esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Software Architecture Solution with a Focus on Long-Term IoT Device Security Support
## 4 Device Architecture
### Hardware Layer
The ambient temperature affects the running time of the compressor and the fan, so this information can be used to detect the malfunctions of the unit. Temperature and flow sensors are connected to the main processing unit. The passive sensors are connected directly to the 8-bit microcontroller. The temperature sensors are NTC thermistors connected to a 10-bit analogue-to-digital converter, which passes the data to the microcontroller via the I2C bus. Rotating flow sensors are directly connected and counted via the digital input line of the microcontroller. The microcontroller executes the device algorithm to provide temperature hysteresis cooling system based on the bath temperature sensor. The device has a two-line liquid crystal display that shows the readings from the sensors and allows the reading and setting of the bath base temperature and hysteresis differential temperature. Cumulative flow sensor readings and settings are stored in EEPROM and are retained even if the unit loses power. Other sensor readings are displayed in real time and are not stored or transmitted to an output device. The unit uses a simple fault detection algorithm based on compressor temperature and the deviation of the average python tube temperature from the bath temperature. For power failure detection, the motherboard is equipped with current sensors that provide information about compressor, pump or fan failure. Due to the low resolution of the current sensors, they cannot provide a detailed current measurement or energy footprint of the device. Figure 4: Python tubing with six beverage lines and a return central line.
# An Approach to Industrial Automation Based on Low-Cost Embedded Platforms and Open Software
## 4 Results  
### Simulation Results  
The control systems described in Section 3.2 are first simulated in MATLAB using the module for the solution of ordinary differential equations. To compare the performance of the proposed control schemes, the tracking performance index described by Equation (12) is used. This index represents the ability of the controller to track a reference. A lower value of this index represents a better performance of the controller [16].  
\[J=\sum_{k=1}^{+\infty}\Big{(}\|h_{1}(k)-r_{1}(k)\|^{2}+\|h_{2}(k)-r_{2}(k)\|^{ 2}\Big{)} \tag{12}\]  
The simulation model is configured using the parameters described in Table 1, considering a sampling period (\(T_{s}\)) of 500 ms and introducing measurement noise with power \(1\times 10^{-7}\). Simulation tests are performed with the purpose of evaluating the performance of the classic PID and fuzzy + PID control schemes before implementing them.  
Table 5 shows the results of the overshoot and settling time obtained in simulation for the two control schemes. The FGS-PID reduces the settling time and overshoot in tank 2.  
Table 6 shows the performance index (Equation (12)) of the control schemes for different tests. The first test stabilizes the tank levels at particular set points. The second test consists of making reference changes in the two tanks. The third test consists of simulating leaks in the tanks to evaluate the fault detection system. It can be seen that, in general, the use of the FGS-PID controller improves the overall system performance.  
Figure 8 shows the simulation results of the control schemes in terms of stabilizing the system at a reference value. Figure 9 shows the residue obtained in the presence of leaks. The system detects leaks when the value of the residue is below a defined threshold (\(lim=-0.010\)). When the system leak disappears, the residue increases its value and allows the identification of the fault-free operating state.  
Figure 8: Simulation results of the control systems.  
Figure 9: Residue simulation results to evaluate fault detection.
# Machine Learning Detection of Cloud Services Abuse as C&C Infrastructure
## 3 Related Works
### Rule-Based Detection  
[PERSON] et al. [11] introduced a dual-level abuse detection system, comprising client-side and server-side mechanisms. On the client side, they defined three features to detect botnets: self-concealment, unusual network traffic, and questionable provenance. They posited that connections to social media platforms might be considered suspicious unless driven by human actions. To differentiate between legitimate users and bots, they employed behavioral biometrics, user input reactions, and graphical user interface (GUI) interactions as detection metrics. On the server side, they operated under the assumption that any communication with social media platforms that involves textually encoded messages or posts is suspicious. They utilized the J48 decision tree algorithm to categorize input messages, differentiating between Base64 or Hexadecimal-encoded text and regular language content. However, these detection methods come with certain constraints: (i) they do not offer real-time detection since the tests were conducted in a post-analysis lab environment, and (ii) crafty adversaries could potentially bypass detection by using image-steganography techniques to embed malicious commands within posts.  
