esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Interacting Multiple Model Filters
### Extended Kalman Filter  
The vectors \(\mathbf{w}_{k}\) and \(\mathbf{v}_{k}\) in Equations (1) and (2) are zero-mean Gaussian white sequences with zero cross-correlation with each other:  
\[E[\mathbf{w}_{k}\mathbf{w}_{i}^{\mathsf{T}}]=\mathbf{Q}_{k}\delta_{ik};\ E[ \mathbf{v}_{k}\mathbf{v}_{i}^{\mathsf{T}}]=\mathbf{R}_{k}\delta_{ik};\ E[ \mathbf{w}_{k}\mathbf{v}_{i}^{\mathsf{T}}]=\mathbf{0}\ \ for\ all\ i\ and\ k\]  
where \(E\left[\cdot\right]\) represents mathematical expectation and the superscript \"T\" denotes the matrix transpose. The symbol \(\delta_{ik}\) stands for the Kronecker delta function given by:  
\[\delta_{ik}=\begin{cases}1,&i=k\\ 0,&i\
eq k\end{cases}\]The discrete-time EKF algorithm is summarized as follows:  
1. Initialize state vector \(\overset{\alpha}{\mathbf{x}}_{0|0}^{\Lambda}\) and state covariance matrix \(\mathbf{P}_{0|0}\).
2. Predict the state vector and state covariance matrix: \[\overset{\alpha}{\mathbf{x}}_{k|k-1}=\mathbf{f}_{k-1}(\overset{\alpha}{ \mathbf{x}}_{k-1|k-1})\] \[\mathbf{P}_{k|k-1}=\mathbf{\Phi}_{k-1}\mathbf{P}_{k-1|k-1}\mathbf{ \Phi}_{k-1}^{T}+\mathbf{Q}_{k-1}\] 3. Compute the Kalman gain matrix: \(\mathbf{K}_{k}=\mathbf{P}_{k|k-1}\mathbf{H}_{k}^{\top}[\mathbf{H}_{k}\mathbf{ P}_{k|k-1}\mathbf{H}_{k}^{\top}+\mathbf{R}_{k}]^{-1}\).
4. Update the state vector: \(\overset{\alpha}{\mathbf{x}}_{k|k}=\overset{\alpha}{\mathbf{x}}_{k|k-1}+ \mathbf{K}_{k}[\mathbf{z}_{k}-\mathbf{h}_{k}(\overset{\alpha}{\mathbf{x}}_{k| k-1})]\).
5. Update the error covariance: \(\mathbf{P}_{k|k}=[\mathbf{I}-\mathbf{K}_{k}\mathbf{H}_{k}]\mathbf{P}_{k|k-1}\).  
where the linear approximation equations for the system and measurement matrices are obtained through the following relations:  
\[\mathbf{\Phi}_{k-1}\approx\left.\frac{\partial\mathbf{f}_{k}}{\partial \mathbf{x}}\right|_{\overset{\alpha}{\mathbf{x}}=\overset{\alpha}{\mathbf{x }}_{k|k-1}};\;\mathbf{H}_{k}\approx\left.\frac{\partial\mathbf{h}_{k}}{\partial \mathbf{x}}\right|_{\overset{\alpha}{\mathbf{x}}=\overset{\alpha}{\mathbf{x }}_{k|k-1}}\]
###### Abstract  
The present paper investigates the application of convolutional neural networks (CNNs) for the classification of diabetic foot ulcers, using VGG16, VGG19 and MobileNetV2 architectures. The primary objective is to develop and compare deep learning models capable of accurately identifying ulcerated regions in clinical images of diabetic feet, thereby aiding in the prevention and effective treatment of foot ulcers. A comprehensive study was conducted using an annotated dataset of medical images, evaluating the performance of the models in terms of accuracy, precision, recall and F1-score. VGG19 achieved the highest accuracy at 97%, demonstrating superior ability to focus activations on relevant lesion areas in complex images. MobileNetV2, while slightly less accurate, excelled in computational efficiency, making it a suitable choice for mobile devices and environments with hardware constraints. The study also highlights the limitations of each architecture, such as increased risk of overfitting in deeper models and the lower capability of MobileNetV2 to capture fine clinical details. These findings suggest that CNNs hold significant potential in computer-aided clinical diagnosis, particularly in the early and precise detection of diabetic foot ulcers, where timely intervention is crucial to prevent amputations.  
