esa-sceva/satcom-chunk-collection huggingface dataset:
# DCS-TransUperNet: Road Segmentation Network Based on CSwin Transformer with Dual Resolution
## 3 Method
### Mixed Loss Function  
Road extraction is often considered a pixel-level classification problem. Each pixel in the image is classified as road or non-road. Generally, we solve this problem by a binary cross-entropy loss function, whose formula is expressed as follows:  
\[L_{BEC}=-\frac{1}{N}\sum_{n=1}^{N}\left(y_{n}\log(y^{\prime}_{n})+(1-y_{n})\log (1-y^{\prime}_{n})\right) \tag{7}\]  
However, most of the pixels are non-road in high-resolution remote sensing images, and the pixels of roads only account for a small proportion. This means that the two types of pixel distributions are very uneven. In this case, if the loss function treats road pixels and non-road pixels equally, the model constrained by the loss function is optimized toward non-road categories to fall into the local optima. Furthermore, the road's pixel accuracy is declined under the constraint of the loss function. Therefore, in order to solve the problem of classes imbalance, we propose a new loss function \(L_{MIX}\) based on binary cross-entropy loss and the Dice loss. The \(L_{DICE}\)\(L_{MIX}\) formulaic expressions are as follows:  
\[L_{DICE}=1-\frac{1}{N}(\frac{2\sum_{m=1}^{N}y_{n}y_{n}^{\prime}+o}{\sum_{n=1}^{N }y_{n}+\sum_{n=1}^{N}y_{n}^{\prime}+o}+\frac{2\sum_{n=1}^{N}\left(1-y_{n} \right)(1-y_{n}^{\prime})+o}{\sum_{n=1}^{N}\left(1-y_{n}\right)+\sum_{n=1}^{N} \left(1-y_{n}^{\prime}\right)+o}) \tag{9}\]  
where \(L_{DICE}\) represents Dice loss and \(y_{n}\) is the ground truth label, whose value is either 0 or 1. \(y_{n}^{\prime}\) is the probability of the road.  
\[L_{MIX}=L_{BEC}+\lambda L_{DICE} \tag{10}\]  
where \(L_{DICE}\) is the Dice loss, \(L_{BEC}\) is the binary cross-entropy, and \(\lambda\) is a coefficient to balance between \(L_{BCE}\) and \(L_{DICE}\).  
From Formula (10), it can be concluded that the similarity between the predicted value and the real label value only determines Dice, which is independent of the number of training samples. Therefore, it can alleviate the problems caused by sample imbalance. However, Dice loss has some flaws, and strong gradient changes, which will lead to unstable training. Considering the above, we propose the new Loss function \(L_{MIX}\), which not only maintains the stability of the gradient but also prevents falling into the local optimum.
# Intelligent End-Edge Computation Offloading Based on Lyapunov-Guided Deep Reinforcement Learning
## 3 System Model  
### Network Model  
In this paper, we propose an end-edge industrial wireless network system with edge computation capability, which consists of an edge layer and an end layer. As depicted in Figure 1, the industrial wireless network is composed of \(M\) industrial base stations and \(N\) industrial terminals to support industrial production and manufacturing in factories. We define \(\mathcal{M}=\{1,2,\ldots,M\}\) as the set of \(M\) industrial base stations and \(\mathcal{N}=\{1,2,\ldots,N\}\) as the set of \(N\) industrial terminals.  
In this scenario, the industrial base station is equipped with an edge computing server, which is designed to supply computation resources for multiple industrial terminals. It also facilitates the scheduling of industrial terminals within its service area. The end layer comprises numerous industrial terminals with sensing, computing, communication and control capabilities, each of which generates an indivisible task that necessitates processing. Initially, each industrial terminal endeavors to process the task data using its own resources. However, when the task's computation requirements, in terms of cycles, are substantial, the industrial terminal may find itself unable to meet the designated deadline due to resource limitations. In such case, the industrial terminal must offload the task to a proximate industrial base station to ensure timely completion.  
Figure 1: System model.  
In \(t\)-th time slot, the channel gain between \(m\)-th industrial base station and \(n\)-th industrial terminal is denoted as \(h_{n,m}\), where \(h_{n,m}\) remains constant during the time slot, but it varies independently from time slot to time slot.
