esa-sceva/satcom-chunk-collection huggingface dataset:
# Modified Generalized Feistel Network Block Cipher for the Internet of Things
## 5 Result and Discussion
### The Avalanche Effect Test  
The avalanche effect refers to measuring and analyzing an algorithm's non-linearity characteristics. Therefore, the bit error and key sensitivity tests were performed to observe the avalanche effect of the proposed MGFN lightweight block cipher.  
As previously explained, the bit error test measures the relationship between the plaintext and ciphertext. The results of testing random key and random plaintext samples on the MGFN lightweight block cipher are illustrated in Figure 3. The MGFN recorded 32 distinct bits out of 64 bits of plaintext, which corresponds to a bit error rate of 50%.  
In addition, comparative research has been undertaken to analyze the avalanche effect and evaluate the efficacy of the MGFN to other leading lightweight block cipher algorithms, as shown in Table 6. In terms of the avalanche effect, the comparison analysis reveals that the MGFN surpasses the other algorithms.  
The key sensitivity test assessed the link between the key and the encrypted message. The test was conducted by subjecting a random set of keys and plaintext samples to the MGFN lightweight block cipher; the results are depicted in Figure 4's scatter plots.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Average Avalanche Effect** & **Reference** \\ \hline MGFN & 50.00\% & This paper \\ LED & 52.83\% & [66] \\ LRBC & 58.00\% & [66] \\ PRINCE & 51.18\% & [66] \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of avalanche effect on plaintext modifications.  
Figure 3: Scatter chart for bit error test result.  
According to our analysis, the MGFN lightweight block cipher created 31 distinct bits on average and had a 50% error rate when the key bits were changed. Importantly, despite fluctuations in the key bits, the bit rate (BR) of the MGFN lightweight block cipher remained near to 0.5, showing a high degree of key sensitivity to the ciphertext. The findings show that the MGFN lightweight block cipher is highly sensitive to key changes.  
Table 7 compares the MGFN and existing algorithms in terms of key modification while keeping the plaintext fixed. The results show that the MGFN lightweight block cipher outperformed all other algorithms regarding the avalanche effect on key sensitivity.  
In summary, the avalanche effect results of the proposed modified Feistel-based lightweight block cipher demonstrate its ability to provide a diffusion property to the cipher output. While some results slightly deviated from the threshold value of 32 bits, our findings suggest that the permutation methods used can maintain the optimal output changes with minor or major input modifications.
# Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold
## 2 Materials and Methods
### Methods  
The flow of oil film classification is shown in Figure 7. The effective echo pixels were extracted from the preprocessed image using the SVM [39] classification method. Mean filtering was then applied to the effective wave echo image to generate the gray gradient matrix, and the mask of the effective wave area was generated accordingly. Next, the local adaptive threshold was used to preliminarily extract the oil films in the mask. Finally, the final oil film results were obtained by the pixel area threshold method.  
Figure 6: Result of data preprocessing showing (**a**) convolution of the Laplace operator, (**b**) threshold segmentation, \(T=128\), (**c**) in the \(1\times 7\) window centered on the highlighted pixels of the original image, with two nearest non-highlighted points selected to replace the highlighted pixel with the mean value, (**d**) segmentation of isolated targets, \(T_{area}=200\), and (**e**) the window of median filtering, which is 21\(\times\)21.
# Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks
## 2 Methods
### EEG Preprocessing and Generation
#### 2.1.2 Data Preprocessing  
Since deep learning requires relatively large datasets, limited data can lead to severe overfitting. Typically, overfitting in neural network models is mitigated by expanding the dataset size to leverage prior knowledge. To address this, EEG data are segmented using a fixed sliding window of size \(m\) (set to 1 s) and overlap \(\delta_{m}\) (set to 0\(\sim\)1), thereby augmenting the training dataset.  
Using a sliding window \(m\) (1 s) and overlap \(\delta_{m}\) (0), each 23.6-s EEG file is sequentially segmented. The segmented data are shuffled randomly before training. From the five EEG datasets, 11,500 samples are obtained, each containing 178 data points and 1 class attribute. To combat statistical uncertainty from small-sample datasets during testing, EEG data were first partitioned into training and testing sets at a 7:3 ratio. Then a three-fold cross-validation procedure is employed to evaluate the performance of the epilepsy detection algorithm. Compared to seizure detection models using other deep learning networks trained on tens of thousands of samples [33], this study utilizes small-scale datasets.
