esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Supplementation Group**} & \multicolumn{4}{c}{**Placebo Group**} \\ \hline  & \multicolumn{2}{c}{**Day 0**} & \multicolumn{2}{c}{**Day 60**} & \multicolumn{2}{c}{_P_} & \multicolumn{2}{c}{**Day 0**} & \multicolumn{2}{c}{**Day 60**} & \multicolumn{2}{c}{_p_} \\  & **Mean** & **SD [\(\pm\)]** & **Mean** & **SD [\(\pm\)]** & **Mean** & **SD [\(\pm\)]** & **Mean** & **SD [\(\pm\)]** & **p** \\ \hline FM [kg] & 37.85 & 12.10 & 39.46 & 12.48 & 0.07 & 31.51 & 8.90 & 31.31 & 8.87 & 0.80 \\ FM\% & 39.73 & 4.69 & 40.01 & 4.71 & 0.06 & 37.76 & 4.51 & 37.71 & 4.29 & 0.81 \\ Creating [mg/dL] & 0.64 & 0.10 & 0.64 & 0.09 & 0.91 & 0.71 & 0.26 & 0.69 & 0.24 & 0.22 \\ GFR [mL/min/1.73 m\({}^{2}\)] & 111.84 & 25.12 & 111.26 & 24.95 & 0.97 & 101.70 & 20.84 & 106.36 & 24.00 & 0.26 \\ Uric acid [mg/dL] & 5.15 & 1.06 & 5.25 & 0.99 & 0.55 & 4.91 & 1.02 & 4.66 & 1.03 & 0.08 \\ GGTP [U/L] & 29.57 & 20.29 & 29.51 & 17.21 & 0.88 & 30.00 & 48.50 & 22.93 & 26.84 & 0.71 \\ Amylase [U/L] & 54.41 & 31.06 & 57.11 & 33.78 & 0.24 & 57.38 & 18.76 & 57.56 & 14.80 & 0.19 \\ Total cholesterol [mg/dL] & 220.15 & 36.69 & 208.43 & 37.09 & 0.04 & 187.57 & 20.30 & 181.93 & 26.49 & 0.28 \\ HDL [mg/dL] & 59.00 & 31.68 & 62.64 & 30.43 & 0.17 & 53.21 & 10.37 & 56.57 & 12.88 & 0.08 \\ LDL [mg/dL] & 133.23 & 38.42 & 117.86 & 38.56 & 0.02 & 111.57 & 16.70 & 103.29 & 19.68 & 0.03 \\ TGg [mg/dL] & 140.04 & 64.75 & 139.24 & 69.06 & 0.34 & 114.13 & 41.52 & 110.15 & 53.20 & 0.63 \\ Non-HDL [mg/dL] & 161.17 & 41.00 & 145.69 & 41.75 & 0.02 & 134.34 & 18.37 & 125.26 & 26.60 & 0.09 \\ AST [U/L] & 20.51 & 6.96 & 17.93 & 3.41 & 0.12 & 15.90 & 3.62 & 18.59 & 11.09 & 1.00 \\ ALT [U/L] & 25.41 & 12.05 & 19.07 & 6.13 & 0.01 & 15.86 & 6.03 & 14.22 & 4.76 & 0.75 \\ Glucose [mg/dL] & 102.29 & 9.35 & 101.21 & 10.56 & 0.43 & 97.93 & 6.64 & 96.86 & 5.60 & 0.62 \\ HbA\({}_{\rm 1C}\) [\%] & 5.33 & 0.38 & 5.22 & 0.30 & 0.05 & 5.16 & 0.17 & 5.09 & 0.15 & 0.18 \\ Insulin [mU/mL] & 17.01 & 8.86 & 15.01 & 7.26 & 0.29 & 9.56 & 3.03 & 10.00 & 4.30 & 0.71 \\ HOMA-IR & 0.16 & 0.09 & 0.14 & 0.09 & 0.50 & 0.10 & 0.04 & 0.12 & 0.04 & 0.15 \\ Albumin [g/L] & 45.41 & 2.98 & 44.71 & 2.11 & 0.38 & 46.28 & 2.54 & 45.18 & 1.88 & 0.03 \\ Body mass [kg] & 93.56 & 20.04 & 94.41 & 21.06 & 0.35 & 82.19 & 15.54 & 81.89 & 15.61 & 0.89 \\ BMI [kg/m\({}^{2}\)] & 33.17 & 6.03 & 33.37 & 6.29 & 0.38 & 29.85 & 4.54 & 29.76 & 4.52 & 1.00 \\ \hline \hline \end{tabular}  
* ALT: alanine aminotransferase, AST: aspartate aminotransferase, BMI: body mass index, FM: fat mass, GFR: glomerular filtration rate, GGTP: gamma-glutamyl transpeptidase, HOMA-IR: homeostatic model assessment of insulin resistance, HDL: high-density lipoprotein, LDL: low density lipoprotein, TG: triglycerides.  