## 3 Error Overboundings against IMU Faults  
Different from the full-state EKF, the error-state EKF has no input of the control quantity during the entire filtering process. Compared with the full-state EKF, the error-state EKF keeps the magnitude of each dimension of the state vector close or consistent, which avoids the occurrence of singular matrices in the filtering calculation process, reduces the convergence time and improves the calculation efficiency.  
This part analyzes the propagation process of IMU faults for the error-state EKF in detail. Through the analysis, the recursive propagation equation between adjacent epochs of the EKF state error is obtained. Based on the EKF state error propagation equation and the EKF innovation equation, the error overboundings of the EKF state error are given.
# On Maximizing the Probability of Achieving Deadlines in Communication Networks
## 6 Simulation Implementation
### Application Layer (APP)  
In our simulation, the application layer (APP) laid right above the NET. It produced data units of 10 bytes in size which needed to be delivered to a sink node within an initial TTD that was set for all nodes equally, ranging from 12 ms to 100 ms in the various simulation runs. Data units were generated at each node at a rate of one unit every 80 s.
## 6 Results and Discussion  
Overall, this advanced 1D-CNN uses multiple convolutional layers, and aims to capture and classify intricate patterns in sEMG signals. The network's architecture is specifically designed to handle the high dimensionality and complexity of sEMG data, making it an effective tool for distinguishing healthy from neuropathic subjects based on muscle electrical activity.  
The results clearly show that the proposed neural network achieved high accuracy on both the training (96.732%) and test sets (94.234%). These findings indicate that the model is highly effective (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer Number** & **Layer Name** & **Input Shape** & **Output Shape** & **Kernel Size** & **Stride** \\ \hline
1 & Conv1D & (batch\_size, 20,000, 6) & (batch\_size, 19,996, 64) & 5 & 1 \\
2 & BatchNormalization & (batch\_size, 19,996, 64) & (batch\_size, 19,996, 64) & -1 & - \\
3 & MaxPooling1D & (batch\_size, 19,996, 64) & (batch\_size, 998, 64) & 2 & 2 \\
4 & Dropout & (batch\_size, 9998, 64) & (batch\_size, 9998, 64) & - & - \\
5 & Conv1D & (batch\_size, 9998, 64) & (batch\_size, 9994, 128) & 5 & 1 \\
6 & BatchNormalization & (batch\_size, 9999, 128) & (batch\_size, 4997, 128) & - & - \\
7 & MaxPooling1D & (batch\_size, 9994, 128) & (batch\_size, 4997, 128) & 2 & 2 \\
8 & Dropout & (batch\_size, 4997, 128) & (batch\_size, 4997, 128) & - & - \\
9 & Conv1D & (batch\_size, 4997, 128) & (batch\_size, 4993, 256) & 5 & 1 \\
10 & BatchNormalization & (batch\_size, 4993, 256) & (batch\_size, 2496, 256) & - & - \\
11 & MaxPooling1D & (batch\_size, 4993, 256) & (batch\_size, 2496, 256) & 2 & 2 \\
12 & Dropout & (batch\_size, 2496, 256) & (batch\_size, 2496, 256) & - & - \\
13 & Conv1D & (batch\_size, 2496, 256) & (batch\_size, 2492, 512) & 5 & 1 \\
14 & BatchNormalization & (batch\_size, 2492, 512) & (batch\_size, 2492, 512) & - & - \\
15 & GlobalAveragePooling1D & (batch\_size, 2492, 512) & (batch\_size, 512) & - & - \\
16 & Dropout & (batch\_size, 512) & (batch\_size, 512) & - & - \\
17 & Dense & (batch\_size, 512) & (batch\_size, 256) & - & - \\
18 & Dropout & (batch\_size, 256) & (batch\_size, 256) & - & - \\
19 & Dense & (batch\_size, 256) & (batch\_size, 1) & - & - \\ \hline \hline \end{tabular}  
* No value.  
\end{table}
Table 3: Accuracy of the proposed SLM.  
Figure 8: SLM architecture: advanced 1D-CNN.  
The high accuracy of the model in detecting neuropathies is promising for potential integration of such neural networks into automated clinical diagnostic systems. These systems could assist medical professionals in diagnosing neuromuscular conditions, reducing the time required for manual analysis of sEMG signals and increasing diagnostic accuracy [62, 63]. Although the results are promising, the size of the dataset used is relatively limited. Future studies could benefit from using larger and more diverse datasets to confirm the robustness and generalizability of the model. To translate these results into practical clinical applications, extensive clinical validation is necessary.
