esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Study Design  
The study applied a double-blind, crossover design to examine the viability of tDCS in improving performance during a maximal effort task, particularly the \"No-Jump Burpee\" exercise. The tDCS was uniformly administered with an intensity of 2 mA for 20 min across two conditions: targeting the DLPFC and the M1. Subjects attended the laboratory on four separate occasions, one week apart. The initial visit was dedicated to familiarizing the subjects with the experimental protocol procedures. Subsequent sessions were randomized using the Research Randomizer website ([[https://www.randomizer.org](https://www.randomizer.org)]([https://www.randomizer.org](https://www.randomizer.org)); accessed on 8 January 2024), covering three experimental conditions: DLPFC, M1, and SHAM. In each experimental session, subjects completed the Profile of Mood States (POMS), Beck Anxiety Inventory (BAI), and Pittsburgh Sleep Quality Index (PSQI) questionnaires to assess mood, anxiety levels, and sleep quality, respectively. Following the administration of tDCS, participants were tasked to perform burpee exercises until exhaustion. The exercise regimen was standardized at a pace of 20 burpees per minute. The total number of repetitions of each condition was recorded, as was the heart rate, muscle oxygen saturation in the _vastus lateralis_, and rate of perceived exertion (RPE) throughout the exercise. To ensure consistency in experimental conditions, all sessions were conducted in the morning at the same time for each subject under similar ambient conditions (\(\sim\)22 \({}^{\circ}\)C and \(\sim\)60% humidity).
## 7 Conclusions and Future Research  
The Internet of Things (IoT) and blockchain are emerging technologies that have raised many new research avenues. Blockchain technology holds substantial potential to protect low-powered IoT end devices [47]. However, integrating blockchain and IoT technologies raises several research questions, including scalability limitations. The integration of blockchain and IoT technologies may address many of the cyber security issues [48].  
Little research has been conducted to identify the scalability limitations of blockchain-based, low-powered, wireless sensor networks [48]. We have analysed the scalability of Hydrachain, Monero, and Duino coin blockchain networks to contribute to this research gap. Based on the experimental results of the research, we can emphasize that the scalability of blockchain-based, low-powered sensor networks can vary due to the number of blockchain nodes and the type of blockchain algorithm [48].  
The integration of blockchain and IoT opens future research possibilities. One of the possible new research avenues is the performance analysis of different blockchain algorithms on IoT single-board computers [49]. Another possible research area is the energy consumption of blockchain networks. The integrity and security anomaly detection of blockchain sensor networks would be another future research avenue. Finally, the scalability analysis of low-powered sensor networks may play a key role in addressing future IoT-based research issues [49].  
In this paper, the idea and primary evaluations were conducted by [PERSON], [PERSON] and [PERSON] supervised the experiments conducted and the analysis of the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All datasets generated during the study are available upon request from the primary author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion  
### Properties of Mechanical  
#### 3.1.1 Flexural Strength Test Results  
Figure 6 presents the average flexural strength test results of MFRCM and HyFRCM compared to PM. As is clear from Figure 6, the average flexural strength of PM at the age of 28 days was 3.09 MPa. The average flexural strength of MFRCM containing only CF or SF was 4.96 MPa and 5.85 MPa, respectively, which was about 60.5-89.3% compared to PM. According to the results, HyFRCM containing 0.75% SF and 0.25% CF had the highest average flexural strength of 7.68 MPa, revealing the highest flexural strength improved by about 148.5% compared to PM. It also turns out that the average flexural strength of HyFRCM containing 0.75% SF and 0.25% CF or 0.5% SF and 0.5% CF increased by about 31.3% and 28.2%, respectively, compared to the flexural strength of MFRCM containing only SF. This is thought to be because the incorporated fiber plays a cross-linking role in preventing cracks from growing and improving flexural strength through redistribution of stress. That is, it is considered that the performance is improved by controlling the rather macro-cracks of the relatively long steel fibers while the short carbon fibers control the micro-cracks [31]. Once SF and CF are used in an appropriate fiber combination, a synergistic effect could be obtained by increased flexural strength even in the low fiber volume fraction range. Since the optimal fiber combination to obtain the maximum flexural strength is found to be 0.75% SF and 0.25% CF in this experimental investigation, it is believed to be the most appropriate mixing ratio to improve flexural strength.  