\end{table} 
Table 3: The overall results of the supplementation and placebo control.
## 5 Conclusions  
As the nonlinear loads are proliferating in the grid, the accurate assessment of power quality problems is becoming vital. The variation in current harmonics for the switch-mode power supplies has been discussed in this paper. The power supplies are categorized into two groups based on the filter circuits. The type 1 power supplies contain filtering circuits, while type 2 power supplies are without filters. The difference between cold and stable state measurements of current harmonics magnitude and phase angles are more significant for type 2 power supplies contrary to type 1. The \(\text{THD}_{i}\) values of SMPS also vary with time until they become stable. The power supplies without filters show more difference between \(\text{THD}_{i}\) measurements in the cold state and stable state. The \(\text{THD}_{i}\) variation was between 15 to 20% for type 2 power supplies and 0.5 to 1.2% for type 1 power  
Figure 8: Current harmonic stability time (\(T_{stable}\)) variation against active power of all SMPS.  
Figure 9: Current RMS difference between Cold and Stable state.  
supplies. The maximum time taken by power supplies to have stable current harmonics was 85 min. The absolute difference between cold and stable measurements was more prominent for higher-order harmonics for power supply without harmonic filters. For power quality estimation in the real-time scenario where a large number of power supplies have been involved, this difference may result in a significant error. Therefore, it is recommended to perform power quality measurements when the power supplies become stable. Future work will include power supplies with more rated power and other electronics-based equipment to assess time-dependent variation in harmonic emission.  
Conceptualization, M.N.I. and L.K.; methodology, M.N.I., B.A. and T.V.; validation, A.R. and M.N.I.; data curation, M.N.I. and G.L.D.; writing--original draft preparation, M.N.I. and B.A.; writing--review and editing, A.R. and G.L.D.; supervision, L.K. All authors have read and agreed to the published version of the manuscript. This work was supported by the Estonian Research Council grant PSG 142. This work was financially supported by Government of Russian Federation, Grant 08-08. The authors declare no conflict of interest.
## 4 Discussion
### The Effect of the Dis-Pipe Pressure Pulsation  
The dynamic pressure of the internal discharge pipe near the hyper-compressor (Figure 5) was monitored in the test and simulation simultaneously. As shown in Figure 13, the pressure changes with the crank angle showed similar overall trends between the numerical results and experimental data. There was one large pressure wave during the exhaust process, then the pressure was slowly decreased with the crank angle. The estimated pressure irregularity (\(100(p_{\max}-p_{\min})/p_{0}\)) was 11.70%, which deviated from the experimental value (12.58%) by 7.01%. The first and second orders of amplitude with the simulation results and experimental data had relative deviations of 7.91% and 6.66%, respectively.  
The pressure fluctuations in the dis-pipe can be attributed to the hyper-compressor's periodic exhaust. In addition, adding 4.1 and 4.2 reveals that the valve loss accounted for just 9.60% of the discharge power loss, while the remaining power loss was mainly caused by the pressure pulsation. As shown in Figure 13, there was a large wave during the discharge process, as no pulsation attenuator was installed in the discharge pipeline.
## 2 Background
### Resource Flexibility  
By introducing flexibility in resource allocation, the problem extends the RCPSP, leading to an optimal makespan that is at least as efficient as the makespan of the RCPSP. In this new scenario, the usage of resources and the duration of each activity are not predetermined. Instead, they are determined when scheduling the activities based on their starting times. This problem is known as the RCPSP with Flexible Resource Profiles (FRCPSP) [7].  
This problem is initially introduced by [PERSON] et al. [17] in the context of pharmaceutical research. Despite its significant potential, the problem has not been as extensively explored as the RCPSP. However, in recent years, the FRCPSP has attracted wider attention from researchers, resulting in the development of various model formulations and heuristic methods.  