## 2 Materials and Methods
### Experimental Environment Settings and Model Evaluation Indicator  
The proposed model was implemented using Python 3.10 on Ubuntu 22.04 LTS OS, Intel(R) Xeon(R) CPU@ 2.20 GHz, RAM 53 GB, and GPU RAM 22.5 GB. The performance of the model was measured using the confusion matrix, F1-score, accuracy, precision, and recall.  
A confusion matrix is a visual tool that helps assess the effectiveness of a classification model. It presents a summary of the actual versus predicted classifications, making it easier to identify how well the model is performing for each class. The matrix includes four key components as explained below.  
* TP (true positives): Correctly predicted positive cases;
* TN (true negatives): Correctly predicted negative cases;
* FP (false positives): Incorrectly predicted positive cases;
* FN (false negatives): Incorrectly predicted negative cases.  
Accuracy is the proportion of total correct predictions made by the model, including both true positives and true negatives, out of all predictions. It can be calculated using Formula (7):  
\[\text{Accuracy}=\frac{(\text{TP}+\text{TN})}{(\text{TP}+\text{TN}+\text{FP }+\text{FN})} \tag{7}\]  
Figure 6: Adaptive MobileNetV2 architecture.  
Precision is the proportion of correctly predicted positive cases out of all cases that the model predicted as positive, measuring the accuracy of positive predictions. It is calculated using Formula (8):  
\[\text{Precision}=\frac{\text{TP}}{(\text{TP}+\text{FP})} \tag{8}\]  
Recall (sensitivity) is the proportion of actual positive cases that the model correctly identified, measuring the model's ability to detect positive instances. It is calculated using Formula (9):  
\[\text{Recall}=\frac{\text{TP}}{(\text{TP}+\text{FN})} \tag{9}\]  
The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both precision and recall, especially useful when dealing with imbalanced datasets. It is calculated using Formula (10):  
\[\text{F1 score}=2\frac{(\text{Precision}*\text{Recall})}{(\text{Precision}+ \text{Recall})} \tag{10}\]
# Multi-Scenario Robust Distributed Permutation Flow Shop Scheduling Based on DDQN
## 2 Related Theory
### NEH and DDQN Algorithms  
The NEH algorithm is widely used to generate high-quality initial scheduling sequences [32]. Its core idea is to sort the workpieces in descending order according to their total machining time in each process. Workpieces are then sequentially inserted into all possible positions in the currently constructed scheduling sequence. For each insertion position, the makespan is calculated, and the position yielding the minimum makespan is selected. This process iteratively extends the existing scheduling sequence until all the artifacts are inserted. Figure 3 below illustrates a simplified flowchart of seven tasks assigned to three workshops.  
The core of reinforcement learning is the process by which an agent learns to obtain the best behavior based on environmental rewards, i.e., there exists a mapping relationship between the state and the action [33]. This mechanism learns to maximize cumulative rewards by iteratively refining the policy through exploration and exploitation. The agent continuously experiments in the environment and optimizes the state-action correspondence through the inverse of the environment.  
DDQN is a deep reinforcement learning algorithm optimized and improved on the basis of DQN. Its main goal is to alleviate the overestimation of Q value that tends to occur in the training process of DQN. In DQN, both the selection of actions and the evaluation of action values are performed by the same neural network, and this mechanism often leads to the Q-value deviating from the actual return, thus affecting the optimality and convergence of the strategy. To solve this problem, DDQN introduces two independent neural networks: the Evaluation Network used to select the optimal action in the current state, and the Target  
Figure 2: Model elements and their names in the Plant Simulation software.  
Network responsible for evaluating the value of the action. This improves the stability of the policy learning through the separation mechanism of action selection and action evaluation, effectively reduces the Q-value estimation bias, and demonstrates superior stability in addressing workshop scheduling problems [34].