## 5 Results and Discussion  
In this section results of the pathloss model and BER closed-form expressions are described. Figure 4 displays our pathloss for two vehicles (obstacles). It can be remarked with more obstacles, i.e., \(n=0,1\) and \(2\), pathloss also increased. Further, when increasing distance between tx and rx, pathloss also rose. When the number of obstacles was \(2\), pathloss was \(-135\) dB at \(30\) m.  
Figure 5 shows the results of BCH in the Rayleigh fading using equation 14. The results are compared with the AWGN channel and the simulated BER in the Rayleigh channel. From Figure 5 it can be noticed that the BER was getting low when EbNo was increasing. The former's code rate (127,36) was superior. In Figure 6 the results using the conventional ASTBC equation and our proposed expression, i.e., expression 19 (BER using ASTBC with BCH coding), are shown. The BER of ASTBC with BCH coding was minimum in contrast with the BER without BCH coding. The simulated BER in the Rayleigh channel and the BER without BCH coding were similar. The former's code rate (127,36) was superior. Table 4 shows that our BER computation approaches outmatched other techniques.  
\begin{table}
\begin{tabular}{c c l l} \hline \hline
**Sr No.** & **Paper** & **Approach** & **Comparison with Proposed Approximations** \\ \hline
1 & [19] & MIMO-OFDM System using ASTBC & Low BER is obtained using proposed approximations \\ \hline
2 & [20] & ASTBC Impact on VANET & The Performance is evaluated using different modulaton schemes than code rate. \\ \hline
3 & [21] & BCH-STBC & Low BER is obtained using proposed approximations \\ \hline
4 & [22] & Fast Frequency Hopping Orthogonal Frequency Division Multiplexing (FFHOFDM) Pre-Coding & The results of proposed approximations are optimized. \\ \hline \hline \end{tabular}
\end{table}
Table 4: ASTBC and VANET BER computation approaches.  
Figure 4: Pathloss in the presence of vehicles.  
Figure 5: BER of BCH in Rayleigh fading.  
Figure 6: BER of BCH-ASTBC.
## 3 The Location-Based Interactive Model
### Mapping Functions
The cloud first checks whether or not the current location of the mobile user \(\mu\) is within the registered location of interest of the corresponding application \(\alpha_{RI}\). _Function 3_: If \(\mu_{L}^{current}\in\alpha_{RI}\), a specific subset of virtual sensors that are responsible for that area will be determined to serve the user:  
\[f_{3}(\mu_{RI},\gamma_{\alpha}^{*})=\gamma_{\mu}^{*}(\gamma_{\mu}^{*}\in\gamma _{\alpha}^{*}\&\gamma_{j->location}\in\mu_{RI}), \tag{8}\]  
where \(\mu_{RI}\) is the region of interest of the user \(\mu\) with radius of \(R\) around his location. The specific range \(R\) around the current location of \(\mu\) depends on applications, which is not covered in this paper. The cloud can reversely map the set of virtual sensors \(\gamma_{\mu}^{*}\) to a set of corresponding physical sensors \(\zeta_{\mu}\). We model the reserved mapping function as follows:  
\[f_{vir->phy}(\gamma)=\zeta=f_{phy->vir}^{-1}(\zeta). \tag{9}\]  
We now discuss two interactive models for physical WSNs with the cloud. In the first model, the sensor periodically reports sensing data to the cloud. In the second model, sensors are scheduled to report sensing data on-demand based on the location of mobile users.
# Novel Approach in Vegetation Detection Using Multi-Scale Convolutional Neural Network
## 2 Materials and Methods
### Proposed Architecture
#### 2.1.6 Softmax and Pixel Classification  
The softmax layer converts the output logits into probabilities for each class, enabling pixel-wise classification. This layer is essential for producing the final segmentation output, where each pixel is assigned to the most likely class based on the learned features.  
Thus, the proposed MSCNN architecture integrates various convolutional, batch normalization, pooling, and transposed convolution layers to effectively extract and refine features from high-resolution images. Each layer is purposefully designed to enhance the model's ability to learn complex patterns, making it suitable for tasks such as semantic segmentation in diverse applications.