Figure 5: Impact resistance test; (**a**) schematic diagram for specimen fabrication, and (**b**) used apparatus for implementation of impact resistance test.
# Novelty Detection with Autoencoders for System Health Monitoring in Industrial Environments
## 5 Experimental Evaluation
### Evaluating the Classification of the Operating Condition
#### 5.2.1 Unsupervised Implementation  
Description. This experiment aims to evaluate the implementations proposed for the unsupervised classifier. The model is based on a hierarchical clustering technique applied to the encoding of the time series generated by the AE encoder. The evaluation takes into account many settings by varying the AE model and the metrics adopted to evaluate the clusters. Tables 10-12 show the accuracy obtained by considering different window sizes, averaging the results obtained in all scenarios, and adopting the homogeneity score, rand score, and adjusted mutual information as metrics. The column RAW is the reference baseline obtained by applying the hierarchical clustering technique to the (original) input time series.  
Discussion. The results show that the baseline is outperformed by our approach independently of the selected window size and metrics. The BiLSTM-based AEs provide the best results (by considering the average and the best performance obtained with the smallest window size) with all metrics. Note that, different to the AE methods, the RAW approach is almost not sensitive to the window size.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Window** & **Raw** & **FC** & **CNN** & **LSTM** & **BiLSTM** \\ \hline
40 & 83.77 & 89.14 & 85.35 & 82.04 & 100 \\
80 & 83.67 & 93.68 & 86.66 & 94.87 & 94.34 \\
120 & 84.47 & 98.5 & 88.44 & 88.64 & 96.49 \\
200 & 86.35 & 97.43 & 86.02 & 84.07 & 94.71 \\
240 & 83.93 & 93.27 & 88.03 & 95.90 & 93.86 \\
360 & 83.57 & 98.74 & 89.74 & 92.68 & 97.54 \\ \hline Avg & 84.29 & 95.13 & 87.37 & 89.70 & 96.16 \\ Std & 1.06 & 3.77 & 1.65 & 5.75 & 2.34 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Accuracy obtained by the unsupervised classifiers: Homogeneity Score.
## 2 Related Works
### Nms  
The NMS algorithm, initially known as Greedy NMS, is widely utilized as a post-processing method in object detection. It uses a rigid threshold to divide a uniform suppression interval for each pedestrian and directly discards all the other proposals within the interval to remove duplicates. However, the rigid threshold and discarding operation utilized in Greedy NMS tend to incorrectly remove the proposals belonging to occluded pedestrians in crowded scenes. In order to handle this problem, Soft NMS [16] uses a re-scoring strategy that replaces the discarding operation used in Greedy NMS. Furthermore, Adaptive NMS [17] uses a dynamic threshold to replace the rigid threshold in the existing NMS algorithms, and demonstrates improved performance, especially combined with Soft NMS. Considering the sensitivity of hyperparameters in Soft NMS, a normalized suppression function [39] was proposed to enhance the robustness of NMS. In addition, MAPD [19] employs a triplet loss function to learn the high-level ID features of pedestrians and uses a new segmented NMS algorithm. In [40], each pedestrian was predicted to have a nearby object, and suppression was conducted based on the proximity between the current bounding box and the nearby object. Alternatively, a pedestrian detector [41] predicts two proposals for each pedestrian and conducts NMS within each branch. MB-CSP [42] predicts the upper, middle, and lower parts of each pedestrian, and applies NMS based on occlusion modes. Although the above-mentioned methods employ different approaches that attempt to retain occluded pedestrians, they still encounter challenges where partial false positives are mistakenly retained while highly occluded pedestrians are still removed.
