esa-sceva/satcom-chunk-collection huggingface dataset:
# Novelty Detection with Autoencoders for System Health Monitoring in Industrial Environments
## 5 Experimental Evaluation
### Evaluating the Classification of the Operating Condition
#### 5.2.2 Supervised Implementation  
Description. This experiment evaluates a classifier trained on the encodings generated by the AE approach and using target classes provided by the user. In particular, the implemented classifier is based on a Feed-Forward Fully Connected Neural Network. The network is composed of 1 layer with 64 neurons. Table 13 shows the accuracy obtained as the mean on all scenarios with a window size equal to 200 (Acc 200), and the mean/standard deviation on all scenarios with all evaluated window sizes. Table 14 shows the F1 score in classifying the different operating conditions by considering a window size equal to 200 and all evaluated window sizes.  
Discussion. The classifier shows a high accuracy score in all AE implementations. The low standard deviation measure shows the window size invariance of the approach.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Window** & **Raw** & **FC** & **CNN** & **LSTM** & **BiLSTM** \\ \hline
40 & 88.67 & 95.28 & 88.91 & 89.81 & 100 \\
80 & 88.53 & 97.59 & 94.66 & 98.20 & 98.03 \\
120 & 89.05 & 99.29 & 95.07 & 93.29 & 98.79 \\
200 & 89.61 & 98.66 & 89.30 & 90.55 & 98.11 \\
240 & 89.32 & 97.39 & 94.84 & 98.64 & 97.64 \\
360 & 88.47 & 99.34 & 94.12 & 95.12 & 99.32 \\ \hline Avg & 88.94 & 97.93 & 92.82 & 94.27 & 98.65 \\ Std & 0.46 & 1.54 & 2.89 & 3.74 & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Accuracy obtained by the unsupervised classifiers: Adjusted Rand.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Survived** & **Raw** & **FC** & **CNN** & **LSTM** & **BiLSTM** \\ \hline Acc 200 & 97.61 & 99.63 & 98.34 & 98.13 & 99.60 \\ Acc Avg & 97.48 & 99.08 & 98.78 & 98.52 & 98.86 \\ Acc Std & 0.24 & 0.58 & 0.66 & 0.26 & 0.51 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Evaluation of the supervised classifier: Accuracy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Conf.** & **Raw** & **FC** & **CNN** & **LSTM** & **BiLSTM** \\ \hline C1 & 100 & 100 & 100 & 97.90 & 100 \\ C2 & 98.81 & 99.65 & 99.47 & 99.38 & 99.62 \\ C3 & 97.23 & 100 & 97.27 & 99.23 & 100 \\ C4 & 78.42 & 97.18 & 85.90 & 83.87 & 96.91 \\ \hline Avg 200 & 93.62 & 99.21 & 95.66 & 95.10 & 99.13 \\ Std 200 & 10.19 & 1.36 & 6.61 & 7.51 & 1.49 \\ \hline Avg all & 93.22 & 97.91 & 96.96 & 96.1 & 97.02 \\ Std all & 9.82 & 3.91 & 4.96 & 5.29 & 4.44 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Evaluation of the supervised classifier: F1 score for each class.  
As expected, the F1 score measured when detecting C4 is largely lower than the other operating conditions. This is due to the anomalies existing in the C4 operating condition that make the classification process more complex.
## 3 Model and Scheme
### Scheme Implementation  
#### 3.2.1 The Communication Scenario  
We first suppose a communication scenario for the CCES scheme in which message sender \(P_{r}\) wants to covertly communicate with the message receiver, but an attacker locates and launches serious threats to the system, including scanning the active server host to collect intelligence, eavesdropping and analyzing network traffic, and launching denial of service (DoS) attacks to compromise server hosts or congest the network. The communication scenario is shown in Figure 3.  
In this communication scenario, the end hopping client acts as the message sender, while the end hopping server serves as the message receiver. The two parties achieve covert communication through the transmission of end spreading sequence from the sender. During this process, the server remains silent and captures end spreading sequence by monitoring processes. This mechanism prevents attackers from determining the host status through scanning attacks, thereby thwarting attempts to launch targeted attacks and effectively protecting the server.  
Scanning and Reconnaissance: The attacker can perform reconnaissance by scanning the network to gather information about active server hosts. This can help the adversary understand the system's structure and potentially exploit vulnerabilities.  
Eavesdropping and Traffic Analysis: The adversary can engage in eavesdropping on the network traffic. By intercepting packets, the attacker attempts to analyze the data being transmitted between the sender and receiver. This could allow the attacker to detect the covert communication or extract sensitive information from the packets.  