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 6 Evaluation and Results
### Adding Time Information  
For the last tested configuration, we combine position, accelerometer, and time data to train the classifier. The AUC for a single gestures is about 95% and also the EER is the lowest for all evaluated single gesture configurations, with a value of 12.97% when considering gestures of lengths bigger than one.  
When considering multiple gestures, with gesture lengths greater than one, the configuration using position, accelerometer, and timestamp features does also perform better than any other tested. The AUC reaches up to 98% with 25 consecutive gestures grouped as seen in Figure 7. The EER of the classifier drops to about 11.4% when combining 25 gestures.  
When we are considering a classifier (again with four hidden states and four mixtures), where the lengths of the gestures have to contain more than 50 data points, the AUC is with 94% for single gestures a little bit worse than when considering gestures of all length. Furthermore, the EER is about 3% higher with 16.2%.  
As soon as the input gestures are grouped, the AUC rises up to 100% with 25 combined gestures as illustrated in Figure 8. Furthermore, the EER drops to 3.6% when grouping that many gestures.
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 5 Result Analysis and Performance Evaluation
### Under Jellyfish Reordering Attacks
#### 5.3.1 Packet Delivery Ratio (PDR)  
The impact of Jellyfish reordering attacks on the PDR is depicted in Figure 16. Similar to periodic drop and delay variance attacks, the PDR decreases whenever the number of attackers increases. In comparison to [37], 2017 (about 79.91%), and [20], 2018 (about 89.57%), the proposed protocol performs at a higher ratio of 93.91% for three malicious nodes. The PDR of [37], 2017, drastically drops to 56.85% when the number of attacker nodes is raised to nine, whereas [20], 2018, has a PDR of 74.84%, and our proposed protocol has the highest PDR of 78.18%. The results show that SecE-V2X performs noticeably better  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Attackers** & **Metrics** & **SecE-V2X** & **[20], 2018** & **[37], 2017** \\ \hline \multirow{4}{*}{0} & PDR (\%) & 95.34 & 93.80 & 91.60 \\  & EED (ms) & 6.80 & 7.11 & 8.64 \\  & Throughput (packets/s) & 638.15 & 596.33 & 534.55 \\ \hline \multirow{4}{*}{3} & PDR (\%) & 93.92 & 89.57 & 79.92 \\  & EED (ms) & 7.70 & 8.64 & 13.03 \\  & Throughput (packets/s) & 612.82 & 559.42 & 453.76 \\ \hline \multirow{4}{*}{6} & PDR (\%) & 90.50 & 79.19 & 72.07 \\  & EED (ms) & 8.71 & 11.49 & 17.21 \\  & Throughput (packets/s) & 556.02 & 472.08 & 402.10 \\ \hline \multirow{4}{*}{9} & PDR (\%) & 78.18 & 74.84 & 56.86 \\  & EED (ms) & 10.88 & 14.19 & 22.24 \\ \cline{1-1}  & Throughput (packets/s) & 441.15 & 415.64 & 290.37 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance values comparison under Jellyfish reordering attacks.  
Figure 15: Nodes’ data throughput under Jellyfish delay variance attacks [20,37].  
in terms of the PDR than other approaches, indicating that our protocol performs efficiently in the presence of Jellyfish reordering attacks.
None of the studies utilized radiomics for the quantification of [18F]FDG PET/CT, although it is considered a useful tool in the evaluation of bone marrow in other clinical contexts with bone marrow alterations [12; 13; 14; 15] or in other studies where the primary disease is lymphoma [16; 17]. The studies included in the three previous reviews evaluate the differentiation between positive BMI and negative BMI. However, the heterogeneous and diffuse uptake pattern of the BMI observed in [18F]FDG PET scans is also seen in patients affected by inflammation or infection as a response to the need for the overproduction of immune cells since bone marrow is the main hematopoietic organ [18; 19; 20; 21]. No studies have been identified that differentiated this inflammatory status from those previously mentioned. For these reasons, quantification methodologies beyond SUV metrics are needed to assess the potential of PET in distinguishing positive BMI from negative BMI to support clinical decision making in a less subjective way. Furthermore, these methodologies not only should include the distinction between these two bone marrow states but also the differentiation between inflammatory and infectious states. The aim of this study is to develop classification models to distinguish three bone marrow states: non-infiltrated (BMI\(-\)), infiltrated (BMI\(+\)), and inflammatory or reactive (rBM). To this end, different machine learning models based on bone marrow radiomics features are proposed. In order to obtain the features exclusively from the bone marrow region, a U-Net for segmenting bone marrow from CT images is trained and applied to the [18F]FDG PET images for extracting the metabolic information.
# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
## 5 Experiments and Results
### Attack Simulation
#### 5.2.4 Collusion Attack  
In this collusion attack, three out of five orderer nodes in an HLF channel were compromised. These malicious orderers sabotaged the consensus protocol to prevent new nodes from joining the network by disrupting the consensus process required for enrollment. They also interfered with the block finalization based on a probabilistic decision function. The malicious nodes employed a probability function tied to a block receipt counter. When the function output 1, the nodes colluded to invalidate the consensus for the current block (e.g., withholding approvals). A 0 output allowed normal validation. To evade detection, attackers could limit how frequently the function output 1 (e.g., triggering disruption in 30% of blocks within a window). The collusion attack scenario is shown in Figure 7. To demonstrate this attack, we modified the orderer.yaml files of the three orderers and configured them to drop transactions that exceeded a certain buffer size. Next, we developed a custom client (using HLF SDK) to generate transaction proposals with payloads that deliberately exceeded the buffer size. Standard-sized transactions were created to mimic legitimate traffic, while large payloads were generated with a predefined probability. Malicious orderers rejected oversized transactions during consensus. The attack prevented the required quorum (\(\geq\)3 confirmations) for block validation. This resulted in stalled block finalization, reduced throughput, and ultimately crashing the ordering service.  
Figure 6: Boycott attack scenario where the attacker modifies MSP configuration to revoke victim’s certificate resulting in denial of service to victim.  
Figure 7: Collusion attack scenario where malicious ordering nodes collude to sabotage the consensus protocol and prevent new nodes from joining the network.  
Figure 7(a),b demonstrate the attack's effects. We saw a 43% decline in the transaction throughput, down from 22 TPM to 12 TPM, and a 42% reduction in successfully recorded transactions, down from 93 to 54 successfully recorded transactions at a 60% probability of disruption. At 80% probability, the throughput was reduced by roughly 90%.  
A low throughput over extended periods is likely to breach the performance expectations defined in SLAs or user contracts, resulting in cascading delays across integrated systems. The attack highlights how malicious coordination among orderers can not only slow the network but also render it unreliable from a service delivery perspective. It also underscores the risks of insider collusion in permissioned blockchains, where compromised orderers can cripple both the availability and trust.
## 1 Introduction
* The proposed reward predictor and fake node generator can fully leverage querying results. The reward predictor fine-tunes the model parameters using the query results, ensuring that predictions are closer to the actual query results. The fake node generator enhances the success rate of the attack by generating fake nodes that are dissimilar to those from previous unsuccessful cases. * Through extensive experiments conducted on various recognized benchmark datasets, the effectiveness of the proposed GCIA method has been demonstrated in comparison to state-of-the-art attack models across varying attack budgets. In the single fake node-single fake edge injection attack scenario, when the target black-box model is a GCN, after conducting 10 queries, the GCIA method achieved attack success rates of 43.5%, 60.3%, 100%, and 91.1% on the Cora, Citeseer, Reddit, and PubMed datasets, respectively. This work is an extension of our previous study, presented at the ICASSP 2024 conference [17]. The current manuscript expands upon the preliminary findings reported in the conference paper by including multi-fake node attacks and multi-fake edge attacks. Extensive experiments have been conducted to test the impact of the fake node number and fake edge number on the attack performance. The remainder of the paper is organized as follows: In Section 2, we review the relevant literature on graph injection attacks and graph contrastive learning. In Section 3, we introduce the preliminaries of graph contrastive learning and graph injection attacks. Then, in Section 4, we introduce the proposed Graph Contrastive Learning-based Graph Injection Attack (GCIA) method. The node encoder, the reward predictor, and the fake node generator are detailed in Section 4.2, Section 4.3, and Section 4.4, respectively. Section 5 describes our experimental results, after we evaluated the performance of the proposed GCIA method across four adversarial settings. Finally, Section 6 concludes with a summary and an outline of promising directions for future works.