esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Delay and Energy Consumption Evaluation
### DFSA Delay and Energy Consumption Evaluation  
The average delay and energy consumption of the coordinator using DFSA are depicted in Figures 6 and 7a, respectively, whereas for end-devices the average energy consumption is depicted in Figure 7b. In all cases, the average delay and energy consumption are presented as a function of \(\rho\) and considering \(n\in\{25,50,100\}\). Notice that, in DFSA, the number of slots per frame (\(m_{i}\)) is proportional to the number of end-devices that contend in every frame (\(\hat{n}_{i}\)). That is, \(m_{i}=\lceil\rho\cdot\hat{n}_{i}\rceil\), where \(\rho\) is a positive real number.  
Figure 4: Coordinator and end-device average delay using FSA-ACK and FSA-FBP.  
Figure 5: Coordinator and end-device average energy consumption using FSA-ACK and FSA-FBP.  
As it can be observed, the results show that the delays and the energy consumption of the coordinator have a minimum at \(\rho=1\). In contrast, when \(\rho<1\), the number of slots per frame becomes lower and the probability of collision in a given slot increases, leading to an increase in the number of frames required to complete a DCR. Otherwise, if \(\rho>1\), the number of slots per frame increases and the probability of collision decreases, leading to a decrease in the the number of frames required to complete a DCR.  
In both cases, the network delay and the energy consumption of the coordinator increase. In the first scenario (\(\rho<1\)), the coordinator has to transmit more FBP packets, whereas in the second scenario (\(\rho>1\)), the number of slots per frame is higher, thus requiring the coordinator to waste more energy in idle-listening. Taking that into consideration, the number of slots per frame in DFSA has to be adjusted to be equal to the number of end-devices that contend in every frame in order to minimize the delay and the energy consumption of the coordinator.  
Regarding end-devices, results show that energy consumption is minimized as \(\rho\) increases. This can be explained by the fact that an increase in the frame length leads to a lower collision probability among nodes and, hence, a lower energy consumption. It is also important to notice that end-device energy consumption is almost insensitive to the number of end-devices. Here, the average number of re-transmissions of an end-device in DFSA for a given \(\rho\) does not change with the number of end-devices. This holds because the probability that an end-device succeeds to transmit in a given frame is constant regardless of the number of end-devices, as the protocol is able to re-configure the frame length after each frame of a DCR.  
Figure 6: DFSA average delay (seconds) depending on \(\rho\).
# A Trustworthy Framework for Skin Cancer Detection Using a CNN with a Modified Attention Mechanism
## 4 Results and Discussion
### Dataset Description  
The HAM10000 dataset comprises 10,015 dermatoscopic images collected over 20 years from two distinct locations: the Department of Dermatology at the Medical University of Vienna, Austria, and the skin cancer clinic of [PERSON] in Queensland, Australia. The HAM10000 dataset is widely regarded as a benchmark dataset in the field of skin cancer detection and analysis. It consists of 10,015 dermatoscopic images representing seven different types of skin lesions including melanoma, basal cell carcinoma, and benign lesions, making it a comprehensive and diverse resource for training and evaluating machine learning models. Its broad acceptance within the research community stems from the high-quality annotations provided by experienced dermatologists and its representation of a wide spectrum of real-world skin conditions. Furthermore, the dataset's size, variety, and availability have enabled researchers to develop and compare innovative methods for skin lesion classification and segmentation effectively. While there are other datasets in the domain, such as the ISIC Challenge datasets, the HAM10000 dataset is considered a reliable standard for benchmarking and is particularly well-suited for studies like ours that focus on improving the early detection and classification of skin cancer. The dataset consists of seven different classes: actinic keratoses (Akiec), basal cell carcinoma (Bcc), benign keratosis-like lesions (Bkl), dermatofibroma (Df), melanoma (Mel), melanocytic nevi (Nevi), and vascular lesions (Vasc). It is a highly imbalanced and challenging dataset. Various preprocessing steps were used to improve visibility and increase the number of samples in the dataset to enhance the generalization capability of the model. Furthermore, these preprocessing steps reduced the data imbalance.  