Denial of Service Attacks: The adversary can launch DoS attacks against the network, which would attempt to block or disrupt the communication between the sender and receiver. This could overwhelm the network with traffic, preventing the legitimate communication from being transmitted effectively or causing packet loss.  
Network Congestion: The attacker might also attempt to induce network congestion by flooding the network with excessive traffic or through other malicious techniques. This can degrade the performance of the covert channel and cause communication delays or failures.  
Figure 3: The communication scenario for CCES.  
Figure 2: The CCES model.
# Evaluation of Diversification Techniques for Legal Information Retrieval +
## 2 Legal Document Ranking Using Diversification
### Ranking Features/Distance Measures  
Typically, diversification techniques measure diversity in terms of content, where textual similarity between items is used in order to quantify information similarity. In the vector space model [10], each document \(u\) can be represented as a term vector \(U=(is_{w1u},is_{w2u},...,is_{wmu})^{T}\), where \(w_{1},w_{2},...,w_{m}\) are all of the available terms, and _is_ can be any popular indexing schema, e.g., \(tf,tf-idf,logtf-idf\). Queries are represented in the same manner as documents.  
Following, we define:  
* Document similarity: Various well-known functions from the literature (e.g., Jaccard, cosine similarity, etc.) can be employed for computing the similarity of legal documents. In this work, we choose cosine similarity as a similarity measure; thus, the similarity between documents \(u\) and \(v\), with term vectors \(U\) and \(V\) is: \[sim(u,v)=\cos(u,v)=\frac{U\cdot V}{\parallel U\parallel\parallel V\parallel}\] (2)
* Document distance: The distance of two documents is: \[d(u,v)=1-sim(u,v)\] (3)
* Query document similarity. The relevance of a query \(q\) to a given document \(u\) can be assigned as the initial ranking score obtained from the IR system, or calculated using the similarity measure, e.g., cosine similarity of the corresponding term vectors: \[r(q,u)=\cos(q,u)\] (4)
## 3 Results
### Hyrax vs. Haas Group  
None of the dental and skeletal measurements were statistically significant between the Hyrax and Haas groups in the interval T0-T1. Considering the anchorage unit on the second primary molars and the E + E distance as 100% of the dental expansion performed, the following percentages can, therefore, be calculated: the Hyrax expander allowed a skeletal expansion of 27% (NF = 1.76 \(\pm\) 0.86 mm) compared to the dental expander (E + E = 6.47 \(\pm\) 1.54 mm), with an indirect expansion of the upper first permanent molar of 53% (6 + 6 = 3.42 \(\pm\) 1.50 mm), while the Haas expander guaranteed a greater skeletal expansion of 44% (NF = 2.97 \(\pm\) 1.84 mm) compared to the dental expander (E + E = 6.80 \(\pm\) 2.26 mm) with a spontaneous drift of the first molars of 67%  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**Hyrax**} & \multicolumn{3}{c}{**Haas**} & \multicolumn{3}{c}{**Hyrax vs. Haas**} \\ \cline{2-7}
**Measurements** & **Mean** & **SD** & \(p\) & **Mean** & **SD** & \(p\) & \(p\) \\ \hline Skeletal & & & & & & \\
**NF** & 1.76 & 0.86 & \textless{}0.001 \({}^{*}\) & 2.97 & 1.84 & 0.003 \({}^{*}\) & 0.081 \\
**LPt** & 1.93 & 1.69 & 0.006 \({}^{*}\) & 1.91 & 1.12 & 0.002 \({}^{*}\) & 0.998 \\ \hline Dental & & & & & & \\
**6 + 6** & 3.42 & 1.50 & \textless{}0.001 \({}^{*}\) & 4.57 & 2.32 & 0.001 \({}^{*}\) & 0.222 \\
**E + E** & 6.47 & 1.54 & \textless{}0.001 \({}^{*}\) & 6.80 & 2.26 & \textless{}0.001 \({}^{*}\) & 0.724 \\
**FURCA** & 4.36 & 1.79 & \textless{}0.001 \({}^{*}\) & 4.14 & 2.58 & 0.003 \({}^{*}\) & 0.837 \\ \hline \end{tabular}
\end{table}
Table 6: Intraclass comparisons between transversal differences (Mean and Standard Deviation) between T0 and T1 and interclass significance (\({}^{*}p\)! 0.05).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{3}{c}{**Hyrax**} & \multicolumn{3}{c}{**Haas**} & \multicolumn{3}{c}{**Hyrax vs. Haas**} \\ \cline{2-7}
**Measurements** & **Mean** & **SD** & **Mean** & **SD** & \(p\) \\ \hline Skeletal & & & & & \\
**NF** & 29.55 & 1.96 & 28.17 & 2.52 & 0.227 \\
**LPt** & 50.74 & 4.53 & 49.51 & 3.64 & 0.544 \\ \hline Dental & & & & & \\
**6 + 6** & 42.30 & 3.72 & 39.06 & 3.13 & 0.067 \\
**E + E** & 39.20 & 2.95 & 35.85 & 2.26 & 0.018 \({}^{*}\) \\
**FURCA** & 47.52 & 4.04 & 38.97 & 3.28 & \textless{}0.001 \({}^{*}\) \\ \hline \end{tabular}
\end{table}
Table 5: Starting forms intergroup comparison at T0 (\({}^{*}p\)! 0.05).  