# Remote Sensing Image Enhancement Based on Non-Local Means Filter in NSCT Domain
## 2 Theoretical Analysis  
### Nonsubsampled Contourlet Transform  
Nonsubsampled contourlet transform (NST) is put forward on the basis of contourlet transform, and it is an overcomplete transformation. Though the structure of the NST transformation is similar to the contourlet transform, NST does not use the sampling link. NST is composed of the Nonsubsampled pyramid filter bank (NSPFB) and the Nonsubsampled directional filter bank (NSDFB) [25]; the corresponding structure is shown in Figure 1. NST is a transformation characterized by multi-scale, good spatial and frequency domain, shift-invariance and higher redundancy. NST transform first uses NSPFB to obtain the multi-scale decomposition of the input image, and then NSDFB is applied to decompose the obtained sub-band images in order to achieve the sub-band coefficients of different scales and different directions. Figure 2 is the flow chart of NST. The test image (zoneplate) is decomposed by NST as shown in Figure 3.  
Figure 1: **(a)** The frequency model of nonsubsampled pyramid filter bank (NSPFB); **(b)** the frequency model of nonsubsampled directional filter bank (NSDFB); **(c)** the four-channel model of NSPFB.  
For the nonsubsampled pyramid (NSP) decomposition, the analytical filter \(\{H_{0}(z),H_{1}(z)\}\) and the integrated filter \(\{G_{0}(z),G_{1}(z)\}\) satisfy the Bezout identity. The corresponding equation is expressed as follows:  
\[H_{0}(z)G_{0}(z)+H_{1}(z)G_{1}(z)=1 \tag{1}\]  
Figure 3: Nonsubsampled contourlet transform (NSCT) decomposition of zoneplate image. (**a**) Zoneplate (\(256\times 256\)); (**b**) approximate NSCT coefficients; (**c**) detailed coefficients.  
Figure 2: The structure diagram of nonsubsampled contourlet transform (NSCT). (**a**) Non-subsampled filter bank model; (**b**) idealized frequency partitioning.  
For the nonsubsampled directional filter bank, the analytical filter \(\{U_{0}(z)\), \(U_{1}(z)\}\) and the integrated filter \(\{V_{0}(z)\), \(V_{1}(z)\}\) also satisfy the Bezout identity. The corresponding formula is described as follows:  
\[U_{0}(z)V_{0}(z)+U_{1}(z)V_{1}(z)=1 \tag{2}\]
## 4 Results and Discussion
### Distribution of DSM Services by Communication Protocol
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario** & **Services** & **Topology** \\ \hline Smart Home in neighborhoods & SM, SP, SS, QEE, DR, HEM, Home & LoRaWAN or \\  & Automation, consumption forecast, load & LoRaMESH \\  & profile and solar energy generation & or Hybrid \\ \hline Smart Home & SM, SP, SS, QEE, DR, HEM, Home & \\  & Automation, consumption & \\  & forecast, load profile & LoRaWAN or Hybrid \\  & and solar energy generation & \\ \hline Smart Homes & SM, SP, SS, QEE, DR, HEM, & \\  & Home Automation, & Hybrid \\  & consumption forecast, & \\  & load profile, Gameficacao & \\  & and solar energy generation & \\ \hline Building Condominium & SM, SP, SS, QEE, DR, HEM, & \\  & Automacao Residencial, consumption & \\  & forecast, load profile, Gameficcion & Hybrid \\  & and solar energy generation & \\ \hline Public Buildings & SM, SP, SS, QEE, & \\  & and Universities & and solar energy generation & LoRaWAN or Hybrid \\ \hline Industry 4.0 &
\begin{tabular}{c} SM, SP, SS, QEE, DR, \\ Industrial Automation \\ and solar energy generation \\ \end{tabular} & LoRaWAN or Hybrid \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Scenarios and services to be used by topology. terms of AMI deployment and implementation, either with communication failures or due to the price of implementing AMI. From the results obtained in the LoRaWAN network, a range of 2 km to 2.35 km was achieved between the gateway and the more distant SMs, and in LoRaMESH a distance of up to 600 m between two devices could extend the network coverage area by connecting more mesh devices. Both results were obtained in an urban scenario with all the main barriers existing in the environment, as well as all the barriers present in a city that may or may not affect the communication infrastructure. Thus, with the implementation of the hybrid network, it will be possible to cover long range areas with LoRaWAN, as well as to connect several nearby devices through the LoRaMESH protocol in order to reach a greater amount of distance between the SMs, in order to propose one more possibility of topology to be used to supply all the main different scenarios present in the SG according to the contexts cited in this work, such as SHs and buildings condominiums, industries 4.0, universities and commerce among other end consumers of the EEC. Using the DAP studied, implemented and proposed by this work AMI will be possible to be deployed in the real scenario keeping essential characteristics for this deployment as low cost and long range. In this way, the cost of AMI deployment will be significantly reduced compared to other technologies present in the market today. It is worth mentioning that the formulation implemented and modeled in this work considers that all SMs are synchronized without failures, however, it is known that in practice this synchronization is a challenge to be tackled in future work in order to think about the scalability of this infrastructure.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 5 BackCom: Wireless Communications
### Multiple Access Techniques for BackCom  
Various multiple access techniques are used to adjust for a greater number of users at the same time. The method includes multiple access based on time division, code division, and frequency division [68; 99; 100; 101; 105]. Figure 24 depicts the basic concept of multiple access schemes, demonstrating multiple-tag backscattering carrier signals to a single reader.
