esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating Elliptic Curve Cryptography with the Modbus TCP SCADA Communication Protocol
## 4 Architecture and Implementation
### Encrypted Communication  
After the keys have been established the encrypted communication can be initiated. The library we have used for our implementation contains the complete Modbus functionality implemented as a set of C functions. Modbus was first developed as a serial protocol and to this day the serial version is, still, used. Libmodbus provides both serial and TCP modes of operation for the Modbus protocol suite. Our implementation is a part of the Modbus TCP backend. The TCP backend \"send\" function includes, now, the full encryption functionality (point-to-point encryption). However, the decryption functionality was implemented on a higher level in the \"receive\" Modbus function, due to the way Modbus handles the received messages.  
In some more detail, the backend \"send\" function encrypts each message using the key that is stored in the appropriate Modbus data structure of the device. The encrypted message is a character array and, thus, it does not need any type conversions. The Modbus \"receive\" function has been modified, however, more extensively. For a Modbus request to be processed correctly, it has to be received according to a specific sequence of steps. At first, the function's return code is received and checked to determine how many bytes remain in the request. Based on the value of this code, the recipient knows how many more data items remain to be decrypted in the \"receive\" Modbus function. Thus, the remaining bytes are read and processed accordingly. This Modbus functionality must be preserved in the modified version of the protocol. Consequently, the decryption process should also be performed in the same sequence of steps followed by the \"receive\" function. As we deploy the one-time pad algorithm, the messages are decrypted using an XOR mask comprised of the shared key.  
The encryption/decryption functionalities embedded in the Modbus send/receive functions, respectively, were implemented with the corresponding cryptographic functions of the ECCLIB, based on two separate functions. The first function is responsible for the generation of the XOR mask which is then stored to be used for the decryption process. The mask length was set to the largest possible value. This was an essential decision since Modbus messages can vary in size and the encryption/decryption process should be able to handle all possible message sizes. The second function is responsible for executing the one-time pad algorithm in the corresponding parts of the message and the mask.
## 5 Evaluation
### Location Contexts Semantic Analysis  
To evaluate the effectiveness of representation in characterizing different contexts, we firstly build up a context database of all training trips by adopting a bottom-up clustering process (i.e., lower-level clustering followed by upper-level clustering) with DBSCAN. For 20 training trips, we finally get 86 lower-level clusters, 7 upper-level clusters, and 14 small clusters remain as noise at the upper level, given their unique satellite patterns. To provide ground truth for the training set, we manually assign predefined labels, as described in Section 5.1.1, to all the representations in each cluster. Noise points detected through DBSCAN at lower-level clustering are labeled as \"Noise\".  
At the query phase, we conduct a top-down search within our context database. We assign each representation the same detailed context label of its \(k\) nearest neighbors, using cosine distance as a metric. If the nearest distance is larger than our predefined threshold, we record it as a \"failure case\". We manually examine the assigned labels based on their corresponding locations for validation. As shown in Figure 9, we firstly evaluate the success rate given different thresholds. The success rate of queries increases when the threshold increases, and a threshold of 0.5 can cover 98.6% of queries. We then evaluate classification accuracy under threshold of 0.5 for different \(k\) values, and achieve the highest classification accuracy of 91.62% when \(k=20\); the accuracies at different \(k\) values are all above 90%. The results show that our model can successfully recognize most contexts given a sufficient and well-labelled database.  
To better showcase the performance in the context semantic analysis, we demonstrate the result of two test traces along with their snapshots, taken from Google Maps in Figure 10. Most of the location points are correctly labeled, except some noise points and a mismatch between \"on void deck\" and \"on sheltered street\" in both traces, given that the two scenarios share quite similar contexts and, thus, it is hard to  
Figure 8: Performance of localization accuracy indicator. (**a**) Mean probability of error falling within the confidence range at different time resolutions; (**b**) median probability of error falling within the confidence range at different time resolutions.  
Figure 9: Clustering performance with various threshold and \(k\).