caponvolutional neural networks; medical image classification; diabetic foot ulcers; deep learning Article  
1
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
## 4 A Novel Battery SoC Estimation Using a Data-Driven PHM Approach
### Additive Features Attribution  
Feature selection identifies the most relevant attributes for analyzing the system-of-interest [26]. Within the proposed framework, this process is employed to choose features that significantly influence model training while excluding those with minimal or negligible impact on the final algorithm. Additionally, redundant features may also be removed [27].  
Recently, feature selection has been used to explain machine learning data-driven models [28], such as \(xAI\), which determines each feature's contribution to the model performance, helping to determine an optimal feature's subset. Here, a Shapley tree-based model is used to quantify the contributions of each individual feature to the final performance of the model, given a set of data points [13].
## 3 Proposed Methodology
### Estimation of Link Delay
#### 3.3.4 Computing Average Per-Channel Delay at Each Hop  
We can find the average value for propagation delay at the \(i\)th node (\(\overline{t_{prop,i}}\)) using Equation (30):  
\[\overline{t_{prop,i}}=\frac{\sum_{j=1}^{N_{i}}D_{ij}}{N_{i}\times v} \tag{30}\]  
In Equation (30), \(D_{ij}\) is the transmission distance from node \(i\) to its \(j\)th neighbor, and \(v\) is the speed of signal propagation in the given medium.  
Furthermore, in order to estimate the delay of each link, it is necessary to compute the average queue size of node \(i\) (\(\overline{\mathcal{R}_{i}}\)), as shown in Equation (31):  
\[\overline{\mathcal{R}_{i}}=\frac{\sum_{j=1}^{M}Y_{ij}}{M} \tag{31}\]  
In Equation (31), \(Y_{ij}\) is the queue size of node \(i\) at the \(j\)th time step, and \(M\) is the total number of time steps.  
Finally, using the collected \(\overline{\mathcal{R}_{i}}\), computed \(\overline{t_{cont,WI}}\), \(\overline{t_{cont,WL}}\), and \(\overline{t_{prop,i}}\), and Equations (11) and (12), the final delay equations can be formulated as given in Equations (32) and (33):  
\[\overline{\mathcal{D}_{iWL}}=\overline{\mathcal{R}_{i}}\times(t_{trans,i}+4 \overline{t_{prop,i}}+4t_{proc,i}+t_{trs}+t_{tts}+t_{ack}+\overline{t_{cont, WL}}) \tag{32}\]  
In Equation (32), \(\overline{\mathcal{D}_{iWL}}\) is the average one-hop wireless link delay at node \(i\) using CSMA-CA.  
\[\overline{\mathcal{D}_{iWI}}=\overline{\mathcal{R}_{i}}\times(t_{trans,i}+ \overline{t_{prop,i}}+t_{proc,i}+\overline{t_{cont,WI}}) \tag{33}\]  
In Equation (33), \(\overline{\mathcal{D}_{iWI}}\) is the average one-hop wired link delay at node \(i\) using CSMA-CD.  
Thus, for each link that exists in the network, an average value for the link delay can be estimated using Equations (32) and (33). Note that the one-hop delays that are obtained from Equations (32) and (33) are only estimates for the average wireless or wired communication channel delay at the \(i\)th node and may not depict the exact delay that occurs at node \(i\). The main reason for that is that the average contention delay for a given communication channel (\(\overline{t}_{cont,WI}\), \(\overline{t}_{cont,WL}\)) is computed for the whole network, not for individual links. However, the average values obtained from Equations (32) and (33) can be used as very good initial estimates in deriving the exact delay per communication channel. In order to predict the exact delay, we use the concept of machine learning, as described below. Sample delay calculations using the above-derived Equations are given in Appendix B.