While the RCPSP assumes that the resources are allocated in constant amounts over the entire duration of each activity, [PERSON] et al. [17] proposed a model in which resource allocation must be determined. In [PERSON] and [PERSON]'s [18] proposal, RCPSP-FWP (RCPSP with Flexible Work Profiles) is used with the same meaning as FRCPCP. The RCPS-FWP is a different version of the well-known RCPSP, which consists of interrelated activities with a zero-time lag that are interconnected via finish-start-type precedence relations, where a single renewable resource is available, and activity duration and resource usage to a single renewable resource are known constants. The total work content of each activity is given, instead of the duration and resources required for each activity, which essentially indicates how much work needs to be done. In other words, activities' durations and resource usage at any time are unknown. FRCPSP assumes that activity duration is not set, being part of the problem to be solved [18].  
[PERSON] and [PERSON] [7] presented the FRCPSP. They used a Mixed Integer Programming (MIP) approach to address this problem. In addition to the MIP formulation, a priority rule (PR) heuristic is proposed, along with a scheme for creating both serial and parallel schedules. In their schedule generation scheme (SGS), activities are scheduled as early as possible, and the greatest number of resources is allocated per iteration.  
This means that, in each iteration of the scheduling process, resources are assigned to activities based on their availability and the requirements of the activities being scheduled at that point [8].  
This new approach to RCPSP, renamed Resource Constraint Project Scheduling Problem with Flexible Resource Management (RCPSP-FRM), takes into account the flexibility of limited resources [5].
# An Adaptive Hybrid Automatic Repeat Request (A-HARQ) Scheme Based on Reinforcement Learning
## 4 The Proposed Adaptive HARQ Q-Learning Model
### Model Description
#### 4.1.3 Action Space
The selection of a trans  
Figure 3: \(T\)-delay scheme where T = 1 slot (**a**) and T = 2 slots; (**b**) \(K\)-repetition scheme where K = 1 (**c**) and K = 2 (**d**); [T, K]-overlap scheme of \(T\)-delay and \(K\)-repetition where [T, K] = [1, 1] (**e**), [T, K] = [1, 2] (**f**), [T, K] = [2, 1] (**g**), and [T, K] = [2, 1] (**h**). mission scheme cannot focus on a single parameter but needs to comprehensively consider factors such as the CQI, UE distance, and the number of resource blocks. The 5G HARQ and eight designed alternatives together constitute the action space \(A_{k},(k=0,\ 1,\ \ldots,\ 8)\) of the A-HARQ scheme in this study.
# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
## 3 Separation Calculation Model Based on Track Prediction  
### Track Prediction Model  
To achieve autonomous separation maintenance, firstly, it is necessary to conduct autonomous track prediction. To improve prediction accuracy, a two-stage model is constructed. The prediction process is shown in Figure 1.  
This study combines the K-means classification model and the Adam-LSTM prediction model. The K-means model clusters track types, and the optimal number of clustering categories is evaluated using the silhouette coefficient. Subsequently, the Adam-LSTM model is used for each type of track data, thereby conducting training to realize the time series prediction of individual tracks.
## 3 Results
### Running Initiation Key Events of Interest/Running Initiation Performance
#### 3.3.2 Within-Participant Variability  
The within-athlete variability (between trials) analysis shows a gradual reduction in variability over time in both the RWE and VE conditions (Figure 5). Wave initiation is much more variable (468 (\(\pm\)216) ms and 484 (\(\pm\)228) ms in the RWE and VE conditions, respectively) than both gaze release variability (98 (\(\pm\)53) ms and 85 (\(\pm\)34) in the RWE and VE conditions, respectively) and foot lift variability (81 (\(\pm\)34) ms and 102(\(\pm\)66) ms in the RWE and VE conditions, respectively). A repeated measures ANOVA confirmed that event variability differed significantly (F (2,66) = 52.049; \(p\) < 0.001; \(\eta_{p}^{2}\) = 0.826). A post hoc comparison revealed that wave variability was significantly higher than both gaze release (\(p\) < 0.001; \(d\) = 2.867) and foot lift variability (\(p\) < 0.001; \(d\) = 2.865). No significant difference was found between the RWE and VE variabilities (\(p\) = 0.859; \(d\) = 0.043), and there was no interaction between the conditions and key events (\(p\) = 0.639; \(\eta_{p}^{2}\) = 0.021).  
Figure 4: The group-scale average timings of each key event (wave initiation shown in blue, gaze release in red, and foot lift in green) in the real-world environment (RWE) and the virtual environment (VE) relative to the moment the partner passes over the mark. The error bars represent the between-participant variability of the initiation timings for each event in each condition. The clouds of points represent the average initiation timings of each participant in each condition for each event.
