esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Encapsulation Protocol
### Dns
#### 3.3.3 Statistic Feature  
There has been a significant interest in detecting DNS tunnel traffic based on the time and space statistical characteristics of packets/streams, such as the number of packets/streams, the number of bytes, and the duration of streams. These features have been used by [PERSON] [55]. The average TTL value of domain records, total number of domain name requests during the observation period, request ratio in each time period, whether the domain name is blacklisted, and other characteristics were also extracted by [PERSON] [55].  
There is also a high degree of entropy detection in DNS tunnel traffic. Meanwhile, [PERSON] [18] determined whether the packet size distribution was met with cross entropy, and then defined a distance function to measure entropy, which controlled the threshold range to identify DNS tunnel traffic effectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Ref.** & **Year** & **Datasets** & **T/ML** & **Fingerprint** & **Behavior** & **Flow** & **Packet** & **Spatial** & **Temporal** & **I** & **II** & **Model** \\ \hline [PERSON] [52] & 2009 & Private & T & & \(\surd\) & \(\surd\) & \(\surd\) & & & Payload & Size & MNB \\ Sun [17] & 2010 & Private & T & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & & \(\star\) Payload & Match + Bayes \\ [PERSON] [15] & 2013 & Private & T & \(\surd\) & & \(\surd\) & \(\surd\) & & Handshake & Entities & Match \\ [PERSON] [25] & 2014 & Public & T & \(\surd\) & & \(\surd\) & & \(\surd\) & Handshake & ClientHello & Markov \\ [PERSON] [40] & 2016 & Public & ML & & \(\surd\) & \(\surd\) & & \(\surd\) & Payload & Duration & & C4.5 + KNN \\  & & & & & & & & & & & Linear regression + \\ [PERSON] [29] & 2017 & Private & ML & & \(\surd\) & & \(\surd\) & & Payload & & & & \\  & & & & & & & & & & Duration & & SVM + Decision Tree \\  & & & & & & & & & & & & + Multi-layer \\ [PERSON] [38] & 2018 & Private & T & & \(\surd\) & & & \(\surd\) & Payload & Duration & & \\  & & & & & & & & & & & & + Order & MAMPF \\ [PERSON] [34] & 2020 & Public & ML & & \(\surd\) & \(\surd\) & \(\surd\) & & & Payload & & Size + \\  & & & & & & & & & & & Duration & \\  & & & & & & & & & & & & Size + \\  & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of HTTPS tunnel detection. Summary of HTTPS tunnel detection. T represents traditional method. ML represents machine learning method. I represents detecting with coarse content. II represents detecting in a specific location.
## 4 Performance Results
### Performance Result  
Figure 12 shows the MSE predicted by the cell center and elevation angle XGBOOST regression prediction model for each propagation delay.  
As a result of the evaluation, the performance was about 0.01252 on average when measured by MSE, which is a regression performance index. Figures 13-17 show the results of using the results predicted by the regression prediction model as test data for the handover triggering prediction model using the cell center and elevation angle. Figure 18 is a graph showing the improved performance as a percentage when each model is applied.  
Figure 12: MSE by propagation delay.  
Figure 13: Accuracy score.  
Figure 16: F1 score.  
Figure 14: Precision score.  
Figure 15: Recall score.  
Among the graphs showing the results, the recall and precision scores are indicators that evaluate the degree to which handover triggering occurs well at the time when it should occur, and that it occurs at the time when it should not occur. In the case of recall, this can consist of Radio Link Failure or Handover Failure, and in the case of precision, it can lead to Unnecessary Handover (UHO) or PP. In the experiment, 40 out of 50 UEs were used as training data, and 10 UEs were used as test data. Each UE was simulated for 600 s. Figure 19 shows the RLF + HOF, UHO + PP of the handover triggering test results considering the propagation delay.  
Figure 19: Two-step XGBOOST performance results.  