# Deep Learning for Joint Adapatations of Transmission Rate and Payload Length in Vehicular Networks
## 6 Conclusions  
This work proposes an adaptive scheme for selecting both the MCS and payload length in IEEE 802.11p V2V communication systems. We consider practical receivers that involve realistic timing synchronization, carrier frequency offset correction and channel estimation. Furthermore, we employ a deep learning approach to solve the AMCPLS scheme. We design a deep convolutional neural network in order to be trained using the estimated channel coefficients and the noise standard deviation as input features. Our deep learning approach can predict the suitable combination of the payload length along with the modulation and coding scheme. Simulation results show that the proposed AMCPLS scheme outperforms fixed MCS and payload length schemes. In addition, the proposed DCNN AMCPLS approach has better prediction time, frame error rate, and effective physical layer throughput performance compared to both k-NN AMCPLS, and SVM AMCPLS approaches. In a future work, we will consider the application of the proposed AMCPLS scheme in the broadcasting scenario.  
Conceptualization, M.E., T.W. and S.Z.; methodology, M.E. and T.W.; software, M.E.; validation, M.E. and S.Z.; formal analysis, M.E.; investigation, M.E.; resources, S.Z. and T.W.; data curation, M.E. and T.W.; writing-original draft preparation, M.E.; writing-review and editing, M.E., T.W. and S.Z.; visualization, M.E.; supervision, S.Z.; project administration, S.Z.; funding acquisition, T.W. and S.Z.  
This research was partially funded by research grants from the Natural Science Foundation of Guangdong Province, China (Grant No. 2017A030310334), the Chinese NSF project (Grant No. 61771315), and the Shenzhen NSF Project (Grants No. JCYJ20170818095107583, JCYJ20160226192223251, JCYJ20170412104656685, JCYJ20170302142312688).  
The authors declare no conflict of interest.
# Stability of a Compacted Sand Slope Model Subject to
## 1 Introduction
[PERSON] et al., 2021, [9] studied the use of Recycled Plastic Pins (RPPs) to reinforce slopes ranging from 25% to 50% constructed with a highly plastic Yazoo clay (HPYC). Three uniform and varied RPPs spacings were investigated by using the finite element Plaxis 2D code. The numerical results indicated that RPPs provide shear resistance for the sloping HPYCS embankment. For instance, by maintaining a uniform spacing between the RPPs, the factor of safety increased to about 1.7 for the 50% slope case. [PERSON] et al., 2022, [13] determined the properties of phosphogypsum stockpiles in Aqaba, Jordan in order to evaluate their stability by utilizing limit equilibrium and finite element methods. They determined that all studied slopes were stable under all static and seismic loading conditions. The main objective of the present work is the determination of the failure load induced by a rigid plate on a compacted sand slope model. The applicability of such an investigation is wide and should be followed up during the construction phase in terms of the stability of excavations in the presence of surcharge loads located at the upstream side. In particular, for soils of negligible cohesion, it is impossible to test vertical slopes without the retaining systems when the excavation depth exceeds 5 m. For this reason, slope angles between 30\({}^{\circ}\) and 35\({}^{\circ}\) are expected to provide reliable results. An existing experimental laboratory model, [10], permitted us to carry out a series of loading tests to record the failure load of the compacted sand slope model. Then, a parametric study was performed to highlight the influence of the slope angle \(\alpha\) and the location of the applied loading at the crest of the compacted sand slope. A built numerical model including the boundary conditions and the geotechnical parameters of the compacted sand slope was presented. Then, using the 3D Plaxis code, the behavior of the experimental slope model, subjected to a constant rate of vertical displacement, was simulated up to the failure load. Furthermore, this research investigated the prediction of the failure load of tested models in the yield design framework. The numerical and analytical predictions were compared to the experimental results to assess the performed numerical and analytical modelling.
