esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 6 Conclusions  
To reduce the complexity of the communication anti-jamming decision-making process, make full use of domain prior knowledge, and improve the intelligent decision-making level, a communication anti-jamming decision-making algorithm based on knowledge graph is proposed. The proposed algorithm is based on the anti-jamming knowledge graph, and separately processes the jamming cognitive data. For known jamming patterns, the anti-jamming strategy is directly obtained by searching the graph; for unknown jamming patterns, the proposed feature extraction method of the time-frequency domain is used to calculate the time-frequency domain characteristic parameters of the jamming pattern, and the time-frequency domain of the feature is judged. Then, the anti-jamming strategy corresponding to the time-frequency domain features is searched in the knowledge graph. Anti-jamming strategy fusion is performed, and the final anti-jamming decision-making results are outputted. The simulation results show that the time-frequency domain feature extraction method can accurately extract the time-frequency domain features of unknown jamming patterns, and it is still robust in the presence of errors in jamming sensing. The communication anti-jamming decision-making algorithm based on the knowledge graph can deal with multiple different jamming patterns, and the decision-making results are robust to jamming sensing errors, which can achieve accurate anti-jamming decision-making results under the complex and changeable electromagnetic environment and inaccurate interference cognition.  
The principal theoretical implication of this study is that utilization of a domain knowledge graph can reduce algorithm complexity. The major limitation of this study is that new jamming patterns are constantly emerging and the anti-jamming knowledge graph is difficult to complete. A further study could usefully explore how combining existing knowledge graphs with reinforcement learning could improve the convergence speed of the algorithm. This would be a fruitful area for further work.  
Conceptualization, Y.N. and X.F.; methodology, S.K.; software, S.K.; validation, X.F. and S.K.; formal analysis, Y.N.; investigation, Y.N.; resources, Y.N.; data curation, X.F.; writing--original draft preparation, S.K.; writing--review and editing, X.F. and P.X.; visualization, X.F.; supervision, Y.N. and X.F.; project administration, X.F.; funding acquisition, Y.N. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Science Foundation of China under contract No. U19B2014.  
Not applicable.  
The data presented in this study are available from the author, [PERSON], upon reasonable request.  
The authors declare no conflict of interest.
## 4 System Architecture
### Packet Structure
#### 4.7.2 Oversampling  
When a rolling-shutter camera's frame rate exceeds (at least doubles) the transmitter's packet rate, every data packet is sampled at least twice (i.e., two pictures). We receive the same packet at the receiver, generating packet merger confusion. The SN is added to DS to assist the receiver in decreasing the influence of the camera's frame rate variation. Each packet contains identical DSs, which aids the receiver in removing superfluous data. When a DS is received, the receiver chooses which one has a compatible SN. As seen in Figure 5, the receiver side discards consecutive packets with the same SN and merges packets with successive SN (n - 1, n, n + 1).
# Effective Privacy-Preserving Collection of Health Data from a User's Wearable Device
## 5 Experimental Evaluation
### Experimental Setup  
Three different levels of privacy, \(\epsilon=0.5\), \(\epsilon=1.0\), and \(\epsilon=2.0\), were used in the experiments. In the experimental data set, the difference between a possible minimum value and a possible maximum value of daily cumulative step-count is less than 8000, and thus, the local sensitivity \(\Delta s\) was set to 8000. Furthermore, the predefined parameters, \(\alpha\) and \(\beta\), in Algorithms 1 and 2 were assigned the values of 6 and 200, respectively.  
To measure the estimation accuracy, we use an error rate, \(e\), defined as follows:  
\[e=\frac{1}{n}\times\sum_{d=1}^{n}|ax_{d}^{actual}-ax_{d}^{est}|.\]  
Here, \(n\) denotes the length of the health data sequence, and \(ax_{d}^{actual}\) and \(ax_{d}^{est}\), respectively, correspond to the actual and the estimated value of \(ax_{d}\) at the timestamp, \(t_{d}\), in aggregate static \(AS=\Big{(}(t_{1},ax_{1}),(t_{2},ax_{2}),\cdots,(t_{n},ax_{n})\Big{)}\).