Figure 17: AUC-ROC score.  
Figure 18: Improved Performance Comparison.  
The result of Figure 19 is a graph showing the point at which the UE can predict handover triggering for 1 min on average when the model makes predictions by considering the propagation delay. When conducting the experiment in paper [29], HOM was set to 0 db and TTT to 0 ms, and the test was conducted. This means that it is a graph that measures the result of performing a measurement report immediately when the condition of handover triggering is satisfied. Therefore, in the case of the prediction model using LHT + EHT, the graph is shown based on whether the prediction of the first point where handover triggering is possible is consistent with the assumption that HOM operates like 0 db and TTT operates like 0 ms. When the two-step XGBOOST model is applied, it shows good performance in all propagation delay error intervals. Even when applied to the actual NTN environment, this represents a more accurate prediction than handover triggering, which does not consider the existing propagation delay for the section of the moving satellite. In particular, the recall score indicates a score that predicts the point in time when handover triggering should occur, and it can be seen that the prediction succeeds in most cases. This model shows strength in situations that can lead to RLF and HOF, which are used as KPIs for simulations in several papers.
# A Reliability Scheduling Algorithm for the Static Segment of FlexRay on Vehicle Networks +
## 3 Experimental Results  
In this section, the experimental environment and validation for the proposed method are described as follows. Then, we will discuss the experimental results, comparing our approach and three related studies.
# Efficient Shot Detector: Lightweight Network Based on Deep Learning Using Feature Pyramid
## 4 Experiment Results  
We used the public data sets PASCAL VOC [17] and MS COCO2017 [18] to verify and evaluate the performance of ESDet. The backbone network was pretrained with the ILSVRC CLS-LOC data set before conducting the experiment. Then, the network was extended up to ESDet-B7 in proportion to the input image size. Consequently, the EfficientNet used as the backbone was extended to EfficientNet-B7 in proportion to the input resolution. When training the network in the experiment, it was conducted in Tensorflow 2.4.1, NVIDIA Geforce RTX 3090 X2 48 GB environment. For all networks, we used the SGD optimizer and set the momentum value to 0.9. The initial training rate was 0.005, and 0.0005 weight attenuation was applied to the weights and biases of the convolution filter. To measure the network fair inference time, we set the batch size to 1 in NVIDIA Geforce GTX TITAN X 12 GB environment. Based on the proposed ESDet-baseline network, 10.1 ms GPU latency (99 FPS) and 3.3 BFLOPS were measured. Table 3 shows the network configuration according to the input resolution and backbone change.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Detector**} & **Input** & \multirow{2}{*}{**Proposals**} & **Pyramid** & \multirow{2}{*}{**Params (M)**} \\  & **Resolution** & & **Times/Channel** & \\ \hline ESDet-Baseline & \(512\times 512\) & 16,368 & 3/64 & 3.81 M \\ ESDet-B1 & \(544\times 544\) & 18,447 & 4/88 & 6.64 M \\ ESDet-B2 & \(576\times 576\) & 20,652 & 5/112 & 8.08 M \\ ESDet-B3 & \(608\times 608\) & 23,061 & 6/160 & 11.91 M \\ ESDet-B4 & \(640\times 640\) & 25,518 & 7/224 & 20.42 M \\ ESDet-B5 & \(672\times 672\) & 28,158 & 7/288 & 33.10 M \\ ESDet-B6 & \(704\times 704\) & 30,867 & 7/288 & 45.34 M \\ ESDet-B7 & \(736\times 736\) & 33,798 & 7/288 & 68.24 M \\ \hline \hline \end{tabular}
\end{table}
Table 3: Expand the network based on the ESDet-baseline.