[PERSON] et al. [12] developed the API Verifier, a tool that uses CAPTCHA verification to authenticate social media account access based on MAC addresses. This tool determines whether an API call is made by a human or a bot, adding a protective layer against automated bot activities. However, the API Verifier proposed by [PERSON] et al. has several drawbacks. First, the CAPTCHA verification system can be vulnerable to relay attacks, potentially enabling botnets to circumvent the verification. Second, depending solely on MAC addresses for user identification might fall short in situations where users switch between multiple devices or when MAC addresses are easily spoofed.  
[PERSON] et al. [13] delve into the study of stego-botnets that utilize steganographic images on online social networks for C&C operations. They proposed a system named SocialClymene, designed to detect covert botnets in social networks using stego-images. The system has a negative reputation subsystem that analyzes images shared by social network users and calculates a reputation score for each user based on their history of participating in suspicious activities. The goal is to recognize botnets by analyzing the behavior of the users and their history of involvement in suspicious activities. Nevertheless, these detection approaches have certain limitations: (i) the system might not detect new botnets lacking a history of suspicious behavior, and (ii) it can be challenging to accurately identify a user's reputation, especially in dynamic online settings where reputations can shift swiftly.
###### Abstract  
Practical Byzantine Fault Tolerance (PBFT) has been widely used in consortium blockchain systems; however, it suffers from performance degradation and susceptibility to Byzantine faults in complex environments. To overcome these limitations, this paper proposes RE-BPFT, an enhanced consensus algorithm that integrates a nuanced node credibility model considering direct interactions, indirect reputations, and historical behavior. Additionally, we adopt an optimized ID3 decision-tree method for node classification, dynamically identifying high-performing, trustworthy, ordinary, and malicious nodes based on real-time data. To address issues related to centralization risk in leader selection, we introduce a weighted random primary node election mechanism. We implemented a prototype of the RE-BPFT algorithm in Python and conducted extensive evaluations across diverse network scales and transaction scenarios. Experimental results indicate that RE-BPFT markedly reduces consensus latency and communication costs while achieving higher throughput and better scalability than classical PBFT, RBFT, and PPoR algorithms. Thus, RE-BPFT demonstrates significant advantages for large-scale and high-demand consortium blockchain use cases, particularly in areas like digital traceability and forensic data management. The insights gained from this study offer valuable improvements for ensuring node reliability, consensus performance, and overall system resilience.  
PBFT; consensus algorithm; node credibility; ID3 decision tree; consortium blockchain 2025
# Neighbor Discovery for IPv6 over BLE Mesh Networks
## 3 6 LoBLEmesh Neighbor Discovery
### Neighbor Discovery Based on BLE 5.0  
The neighbor discovery process is the first step for devices to establish a connection. According to the latest BLE 5.0 specification [14], the NDP includes two main events, the advertising, and the scanning event. Advertisers periodically send advertising packets on primary channels (CH37, CH38, and CH39), and scanners, within the discovery range, scan for these packets. For the purpose of neighbor discovery, once the scanner receives the ADV_EXT_IND packet from the advertiser, the related information about AUX_ADV_IND such as offset (offset37, offset38, offset39), and the channel used for packet transmission (CHx1) is obtained by the scanner. Owing to the offset, the scanner can know when AUX_ADV_IND would be sent. For AUX_ADV_IND, one of the 37 channels CHx1 (channel_index= 0, 1,..., 36) may be used for packet transmission. The scanner additionally scans on CHx1 at the time that the corresponding offset indicates. After the scanner receives the AUX_ADV_IND packet from the advertiser, it will send the connect_req to the advertiser and wait for  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Character** & **6 LoWPAN** & **6 LoBLE (Mesh Topology)** & **6 LoBLE (Star Topology)** \\ \hline Border router & 6 LRR & Mesh Gateway & Central \\ The router & 6 LR & Relay node & N/A \\ Host & 6 LN & Node, Friend node & Peripheral \\ \hline \hline \end{tabular}
\end{table}
Table 1: The nodes in IPv6 over Low Power Wireless Personal Area Network (6 LoWPAN) and IPv6 over BLE (6 LoBLE).  
Figure 2: IPv6 over BLE Networks.  
the connect_req_ACK. As shown in Figure 3, the advertiser and the scanner can set up the connection on Channel CHx1.  