# Monitoring System of Transmission Line in Mountainous Area Based on LPWAN
## 4 Experiment and Simulation Analysis  
In this paper, the parameters of the sensors used are shown in Table 1. In general, the communication energy consumption was far greater than other energy consumption, so the calculation, data fusion, information maintenance, and packet transceiver were not considered in the simulation. The transmission rate of the LoRa module used in the experiment was 5 kb\(\sim\)30 kb, and the transmission distance was 1 km-5 km. The transmission distance is inversely proportional to the transmission rate. The transmission rate of the cellular wireless transmission module was 125 m/s. In this paper, a 500 kV double circuit line on the same tower in southwest mountain area was taken as an example. The delay caused by the state transition delay, access delay, and handover of the cellular link was taken as 50 ms. The transmission tower was located at the top of a narrow slope with an absolute altitude of 2600 m and a relative height difference of about 400 m. Assuming that there are 100 transmission towers with a fixed distance of one kilometer between transmission towers, each transmission tower is equipped with a sensor group. According to the data in Table 1, the sensor group generates 2.73 kb of data each time [41].
## 4 Prototype Implementation  
We implemented a prototype pixelated shutter receiver as shown in the setup in Figure 3. The design parameters are all listed in Table 1. The key components of the hardware system include 2 RED LEDs, a PDA10A2 Amplified Photodetector, a custom made 1 \(\times\) 2 pixelated LCD shutter and an aspheric condenser lens (outer diameter 80 mm and BFL = 37.5 mm). We implemented the automated shutter control algorithm in a Raspberry Pi 3 Model B+ which interfaces with a 2 \(\times\) 2 pixelated shutter, built using off-the-shelf LCD shutter elements from AdafruitDue to the leakage of LED light we use the 2 \(\times\) 2 LCD setup in 1 \(\times\) 2 reception mode. We used two N210 USRPs as the computing units at the transmitter (controlling LED transmissions) and receiver ends (decoding signals from photodiode). We used a LFTX daughterboard capable of operation from 0-30 MHz and a RFTX daughterboard. The 2 LEDs were controlled using two different USRPs, each hosting a LFTX, and one of the USRPs hosting a LFRX that also conducted the reception. We used GNU Radio blocks (block diagram of GNU applications shown in Figure 4) to transmit and receive signals using the USRPs. We chose to use the state-of-the-art Gaussian Minimum Shift Keying (GMSK) as the modulation strategy in our design, however, any type of modulation can be used in the system. We use 13-bit and 11-bit barker sequences for LED1 and LED2 header bits, respectively. We have implemented the transmissions in the form of UDP packets of size 2096 bits (as per IEEE 802.15.7 VLC standard [28] packet definitions).  
Figure 2: Block Diagram of Shutter Controlling Algorithm.  