## 4 Experimental Results and Analysis
### Model Robustness  
Finally, the robustness of the developed models has been assessed. While the images in the IP102 contain clear, high-resolution acquisitions of pests, images acquired by in-field sensors may be affected by reduced resolution due to the resource constraints of low-power sensors. Moreover, dust, rain, and terrain particles may dirty the image and cover parts of the pests being acquired. In some cases, in-field sensors are only responsible for data acquisition and transmission to remote stations where the detection and classification models are executed. Due to the low resource usage and network bandwidth constraints, the pictures acquired on the field sometimes need to be downsampled in order to be transmitted over the network.The purpose of this study is to stress the conceived models under real-world conditions. The proposed levels of added noise have been chosen to cover the entire 100% interval in a few steps. In this way, an idea is given of the performance degradation the network can face during the training phase in the case of low signal-to-noise ratio in the acquisition system, The case for the resolution has also been addressed with the same approach.  
For this reason, the study aims to evaluate the robustness of the model to decreasing levels of image resolution and to increasing levels of pixel-wise noise.
## 3 Friction Experiments of Granitoid Fault
### Velocity-Stepping Experiment  
Velocity-stepping (VS) experiments are widely used in the study of fault friction properties. In VS friction experiments, variations in shear velocity are systematically applied to investigate the rate-dependence of frictional behavior in fault materials, which is critical for assessing slip stability in earthquake nucleation zones (e.g., the values of (\(a-b\))). These experiments are typically conducted using high-stiffness shear apparatuses, most commonly direct-shear, saw-cut, and rotary shear configurations [64,70]. The direct-shear apparatus is mechanically straightforward and allows for easy sample assembly and force alignment (Figure 1a,d). It is well-suited for low-to-moderate slip rates (\(\mu\)m/s-mm/s) and small displacement (\(<\)10 mm) experiments, making it ideal for studying quasi-static frictional properties. Its primary advantages include the direct application of shear load and ease of data interpretation. However, its limitations include relatively small cumulative displacements and potential boundary effects, such as stress concentrations at the sample edges, which can affect the uniformity of deformation. In the saw-cut test, the servo-controlled triaxial machine is capable of independently controlling axial stress, confining pressure, and pore pressure. This setup allows the simulation of realistic in situ stress conditions relevant to crustal fault zones, particularly under elevated pressure and temperatures. However, the total shear displacement achievable is typically constrained (\(<\)4-5 mm). In contrast, the rotary shear apparatus employs torsional motion to impose shear displacement, allowing for continuous sliding at a wide range of velocities and large total displacements. This makes it particularly suitable for simulating seismic slip conditions and capturing thermomechanical processes during rapid fault motion. Despite these advantages, rotary systems are generally more complex, require careful alignment, and involve non-uniform stress distributions due to their radial geometry. Data interpretation also requires the conversion of torque and angular displacement into shear stress and slip, which adds to the analytical complexity. The choice of apparatus depends on the experimental objectives and the specific slip conditions of interest. We summarized the VS test on granitic examples and discussed the mechanisms that influence their sliding stability. At dry conditions (\(P_{\text{f}}\) = 0 MPa), the velocity dependence of friction, that is, on (\(a-b\)) values only, shows the positive (V-S behavior) under all temperatures (\(T\)) and velocities (\(V\)) (Figure 2) [36]. However, V-W behavior can also be observed in the nominally dry tests, although such results should be interpreted with greater caution [45]. [PERSON] et al. [38] conducted friction experiments under room \(T\) at an effective normal stress (\(\sigma_{\text{n}}^{\text{eff}}\)) of 25 MPa, and granite fault gouges show the V-W behavior occurring up to \(\sim\)40-100 mm displacement, then giving way to V-S behavior.