# Metal-to-Insulator Transition in Ultrathin Manganite Heterostructures
## 4 Mechanism of the MIT
### Anderson Localization Effect  
The electric dead layer is normally thicker than the magnetization dead layer. In other words, we have a ferromagnetic insulator (FMI) phase in ultrathin LSMO. This is not consistent with the double exchange mechanism which couples the ferromagnetism to metallicity, as in bulk LSMO. Transport analysis shows that the anomalous FMI phase is due to the Anderson localization effect [39; 57; 71]. As shown in Figure 13, the temperature-dependent resistivity indicates that LSMO films on STO gradually evolve from weak localization to strong localization, with reducing thickness. For metallic film (t > t\({}_{\rm c}\)), the resistivity linearly depends on Log [\(T\)], as shown in Figure 13a,b. This is a strong signature of the weak localization where enhanced backscattering needs to be considered with the presence of impurities [72]. While at critical thickness, the transport does not follow the weak localization behavior (see Figure 13c); instead, the ultrathin critical film follows the Mott's variable range hopping (VRH), \(\rho=\rho_{0}\exp\left(T_{0}/T\right)^{\alpha}\) with exponent \(\alpha=1/3\), as expected in two dimensions (see Figure 13d) [73]. For thinner films (t < t\({}_{\rm c}\)), the films enter into the thermal activation region and the resistivity can be described by an Arrhenius function (\(\rho=\rho_{0}e^{-\Delta E/kT}\)) (see inset in Figure 13c). The transition from weak to strong localization is characterized by the Ioffe-Regel (IR) limit of resistivity [72] above which the system is turned into a strong localization region. For a 2D system, the IR limit is related to the sheet resistance of \(\text{R}_{\text{IR}}\approx 25\) K\(\Omega\). As shown in Figure 13e, we can find metallic films with weak localization when the resistivity of the LSMO films on STO (001) is below the IR limit, while above the IR limit, the films are nonmetallic. The characteristic transport properties indicate that the thickness driven metal-nonmetal transition is associated with a 3D metallic state to a 2D insulating state-dimensional cross-over transition. The carrier localization likely arises from an intrinsic A-site cation disorder, or/and residual oxygen vacancies [39; 57; 74]. Suppressed localization effect and reduced critical thickness have been realized by minimizing the oxygen vacancies concentration [39; 57]. Since a perfect crystal is not realistic, this disorder-induced localization serves as the universal driving force for the MIT in many ultrathin film oxide materials.  
Figure 13: The resistivity versus Log(\(T\)) for (**a**) 60, (**b**) 7, and (**c**) 6 u.c. LSMO films on STO (001). The inset in (**c**) shows the logarithmic plot of resistivity of a function of \(1/T\) for 5 u.c. film. (**d**) Logarithmic plot of resistivity as a function of \(1/T^{1/3}\) for 6 u.c. LSMO. (**e**) \(T\) dependence of the sheet resistance for different thicknesses of LSMO films. The red dotted line indicates the Ioffe–Regel (IR) limit for the onset of strong localization. Figure is adapted from Ref. [39].
# LSANet: Lightweight Super Resolution via Large Separable Kernel Attention for Edge Remote Sensing
## 4 Implementation Details
### Ablation Study
#### 4.4.3 Effectiveness of Hard Swish Activation Function  
To validate the impact of the Hard Swish activation function on super-resolution reconstruction performance, a set of ablation experiments was designed to compare the effects of different activation functions on model parameters, computational complexity, and reconstruction quality. The results are presented in Table 5. The experimental findings show that compared with configurations without an activation function (Experiment m) or using common functions (ReLU, Sigmoid, ELU, Tanh), Hard Swish achieves superior reconstruction performance in terms of PSNR and SSIM. Although integrating Hard Swish introduces a slight increase in both parameter count and computational complexity, the magnitude of this increase is minimal. In contrast, the performance enhancement is significant, indicating that this activation function not only maintains high computational efficiency but also effectively improves the super-resolution reconstruction quality of remote sensing images. Thus, as an efficient activation function, Hard Swish demonstrates good applicability and effectiveness in the super-resolution tasks of this study.
