esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Methodology
### Construction Auditing Mechanisms and Data
Data on defects and risk factors should be compiled and documented. Moreover, missing, incomplete, abnormal, outlier, inconsistent, and invalid data should be eliminated. Data selection: In accordance with the step-by-step process of machine learning algorithms, key features in the data should be selected to obtain optimal training results. Data transformation: Text data should be transformed into a binary format and discrete numerical data for ANN processing. [PERSON] et al. [39] indicated that a high number of predictor variables might result in overlearning. Therefore, a screening method for selecting key factors from big data is essential for ensuring the accuracy of ANN predictions. The PCMIS is a quality inspection scoring system for public works established by the Taiwan government in 1993. In this study, 948 construction auditing records from 1993 to 2020 were collected from the PCMIS. These records contain a total of 948 auditing scores, 499 defects items (risk factors), and 9596 defect frequencies. The experts were interviewed to identify five risk dimensions (46 risk factors) from PCMIS, and a questionnaire survey (Likert scale) was performed among personnel working in construction-related fields to identify 19 major risk factors from five risk dimensions. The four main research procedures are detailed as follows (Figure 2):  
1. Evaluation based on a Likert scale: The risks extracted from the PCMIS were divided into five dimensions, namely the owner and supervisory unit dimension, contractor and labor dimension, material and equipment dimension, construction defects dimension, and relevant risks dimension. A Likert scale was used to perform a questionnaire survey, quantify the importance of each risk factor, and identify major risks in construction projects. 2. AHP: The AHP was used to conduct pairwise comparison between the five risk dimensions. The Likert scale scores of the risk dimensions were used as a comparison scale to determine the weight of each risk dimension and importance of each major risk. The major risks were used as the input variable for predicting project quality, and auditing scores served as the basis for project quality predictions. 3. ANN processing: An ANN with a size of 1500 \(\times\) 1500 \(\times\) 1 was constructed. The input layer comprised 19 input variables (major risks). The first and second hidden layers contained 1500 neurons. The output layer contained one neuron, which was used to predict the output value (probability) of the project quality. A total of 70% of the collected data were used for training, and the remaining 30% of the data were used for testing. ReLU was used as the activation function in the input layer and hidden layers, and the sigmoid function was used as the activation function in the output layer. 4. Model assessment: The developed AHP-ANN model was assessed in terms of its prediction accuracy and learning loss. The results obtained with this model were used to determine the relationships between major risks and project quality.
## 2 Results and Discussion
### Validation Assays  
The proposed methodology was validated following the parameters in accordance to Section 3.5 which included selectivity, linearity, sensitivity, accuracy, precision, as well as recovery yields and matrix effects. Table 2 shows most of the results for the validation results. Selectivity was assessed by verifying the absence of interfering peaks in the retention times of the target compounds using blank urine samples (\(n=10\)). Each calibration plot showed good linearity (\(r^{2}\geq 0.999\); residuals \(\leq 9.7\%\)) over the range of 5.0 to 1000 \(\upmu\)g L\({}^{-1}\). The linearity was also estimated (\(F_{\text{calc}}\)) using a lack-of-fit test (at confidence interval 95%) performed for both, which was always below the \(F_{\text{tab}}\). As it can be observed the average recoveries yields and matrix effects using urine matrices at four spiking levels were between 84.9-105.0% (RSD \(\leq 9.2\%\)) and between -9.1-9.0% (RSD \(\leq 14.1\%\)), respectively. The accuracy values ranged from 87.2 to 110.0% (RSD \(\leq 10.1\%\)) and 85.5 to 112.1% (RSD \(\leq 12.6\%\)) for KET and NKET, respectively. These results show that the developed analytical approach is suitable for the analysis of KET and NKET in urine matrices.
# Reducing Defense Vulnerabilities in Federated Learning:
## 4 Attack Scenarios and Results
### Performance Trade-Offs Analysis  
While NC-FLD demonstrates superior robustness against poisoning attacks, it is important to consider potential trade-offs. Our computational overhead analysis reveals that NC-FLD introduces approximately 12-15% additional processing time compared to FedAvg due to the neuron selection and dimensionality reduction steps. However, this overhead remains constant regardless of the attack intensity and is significantly lower than the 25-40% overhead reported for some alternative defense mechanisms like Mkrum.  
Under benign conditions (0% malicious participants), we observe a minor accuracy reduction of 0.5-1.2% compared to standard FedAvg across datasets. This slight performance impact stems from the inherent caution of our defense mechanism, which occasionally misclassifies benign updates with unusual gradient patterns as potentially malicious. However, this trade-off becomes negligible when even a small percentage of participants are malicious (5% or more), where NC-FLD significantly outperforms undefended systems.  
The memory requirements for NC-FLD scale linearly with the number of selected neurons rather than the total model size, making it feasible for deployment even in resource-constrained federated environments. For instance, in our CIFAR-10 experiments with ResNet18 (11 M parameters), the memory footprint for gradient analysis was reduced by approximately 85% compared to methods that analyze full gradients\".