## 3 Application of AIEgens in the Detection of Pesticide Residues in Fluorescence Sensors
### Detection of Pesticides by Metal-Organic Framework Sensors Based on AIEgens
between the green/red fluorescence ratio and the concentrations of Cu\({}^{2+}\) and glyphosate in the range of 0-5 uM, with detection limits estimated at 0.05 uM for copper ions and 0.93 nM for glyphosate, based on 3\(\sigma\)/k calculations. This sensor enables both semi-quantitative detection by the naked eye and quantitative/accurate detection via a smartphone platform, underscoring its potential for pollution monitoring. The aforementioned studies leverage the unique properties of AIEgens and MOF materials (such as UiO-66-NH2 and GSH-Au NCs@ZIF-8). By employing various mechanisms (including fluorescence enhancement, AIE, metal-ion adsorption, and fluorescence quenching), they achieve highly sensitive and selective detection of pesticides. Some research has focused on developing portable detection platforms (like 2D sensing pads and hydrogel sensors based on smartphone technology) or validating practical applications, thereby highlighting the significant application potential of these composite materials.
# Escaping Printable Encoded Streams to Embed Out-of-Band Data
## 5 Discussion and Results
### Client-Server Parameter Passing  
Let us consider passing a variable number of parameters from a Web client to a server. As a concrete example, suppose conveying a 16 bit integer valued 41, a 16 bit integer valued 65,535 and a character string valued \"BASE\". Having built \(\mathcal{Z}\) with the unused sequences \"xBA\", \"xBB\", \"xBC\", \"xBD\", \"xBF\", \"xBG\", \"xBH\", and \"xBJ\" to represent S\({}_{i}\), S\({}_{f}\), S\({}_{o}\), S\({}_{p}\), S\({}_{rs}\), S\({}_{re}\), S\({}_{d}\), and S\({}_{t}\), respectively, to perform an encoding that follows the proposal shown in Figure 5, the resulting printable stream will be:  
xBAABAvzVxBCMDkQivxBJ  
Corresponding to:  
S\({}_{i}\) 41  S\({}_{i}\) 65,535 S\({}_{o}\) \"BA\" \"SE\" S\({}_{t}\)  
It is obvious that the resulting Base41 string can be immediately and unambiguously decoded by a procedure aware of the Base41 encoding symbols assignment and expecting the corresponding parameters.  
One disadvantage is that the insertion of extra data in the encoding increases the size of the processed stream and this might be limiting the application on low-capacity links or small-capacity devices.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Block Size** & **Block Size** & **Encoded Block Size** & **CRC Size** & **CRC Overload** & **CRC Overload** & **CRC Overload** & **CRC Overload** \\
**Block Size** & **Block Size** & **Block Size** & **Block Size** & **(Proposed Method)** & **(Standard Method)** & **(Standard Method, 11 bits)** & **(Standard Method, 14 bits)** \\
**[bits]** & **[octets]** & **[octets]** & **[octets]** & **[octets]** & **\%** & **\%** & **\%** \\ \hline
128 & 16 & 24 & 27 & 11.2 & 8.0 & 9.9 \\
256 & 32 & 48 & 51 & 5.9 & 4.2 & 5.2 \\
512 & 64 & 96 & 99 & 3.1 & 2.2 & 2.7 \\
1024 & 128 & 192 & 195 & 1.6 & 1.1 & 1.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computation and comparison of CRC overloads for Base41 and Base45 encoded CRCs (11 and 14 bits, respectively).  
Concerning the security issues of the proposed framework, it should be pointed out that any printable encoding presents the same security issues, being just an encoding. We merely present a way in which an application can make use of unused configuration to insert extra information in the encoding. When this extra information is a MAC or a signature, the encoded data are protected against modification attacks. Transferring the resulting encoding in a secure way is out of the scope of the present work, and mainly relies on the use of proper security measures (for instance, cryptography, secure protocols such as https, SSL, and TLS, etc.).