## 6 Experimental Results and Analysis  
### Evaluation Index  
To evaluate the stability and robustness of the algorithm in this study, recall rate, accuracy rate, average IOU value, and _F1_ value are defined to evaluate the model. Recall is the proportion of precision parts detected in images. Precision is the correct proportion of all tested candidate frames. Average IOU is the coincidence degree between the prediction and actual boxes, thereby reflecting the accuracy of the detected location of the object precision parts. The performance of the proposed test model is evaluated by using the statistics of _F1_ measure, where \(P\) and \(R\) denote the precision and recall rates, respectively.  
\[F1=\frac{2 PR}{P+R} \tag{12}\]
## 3 Design of the Learning System and Content Structure
### System Interface Introduction  
The user interface of this system, as shown in Figure 1, is mainly divided into the three main sections--Source Coding, Answer, and Related Information--to enhance learning experiences and students' understanding of _Verilog_ syntax and digital circuit design.  
1. Source Coding: This section provides a complete _Verilog_ code corresponding to the implementation of a specific digital circuit. The fill-in-the-blank sections request students to supplement the missing code according to the requirements of the question to enhance their understanding of grammar and program logic.
2. Answer: This section contains quizzes on concepts related to the code and is designed to assess students' understanding of grammatical structures, digital circuit behaviors, and key design concepts. Learners must apply their knowledge to interpret the provided code and solve each problem, with the platform's real-time feedback mechanism immediately verifying the correctness of their answers.
3. Related Information: This section offers supplementary learning materials aligned with each exercise, including an annotated example code, in-depth conceptual explanations, and digital-circuit schematics or timing waveforms. Designed to support self-directed studies, it enables students to consult these resources during problem-solving, reinforcing their understanding of programming constructs and digital-logic fundamentals.  
Through the interactive design of the above sections, the system can not only strengthen students' understanding of _Verilog_ syntax and digital circuit behaviors, but also improve learners' code-writing and error correction capabilities through real-time evaluation and feedback mechanisms, further optimizing their learning effects.
## 3 Comparative Analysis of Numerical Forward Simulation and Soil Trench Modeling Experiments
### Experimental Program Design  
The test area (6 m \(\times\) 2 m) was selected and the average resistivity of the surface soil in the tested area was 537 \(\Omega\cdot\)m. The specification of the chromium-contaminated body was 50 cm \(\times\) 40 cm \(\times\) 20 cm as shown in Figure 3. The bottom layer of 15-20 cm is dry clay with a resistivity of about 759 \(\Omega\cdot\)m, which serves to ensure that the chromium solution stored in the fine sand will not be lost by downward infiltration. The middle layer of 5-15 cm is the chromium-contaminated area, which is made of fine river sand with high water absorption and permeability. By grinding fine river sand to a particle size of 0.25-0.35 mm (see Figure 4) and compacting it after passing through a soil sieve, the final layer was made. After the fine sand was filled and compacted, 2 mol/m\({}^{3}\) of Na\({}_{2}\)CrO\({}_{4}\) solution (yellow liquid in the beaker in Figure 4) was uniformly placed on the surface and left for 1 h to make the chromium solution penetrate and mix with the fine sand. The soil at the top of the pollutant was sandy soil from the experimental area, and this type of sandy soil was placed at the top, 0-5 cm. After the preparation of the chromium-contaminated body was completed, it was determined that the average resistivity of the chromium-contaminated body at the depth of 5\(\sim\)15 cm was about 93 \(\Omega\cdot\)m.  
Figure 4: Handling of sandy soil particle sizes.  
Figure 3: Structure of chromium contaminant.  
The physical experimental model of the constructed soil trench is shown in Figure 5. The chromium-contaminated body was placed in the center of the area, and the burial depth was from 5 cm to 25 cm. The middle gradient is used to lay the electrodes, A and B are the power supply electrodes, and the pole distance is 6 m. M and N are the measuring electrodes, and the pole distance is 10 cm. YDZ75(A) parallel DC electrometer is used for the detection, and the transmitter module transmits the 100-mA current as the excitation, and the receiver module is used for the data acquisition and processing.