## 4 Residual Error Optimization Strategy Based on Root-Mean-Square and Peak-to-Valley Maps
### Derivation of the RMS and PV Maps  
The RMS (PV) map characterizes the relationship of the RMS (PV) value of the residual error obtained after theoretical deterministic polishing with the path spacing and the surface form error. From the results of the third set of simulation experiments in Section 3, the experimental data in the range of reasonable path spacing (0.1 mm to 0.6 mm) were extracted and collated, and then, the initial RMS and PV maps were obtained, as shown in Figure 5a,c. Using surface fitting techniques, we obtained the final smoothed RMS and PV maps, as shown in Figure 5b,d. The proposed RMS and PV maps provide a theoretical model reference for predicting the RMS and PV values of the residual error after practical deterministic polishing.  
Figure 5: (**a**) Original RMS map; (**b**) fitted map of RMS; (**c**) original PV map; (**d**) fitted map of PV.
# Evaluation of IEEE802.15.4g for Environmental Observations
## 4 Analysis
A dotted line is added as a reference on every figure to easily compare the 2 FSK-50 kbps radio setting with the rest of the PHYs. Although in most cases 2 FSK-50 delivers a good PDR in comparison with most of the other PHYs, this paper shows that there may be more interesting PHYs depending on the constraints of range, throughput, duty cycle and electric charge consumption.
# MDPN: Multilevel Distribution Propagation Network for Few-Shot Image Classification
## 3 Proposed Method
### Problem Definition  
In standard few-shot classification, there are two datasets: the training set \(D_{base}=\{(x_{i},\ y_{i}),\ y_{i}\in C_{base}\}\) and the test set \(D_{novel}=\{(x_{i},\ y_{i}),\ y_{i}\in C_{novel}\}\), where \(C_{base}\) and \(C_{novel}\) represent the base classes in the training set and novel classes in the test set, and they do not overlap (\(C_{base}\cap C_{novel}=\varnothing\)). Training and testing for few-shot image classification consist of a number of episodes. Each episode is constructed by randomly sampling \(K\) categories, which consist of \(N\) labeled images and \(U\) unlabeled images, i.e., the \(k\)-way \(n\)-shot setting. The labeled images are called support set \(S=\{(x_{i},\ y_{i})\}_{j=1}^{K\times N}\), and the unlabeled images are called query set \(Q=\{(x_{i},\ y_{i})\}_{j=1}^{K\times U}\), where they do not overlap (\(S\cap Q=\varnothing\)).  
Take the 5-way 1-shot (that is, \(K=5\), \(N=1\)) episode setting as an instance:  
1. Divide the dataset into a training set \(D_{base}\) and a test set \(D_{novel}\) by category.
2. Sample five categories from \(D_{base}\). Then, sample 1 image from each category as a labeled image to form the support set \(S\), and \(U\) images from each category as unlabeled images to form the query set \(Q\) (\(U\) can be set according to your needs, such as 1, 15, etc.). In our experiment, \(U=1\), \(S\cap Q=\varnothing\). The model learns the features of the images in \(S\) according to the specific algorithm and predicts the labels of the images in \(Q\). Calculate the loss between predicted labels and ground truth labels, then backpropagate the loss.
3. Repeat step (2) until the preset number of episodes is reached.
4. Sample five categories from \(D_{novel}\). Then, sample 1 image from each category as a labeled image to form support set \(S\) and \(U\prime\) images from each category as unlabeled images to form query set \(Q^{\prime}\) (\(U\prime\) can be set according to your needs, such as 1, 15, etc.). In our experiment, \(U\prime\)=1, \(S\cap Q^{\prime}=\varnothing\).
5. Repeat step (4) until the preset number of episodes is reached.
6. Repeat steps from (2) to (5) until the preset number of epochs is reached.  
The \(K\)-way \(N\)-shot setting aims to train a classifier that can accurately map a query set to its label based only on a small support set. The 5-way setting is chosen in our experiments instead of more categories, such as 20-way, 36-way, 50-way, etc. [30; 31; 32] Compared to the \(Q\)-way (\(Q>5\)), the 5-way setting requires less data to reduce the risk of model overfitting and is more suitable for training with a small amount of data or limited time. Therefore, we use a 5-way setting in our study.