## 3 Methodology and Solution
### Detector Bank Based on Simplified Welch Algorithm
#### 3.2.1 Welch and Simplified Welch Method
For \(i\)th input window, PSD samples are calculated as the following equation:  
\[PSD_{i}[v]=\ \alpha\times PSD_{i-1}[v]+\ P_{i}[v]\ and\ 0\leq\alpha<1. \tag{9}\]  
As it can be seen, calculated PSD depends on the weighted pervious PSD and current amplitude of the frequency samples. The \(\alpha\) parameter determines the dependency on the past, with a larger \(\alpha\), more PSDs from the past are involved in the calculation and a smoother power spectrum is obtained. Smaller \(\alpha\) results in faster convergence, but rougher estimation of the spectral density. The proposed simplified Welch algorithm has a structure that completely fits the Xilinx DSP generator library and can be easily implemented as a real-time PSD estimator. This method generates the PSD samples in series manner and synchronizes the start and end of each vector by the help of \"Data Ready\" pulse. After estimation of \(N_{F}\) PSD samples, a data ready pulse is generated to synchronize all PSD packets.
## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Precision (\%)** & **NS** & **EAS** & **MS** & **Mean** \\ \hline
**Model** & **NS** & **EAS** & **MS** & **Mean** \\ \hline Multi-task LSTM RNN model designed to simultaneously perform & 97.5 & 100 & - & 98.75 \\ classification of APS types (NS, EAS), classification of PA types (SS, Sb, TR) and estimation of EE. & 98.91 & 99.71 & 99.06 & 99.23 \\ \hline Multi-task LSTM RNN model for classification of APS types (NS, EAS, MS) and PA types (SS, Sb, TR) with ADYSN. & 98.01 & 99.58 & 98.41 & 98.67 \\ \hline Independent LSTM RNN model for classification of APS types (NS, EAS, MS) with Weighted Training. & 97.98 & 99.73 & 97.79 & 98.5 \\ \hline Independent LSTM RNN model for classification of APS types (NS, EAS, MS) with ADYSN. & 96.77 & 99.3 & 97.63 & 97.9 \\ \hline
**Model** & **NS** & **EAS** & **MS** & **Mean** \\ \hline Multi-task LSTM RNN model designed to simultaneously perform & 100 & 98.43 & - & 99.21 \\ classification of APS types (NS, EAS), classification of PA types (SS, Sb, TR) and estimation of EE. & 98.74 & 99.6 & 99.78 & 99.38 \\ \hline Multi-task LSTM RNN model for classification of APS types (NS, EAS, MS) and PA types (SS, Sb, TR) with ADYSN. & 96.68 & 99.3 & 99.02 & 99.0 \\ \hline Independent LSTM RNN model for classification of APS types (NS, EAS, MS) with Weighted Training. & 98.81 & 99.17 & 99.53 & 99.17 \\ \hline Independent LSTM RNN model for classification of APS types (NS, EAS, MS) with ADYSN. & 97.79 & 98.88 & 98.55 & 98.41 \\ \hline
**F1 Score (\%)** & **NS** & **EAS** & **MS** & **Mean** \\ \hline
**Model** & **NS** & **EAS** & **MS** & **Mean** \\ \hline Multi-task LSTM RNN model designed to simultaneously perform & 98.73 & 99.21 & - & 98.97 \\ classification of APS types (NS, EAS), classification of PA types (SS, Sb, TR) and estimation of EE. & 98.73 & 99.21 & - & 98.97 \\ \hline Multi-task LSTM RNN model for classification of APS types (NS, EAS, MS) and PA types (SS, Sb, TR) with Weighted Training. & 98.83 & 99.66 & 99.42 & 99.3 \\ \hline Multi-task LSTM RNN model for classification of APS types (NS, EAS, MS) and PA types (SS, Sb, TR) with ADYSN. & 98.34 & 99.44 & 98.72 & 98.83 \\ \hline Independent LSTM RNN model for classification of APS types (NS, EAS, MS) with Weighted Training. & 98.39 & 99.45 & 98.65 & 98.83 \\ \hline Independent LSTM RNN model for classification of APS types (NS, EAS, MS) with ADYSN. & 97.28 & 99.09 & 98.09 & 98.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Precision, Recall, and F1 score for APS Classification (LSTM models).