(6 + 6 = 4.57 \(\pm\) 2.32 mm). The FURCA point moved differently between Hyrax and Haas groups: in the first group the upper first molars suffered a lingual tipping differently from the second group, in which they showed a slight buccal tipping. Figure 2 represents the amount of dental and skeletal expansion among groups.
# Optimized Architecture for Efficient OFDMA Network Design
## 5 Simulation Results  
The problem of server capacity limitations led us to opt for a simulation with a number of stations equal to 10. The experimental results are divided into two key phases: Validation Study and Robustness Study, each designed to test the proposed model under different conditions. In the Validation Study, the simulation is conducted with assumptions closely aligned with those in the analytical model. This phase aims to verify the model's accuracy by comparing the simulated results with the expected performance metrics under controlled conditions. In the Robustness Study, the assumptions from the analytical model are relaxed to reflect more dynamic and realistic network scenarios. By introducing variations in traffic patterns and channel conditions, this phase evaluates the model's ability to maintain optimal performance under less ideal, real-world conditions.  
In real-world networks, user traffic often exhibits varied patterns, with both light and heavy usage. To simulate these conditions, the validation study incorporates an exponential traffic distribution, while the robustness study employs a truncated Pareto distribution. These two approaches capture the variability in user behavior, with the Pareto distribution providing a more accurate reflection of bursty traffic often observed in modern wireless networks.  
The study also models wireless channel conditions using both memoryless and combined average channel models. The memoryless model simulates environments where channel conditions vary unpredictably between frames, while the combined average model reflects more structured scenarios where users may experience consistent 'good'or 'bad' channel conditions. These models enable a comprehensive evaluation of the scheduling algorithms' performance under various real-world channel dynamics.  
Figure 3: Simulator diagram.
# Modeling Actual Evapotranspirationiration with MSI-Sentinel Images and Machine Learning Algorithms
## 2 Materials and Methods
### Data Extraction for Training  
Images from MSI sensors were divided into two groups according to their spatial resolution. Two approaches were applied: approach 1 using a 10 m spatial resolution and approach 2 with a 20 m spatial resolution. This division aimed to understand the effect of the number of predictor variables and spatial resolution on the performance of predictive models.  
The primary variables selected for approach 1 were reflectance of blue (\(\rho\)B), green (\(\rho\)G), red (\(\rho\)R), and near-infrared (\(\rho\)NIR) bands; the variables for approach 2 were reflectance of blue (\(\rho\)B), green (\(\rho\)G), red (\(\rho\)R), red edge 1 (\(\rho\)Re1), red edge 2 (\(\rho\)Re2), red edge 3 (\(\rho\)Re3), near-infrared (\(\rho\)NIR), shortwave infrared 1 (\(\rho\)SWIR1), and shortwave infrared 2 (\(\rho\)SWIR2). As approaches 1 and 2 have, respectively, a 10 and 20 m spatial resolution, it was necessary to match their dimensions with the dependent variable, that is, reduce their resolution to 30 m \(\times\) 30 m. Therefore, the resample function was used with the bilinear method of the raster package [28] for the R language based on the images of the dependent variable. With images in the same spatial resolution, the central irrigation pivots were cut within each scene using a shapefile, and then the pivots were separated into two groups. The first group encompassed 70% of the total pivots and was used to train the model. The remaining 30% of center pivots were used to test the model (Figure 1).  