# MRI Diffusion Connectomics-Based Characterization of Progression in Alzheimer's Disease
## 2 Materials and Methods
### Adaptions to Methodology
The F1-score was 0.64, providing a harmonic mean of precision and recall, and the overall accuracy of predicting the correct class was 71%. The model's ability to detect \"Converters\" (true positive) has room for improvement, with a sensitivity of 0.52. The specificity was much better, 0.90, suggesting a strong ability to identify Normal subjects. This was expected given the large class imbalance. The model exhibited a Cohen's Kappa of 0.42, indicating that there is moderate agreement between the predicted classifications and the actual classifications. These results are consistent with other studies who attempted to predict future disease conversion by using DWI-only data (Table 8). To enhance the predictive power of this model, we integrated these two diffusion metrics with traditional clinical variables, including MMSE, MoCA, RAVLT, CDR-SB, FAQ, hippocampal volume, entorhinal volume, and A\(\beta\) and Tau indicators. This hybrid model achieved a significant improvement, yielding an accuracy of 86%, a sensitivity of 86%, and a specificity of 93%. These results surpass the diagnostic performance of current clinical assessments, where the sensitivity ranges from 70.9% to 87.3% and the specificity from 44.3% to 70.8% [69]. Our findings emphasize the value of a hybrid machine learning approach that combines advanced neuroimaging techniques with conventional clinical assessments. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Model** & **AUROC** & **AUPRC** & **F1** & **Accuracy** & **Sensitivity** & **Specificity** & **Kappa** & \(p\)**-adj** \\ \hline RF 1 & 0.74 & 0.75 & 0.64 & 0.71 & 0.52 & 0.90 & 0.42 & \(1.11\times 10^{-14}\) \\ XGBTree 2 & 0.77 & 0.72 & 0.57 & 0.67 & 0.44 & 0.90 & 0.34 & \(3.78\times 10^{-10}\) \\ MARS 3 & 0.64 & 0.56 & 0.50 & 0.61 & 0.48 & 0.74 & 0.26 & \(7.20\times 10^{-5}\) \\ SVM 4 & 0.54 & 0.50 & 0.24 & 0.50 & 0.16 & 0.84 & \(-0.00\) & \(0.05\times 10^{1}\) \\ \hline \hline \end{tabular}  
* Random forest mtry = c(2, floor(sqrt(num_features)), floor(num_features/3)) 2 eXtreme gradient boosting \(n\)rounds = seq(from = 25, to = 100, by = 25); max_depth = seq(from = 5, to = 35, by = 10); eta = seq (from = 0.2, to = 1, by = 0.2); gamma = seq(from = 1, to = 10, by = 1); csample_bytree = seq(from = 0, 6, to = 1, by = 0.2); min_child_weight = seq(from = 2, to = 5, by = 1); subsample = 1 3 Multivariate adaptive regression splines; degree = seq(from = 1, to = 3, by = 1); nprune = seq(from = 1, to = 10, by = 1) 4 Support vector machine; sigma = (0.001,0.01,0.1,1,10,100); C = (0.001,0.01,0.1,10,100).  
\end{table} 
Table 9: Best prediction models of Normal vs. Converter within 3 years.
# Detection of Welding Defects Using the YOLOv8-ELA Algorithm
## 1 Introduction
First, the feature fusion module is redesigned into a Hierarchical Scale-based Feature Pyramid Network (HS-FPN), which boosts the model's ability to generalize while reducing the number of parameters and improving the accuracy. Next, the Coordinate Attention (CA) attention mechanism [24] is replaced with the Efficient Local Attention (ELA) mechanism [25], which improves the detection of small defects such as porosity, inclusions, and incomplete welds in DR images of aluminum alloy welds, thus meeting the requirements for real-time performance, including the model speed, accuracy, and efficiency. Furthermore, the Simple Attention Mechanism (SimAM) module is incorporated into the framework to enhance the detection performance for micro-defects, particularly in complex industrial inspection scenarios where subtle surface anomalies are easily obscured by background noise. The experimental results show that the proposed model achieves enhanced detection accuracy and speed, making it suitable for most industrial applications.