## 4 Discussion
### Gaps and Challenges  
Several gaps in the current literature were identified. First, the predominant reliance on preclinical models limits the generalizability of findings to human populations. While these models simulate immunocompromised environments, they do not fully replicate the complexities of human physiology, particularly in chronic conditions like diabetes and cancer.  
Second, the heterogeneity within immunocompromised populations remains under-explored. Variations in glycemic control among diabetic patients or differences in cancer treatment regimens likely influence GTR membrane performance but were not adequately stratified in the reviewed studies. Future studies should account for these variables to develop personalized regenerative solutions.  
Finally, the long-term outcomes and potential adverse effects of GTR membranes are poorly understood. Chronic conditions may introduce systemic factors that affect the performance and longevity of these technologies, necessitating longitudinal studies to assess sustained efficacy and identify delayed complications.
# Network Performance Optimization for Low-Voltage Power Line Communications
## 3 LVPLC Network Scheme
### Network Problem Description and Modeling
#### 3.1.2 Network Objective Function  
Network optimization is a typical multi-constrained and nonlinear optimization problem. The LVPLC network is modeled as a non-negative weight-directed graph \(G(V,E,C)\), where \(V=\{v_{0},v_{1},v_{2},\cdots,v_{n}\}\) denotes the set of CCO \(v_{i}\) values and stations \(v_{j}(j\
eq i,1\leq j\leq n)\); \(E=\{e_{1},e_{2},\cdots,e_{m}\}\) denotes the set of directed and no-loop links; and \(C=\{c_{ij}|i,j\in V\}\) denotes the set of non-negative weights, whose value dynamics change, showing the link connection state.  
The objective function minimizes several dynamic random shortest paths from CCo \(v_{i}\) to stations \(v_{j}\) in the graph \(G(V,E,C)\). Assuming that the CCo is \(O\); the destination station is \(D\); the proxy set is \(V_{q}\); and the shortest path of the station set is \(V_{p}\), the elements in \(V_{p}\) are represented as \(i_{1},i_{2},\ldots,i_{h}(h\leq n)\) in order in the shortest path. The objective function of the shortest path can be expressed as:  
\[L=\min\sum_{i=s}^{D}\sum_{\begin{subarray}{c}j=s\\ i\
eq j\end{subarray}}^{D}c_{ij}x_{ij} \tag{1}\]  
s. t.  
\[\sum_{(i,j\in\mathrm{E})}x_{ij}-\sum_{(j,i\in\mathrm{E})}x_{ji}=\begin{cases}1& i=O\\ 0&i\in V-\{O,D\}\\ -1&i\in D\end{cases} \tag{2}\]  
\[x_{ij}=\begin{cases}1&link\ from\ i\ to\ j\ exist\\ 0&else\end{cases} \tag{3}\]  
\[\sum_{i\in A_{k}^{+}}x_{ik}=1,\sum_{j\in A_{k}}x_{kj}=1,\forall k\in V_{q} \cap V_{p} \tag{4}\]  
\[V_{q}=V_{q_{1}}\cup V_{q_{2}}\cup\cdots\cup V_{q_{w}} \tag{5}\]  
\[V_{q_{k}}\cap V_{q_{l}}=\phi,\forall k\
eq l \tag{6}\]  
\[\forall k,\exists i_{q_{k}}\in V_{q_{k}}\cap V_{p} \tag{7}\]  
\[\forall l<k,\forall i_{k}\in V_{q_{k}}\cap V_{p},\exists i_{t}\in V_{q_{l}} \cap V_{p}\]  
\[1\leq t<s\leq h \tag{8}\]  
Equation (1) shows the objective function. Formula (2) shows the constraint conditions of the CCo, proxy, and destination station. Equation (3) shows the definition of the decision variables \(x_{ij}\). If the link exists, \(x_{ij}=1\); otherwise, \(x_{ij}=0\). Equation (4) shows a necessary constraint on the proxy. Equation (5) indicates that the proxies are divided into w classes. Equation (6) indicates that there is no loop between every class. Equation (7) indicates that at least one station in each class is stations. Equation (8) denotes the grouped and order-preserving stations shortest path, which in turn, passes stations \(V_{q_{1}},V_{q_{2}},\cdots,V_{q_{w}}\). We propose an improved \(Q\)-learning method for the LVPLC network for the grouped and order-preserving proxy shortest paths.