A traditional BLE network turns to the connected communications mode when the neighbor discovery process is performed on channel CHx1. However, to achieve the transmission of the IPv6 packets, the IPv6 address is needed for the nodes in 6 LoBLE networks. In 6 LoBLEmesh ND, following the connection setup, the new joined node performs the IPv6 address auto-configuration mechanism on channel CHx2. The configuration method will be described in detail in Section 3.2. Based on BLE 5.0, the frequency hopping strategy is used to change channels after each information exchange between nodes. Therefore, CHx3 and CHx4 are used for the 6 LoBLE communications and address update as shown in Figure 3.
## 2 The Learning Algorithm
### Quantum-Enhanced Algorithm with DJ  
Given a Boolean function \(f:\{0,1\}^{n}\to\{0,1\}\), that is guaranteed to be constant or balanced, and its reversible circuit \(U_{f}:|x\rangle|y\rangle\to|x\rangle|y\oplus f(x)\rangle\), the DJ algorithm determines whether \(f\) is constant or balanced with one call to \(U_{f}\). A balanced function means \(f(x)=1\) for half of the possible input string \(x\). The DJ algorithm is conducted using operator \((H^{\otimes n}\otimes 1)U_{f}H^{\otimes(n+1)}\) on the initial state \(|0\rangle^{\otimes n}|1\rangle\). The probability to measure an all-zero string \(x=00\ldots 0\) is \(P_{0}=\lfloor\frac{1}{2\pi}\sum_{x}(-1)^{f(x)}\rfloor^{2}\). Hence, if \(f\) is constant, the probability to obtain a measurement result \(00\ldots 0\) is \(P_{0}=1\). If \(f\) is balanced, then \(P_{0}=0\). If \(f\) is not constant nor balanced, then \(1>P_{0}>0\).  
Given the data \(\{(\vec{x}_{i},y_{i})\}\) in a sphere, we construct the reversible circuit for the function \(f:\vec{x}_{i}\to(1+y_{i})/2\). This serves as the oracle function in the DJ algorithm. A four-data point example is shown in Table 1 and Figure 3. For \(n\) data points, the reading of the data and the construction of the oracle \(U_{f}\) take \(O(n)\) time. However, once given the oracle, the DJ algorithm can determine whether the function is constant or balanced with a single call. In the case where the data are not constant, there is some probability to obtain measurement results other than \(0\ldots 0\). We could repeat the algorithm several times (the number of repetitions is another hyper-parameter that determines the precision) to obtain a reasonable estimation of the probability of how likely that is a constant function. Then, this probability provides an estimation for \(c_{i}\). For an efficient implementation ofthe \(U_{f}\) circuit, one could utilize the single-step \(n\)-Toffoli gate [32] in a quantum computer. While this scheme works in principle, it is difficult to implement and the overhead is large. In our simulation, we take an simplified oracle consisting of one \(n\)-Toffoli gate and one \(n\)-Toffoli gate with negative control, as shown in Figure 4. Each qubit is encoded in a classical indicator state \((1+y_{i})/2\in\{0,1\}\). Then, a single query to the oracle gives the measurement result \(\prod_{i}(1+y_{i})/2\), which indicates whether the data are constant or not. This approach is simpler to implement, but requires more encoding qubits. In the original DJ, it takes \(O(\log n)\) for \(n\) data points, while, in this simplified approach, it takes \(O(n)\) qubits. However, it has a potential acceleration similar to that of DJ, where a single call to the oracle gives the desired result.