## 2 Materials
### Remote Sensing Image Data  
The European Space Agency launched Sentinel-2 [36; 37], an advanced Earth observation satellite specifically intended to detect subtle changes on the Earth's surface using the visible and near-infrared spectral bands. Google Earth works as a comprehensive global platform for geographical information, including satellite images and geographic information services on a worldwide scale. Nevertheless, Google Earth images just cover the fundamental red, green, and blue spectral bands. Thus, in this study, we see it as additional data to Sentinel images, using its abundant surface texture information to improve the analytical efficacy of the research. The research area is shown in the following Figure 2.  
Figure 1: Panda trajectory information.  
Figure 2: Remote sensing image map of the research area. (**A,B**) Sentinel-2 remote sensing image example. (**C,D**) Google Earth remote sensing image example.
# Model Test on the Behaviors of Deep Excavation with Lateral Confined Water
## 3 Results
### Analysis of Bending Moment of diaphragm Wall  
The bending moment variation curve of the right diaphragm wall is shown in Figure 9. The sign convention for the bending moment is defined such that positive values correspond to the inward deflection of the diaphragm wall, while negative values correspond to outward deflection. The bending moment distribution along the diaphragm wall follows a distinct pattern: negative bending moments are observed at the top and bottom, while positive bending moments dominate the middle section. Moreover, the maximum positive bending moment exceeds the maximum negative bending moment. Regardless of whether the excavation (K1-K4) or the lateral confined water level increase (F), the maximum bending moment shows a general increasing trend due to soil stress release and the growing pressure from the lateral confined aquifer. The bending moments are largest in the middle section (0.20-0.38 N\(\cdot\)m), followed by the bottom (0.21-0.24 N\(\cdot\)m), with the smallest values at the top (0.02-0.09 N\(\cdot\)m). A negative bending moment develops at the top of the diaphragm wall, increasing gradually. Prior to excavation, this negative bending moment is induced by the rise in lateral confined aquifer pressure. After excavation, the axial force in the struts increases (Figure 11), transmitting outward forces to the top of the diaphragm wall, resulting in an increase in the bending moment in this region. The maximum positive bending moment occurs at the middle of the diaphragm wall, where the confined aquifer pressure causes significant inward displacement, producing positive bending. The negative bending moment at the bottom of the diaphragm wall is relatively small, as the bottom end is embedded in the fine sand layer, which is significantly affected by excavation disturbances. Below the excavation level, the diaphragm wall experiences negative bending due to soil aggregation effects [20], with the bending moment at a depth of 530 mm concentrated at \(-\)0.22 N-m.  
In the design of the excavation, attention should be given to the maximum positive bending moment at a depth of 210 mm, where the inward deflection of the diaphragm wall may cause excessive deformation or damage to the support structure. At a depth of 50 mm, the maximum negative bending moment, which leads to outward deflection, could result in cracking at the top of the diaphragm wall and excessive displacement in the surrounding soil.
# Continuous-Wave THz Imaging for Biomedical Samples
## 2 CW THz Single-Point Scanning Imaging
### CW THz Attenuated Total Reflection Imaging  
CW THz reflection imaging systems often use a quartz plate or poly acrylic window to eliminate the inhomogeneity of the sample, which is inevitable for the sample surface. By contrast, CW THz attenuation total reflection (CW THz-ATR) imaging geometry is adjusted to the image for liquid, powder, or thin film samples without further preparation. Additionally, it can obtain the exact absorption distribution of the sample [43]. The samples are directly deposited on top of a prism. If the incident THz beam on the top surface of the prism has a higher refractive index \(n_{1}\) and the surface of the sample has a lower refractive index \(n_{2}\), then the refraction angle will exceed the incident angle \(\theta_{\text{c}}\) (\(\theta_{\text{c}}=\arcsin\left(n_{2}/n_{1}\right)\)). When the incident angle increases to a certain degree, the refraction angle becomes 90\({}^{\circ}\), and ATR occurs [44]. At this time, not all of the THz beam returns from the surface of the prism, but it transmits into a depth of wavelength magnitude and is absorbed by the sample along the interface, and then shot out from the prism along the direction of the  
Figure 4: Schematic of optical setup for CW-THz CP and LP imaging.  
reflected beam. ATR mode of biological samples is working close to the critical condition when transmitted mode can take place since the angle of incidence is usually 45 degrees. If refractive index of the sample is larger than 1.5 (very probable), ATR is not working [43]. This method can be used to rapidly and sensitively detect highly absorbable solid or liquid samples on the top of the ATR prism, and especially the surface information of the samples can be acquired without complex preparation.  