## 5 Discussion and Implications
### Future Research Directions  
Emerging deepfake generation techniques demand continuous innovation in detection methodologies. The following research avenues warrant exploration:  
1. Adversarial Defenses: Developing advanced strategies, including PGD-based training, ensemble adversarial learning, or gradient obfuscation, can improve model robustness. Incorporating these techniques early in the pipeline may ensure resilience across diverse adversarial scenarios.
2. Diffusion-Based Detection: With the advent of generative diffusion models like DiffusionFake, evaluating and adapting existing CNN architectures to detect diffusion-specific artifacts is crucial. Future work can explore specialized layers or temporal consistency checks that capture the iterative refinements inherent in diffusion processes.
3. Cross-Dataset Learning: Expanding training to incorporate diverse datasets, including newer benchmarks, can improve cross-domain robustness. Incremental or continual learning strategies that accommodate novel forgery patterns without catastrophic forgetting represent another promising path.
4. Ensemble Approaches: Combining complementary models, such as XCeption and VGG16, or incorporating task-specific modules, can enhance overall performance. Hybrid systems could dynamically allocate computational resources, achieving a trade-off between speed and accuracy, especially in real-time deployments.
5. Human-in-the-Loop Systems: Integrating human reviewers for borderline cases can significantly reduce error rates. Future frameworks could incorporate automated confidence scoring or active learning to determine when expert intervention is warranted, optimizing both throughput and reliability.
## 1 Introduction
### Basics of the Smart City
#### 1.1.4 Smart Healthcare  
Smart healthcare is a crucial key enabler for smart cities [32; 33]. Smart healthcare can provide intra-hospital observation, remote patient monitoring, virtual healthcare, long-distance surgery, remote learning for doctors and nurses, remote diagnoses, emergency services, and remote consultations [17]. These applications required new technologies like virtual reality (VR) and augmented reality (AR) which require real-time, reliable, low latency, and flexible communication networks [1; 17]. Smart healthcare ensures precise and high-quality medical care for the residents of smart cities.
# A Survey of Machine Learning-Based System Performance Optimization Techniques
## 4 Evaluation and Design Consideration
### Evaluation
#### 4.1.2 Machine Learning Model and Traditional Model  
Based on the reviewed papers, the advantages and disadvantages that can be identified when replacing the traditional model with machine learning are summarized. Compared to the traditional B-Tree, Learned B-Tree showed improved results that were up to 1.5-3\(\times\) faster and up to 2\(\times\) less spatially [7], but since Learned B-Tree is trained with training data, it works well only in read operations. Therefore, when a new input comes in, it is necessary to retrain Learning B-Tree. This means that the trained model does not reflect the  
Figure 14: The Glider Predictor [15]: Glider selects the ISVM as a currently accessed PC and determines the cache priority based on the sum of the weights associated with the previously accessed PC.  
diversity of data. Two Level Prefetcher [13] uses perceptron to deny unnecessary proposals from traditional prefetcher. Although Two Level Prefetcher increased accuracy by denying unnecessary decisions, it reduced the cache hit rate slightly because the perceptron can deny the required requests, and the prefetch may not be done during the perceptron training. Learned Prefetcher [14] designed an LSTM-based prefetcher and compared to two hardware prefetchers for experimentation. In various application traces, the LSTM-based prefetchers showed high accuracy and recall compared to two traditional prefetchers, but there were no usability experiments to see if the training model could learn different types of traces. In view of this aspect, machine learning shows an improvement in accuracy when compared to traditional models, but machine learning is limited by the training data and may not be able to guarantee timeliness due to the training time. Table 2 summarizes two pros and cons.