## 4 Experimentation and Analysis of Results
### Experimental Results and Analysis
#### 4.3.1 Performance of Different Classification Algorithms
The results are presented in Figure 10. Figure 10 demonstrates that both models exhibit progressively improving accuracy with increasing epoch count until reaching stabilization. Notably, the validation accuracy marginally exceeds the training accuracy in both architectures. (4) Comparison of SE-WiGR with other gesture recognition systems  
To comprehensively verify the actual performance of the proposed method, we selected two typical indoor scenarios (conference room and office environment) and conducted systematic comparisons with mainstream gesture recognition algorithms under identical experimental conditions, the experimental results are shown in Table 5. In gesture recognition tasks, class imbalance (e.g., limited samples for fine-grained gestures like \"Enlarge/Narrow\") and inter-class feature similarity (e.g., directional gestures \"Left/Right\" and \"Up/Down\") pose challenges for traditional metrics like Accuracy. As a harmonic mean of Precision (TP/(TP + FP)) and Recall (TP/(TP + FN)), the F1 score mitigates bias from dominant gesture categories and provides a balanced assessment of minority classes, while also sensitively capturing misclassifications between similar gestures with subtle feature differences--avoiding the oversimplification of single Accuracy metrics. Additionally, in practical applications like smart homes, the F1 score's penalty of both false positives and negatives aligns with real-world reliability requirements. Figure 10: Comparison of F1 score between VGG16 and SE-VGG16 in training and validation sets under different Epochs. Our experimental results demonstrate significant performance differences among the evaluated approaches. Shallow learning models relying on hand-crafted features (WiGest and HMM) achieve relatively low accuracy (approximately 70%), as manual feature extraction frequently fails to capture critical discriminative characteristics. In comparison, deep learning-based approaches (LSTM and GRU) show superior performance by automatically learning optimal feature representations. Notably, the ABLSTM method outperforms standard LSTM through its bidirectional architecture and attention mechanism. Among all benchmarked methods, our proposed SE-WiGR technique achieves the highest recognition accuracy across all gesture categories. This performance advantage stems from the effective integration of the SE module with the VGG16 network, which synergistically combines their complementary strengths for robust feature extraction and classification.
# Beam-Hopping-Based Resource Allocation in Integrated Satellite-Terrestrial Networks
## 5 Resource Allocation Algorithms for MMCDR
### Optimal Algorithm  
Before the reformulation, the beam allocation pattern matrix \(\mathbf{U}\) and the transmission pattern matrix \(\mathbf{V}\) are formed by using the same approach as in Section 4.1. Then, corresponding to each beam allocation pattern, the transmission capacity of each satellite terminal and each terrestrial system can be calculated by (20) and (21), respectively.  
Introduce the auxiliary variable \(\xi\), which represents the minimum CDR in the ISTN. Then, the optimization problems P2 for MMCDR can be reformulated as the following MILP problem.  
\[\text{P2':}\ \max_{\{\mu_{m}\}}\xi \tag{29}\] \[\text{s.t.}\ \xi\leq\sum_{m=1}^{M_{\text{BS,P}}}\mu_{m} \varepsilon_{i,m}^{sat}\bigg{/}D_{i}^{sat},\forall i\in\mathcal{N}_{sat}\] (30) \[\xi\leq\sum_{m=1}^{M_{\text{H,P}}}\mu_{m}\varepsilon_{j,m}^{Iter} \bigg{/}D_{j}^{Iter},\forall j\in\mathcal{N}_{ter}\] (31) \[\eqref{eq:P2},\eqref{eq:P2}.\]  
Constraints (30) and (31) mean that the value of \(\xi\) depends on the minimum CDR of the satellite terminals and the terrestrial systems.  
The optimal solution of the MILP problem P2' can also be obtained by the branch-and-bound algorithm.
# Image Splicing Location Based on Illumination Maps and Cluster Region Proposal Network
## 3 The Proposed Framework
### The Dual-Stream Framework
#### 3.1.2 The Image and Illumination Stream  
The image stream with the input of \(IMG\left(x\right)\) extract features of strong contrast differences, unnatural tampering boundaries, etc. However, the illumination stream with the input of \(GE_{ill}\left(x\right)/IIC_{ill}\left(x\right)\) focus on the inconsistent feature as supplementary. The input of \(IMG\left(x\right)\) and the \(GE_{ill}\left(x\right)/IIC_{ill}\left(x\right)\) are shown in Figure 2, where the illumination maps of GE and IIC can reveal the tampered regions. The backbone of image and illumination streams are ResNet-50, where the feature maps are recorded as \(\left\{C_{1},\,C_{2},\,C_{3},\,C_{4},\,C_{5}\right\}\) and \(\left\{P_{1},\,P_{2},\,P_{3},\,P_{4},\,P_{5}\right\}\), respectively.  
Figure 2: Traces of tampering with image and illumination stream input. (**a**) splicing image. (**b**) blown-up. (**c**) GE. (**d**) IIC. (**e**) Ground Truth.
# Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery
## 5 Discussion
### Future Applications of Planet Imagery  
#### 5.3.1 Anticipated Growth in Planet CubeSat Image Acquisitions  
Despite the limitations discussed here, the high temporal and spatial resolution of Planet imagery makes it a unique and valuable remote sensing resource. At the time of writing Planet currently operates 148 satellites in sun synchronous orbit, yielding <1 day revisit capacity globally. Owing to orbit convergence, imaging capacity is highest at high latitudes. Here, we simulated both revisit rate and the probability of cloud-free collection when all launched satellites have been phased into staggered sun synchronous orbit. When all satellites are operating at full capacity, the expected revisit time will be sub-daily at high latitudes, and the cloud-free collection probability for most high latitude locations is likely to be between one and three days (Figure 11). Planet plans to maintain a stable imaging capacity in sun synchronous orbits by regularly replenishing the constellation with new deployments.
## 2 Materials and Methods
### Assessment of Environmental Contamination by Trace Elements
#### 2.4.2 Contamination Factor CF  
CF was introduced by [PERSON] [30] as a tool for monitoring soil contamination by TE and indicates the level of contamination. CF is obtained by comparing the TE  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline
**TE** & **Ba** & **Cr** & **Mn** & **Fe** & **Cu** & **Ni** & **Cu** & **Zn** & **Ga** & **Pb** & **Zr** & **Nb** & **Y** & **Ti** & **V** & **Br** & **Sn** \\ \hline Geochemical & & & & & & & & & & & & & & & & & \\ Background & 580 & 90 & 850 & 47,200 & 19 & 68 & 45 & 95 & 19 & 20 & 160 & 11 & 26 & 4600 & 130 & 4 & 6 \\ Values(ppm) & & & & & & & & & & & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Reference values of elements in the Earth’s crust.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Classes** & \
\begin{tabular}{c} **I\({}_{\text{geo}}\)** \\ **Values** \\ \end{tabular} & **Soil Quality** &
\begin{tabular}{c} **EF** \\ **Values** \\ \end{tabular} & **Soil Quality** \\ \hline
0 & I\({}_{\text{geo}}\)\(\leq\) 0 & Not contaminated & EF \(<\) 2 &
\begin{tabular}{c} Deficient to \\ minimal \\ enrichment \\ \end{tabular} \\
1 & 0 \(<\) I\({}_{\text{geo}}\)\(<\) 1 & Not contaminated to & \
\begin{tabular}{c} 2 \(<\) EF \(<\) 5 \\ \end{tabular} &
\begin{tabular}{c} Moderate \\ enrichment \\ enrichment \\ \end{tabular} \\
2 & 1 \(<\) I\({}_{\text{geo}}\)\(<\) 2 & Moderately contaminated & 5 \(<\) EF \(<\) 20 & High enrichment \\
3 & 2 \(<\) I\({}_{\text{geo}}\)\(<\) 3 & Moderately to heavily contaminated & 20 \(<\) EF \(<\) 40 & Very high enrichment \\
4 & 3 \(<\) I\({}_{\text{geo}}\)\(<\) 4 & Heavily contaminated & EF \(>\) 40 & Extremely high enrichment \\
5 & 4 \(<\) I\({}_{\text{geo}}\)\(<\) 5 &
\begin{tabular}{c} Heavily to extremely \\ Contaminated \\ Extremely contaminated \\ \end{tabular} & & & & \\
6 & I\({}_{\text{geo}}\)\(\geq\) 5 & Extremely contaminated & & & & \\ \hline \end{tabular}
\end{table}
Table 2: I\({}_{\text{geo}}\) and EF classification.  
concentrations (C\({}_{i}\)) in samples to the reference TE concentrations (C\({}_{n}\)); its formula is expressed by Equation (3) [4,31]:  
\[CF=\frac{Ci}{Cn} \tag{3}\]  
CF is classified into 4 categories as follows:  
* CF < 1: Low contamination;
* 1 < CF < 3: Moderate contamination;
* 3 < CF < 6: Considerable contamination;
* CF \(\geq\) 6: Very high contamination.  
The contamination degree (C\({}_{\text{deg}}\)) is used as a reference to assess the level of TE contamination in soils [32]. It does not take into account the level of soil pollution and is calculated using Equation (4).  
\[C_{deg}=\sum_{i=1}^{n}CFi \tag{4}\]  
For C\({}_{\text{deg}}\), four classifications have been established. A C\({}_{\text{deg}}\) < 8 indicates a low degree of contamination; a C\({}_{\text{deg}}\) from 8 to 16 indicates a moderate degree of contamination, from 16 to 32 indicates a considerable degree of contamination, and over 32 means a very high level of contamination [33].