In 2017, [PERSON] et al. achieved CW THz-ATR imaging with an FIRL 100 laser with an output frequency of 2.52 THz and a Golay cell detector [45], where the sample was a porcine tissue on top of the fixed prism. The scanning step was 200 \(\upmu\)m, and the resolution of this system was 578 \(\upmu\)m. Figure 5 shows the CW THz-ATR imaging setup. The results showed that lean and adipose tissues could be clearly distinguished. The intensity attenuation of the lean portion was more than twice that of the adipose parts, which demonstrated that different parts of the tissue can be accurately identified by this proposed THz-ATR imaging method for biological tissues.
## 3 Network Anomaly Detection
### Header and Payload Analysis
#### 3.2.1 Data Link Layer  
Source and destination MAC addresses can be spoofed so actual addresses are changed to forged ones. This can be used, for instance, to perform MITM attacks. Detection of MAC spoofing can be achieved with an analysis of the _sequence number_ field, in the MAC header of IEEE 802.11. As a rule, this field is incremented by one (limit 4096) for every transmitted frame. As reported by [63], the existence of a gap in this field for a sequence of packets of the same MAC address evidence the attack.  
[PERSON] and [PERSON] [64] proposed a method for detecting MAC spoofed packets using Simple Network Management Protocol (SNMP) protocol and the second version of Management Information Base (MIB-II) specification. Using the management protocol, it is possible to count the bytes entering and leaving a given port, along with the amount of unicast and non-unicast packets entering and leaving the port. With these counters, the imbalance between the ARP requests and replies may be calculated and depending on the result, the ARP spoof attack is detected.  
[PERSON] et al. [65] studied different attacks in IEEE 802.11 networks, classifying them as key retrieving, keystream retrieving, MITM and availability attacks. They also published a dataset containing the second layer fields that provide evidence of such attacks among normal traffic.
## 4 Results and Discussions
### Analysis of ADMM Effectiveness  
To evaluate the effectiveness of the ADMM algorithm employed in this paper, a comparison of centralized and distributed optimization results is conducted. Additionally, the convergence of residuals and objective values with the number of iterations is analyzed.  
Table 12 presents the objective values from both approaches. The results show that the objective values of distributed optimization and centralized optimization are nearly identical, indicating that the distributed optimization method employed in this paper meets the required standards for computational accuracy.  
Figure 11 presents the convergence behavior of the ADMM algorithm applied to the intraday distributed optimization. The maximum residual values plotted against iterations on a logarithmic scale demonstrate the algorithm's convergence characteristics. The convergence threshold of \(10^{-2}\) is reached at approximately iteration 24, with residual values subsequently stabilizing below this threshold for the remaining iterations. This convergence pattern verifies the effectiveness of the ADMM-based approach for coordinating the operational decisions between AC subnetworks and the DC ring network while preserving the privacy requirements. The corresponding objective function trajectories shown in Figure 12a,b for the AC subnetworks and DC ring network, respectively, confirm that the solution quality stabilizes along with the residual convergence. These results indicate that the adopted ADMM algorithm is capable of solving the intraday optimal scheduling problem for AC/DC distribution systems, with its accuracy and computational efficiency meeting the required standards.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Objective** & **Centralized (CNY)** & **Distributed (CNY)** & **Deviation** \\ \hline \(\sum\limits_{e\in E}C_{\text{AC},e}\) & 79,531.35 & 79,523.65 & 0.01\% \\ \(\text{C}_{\text{DC}}\) & 29,884.75 & 29,859.78 & 0.08\% \\ \(\text{C}_{\text{CSES}}\) & 874.68 & 874.68 & 0.00\% \\ \(\text{C}_{total}\) & 50,521.28 & 50,538.56 & 0.03\% \\ \hline \hline \end{tabular}
\end{table}
Table 12: Objective values comparison of centralized and distributed optimization.  
Figure 11: Relationship between residuals and the number of iterations.