## 3 Architecture and System Evaluation
### Measurements of the Different Delay Components  
Our first priority was to measure all the systems' E2E latency components and see how the encoding rate \(q\) and network status impact them. The E2E latency when processing the set of images is determined by the slowest subtask execution. Namely, when we partition the image set, we essentially create different subtasks and we assign each of them to a different end device. The completion of each subtask requires the respective images to be sent to that device, processed there, and the results (bounding objects and labels) to be returned to the server. Given the different hardware specifications of the end devices and the volatility of the wireless channels, these delays are challenging to predict or capture analytically as a function of the splitting and compression decisions. Our goal was to find the right encoding rate \(q\) in which the image set sizes are as small as possible to minimize the transmission delay or more specifically the time needed to offload the images to the end devices and affect the performance of the YOLO model on every end device as less as possible.
# Advancing Brain Tumor Segmentation with Spectral-Spatial Graph Neural Networks
## 2 Methods and Material
### Characterizing Tumor Sub-Regions for Segmentation Task  
In this study, the segmentation task of the BraTS data involves the accurate identification and classification of three distinct components as in Figure 3 within brain tumors, using sophisticated image processing and machine learning techniques. The following outlines the specific sub-regions of the brain tumor:  
Figure 1: This schematic outlines the research workflow from data collection and preprocessing to advanced supervoxel creation and graph structuring for GNN training. It emphasizes the integration of spectral–spatial GNN models, illustrating the comprehensive steps taken in this study.  
Figure 2: T1-weighted, T1 with contrast, T2-weighted, FLAIR sequences, and a tumor segmentation mask.  
Enhancing tumor: This term refers to the portion of the tumor that displays increased intensity on contrast-enhanced MRI scans, highlighting the active and aggressive parts of the tumor. The presence of an enhancing tumor, which indicates a disruption in the blood-brain barrier that allows contrast agents to leak into the tumor tissue, is crucial for evaluating the tumor's aggressiveness and pinpointing the primary focus for therapeutic interventions. It reflects areas where the tumor is most likely to grow and spread, making it a focal point for surgical and medical treatments [17; 18].  
Edema: In the realm of brain tumors, edema signifies the swelling or accumulation of fluid in the brain tissues surrounding the tumor, visible on MRI scans as areas of hypersensitivity on T2-weighted images. Edema is a vital indicator of the tumor's impact on adjacent brain tissue, significantly affecting a patient's symptoms and overall condition. Accurately identifying edema is essential for assessing the tumor's extent and its implications on brain function, as it can influence decisions regarding the urgency and type of treatment required [18; 19].  
Necrosis: Necrosis within a tumor refers to areas of dead or dying cells that no longer function due to the tumor's growth outstripping its blood supply. On MRI scans, necrosis is typically seen as a non-enhancing core within the tumor, surrounded by the enhancing tumor margin. Identifying necrosis is important for determining the stage of the tumor and its responsiveness to treatment. It also plays a critical role in surgical planning, as these areas can affect the approach to tumor resection and the prediction of treatment outcomes [18; 20].
## 5 Digital Modulation and Demodulation Circuits  
### General Overview  
An overview of the NFC transceiver's digital part is given in Figure 9: An NFC controller block is coordinating the data transfer, either enabling the transmit (TX) chain or the receive (RX) chain and routing data between an SPI interface and the corresponding first-in first-out (FIFO) buffer memories. The TX chain further includes a TX data handler, which builds the packets to be transmitted including carrier burst, preamble, and payload data extracted from the TX FIFO, as well as the actual modulator. The RX chain has the opposite functionality, i.e., it demodulates the data, extracts the actual payload data, and stores it into the RX FIFO. A PLL generates a base and sampling clock of 108.48 MHz from a 40.0 MHz crystal oscillator; the base clock is divided to obtain a 54.24 MHz digital core clock and a 13.56 MHz carrier clock to be used in the NFC controller block.