**OUTPUT:** Allow desired optical signals and block interference  
**Initialization:**  
Open all shutter pixels (\(px1\), \(px2\), \(px3.....pxn\)) to allow signals  
Set a fixed switching time (\(T_{s}\)) for all pxi (\(i=1,2...N\))  
Preset an empirical threshold SNR value (\(SNR_{th}\))  
Refresh unique identifier (ID) look-up table  
**Step 1: Discovery**  
Iterate each pixel OPEN and all others CLOSED for duration \(T_{s}\) and record SNR  
**IF**\(SNR_{pxi}\geq SNR_{th}\)  
(a) OPEN shutter pixels \(pxi\)  
(b) CLOSE all other pixels  
**(c)** Proceed to **Step 2:Identification**  
**ELSE** CLOSE all the pixels and refresh the program  
**Step 2: Identification**  
Correlate decoded signal ID with look-up table IDs  
**IF** ID matches  
(a) OPEN only matched ID containing pixels  
(b) CLOSE all the remaining pixels  
**ELSE** CLOSE all the pixels and goTo **Step 1: Discovery**
## 3 Materials and Methods  
The general work flow is presented in Figure 2. In order to analyze the feasibility of retrieving biophysical variables from TOA radiance, we performed three parallel studies. First, a GSA using an emulator was carried out to determine the relative influence of each biophysical and atmospheric variable in the TOA radiance signal. Secondly, a set of synthetic test scenarios were generated to assess the performance of biophysical variables retrieval under controlled conditions. Three different retrieval scenarios were implemented: (1) ideal surface reflectance data (i.e., without errors from atmospheric correction), (2) TOA radiance, and (3) realistic surface reflectance data (i.e., affected by error propagation from atmospheric correction). Finally, retrieval strategies were applied to a real S2 data at L1C  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Technical Characteristics** & **Value** \\ \hline Imaging principle & Pushbroom-grating \\ Spectral range [nm] & 400–2200 nm \\ Geolocation accuracy & \(<\)12.5 m \\ SNR @\(\mathbbm{I}_{ref}\) & 50–175 \\ Radiometric accuracy & 3\% abs, 1\% rel \\ A/D conversion & 12 bits \\ Coverage & Land and coastal areas \\ \hline \hline \end{tabular}
\end{table}
Table 1: MSI/Sentinel-2 sensor characteristics as per [41].  
Figure 1: MSI-S2A spectral response function for the 13 spectral bands used by the Ground Segment from 15 January 2018 [42].  
(TOA radiance) and L2A (BOA reflectance). A detailed description of the used processing tools and simulated datasets is provided in Sections 3.1 and 3.2. The used GSA and emulation algorithms are described in Sections 3.3 and 3.4. Further information about the implemented method for retrieving the various biophysical variables from the simulated data and evaluating their accuracy is described in Section 3.5. The method to assess the performance on a real S2 image is then described in Section 3.6.
# An Improved YOLOv8 Model for Strip Steel Surface Defect Detection
## 3 Experiment and Analysis
### Ablation Experiments
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**P\%**} & \multirow{2}{*}{**R/\%**} & \multirow{2}{*}{**mAP@50\%**} & \multirow{2}{*}{**GFLOP/G**} & \multirow{2}{*}{**Parament/M**} & \multicolumn{6}{c}{**mAP@50\%**} \\ \cline{5-16}  & & & & & & & **RS** & **Sc** & **Cr** & **Ps** & **Pa** & **In** \\ \hline \(1\) & 76.4 & 76.0 & 82.9 & 8.1 & 2.87 & 75.6 & 96.8 & 58.6 & 53.0 & 72.2 & 90.9 \\ \(1+2\) & 78.3 & 78.1 & 84.2 & 10.1 & 5.32 & 74.1 & 97 & 60.9 & 83.5 & 97.5 & 92.2 \\ \(1+3\) & 77.4 & 74.4 & 82.9 & 8.4 & 3.03 & 74.0 & 97.3 & 56.1 & 82.4 & 95.6 & 92.0 \\ \(1+4\) & 77.6 & 76.8 & 83.4 & 8.1 & 2.62 & 75.7 & 97.0 & 58.1 & 80.9 & 97.3 & 91.6 \\ \(1+5\) & 79.4 & 77.4 & 85.0 & 8.1 & 2.87 & 78.0 & 97.0 & 62.0 & 82.3 & 96.8 & 93.7 \\ \(1+6\) & 79.4 & 79.2 & 85.5 & 8.1 & 2.87 & 73.7 & 97.9 & 63.9 & 84.5 & 96.9 & 92.9 \\ \(1+2+3\) & 78.1 & 78.6 & 84.9 & 10.4 & 5.48 & 74.4 & 98.6 & 61.7 & 83.7 & 98.0 & 92.8 \\ \(1+_{-+}4\) & 78.7 & 80.3 & 85.8 & 10.4 & 5.48 & 75.7 & 98.6 & 62.3 & 87.1 & 97.7 & 93.5 \\ \(1+_{-+}5\) & 79.3 & 79.8 & 86.4 & 10.4 & 5.48 & 78.2 & 97.6 & 64.2 & 87.1 & 98.3 & 94.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experiments. Figure 13: YOLOv8n-BSPB loss function curve: (**a**) YOLOv8; (**b**) YOLOv8-BSPB. detecting most defect categories in the confusion matrix are comparable, indicating higher classification accuracy of the improved model. In order to visually compare the detection performance of the YOLOv8-BSPB proposed in this paper with that of the YOLOv8 model on surface defects of strip steel, the dataset is qualitatively analyzed, and the results are shown in Figure 15. From the Figure, it  
Figure 14: Confusion matrix for the validation set with defects of different classes: (**a**) confusion matrix of YOLOv8n; (**b**) confusion matrix of YOLOv8n-BSPB. can be seen that the original model shows leakage and low detection accuracy, while the improved model improves in accuracy and is able to detect more targets, which verifies the improvement of the improved model in terms of defect sample detection.
## 2 Explanation Methods
### Shap  
Shapley Additive Explanation (SHAP) method was presented by [PERSON] and [PERSON] [13]. SHAP method is based on calculating the Shapley Values [22]. Shapley value represents the contribution of each feature of input instance \(x\) in making the prediction of black-box model.  
The Shapley values are inspired by the cooperative game theory that aims to quantify the contribution of each player (the feature in our case) in the game where a number of players collaborate to acquire a reward (the prediction in our case). The reward is supposed to be paid equitably between the players based on their contributions; such a contribution is the Shapley value. Simply, a Shapley value is defined as the average marginal contribution of a feature in an instance for all possible coalitions. The marginal contribution of a given feature is the difference between the prediction that is generated when the feature is present with respect to the prediction that is generated when that feature is absent. This is carried out for each of the coalitions. The coalitions are all possible subsets that can be generated where the feature we are looking to calculate has its contribution present. The mean of marginal contribution is the Shapley value. For instance \(x\) with \(F\) features, the Shapley value \(\varphi\) of the feature \(i\) could be calculated as  
\[\varphi_{i}(f,x)=\sum_{S\subseteq\{1,\ldots,F\}\setminus\{i\}}\frac{|S|!(F-|S |-1)!}{F!}[f_{x}(S\cup\{i\})-f_{x}(S)], \tag{2}\]  
As a real dataset usually has a large number of features, this causes it to be computationally complicated to figure out the Shapley values for each feature individually in the interested instance. To avoid this, [PERSON] and [PERSON] introduced Kernel SHAP to approximate the SHAP method. Kernel SHAP is a method for calculating Shapley values with a smaller number of coalition instances. It relies on a weighted linear regression with the Shapley values as the solution coefficients.