# A Survey on Different Plant Diseases Detection Using Machine Learning Techniques
## 2 Basic Steps in Identification of Diseases from Leaf Images
### Feature Extraction  
Features play an important role in machine learning. Features are used to describe the disease information in mathematical form, which makes the classification easier. For an effective classification, a feature should contain the necessary information that is required to differentiate the classes. Different types of features are used for the identification of diseases, and they can be classified as color features, shape features [31; 41], texture features [35; 41; 42] and deep-learning-based features. Color features define the different color values of the disease region. The area, perimeter, minor/major axis length, eccentricity, etc., are some of the shape features. Texture-based features such as local binary pattern (LBP) [43], gray-levelco-occurrence matrix (GLCM) [36], gray-level run-length method (GLRLM), Gabor texture features [32] are used for the identification of diseases. Figure 1 shows some of the features that are used in classification of plant diseases.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Wavelength** & **SDC 3** & **SDC 4** & **SDC 5** & **SDC S** & **SDS 3** & **SDS 4** & **SDS 5** & **SDS S** \\ \hline
1 st Wavelength & 429.48 & 437.48 & 437.48 & 433.48 & 417.46 & 433.48 & 437.48 & 433.48 \\ (nm) & (Band 8) & (Band 10) & (Band 10) & (Band 9) & (Band 5) & (Band 9) & (Band 10) & (Band 9) \\ \hline
2 nd Wavelength & 669.74 & 669.74 & 669.74 & 673.75 & 661.73 & 669.74 & 669.74 & 673.75 \\ (nm) & (Band 68) & (Band 68) & (Band 68) & (Band 69) & (Band 66) & (Band 68) & (Band 68) & (Band 69) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Absorption wavelength range of selected green algae. For the analysis of the elongated 449-529 nm wavelength band identified in the spectral spectrum unique to green algae and the third absorption peak wavelength band additionally identified in the spectral spectrum unique to blue-green algae, the overall instantaneous slope was obtained by applying differentiation to the full wave spectrum. It was confirmed that the wavelength range of 417-437 nm, which has the maximum absorption wavelength of both green and blue-green algae, changes to a positive value. It was also confirmed that 661-677 nm, which has the second maximum absorption wavelength, has a negative slope value. Furthermore, in the wavelength range of elongated 449-529 nm, both green and blue-green algae tended to have positive values, so it was determined to be difficult to identify algal species in the 449-529 nm wavelength range. However, in the third wavelength range of 613-633 nm where the absorption peak appears only in blue-green algae, green algae were confirmed to have a positive value while blue-green algae had a negative value. This is similar to the range suggested by [PERSON] (1989) who reported that phycocyanin is strongly absorbed at approximately 620 nm [22]. Based on this, green algae and blue-green algae were classified into the third wavelength band (Figure 9 and Table 5). Figure 8: Graph of algae spectrum from 400 to 1000 nm. (**(a)** green algae spectrum—this graph shows the absorption spectrum of the green algae sample used in this study, ranging from 400 to 1000 nm; (**b**) blue-green algae spectrum—this graph depicts the absorption spectrum of the blue-green algae sample used in this study, ranging from 400 to 1000 nm). Figure 9: Graph of derivative algae result. (**(a)** Green algae derivative spectrum—derivative spectrum results of green algae in the 400–1000 nm range; (**b**) blue-green algae derivative spectrum—the results of the derivative spectrum for blue-green algae in the 400–1000 nm range).
## 4 Experiments
### Evaluation Metrics  
We utilized several key evaluation metrics, including accuracy, recall, precision, F1-score, and AUC (Area Under the ROC Curve), to thoroughly assess the performance of our proposed deep learning model and compare it with baseline machine learning and deep learning models.  