For preprocessing, only the Akiec, Bcc, Df, and Vasc classes were considered because the training samples were very limited, as shown in Table 1. Consequently, 70% of the data were used to train the model, 20% for validation, and 10% for testing. Figure 4 shows sample images taken from the HAM10000 dataset.
## 4 Discussion
### Influencing Factors of ESV  
The net ESV, rice yield, precipitation, ET, and average temperature in July and August of 45 paddy field samples were randomly selected in the remote sensing interpretation marker samples. The Pearson correlation coefficient of each statistic was calculated with the Origin 2022 software and mapped (Figure 6). The results show that net ESV was positively correlated with rice yield and ET and negatively correlated with precipitation and average temperature in August, and passed the 0.01 level significance test. ET has the strongest correlation between net ESV and the absolute correlation coefficient between the average temperature in August, and net ESV is only lower than ET, while ET and the average temperature in August represent the climate regulation service function of a rice field ecosystem, which further confirms that the PFE exerts an important climate regulation function. Rice yield directly affects the ESV of supply services, carbon fixation and oxygen release, and these two EVs account for 20.38% of the positive ESV, which shows a significant correlation between rice yield and net ESV of 0.618. The proportion of positive ESV influenced by ET and rice yield reached 87.01%. Therefore, the accuracy of ET and rice yield indicators directly determines whether the accounting of ESV in PFEs is reasonable. Due to the differences in rice varieties and paddy field management, rice growth and rice yield are spatially heterogeneous. Because rice growth affects the transpiration  
Figure 5: Temperature difference between urban and paddy in July and August.  
of vegetation, the ET will also vary spatially. Previous studies evaluating the ESV of PFEs have adopted uniform yield parameters, and thus could not be used to describe spatial patterns. In this study, the accuracy of rice yield estimation using GPP remote sensing data reached 99.74%. ET data were daily products calculated using the Penman model, and the accuracy of verification with the ground flux tower was greater than 85% [75]. Therefore, the high-precision parameters with spatial differences input in this study can overcome the shortcomings of ignoring the spatial heterogeneity of parameters in previous studies and improve the credibility of ESV assessments.
## 2 Materials and Methods  
### Materials  
#### 2.1.1 Binders  
The three binders used, all European standard EN 197-1-compliant (Table 1), were: a type 1 42.5 R (CEM I 42.5 R) ordinary Portland cement (OPC); a blend of 90% OPC and 10% ground recycled concrete (GRC), labelled RB\({}_{10}\); and a blend of 75% OPC and 25% GRC, labelled RB\({}_{25}\).  
The GRC was obtained by crushing and grinding (to a maximum size of 147 \(\upmu\)m) laboratory-prepared concrete specimens, batched as per the Faury method [18]. The composition and fresh and hardened 28-day characteristics of that concrete mix are given in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Property** & **OPC** & **RB\({}_{10}\)** & **RB\({}_{25}\)** & **EN 197-1 1** \\ \hline
**Setting time** & **Initial** & 84 & 90 & 91 & \(\geq\)60 \\
**(min)** & **Final** & 136 & 138 & 141 & - \\
**Water content (g)** & 143 & 144 & 147 & - \\
**Normal consistency (mm)** & 36 & 35 & 35 & \(34\pm 2\) \\
**28 d compressive strength (MPa)** & \(67.5\pm 1.0\) & \(62.6\pm 1.0\) & \(51.39\pm 2.2\) & \(\geq\)42.5 \\ \hline \hline \end{tabular}  
* Values referred to strength class 42.5 R.  
\end{table}
Table 1: Fresh properties in binders.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Property** & **Value** \\ \hline
**Cement type** & CEM II/A-L 42.5 R & Slump class 2 & S3 \\
**Cement content** & 300 kg/m3 & Compressive strength 3 & 39.8 MPa \\
**w/\({}_{\text{eff}}\)1** & 0.55 & Splitting tensile strength 3 & 2.75 MPa \\
**Coarse aggregate** & 1020 kg/m3 (crushed limestone) & Modulus of elasticity 3 & 38.9 GPa \\
**Fine aggregate** & 910 kg/m3 (natural river sand) & & \\ \hline \hline \end{tabular}  
* \({}^{1}\) w/\({}_{\text{eff}}\): effective water cement ratio; 2 as per European standard EN 206-1 [19]; 3 28 days.  