## 5 Results and Analysis
### Results
#### 5.2.2 Prediction Quality
Thus, the combination of Gini and attribute reduction based on Pearson's correlation coefficient was the default choice in this paper. This result proved the high efficiency of the proposed method for attribute reduction. Third, in all schemes, RF performed the best compared with the other ML/AI models, almost in all cases. In the _Original_, the performance of RF was as good as that in _Entropy_ and _Gini_, which implies that noise attributes had less impact on RF. However, if some critical attributes are lacking, the prediction quality is reduced, as observed in _Pearson_, _Spearman_, and _J-Index_. Considering LSTM, the prediction results were similar to those of RF in _Entropy_, and _Gini_; in particular, it achieved the best results for _Gini_Prs_, which shows that noise attributes influence the results of LSTM. In _Pearson_ and _Spearman_, the performance of LSTM was much worse than that of RF; in this case, unsuitable attribute selection greatly impacted the quality of LSTM, and good attribute selection revealed the performance potential of LSTM. For the other ML/AI models, NB and SVM performed worst in almost all schemes, and CNN had the next worst performance. They showed different comparison results for DM000 and NM0007; for example, in DM000, the performance of SVM was worse in _Pearson_, _Spearman_, and _J-Index_ compared with _Original_. However, in NM0007, the quality improved in _Pearson_, _Spearman_, and _J-Index_. The same trend could be also observed for CNN in _J-Index_.
# Entropy-Regularized Federated Optimization for Non-IID Data
## 4 Experimental Setup
### Evaluation Protocol  
Model performance is evaluated on each dataset's test set after each communication round, reporting both accuracy (percentage correct) and macro-averaged F1. To ensure robust statistical conclusions, we increased our number of independent runs to 50 (seeds 1-50)  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Class** & **C1** & **C2** & **C3** & **C4** & **C5** \\ \hline Benign & 32.1 & 18.4 & 25.7 & 40.8 & 27.3 \\ Exploits & 10.3 & 15.6 & 12.8 & 9.5 & 14.1 \\ Fuzzers & 8.9 & 3.2 & 7.1 & 5.4 & 4.8 \\ Reconnaissance & 12.5 & 11.8 & 14.2 & 7.6 & 13.9 \\ DoS & 22.7 & 37.1 & 28.9 & 31.2 & 30.5 \\ Generic Attacks & 13.5 & 14.0 & 11.3 & 15.5 & 9.4 \\ \hline Avg. JS Div. & & & 0.37 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Label distribution (%) per client after Dirichlet (\(\alpha=0.5\)) partition of UNSW-NB15.  
and now report for each method the mean \(\pm\) standard deviation along with the 95% confidence interval (CI), which was computed under a Student's \(t\)-distribution. For example, if \(\bar{x}\) and \(s\) are the sample mean and sample standard deviation over \(n=50\) runs, the 95% CI is  
\[\bar{x}\ \pm\ t_{0.975,\,n-1}\frac{s}{\sqrt{n}},\]  
where \(t_{0.975,\,9}=2.262\). These CIs are shown in Tables 2 and 3 in parentheses next to each mean. This larger sample size and explicit CI reporting guard against over-interpreting stochastic fluctuations under highly skewed data.
## 2 Method
### Evaluation Metrics  
The accuracy, precision, recall, F1, Area Under roc Curve (AUC) are used to evaluate the benefits and drawbacks of the model. The definitions are as follows:  
\[accuracy=\frac{TP+TN}{P+N} \tag{7}\]  
Accuracy is the most commonly used evaluation index, where TP (True Positive) is the number of positive samples predicted as positive classes by the model, TN (True Negative) is the number of negative samples predicted as negative classes by the model. P + N is the number of all samples.  
\[precision=\frac{TP}{TP+FP} \tag{8}\]  
where FP (False Positive) stands for negative samples predicted as positive by the model.  
\[recall=\frac{TP}{TP+FN}=\frac{TP}{P} \tag{9}\]  
where FN (False Negative) is the number of negative samples predicted as positive by the model.  
\[F1=2\cdot\frac{precision\cdot recall}{precision+recall} \tag{10}\]  
The F1 score indicator combines the results of the outputs of precision and recall. The value range of F1 score is from 0 to 1. 1 represents the best output of the model, and 0 represents the worst output of the model.  
\[AUC=\frac{\sum_{i\in positiveClass}rank_{i}-\frac{M\text{s}(M+1)}{2}}{M\times N} \tag{11}\]  
The result of AUC shows how many pairs of positive samples scored higher than negative samples. Classify the set of samples with known positive and negative values, and sort them from high to low probability. \(Rank_{i}\) refers to the sorting of the ith positive sample. \(M\times N\) represents positive and negative sample pairs, in which M refers to the number of positive samples and N is the number of negative samples.