* **Accuracy** provides a general assessment of the model's performance by calculating the percentage of properly predicted cases relative to all instances, as illustrated in Equation (2). \[Accuracy=\frac{Tp+Tn}{Tp+Tn+Fp+Fn}\] (2)
* **Precision** evaluates how well the model prevents false positives by figuring out what proportion of all positive predictions are true positives, as depicted in Equation (3). \[Precision=\frac{Tp}{Tp+Fp}\] (3)  
Figure 6: The k-fold cross-validation process for splitting data.  
* evaluates the model's ability to minimize false negatives by identifying all true positive cases within the dataset, as defined in Equation (4). \[Recall=\frac{Tp}{Tp+Fn}\] (4)
* computes the harmonic mean of precision and recall, providing a balanced metric that takes into consideration both false positives and false negatives, as demonstrated in Equation (5). \[F1-Score=2\times\frac{Recall\times Precision}{Recall+Precision}\] (5)
* **AUC (Area Under the ROC Curve)** is a commonly used metric for binary classification models. It represents the area under the ROC (Receiver Operating Characteristic) curve, which displays the True Positive Rate (TPR) versus the False Positive Rate (FPR) across various threshold values [85]. These metrics collectively offer an excellent evaluation of the model's effectiveness and reliability.
# Insider Threat Detection Based on Deep Clustering of Multi-Source Behavioral Events
## 2 Related Work
Each behavior event is treated as a vertex in the graph, and different edges are defined using ten rules. Graph embedding learning techniques are then employed to learn vector representations of the vertices (i.e., each behavior event). Finally, the k-means algorithm is used to cluster the vertices, and those in clusters with fewer vertices are identified as anomalies. However, the feature extraction is independent of the clustering method, resulting in suboptimal user behavior feature representations for the clustering algorithm which leads to reduced accuracy in insider threat detection. To learn the feature representation suitable for the clustering task, [PERSON] et al. [23] propose a community-based anomaly detection model. This method represents every user behavior as an embedding vector, which is then iteratively optimized using k-means clustering. Subsequently, clusters of user behaviors can be obtained as communities and abnormal behavior is evaluated based on the distance between a behavior and other behaviors within the same community. However, this method analyzes static behavior and ignores the temporal relationships among user behaviors in the user behavior sequences. Table 1 summarizes existing methods in the field of insider threat detection. It can be seen from Table 1 that user behavior feature extraction for multi-source-data-based insider threats detection remains a challenge for two reasons: (1) traditional artificial feature engineering requires manual work for feature extraction, and (2) the feature extraction is independent of the clustering-based unsupervised machine learning, which results in suboptimal user behavior feature representations.
## 3 Results
### Mechanical Properties  
The most important role of the fibrous component in composite materials is to transfer mechanical load, thus, it is necessary to determine the basic mechanical properties of the obtained fibers. Table 2 and Figures 5 and 6 show the results of measuring the breaking strength and elongation at break for the tested fibers.  
Figure 4: Linear density histograms of (**a**) BBW, (**b**) MBW, (**c**) UBW, (**d**) BBD, (**e**) MBD and (**f**) UBD fibers.  
Figure 5: Effect of plant section used for fiber extraction and the retting method on fiber breaking strength (cN/tex) (Error bars represent standard errors).  
Both the plant fraction and the retting method were found to affect the breaking strength (\(p\) values 0.02 and \(3.24\times 10^{-9}\), respectively). The highest breaking strength (51.80 cN/tex) was obtained from the fibers extracted from the upper part of the okra stem via water-retting. The breaking strength values varied between 42.01-51.80 cN/tex for the water-retted fibers and 32.50-37.12 cN/tex for the dew-retted fibers. In terms of the rate of elongation at break, the extraction fraction had no significant effect, whereas the retting method had a significant effect (\(p\) values 0.57 and \(1.24\times 10^{-6}\), respectively). The water-retted fibers exhibited higher elongation at break rates (3.33-3.48%) compared to the dew-retted ones (2.75-2.90%). The obtained results agree with the literature, which reports the inferior performance of dew-retted fibers compared to water-retted fibers [7]. The comparison of mechanical properties of okra and other types of fibers are shown in Supplementary materials (Table S1).