\end{table}
Table 2: Characteristics of the source concrete.  
Further to the X-ray fluorescence (XRF)-determined chemical composition of the GRC and OPC shown in Figure 1a, the major oxides were SiO\({}_{2}\) (46.10 wt%) and CaO (40.0 wt%). The figure also presents graphically the minimum and maximum ground concrete replacement ratios used in earlier studies [20; 21; 22; 23; 24; 25; 26] on pastes, mortars and concrete.  
The large, irregularly shaped particles visible in the micrograph in Figure 1b were identified as aggregate with bound mortar, and the small clusters with a rough surface as the paste. GRC's density was 2.54 g/cm\({}^{3}\), a value lower than 3.11 g/cm\({}^{3}\) observed in OPC.
# A Glider-Assisted Link Disruption Restoration Mechanism in Underwater Acoustic Sensor Networks
## 4 The GALO Algorithm
### The Glider Movement Description  
To meet the constraints in C3, the trajectory of glider \(g_{i}\) also needs to be studied. Taking the \"Petrel\" glider as an example, the motion characteristics of the glider are studied as follows: in its gliding mode, the glider follows a sawtooth profile through the buoyancy adjusting unit and the attitude adjusting unit. The spiral gyratory movement is achieved by combining the sawtooth movement with the roll adjustment unit.  
It is necessary to consider the stress conditions of the glider to obtain the kinematics formula. Specifically, based on the kinetics formula, the kinematic parameters of the glider at any moment are solved by the Runge-Kutta method with variable step size. Due to the relatively higher efficiency of the glider's buoyancy control unit, the sawtooth profile motion controlled by the buoyancy control unit and the attitude adjustment unit is more suitable for long-range monitoring tasks. The adjustment of the control parameters can control the movement data of the \"Petrel\" and the relationship among these speed parameters is shown in Equation (7):  
\[\left\{\begin{array}{l}V_{x}=u\cos\theta\cos\psi+v\sin\psi\sin\psi-v\sin \theta\cos\psi\cos\phi+w\sin\psi\cos\phi+w\sin\theta\cos\psi\sin\phi\\ V_{y}=u\sin\theta+v\cos\theta\cos\phi-w\cos\theta\sin\phi\\ V_{z}=-u\cos\theta\sin\psi+v\cos\psi\sin\phi+v\sin\theta\sin\psi\cos\phi+w\cos \phi\cos\phi-w\sin\theta\sin\psi\sin\phi\end{array}\right. \tag{7}\]  
where \((V_{x},V_{y},V_{z})\) is the speed vector of the underwater glider floating in the ground coordinate system and \((u,v,w)\) is the coordinates of the velocity vector in the body at the center of the float. \(\varphi\), \(\psi\) and \(\theta\) are the roll angle, pitch angle and flight angle, respectively. From the velocity vector formula of the above equation the underwater glider trajectory can be obtained. The trajectory _trail\((x)\)_ of the underwater glider is the integral of the speed vector.
# A Non-Smooth Numerical Optimization Approach to the Three-Point Dubins Problem (3 PDP)
## 6 Algorithms  
In this section, we present two algorithms employed to find the optimal angle \(\theta_{m}\) that minimizes the total length of the three-point Dubins path. Both algorithms are based on a mixed approach that combines a pattern search method with a non-derivative root finder.  
As highlighted in the previous section, the total length of the path can have discontinuities in the derivative. Therefore, the angle \(\theta_{m}\) that minimizes the total length of the path cannot be found, in general, by simply computing the root of the derivative of the total length. This is true from a global perspective. However, if some regularity conditions are met in a specific region (interval), the root of the derivative can be used to find the optimal angle \(\theta_{m}\).
# Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review
## 2 Overview of Concept Drift
### The Causes of Concept Drift  
According to the definition of concept drift and the characteristics of joint probability, it can have the following three causes:  
1. [label=(0)]
2. Virtual concept drift. When the probability of \(x\) changes, but the probability of \(y\) under the condition of \(x\) does not change, i.e., \(P_{t_{0}}(x)\
eq P_{t_{1}}(x)\) and \(P_{t_{0}}(y\,|\,x)=P_{t_{1}}(y\,|\,x)\). This case belongs to virtual concept drift, which does not affect its decision boundary and only changes the feature space.