Due to computational limitations, it was not possible to use all pixels of each pivot of the seven images. Therefore, 25,000 pieces of information, totaling 175,000 pieces of data, were randomly extracted to train the model (Table 3). To test the model, 10,714 piecesof data were also randomly extracted from each image, totaling 74,998 pixels, maintaining the 30% data proportion for model training. Afterward, ET\({}_{\text{r}}\)F values lower than 0 that eventually contained some pixels generated by noise in some images were filtered out. In the end, data frame files with geographic coordinates, spectral bands, and ET\({}_{\text{r}}\)F were obtained.
## 3 Time Delayed Velocity Feedback Control
### Nonlinear Primary Resonance Response
All initial conditions in the shaded area lead to the steady-state solution on the lower branch \(P_{1}\), while all initial conditions in the unshaded area lead to the upper branch \(P_{2}\). Thus all the shaded area constitutes the domain of attraction of point \(P_{1}\), and all the unshaded area constitutes the domain of attraction of point \(P_{2}\). Again, a small change in the initial conditions can produce large change in the response of the system: this is evidenced in Figure 6 by comparing the laws \(a(t),\gamma(t)\) corresponding to conditions which just differ very little with regarding the amplitude. The peak amplitude of the primary resonance, obtained from the first equation of Equations (12), is given by  
\[a_{p}=\frac{f}{2|u_{e}|\omega}. \tag{13}\]  
The critical force amplitude \(f_{c}\) obtained from Equation (12) is  
\[f_{c}=2\omega\mu_{e}\sqrt{2\omega\mu_{e}/3\alpha}, \tag{14}\]  
for \(f<f_{c}\) there is only one solution while for \(f>f_{c}\) there are three. Figure 4: Loci of saddle-node bifurcation points, for \(p=-0.05\) and different values of \(\tau\) with velocity feedback. Figure 3: Amplitude of the response as a function of amplitude of the excitation for several detunings. Figure 7 corresponds to the critical force amplitude and the peak of the primary resonance changing with time delay \(\tau\). We must point out that the change rule of the critical force amplitude and the peak of the primary resonance are not wholly conformable. So the proper value of \(\tau\) may take into account the requirement of engineering. By way of example, Figures 8 and 9 shown the time history curves, phase diagram and frequency power spectrum of the primary resonance response are obtained numerically at time delay \(\tau=\pi/2\) and \(\tau=\pi\), respectively. Figure 8: The primary resonance response with time delay \(\tau=\pi/2\), **(a)** The time history curves, (**b**) the phase diagram and (**c**) frequency power spectrum. Figure 6: Attraction of amplitude and phase to different constant values. Figure 7: The curves of the critical force amplitude and peak of the primary resonance relate with time delay.
## 2 Materials and Methods
### Experimental Layout-Thermal Testing
More in detail, the temperature sensors for air and the wall were placed in the following way:  
* 4 contact temperature sensors for the walls, one for each of the four outer walls of the test chamber;
* 4 air temperature sensors placed near the opposite side of the entrance door of the test chamber at 0.1 m, 1.1 m, 1.7 m, and 2.5 m height;
* 4 air temperature sensors placed in the climatic room that encloses the test chamber (one for each side of the cell);
* 1 contact temperature sensor placed on the floor of the test chamber;
* 2 temperature sensors placed on the radiant ceiling, one at the inlet of a series of panels, the other one in a point of the ceiling not occupied by panels;
* 2 globe thermometers placed on the side of the entrance door of the test chamber and on the opposite side at the height of 1.1 m for the measurement of the average radiant temperature;
* 3 temperature sensors placed in the space between the radiant ceiling and the ceiling of the room, one in contact with the ceiling of the cell, one in contact with the radiant ceiling, one that measures the temperature of the air in the cavity. The electric radiant panel was installed in the middle of the floor inside the test chamber. Its nominal power is 300 W, and the dimensions are 103 by 33 cm. A picture of the experimental setup is shown in Figure 3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Measurement** & **Instrument** & **Quantity** \\ \hline Water temperature & Resistance Temperature Detector Pt100 & 4 \\ Water Flow & Electromagnetic flowmeter & 1 \\ Electrical power & Volt-Ampere meter & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sensors of the hot and cold water generation system. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Measurement** & **Instrument** & **Quantity** \\ \hline Wall temperature & Resistance Temperature Detector Pt100 & 13 \\ Air temperature & Resistance Temperature Detector Pt100 & 9 \\ Water temperature & Resistance Temperature Detector Pt100 & 2 \\ Mean radiant temperature & Globe thermometer & 2 \\ Heat flux & Heat flux meter & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sensors positioned inside and outside the test chamber. The thermal camera used during the experiments is a FLIR SC660, having a spectral response 7.5 \(\upmu\)m and 13.5 \(\upmu\)m, a noise equivalent temperature difference (NETD) lower than 30 mK, a resolution equal to 640 \(\times\) 480 pixel.