## 4 Results
### Determining Institution Outcome from Text of Written Decision
#### 4.1.1 Comparing Performance of XGBoost and CNN Methods
The graph at the upper right shows the sensitivity and specificity using the balanced \(F1\)-score, which is the harmonic mean of precision (true positives divided by all positive predictions) and recall (true positive rate, i.e., sensitivity), accounting for the imbalance between positive and negative classes. To illustrate the impact of the class imbalance rate, the graphs at the bottom left and right show the recall for the granted and denied classes separately. Which indicate that the CNN model is less sensitive to denials than XGBoost.
# Conservative Interference Injection to Minimize Wi-Fi Sensing Privacy Risks and Bandwidth Loss
## 2 State of the Art
### Fine-Grained Gesture Recognition  
More recent work has applied advanced signal processing techniques to sense microscopic phenomena in the channel. To detect smaller changes in the channel, such as the movement of an arm [17], prominent works compute advanced features such as power distributions [18] or velocity distributions [17]. These higher-order features are typically applied with more complex learning algorithms such as Convolutional Neural Networks (CNNs) [19] or Long Short-Term Memory (LSTM) classifiers [1]. Of particular interest to us is the application of human keystroke recognition [3]. WiKey and other gesture recognition algorithms use moving window approaches to identify the start and end of some gesture event [3]. The identified windows contain waveforms of CSI amplitudes, which vary based on the gesture (i.e., a key press), and their algorithm subsequently uses Discrete Wavelet Transforms (DWTs) and Dynamic Time Warping (DTW) to distinguish between different waveforms. This approach is similar to generalised human activity recognition, which also seeks to classify based on the frequency composition of CSI [20].
## 5 Conclusions  
The classical clustering methods like the LEACH algorithm, LEACH-M algorithm and ALM algorithm have some disadvantages for MWSNs, such as large energy consumption, low topology stability and low data transmission efficiency. The proposed method can solve these problems. In the topology establishment phase, the previous research only considers one or two factors, and in our proposal we optimize some factors comprehensively with multi-objective optimization to ensure the energy left is maximal and the movement is minimal. In addition, the cluster heads are selected to ensure the sum of the distances from member nodes to the cluster head is minimal, which can improve the balance and stability of the network, and also avoid energy holes in the network and extend its lifetime. In the topology maintenance phase, the TDMA time slot assignment wake mechanism and the soft handover communication mechanism are introduced to decrease the energy consumption, ensure the legality of mobile nodes' identity and decrease the attacks from malicious nodes.  
This work was supported by National Nature Science Foundation of China (No. 61273068), International Exchanges and Cooperation Projects of Shanghai Science and Technology Committee (No. 15220721800) and Scientific Research Innovation Project of Shanghai Education Committee (No. 13Y2084).  
[PERSON] conceived and designed the research and experiments, and contributed as the lead author of the article. [PERSON] and [PERSON] wrote the article. [PERSON] gave more valuable suggestion to the research of secure topology control of sensor networks, and audited the article. [PERSON] and [PERSON] performed the experiments, and [PERSON] contributed to revising and proofreading of the article. [PERSON] gave more suggestion to the research and analyzed the experiments.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Structural Identification of GCP-II  
Structural identification of GCP-II was conducted using LC-MS/MS, resulting in the detection of 464 sequences. Among these, 137 sequences were composed of 3 to 7 amino acids, and 327 sequences were composed of 8 to 25 amino acids. A total of 295 sequences had a confidence score of 90 or above. The Base Peak Chromatogram (BPC) of GCP-II is shown in Figure 7a below, while its Total Ion Chromatogram (TIC) is presented in Figure 7b. By analyzing the BPC spectrum, researchers can gain insights into the distribution of different components in the sample and their relative abundances [34]. This analysis aids in identifying the peptide components within the sample and provides an initial assessment of their concentrations. In contrast to the BPC, the TIC reflects the chromatogram of all  
Figure 6: Effects of different concentrations of NaCl **(a)**, glucose **(b)**, and citric acid **(c)** on the antioxidant activity of GCP-II.  
tions in the sample, providing comprehensive information about the overall distribution and abundance of all ions in the sample [35]. It thus offers a more holistic view of the chromatographic distribution of all ions within the sample.