3. Real concept drift. When the probability of \(y\) under the condition of \(x\) changes, the probability of \(x\) remains the same, i.e., \(P_{t_{0}}\left(y\,|\,x\right)\
eq P_{t_{1}}\left(y\,|\,x\right)\) and \(P_{t_{0}}\left(x\right)=P_{t_{1}}\left(x\right)\). This case has a direct impact on the prediction model and is a real concept drift, which not only changes the feature space but also changes its decision-making boundary.
4. Hybrid concept drift. In an open environment, both real concept drift and virtual concept drift can exist in the data stream at the same time, i.e., \(P_{t_{0}}\left(x\right)\
eq P_{t_{1}}\left(x\right)\), \(P_{t_{0}}\left(y\,|\,x\right)\
eq P_{t_{1}}\left(y\,|\,x\right)\). This is a mixed concept drift, which is most common. It is worth noting that according to the Bayesian decision theory [20], we obtain Equation (3): \[P(y|x)=\frac{P(y)*P(x|y)}{P(x)}\] (3)  
It can be seen that \(P_{t}(y)\) and \(P_{t}(x\,|\,y)\) also affect \(P_{t}(y\,|\,x)\), thus indirectly causing a real concept drift. The specific manifestations of the concept drift due to different causes are shown in Figure 1, in which \((X_{1},\,X_{2})\) represents the two-digit feature space and \(y\) represents its category label.  
For example, in stock trading, users can be divided into profitable and non-profit stocks according to profitability. When a user considers purchasing stocks, a change in the channel of purchase or a small change in the number of purchases does not affect the trend of the stock. However, if affected by an outbreak, the trend of stocks may change, thus directly affecting stock returns. This situation belongs to the real concept drift, so users need to reconsider and make decisions. In real life, virtual drift tends to have less impact on the outcome of a decision. There will be no loss to decision makers. However, real concept drift tends to have a direct impact on decision outcomes due to changes in its data relationships. It requires decision makers to discover in time and re-make decisions to avoid losses.
# View-Aware Contrastive Learning for Incomplete Tabular Data with Low-Label Regimes
## 3 Materials and Method
### Dataset Description and Experimental Protocol  
Three publicly available structured datasets were employed for evaluation, corresponding to tasks of credit scoring prediction, transaction behavior classification, and synthetic anomaly detection. These datasets varied in terms of feature dimensionality, missing data rates, and label sparsity, offering a comprehensive test bed for assessing the proposed model's effectiveness under diverse sparse structured data scenarios. For each dataset, experimental settings involved simulating low-label regimes (10-20%) of labeled data and high missingness conditions in order to assess the model's performance in semi-supervised and incomplete data environments. A standard train-test split ratio of 8:2 was adopted to ensure that the test set adequately represented the variability and complexity of each dataset. In addition, 5-fold cross-validation was performed to enhance the robustness and reliability of the experimental results, reducing the impact of random data partitioning on performance evaluation. Key hyperparameters, including the number of views \(k\), learning rate, dropout rate, and hidden dimensions, were selected via grid search based on validation performance to ensure optimal model configuration.
## 4 Performance Analysis
### Simulations Results Corresponding to BS Motion
#### 4.3.2 Analysis of \(\Delta D_{BA1}\) and \(\Delta D_{BA2}\)  
In this section, we examine the differences in the distances due to BS motion in comparison to the distances from a stationary BS. These differences are represented as \(\Delta D_{BA1}\) (=\(D_{BA1F}-D_{BA1M}\)) for BS1 and \(\Delta D_{BA2}\) (=\(D_{BA2F}-D_{BA2M}\)) for BS2, as defined in Table 2. In the leftmost two columns in Table 4, the designation \"F\" denotes the condition where the BS's motion is aligned with the trajectory of an AUV, referred to as the forward direction. Conversely, \"R\" denotes the reverse direction, indicating that the BS is moving opposite to the path of the AUV. \"STD\" stands for standard deviation, a measure indicating the extent of variation from the average distance.  