## 3 Experiments and Analysis
### Quantitative Comparison
#### 3.5.2. Sstlcm  
While spatiotemporal fusion-based methods like SSTLCM demonstrate superior background suppression in cleaner scenarios (BSF > 38, SCRG > 13 in Seq.4-Seq.6), their comparatively lower SCRG (4.41-11.80) coupled with elevated D-statistic values (5.17 \(\times\) 10\({}^{-5}\)-3.88 \(\times\) 10\({}^{-5}\)) in complex scenarios (Seq.1-Seq.3) reveal fundamental limitations in cluttered background rejection stemming from suboptimal fusion of spatial contrast analysis and temporal sampling mechanisms, which fails to filter structured background interference. This persists despite maintaining decent AUC levels, as shown in Figure 9.
## 4 Conclusions  
Due to the random vibrations of the metal sheet caused by the opening and closing of the mold during the stamping process, image registration between the template image and the real-time monitoring image becomes crucial, making homography estimation especially important. Addressing the issues of large estimation errors, poor generalization, and limited industrial applications in current unsupervised homography networks under conditions of significant viewpoint changes and high image overlap, this paper proposes a multi-stage, multi-scale unsupervised homography estimation model. First, the HomographyNet model is improved by introducing a specialized deformable convolution module and GN (Group Normalization) layer, enhancing the model's ability to learn rotation invariance when processing large-scale, high-resolution images. Next, based on the unsupervised homography estimation network model, a multi-scale, multi-stage network structure is constructed. This structure estimates homography at different scales across various stages, minimizing pixel-level photometric loss at each stage for unsupervised training. By progressively refining the homography transformation relationship between images through multiple stages, the accuracy of homography estimation is improved, along with the model's ability to learn scale invariance. Finally, by integrating training with stamping process image data and the MSCOCO2014 dataset, and applying data augmentation through random perturbations, lighting variations, and different levels of blurriness, the model's robustness to the complex changes in the stamping monitoring environment is enhanced. This approach not only ensures the data scale required for network training but also addresses the issue of data scarcity in applying deep learning to specific industrial environments. It is worth noting that, due to the uncertainty in the stamping environment, the images of the sheet surface may be significantly occluded. Therefore, improving the robustness of homography estimation under occlusion will be the focus of future research.  
Figure 10: Ablation study results for eight test images across different network stages.  
**Author Contributions:** Methodology, Y.Z. and Y.D.; validation and formal analysis, Y.D.; supervision, Y.Z.; writing--original draft, Y.D.; writing--review and editing, Y.Z. and Y.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Novel Malware Detection Model in the Software Supply Chain Based on LSTM and SVMs
## 4 Proposed SSC Malware Detection Model
### The Construction of the LSTM Model  
This section deeply explores the construction process of the LSTM model, which is used as the pre-classifier in the LSTM-BO-SVM model. The LSTM model architecture consists of four main parts: an LSTM network layer, a Dropout layer, and two subsequent fully connected layers. The LSTM network layer is designed to capture the long-term dependencies between software with the same name but different version numbers in SSC, which is essential for understanding and predicting software behavior. In order to enhance the generalization ability of the model and avoid overfitting, a Dropout layer is introduced after the LSTM network layer. Subsequently, two fully connected layers are concatenated, where the first fully connected layer aims to improve the classification accuracy of the model. The second fully connected layer is set as the output layer with a single neuron and is used to handle binary classification tasks. The LSTM model flowchart is displayed in Figure 4.  
Figure 4: Flowchart of the LSTM model’s establishment.  
The steps to build the LSTM model are as follows:  
1. Input: The T1 and S1 datasets are input into the model.
2. Split the T1: Divide the T1 into a validation set V1 and a training set T1\({}^{\prime}\).
3. Train the LSTM model: Train the LSTM model using the T1\({}^{\prime}\) and V1 datasets. During the training process, the model parameters are constantly adjusted to obtain the optimal parameters.
4. Evaluate the model with V1: Use the test set V1 to assess the model's performance. Determine whether the stopping criterion is reached (training rounds or model accuracy reaches a threshold), and if so, proceed to step 5; otherwise, go back to step 3.
5. Obtain the trained LSTM model: After the above steps, obtain a trained LSTM model.
6. Obtain the software pre-classification set R: Input the training set T1 into the trained LSTM model to obtain the pre-classification results of T1 and form the set R.