## 3 Network Handover Algorithm Based on Location Prediction
### Seamless Network Handover
When the central controller decides that network handover is required, it sends a handover request to the target AP, and the target AP returns an Ack as a handover confirmation. 3. During handover decisions, the central controller sends an SN status request to the UE to synchronize downlink data transmission. It then forwards the SN status reported by UE to the target AP to ensure data continuity. 4. Throughout the negotiation process, the UE maintains communication with the original network until the handover is successful. In Figure 4, during each iteration, the agent initially observes the environmental state at the current time t and selects an action based on the greedy strategy to obtain immediate rewards. Next, the agent updates the Q-value corresponding to the current state and action based on the maximum expected discounted reward \(\max Q(s_{t+1},a^{\prime})\) at time \(t+1\). The Q-value update equation is as follows:  
\[Q(s,a)\gets Q(s,a)+\alpha[r+\gamma\max Q(s_{t+1},a^{\prime})-Q(s,a)] \tag{21}\]  
where \(\alpha\) represents the learning rate, which affects the update speed of the Q table; \(r\) denotes the immediate reward; \(\gamma\) is the discount rate, representing the impact of future rewards on the current step. When \(\gamma\to 0\), the agent focuses more on the reward obtained by the current action; when \(\gamma\to 1\), the agent considers future rewards more heavily. \(\max Q(s_{t+1},a^{\prime})\) represents the maximum Q value among the available actions in the next state. This process is repeated until the final state is reached, marking the completion of a  
Figure 4: Schematic of seamless handover algorithm. learning episode. Through repeated learning, the algorithm gradually learns the optimal action for each state, corresponding to the behavior that maximizes the long-term reward in each state, forming the optimal policy set. Over time, the exploration rate gradually decreases, the algorithm gradually shifts from the initial exploration to exploiting the best-known strategy.
# Self-Similar Markovian Sources
## 2 Markov Chains and the Markov Traffic Sources
### Poisson Pareto Burst Process (PPBP)  
The PPBP is a process in which the overlapping of multiple bursts can be observed. Burst arrivals follow the Poisson process with intensity \(\lambda_{p}\), and the burst duration is defined by the Pareto (thus, heavy-tailed) distribution. The arrivals of customers during a burst are given by a constant bit rate, denoted as \(r\)[20]. PPBP was implemented, for example, in the broadly used simulation tool NS-3 [44].  
Formally, the Poisson Pareto burst process is a continuous time stochastic process, for which \(t\in[0,T]\to B_{t}\), where \(B_{t}\) is the number of active bursts in the data traffic at the time \(t\). The active burst is a Poisson process of rate \(\lambda\). The time of activity of each burst is independent and given by the Pareto distribution (Pareto(\(\alpha,\beta\))). The function of Pareto(\(\alpha,\beta\)) cumulative distribution is given by  
\[F(x)=\left\{\begin{array}{ll}1-(\frac{\beta}{x})^{\alpha}&\quad\text{for }x \geq\beta\\ 0&\quad\text{otherwise}\end{array}\right. \tag{9}\]  
For \(\alpha\in(1,2)\), the random variable representing the duration of a single burst \(D\) has infinite variance and a finite expectation, defined as follows:  
\[E(D)=\frac{\alpha\beta}{\alpha-1} \tag{10}\]At time \(t=0\), the number of active bursts is a Poisson random variable of parameter \(\Lambda=\lambda E(D)\). The periods of activity of these initial bursts are independent and identically distributed random variables. The cumulative distribution of these bursts can be defined by  
\[G(x)=\left\{\begin{array}{ll}1-\frac{1}{\alpha}(\frac{\beta}{x})^{1}-\alpha& \text{for }x\geq\beta\\ \\ 1-\frac{\alpha-1}{\alpha}(1-\frac{x}{\beta})-\frac{1}{\alpha}&\text{for }0<x<\beta \end{array}\right. \tag{11}\]  
where \(\alpha\), \(\beta\) are the parameters of the Pareto distribution.  
The probability distribution with a cumulative distribution function defined as above is called the distribution of Pareto forward recurrence times. The mean and variance of this function is infinite [45]. The process exhibits LRD when its autocorrelation persists over all times scales. The PPBP process exhibits long range dependence (LRD) as long as the Pareto distributed burst durations have infinite variance.  
The generation of the Pareto burst process requires the following parameters to be set:  
* The traffic time duration \(t\),
* The intensity of the Poisson burst \(\lambda\),
* The mean intensity of bursts \(E(D)\),
* The Hurst parameter \(H\in(0.5,1)\).  
The translation into Pareto(\(\alpha\),\(\beta\)) distribution can be achieved as follows: from \(H\), we get \(\alpha=3-2H\), and from \(E(D)\), we obtain the parameter \(\beta\).