## 4 Experimental Results and Analyses
### Experiment on Image-Query-Based Video Searching  
#### 4.3.1 ReTRiEVED Dataset  
The ReTRiEVED [76] Dataset was created to evaluate methods that require video quality assessment in transmissions. The ReTRiEVED dataset contains 176 test videos obtained from 8 source videos by applying the transmission parameters listed in Table 16.  
Samples of the source videos and test videos are shown in Figure 11. Here, we used this small dataset to assess the robustness of features we used to guard against possible video distortions.  
Here, we compared the SIFT features against some related methods: CST\(-\)SURF [77], CC [78], and {Th; CC; ORB} [24] for video retrieval. In Table 17, the average detection F1-scores are presented.  
The experimental results obtained from the methods CST-SURF, CC, and {Th; CC; ORB} are adopted from the paper [24]. The CST-SURF method uses the difference of the SURF key point numbers in stable frame pairs to generate normalized differences as features. The CC method uses color correlation in the divided non-overlapping blocks of each frame. In the {Th; CC; ORB} method, \"Th\" represents \"Thumbnail\", which is designed as a global feature to resist against flipping transformations.  
In the experiment, since the test videos are short enough, we used the combined features of the selected frames to represent the features of each video. The videos' keyframes are selected at the step of half of the frame rate and they are downsized to 64 \(\times\) 64 pixels. In the retrieval process, we used UBCMATCH to match the features of the tested videos and the source videos. The number of matches is regarded as the similarity value. Since the match numbers differ greatly under different thresholds, we use the 12-step threshold values to find better conditions for SIFT. Figure 12 shows that the Positive  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Attacks** & \multicolumn{8}{c}{**Parameters**} \\ \hline Delay (ms) & 100 & 300 & 500 & 800 & 1000 & / & / \\ Jitter (ms) & 1 & 2 & 3 & 4 & 5 & / & / \\ Packet Loss Rate (\%) & 0.1 & 0.4 & 1 & 3 & 5 & 8 & 10 \\ Throughput (Mbps) & 0.5 & 1 & 2 & 3 & 5 & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 16: The video attacks simulated by the ReTRiEVED dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & \multicolumn{4}{c}{**Attacks**} \\ \cline{2-5}  & **Delay** & **Jitter** & **Packet Loss Rate** & **Throughput** \\ \hline SIFT & 1 & 1 & 1 & 0.9610 \\ CST-SURF & 0.1740 & 0.3737 & 0.2889 & 0.2121 \\ CC & 0.8932 & 0.9777 & 0.9730 & 0.9335 \\ {Th; CC; ORB} & 0.9996 & 0.9930 & 0.9940 & 0.9495 \\ \hline \hline \end{tabular}
\end{table}
Table 17: The average F1-scores for the different transmission attacks in the ReTRiEVED dataset.  
Figure 11: Samples of the videos in the ReTRiEVED dataset. The upper row shows the original videos and the bottom shows the attacked videos.  
Predicted Value (PPV) of ReTRiEVED using the SIFT descriptor under the throughput transmission is lower than that of other transitions.
# Middleware for Plug and Play Integration of Heterogeneous Sensor Resources into the Sensor Web
## 2 Sensor Integration into Observation Platforms
### Protocols and Standards
#### 2.2.1 OGC PUCK Protocol  
The sensor detection requirement can be fulfilled using the OGC PUCK protocol within the acquisition platform controller [28]. This protocol is an add-on that can be implemented in any serial or Ethernet sensor alongside with any proprietary protocol, rather than replacing it. This protocol defines a set of commands that grant transparent access to an internal memory, named _OGC PUCK payload_. This payload is frequently used to store sensor metadata. Another key feature of the OGC Puck protocol is its _softbreak_ operation, which provides on-the-fly detection without any prior knowledge of the sensor, fulfilling requirement 1.