## An Interpretable Modular Deep Learning Framework for Video-Based Fall Detection
###### Abstract
The system optimizes processing power and storage by focusing computational resources on moments of potential falls, making it ideal for extended use in healthcare facilities. * Explanatory Visualizations with GradCAM: We employ GradCAM to enhance the transparency and interpretability of our machine learning model. This technique provides visual explanations by highlighting the critical regions within the data that influence the detection outcomes. Such visual insights are invaluable for medical professionals as they provide a clear basis for understanding and trusting the model's decisions, thereby fostering a deeper integration of AI tools in routine clinical practices. * Modular System Flexibility: Our system's architecture is designed for modular flexibility, allowing for customization according to specific operational requirements and privacy concerns. Facilities can deactivate the Gaussian module where privacy is less concerned, offering unfiltered visual monitoring. Alternatively, in environments where constant, comprehensive data collection is required, the STFT module can be bypassed to maintain continuous monitoring without preselecting motion-induced frames. This adaptability ensures that the system can be adapted to the specific needs of different healthcare environments, enhancing its practicality and applicability across various clinical and care settings. These contributions ensure that our fall-detection system meets the requirements of healthcare applications regarding accuracy, efficiency, and explainability. The rest of the paper is organized as follows: Section 2 provides some of the related work on automated fall-detection algorithms. Section 3 discusses the preliminaries for a basic understanding of the libraries and models. The proposed architecture is explained in Section 4. The performance analysis and experimental results are demonstrated and discussed in Section 5. Finally, Section 6 concludes the work and discusses the future aspects of the proposed study.
## 3 Modeling of the Beam
### Euler-Bernoulli Beam Theory  
The Euler-Bernoulli beam theory is the linear theory of beam elasticity, providing a method for estimating the load-carrying and deflection properties of beams [16]. The Bernoulli-Euler beam theory is based on two key assumptions, and in most situations, these assumptions yield answers that are \"good enough\" for design. As illustrated in Figure 2, the two fundamental assumptions are that \"plane sections remain plane\" and \"deformed beam angles (slopes) are small\" [13].
# IoT-Portrait: Automatically Identifying IoT Devices via Transformer with Incremental Learning
## 2 Novelty and Related Work  
### Device Identification  
IoT device identification has attracted increasing attention from both academia and industry since it makes IoT devices visible to the management system. Many efforts have been devoted to device identification. In this section, we analyze several works on device identification mainly following two methodologies, scanning-based active fingerprinting and learning-based passive recognition, and highlight our innovations different from other works.  
One of the most popular active scanning tools for OS fingerprinting is Nmap [18]. It sends different probing packets to the target and discovers services on the target by analyzing the responses. Shodan [19] and Censys [20] are popular Internet-connected device search engines. They discover connected devices on the Internet using scanning, such as Nmap, with different protocols (e.g., HTTP, SSH, FTP, and Telnet), and manually gen erate device rules in banner grabbing. ARE [21] retrieves data from the application layer (HTML) of an IoT device by using traditional intrusive scanning tools, and then generates descriptions on relevant websites for device annotations with web crawlers. It defines a transaction as a mapping between a unique response and the product description to identify IoT devices. However, scanning-based active fingerprinting, as an intrusive solution, may have a negative impact on the performance of resource-constrained and low-power IoT devices. Moreover, this approach cannot achieve timely recognition for less-opened IoT devices.  
Learning-based passive recognition utilizes machine learning techniques to learn traffic characteristics of devices, sniff the traffic, and identify the device during runtime. ProffilloT [3] is the first work that uses machine learning to learn device network behavior for device identification. It shows that the type of IoT devices can be recognized based on the observed traffic characteristics, such as TCP window size and payload length. IoT-Sentinel [6] implements a recognition scheme based on the random forest algorithm in classical machine learning. It also includes an SDN-based model, which implements vulnerable traffic's interception and filtering mechanism to build a better security system. IoT-Sense [5] analyzes the behavior of a device and generates its fingerprint from a small amount of flow data. It also evaluates the impact of different feature combinations on recognition results. [PERSON] et al. [14] designed a network traffic classifier for IoT devices using CNN and RNN, and investigated the impact of the features chosen. HomeMole [15] is a traffic-analysis system that can automatically infer the IoT devices behind a smart home network. It uses a bidirectional LSTM model that is able to identify IoT devices based on the sniffed packets. However, the above learning-based solutions require complicated feature extraction and extensive manual labeling, especially since the dramatic increase in training data of IoT requires a large number of resources.