# CONCERTS: Coverage Competency-Based Target Search for Heterogeneous Robot Teams
## 5 Simulations, Experiments, and Results
### Comparative Results  
We benchmark CONCERTS against a state-of-the-art algorithm, SCoPP [7], an offline CPP algorithm with the goal to minimize total coverage completion time with the results shown in Table 1. For a fair comparison, the problem setting is the Python environment with static map information given. CONCERTS is not given prior target information (i.e., uniform prior distribution of target location) and the search does not terminate until the entire area has been explored. Furthermore, each trial uses the same number of agents, and  
Figure 4: Simulation results with a different number of agents in different size search regions. Agent position is marked by a circle with crossed lines and each agent is given a matching path color. Black rectangles are obstacles to be avoided by the agents. (**a**) 3-agent case (80 m \(\times\) 80 m) (**b**) 10 agents (160 m \(\times\) 160 m) (**c**) 50 agents (400 m \(\times\) 400 m).  
all have the same skill level. The data in Table 1 is generated from 10 comparative trials with 13 agents, as described in [7].  
In such a setting, CONCERTS performs the same task as a CPP algorithm with the goal of providing complete search as quickly as possible. In this setting, CONCERTS is shown to outperform SCoPP by 9% in coverage completion time.  
These results indicate that in scenarios where team heterogeneity cannot be exploited, CONCERTS still is able to outperform a state-of-the-art method. This is a valid comparison because search for a static target assumed to be in the environment has the same goal as a CPP algorithm, i.e., in the worst case, to cover the entire region as quickly as possible. Similar to SCoPP, our method scaled linearly with the number of agents and the slowest step is the path-planning step. However, our method generates plans more quickly, and experimental validation of online re-planning is provided. Finally, CONCERTS also is scalable and is validated with up to 50 agents.
## 3 Experiments and Analysis  
Previous fusion-based methods for enhancing underwater images face challenges in balancing the quality of enhancement and real-time usage. This is primarily due to the complexity of the process and network structure involved. To validate the effectiveness of the proposed method in this paper, a comprehensive evaluation is conducted in this section through comparative experiments and ablation experiments. Firstly, we discuss the detailed setup of the proposed framework training. Subsequently, we demonstrate the performance of our method by comparing it with other non-deep and deep learning methods on both synthetic and real underwater images. This comparison is carried out using subjective and objective evaluation methods, focusing on parametric quantities, GFLOPs, and real-time performance of different algorithms. Finally, ablation experiments are performed to showcase the effectiveness of each module within the Channel Distillation (CD) module, loss function, and multi-teacher model employed in the framework of this paper.
# _Sci_
## 4 Discussion
### Modifying Satellite ICDS and KPPs for Laser Communications
#### 4.3.2 Key Performance Parameter (KPP) #2: Acquisition, Tracking, and Pointing  
The second key performance parameter links to the first by addressing acquisition, tracking, and pointing. With the enormous speeds and complex geometries of two moving bodies (transmitter, receiver), point-ahead, zeroing, and tracking \"become a formidable challenge\" [51]. The short-range, wide-field-of-view, extremely agile, electronically steered, photonic emitter program of the U.S. defense advanced research project agency (DARPA)'s seeks to increase the speed of development and the cost efficiency. This program places optical scanning technology on a microchip (likely taking advantage of agile beam steering, based on microelectromechanical systems [51]), and can sweep a laser more than one-hundred-thousand times per second [132].