The statistical analysis of \(\Delta D_{BA1}\) and \(\Delta D_{BA2}\) indicates that while the mean values may differ in sign depending on the direction of BS motion, their absolute values remain closely matched. The impact of BS motion on these measurements is pronounced, as the absolute values of the means increase with respect to the sea state code. The standard deviations for \(\Delta D_{BA1}\) and \(\Delta D_{BA2}\) are nearly the same, irrespective of the BS motion direction, and they increase according to the sea state code. This result shows that the variability in AUV-BS distance is more significantly influenced by the magnitude of BS motion than its direction, emphasizing the substantial effect of the intensity of BS motion on distance fluctuations.  
The statistical results indicate an increase in both the mean and standard deviation for \(\Delta D_{BA1}\) and \(\Delta D_{BA2}\) with a higher sea state code, indicating a direct relation between worsening sea conditions and increased variability in AUV-BS distance due to BS motion. Although the analysis sheds light on the variability introduced by BS motion, determining its specific impact on handover decisions remains complex. This analysis provides insights into the presence of the variability, but it does not offer a definitive conclusion on how it affects the likelihood of handover errors, calling for further investigation to fully understand its implications for handover strategies.
# An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing
## 2 Related Work
### Noise Model  
When the image is polluted, part of the image will be replaced by impulse noise of a random value. The probability of this noise determines the degree of image deterioration. The noise model is given in Equation (3) below.  
\[x_{i,j}=\left\{\begin{array}{ll}o_{i,j}&\quad with\ probability\ \ 1-p\\ n_{i,j}&\quad with\ probability\ \ p\end{array}\right., \tag{3}\]  
where \(p\) is the noise ratio or probability of noise. \(x_{i,j}\) is the pixel value at \(i\) row and \(j\) column. \(n_{i,j}\) and \(o_{i,j}\) are pixel intensities of gray-scale images at position \((i,j)\); they correspond to the original pixel and the noisy pixel, respectively. The pixel intensity of \(n_{i,j}\) is between maximum and minimum (\([I_{min},\ I_{max}]\) in an 8-bit gray-scale image, \(I_{min}=0,\ I_{max}=255\)). As a result, there are two forms of noise. One form of noise is known as salt and pepper noise. It has a fixed value at both ends of the gray scale. The other type is uniform impulse noise, which has a uniform distribution at \([I_{min},\ I_{max}]\) and is also known as random-valued impulse noise. The second sort of noise was examined in this research.
## 1 Introduction
presented an online model to estimate the battery state of charge (SOC) in terms of online-identifying the open-circuit voltage (OCV) and the intrinsic relationship of SOC versus OCV for eventually predicting the running time in EV operation [14]. To understand the environmental sustainability performance of the LSB in EV applications, a life cycle assessment (LCA) model was built for comprehensive environmental impact estimation of the LSB in actual EV applications [13]. For new battery technology such as the LSB, an accurate model could greatly help to predict the battery performance in EV applications and guide the research direction in the lab for the optimization of key factors. The LSB is a quite a new energy resource for potential EV applications and thus has many scientific and technological problems to be investigated and solved. Modeling and simulation are very important and are widely used in automotive vehicle and system development. Therefore, we believe that an intensive and systematic investigation of modeling LSB EV applications could speed up the development of LSBs for industrialization and commercialization. In this study, a closed-loop modeling method was established to bridge lab research and scaled-up development for new battery technology in EV applications. We conducted verification of the prediction reliability of the closed-loop model by comparing model prediction and a real prototype vehicle, showing a variation value less than 3% and confirming the applicability for further study of LSB applications in EVs. Then, we further input our tested data of the LSB and LIB cells, especially some experimentally determined critical parameters, into the proven accurate model for simulation to predict LSB performance during EV operations under various driving cycles as well as fast-charging behavior. The simulation results showed that LSBs work well in electric passenger vehicles. Nevertheless, sulfur and its various discharge/charge products including intermediates(Li2 Sx, x = 1-8) possess poor ionic and electronic conductivities, which increase the internal resistance of the battery very much [27]. Consistently, based on our configuration and testing, the internal resistance of the LSB pack is extremely higher than that of the LIB, thereby, the former has more obvious output voltage oscillation and more power and energy loss for automotive applications. Accordingly, the LSB takes a much longer time for a full charge than the LIB. This work vividly demonstrates the power of model simulation with uses of lab research data for the scaled-up development of applications while guiding further research and development directions.