## 6 Parameter Analysis
### The Law of Influence of Prestress on Test Beam Damage  
Both the P1 and P3 beams were loaded using the hierarchical loading method. The P1 beam is an ordinary reinforced concrete beam, while the P3 beam is a prestressed reinforced concrete beam.  
When comparing the experimental and acoustic emission results of the P1 and P3 beams, no significant changes were observed during the loading range of 0-10 kN for either beam. During this stage, the P1 beam exhibited weak acoustic emission signals, whereas the P3 beam showed no acoustic emission signals. The first crack appeared when the P1 beam was loaded to 10 kN, whereas the first crack appeared in the P3 beam at 40 kN. At a load of 20-25 kN, the strain gauge readings for the P1 beam vanished, indicating the formation of through-cracks and the presence of multiple cracks on the side of the beam. This was accompanied by a sudden increase in the acoustic emission signals. When the P3 beam was loaded to 70 kN, the strain gauge reading for gauge 5 vanished, indicating the formation of cracks at the bottom. Additionally, there was a significant increase in the acoustic emission signals. The P1 beam exhibited significant bending and a continuous increase in the percentage representation when loaded to 40-50 kN. Another substantial increase in the acoustic emission signals was observed, indicating that the P1 beam had reached its ultimate load-carrying capacity. In contrast, the P3 beam experienced complete failure when loaded between 80 and 85 kN.  
Figure 14: The influence of each parameter on the failure stage of beam. (**a**) Influence of loading mode on failure stage of beam body; (**b**) influence of prestress on failure stage of beam.  
Based on the comprehensive analysis, it was determined that the load required for the formation of the first crack in prestressed reinforced concrete beams increased by approximately twice compared to ordinary reinforced concrete beams. Similarly, the load for bottom crack formation increased by approximately three times. Moreover, the ultimate load increased by approximately 70%, as depicted in Figure 14b. These findings demonstrate that prestressed reinforced concrete beams possess a significantly enhanced load-bearing capacity compared to ordinary reinforced concrete beams.
# Achievement of Automatic Copper Wire Elongation System
## 4 Design of Mechanical System  
The major mechanical blocks of the proposed system, shown in Figure 6, include A: gripper 1. B: contactless heater. C: gripper 3. D: wire cutter. E: dies mold. F: ring drawing machine (including the server motor with gripper 2).  
Based on the concept of the above theory, a 30 kg weightwas designed to elongate the copper wire. Selecting the mechanical system parameters is shown as follows.  
N\({}_{1}\) = 50, N\({}_{2}\) = 150, d\({}_{1}\) = 0.03125 m, d\({}_{2}\) = 0.09375 m, F\({}_{2}\) = 30 kgw = 30 \(\times\) 9.8 N = 294 N.  
\[\mathrm{T}_{2}=\mathrm{F}_{2}\mathrm{d}_{2}=\mathrm{294(N)}\times\mathrm{0.09375 (m)}\mathrm{\ =\ }27.5625\mathrm{\ (N\cdot m)} \tag{7}\]  
\[\omega_{2}=\frac{2\pi(\mathrm{rad})}{10(\mathrm{sec})}=\frac{\pi}{5}(\mathrm{ rad}/\mathrm{sec}). \tag{8}\]  
Assume  
\[\Rightarrow\omega_{1}=\mathrm{\ }\frac{3\pi}{5}(\mathrm{rad}/\mathrm{s}) \tag{9}\]  
\[\mathrm{T}_{1}=\mathrm{T}_{2}\frac{\mathrm{N}_{1}}{\mathrm{N}_{2}}=27.5625 \times\frac{1}{3}\approx\mathrm{9.19}\mathrm{\ \ \ (N\cdot m)}. \tag{10}\]  
According to Equation (4), the motor power should be selected larger thanthe following value.  
\[\mathrm{P}=\mathrm{T}_{2}\omega_{2}=27.5625\times\frac{\pi}{5}\approx 17.32\mathrm{ \ \ (W)}. \tag{11}\]  
In this case, a 400 W server motor with rating torque: 1.27 is used in this system.  
Figure 5: Profile of proposed system operation. (**a**) Profile of elongating wire; (**b**) profile of dies mold threading.  
Figure 6: Depiction of proposed mechanical system.
# User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
## 4 Design of the PAUM System
### Events Classification
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Disease** & **Probable Location** & **Event Description** & **Event Type** \\ \hline _Acute severe illness or life_ & (i) Emergency (ii) Urgent Operation & (a) Respiratory rate, Heart Rate, \\ _saving patient_ & Theatre & Blood Sugar & (a) Critical Event \\  & (iii) Remote Location & (b) Blood Pressure, Oxygen & \\  & (iii) Remote Location & Saturation, Chest X-Ray, Kidney, & (b) Relevant Event \\  & & Liver Functionalities & \\ \hline _Asthma_ & (i) Emergency (ii) Urgent Operation & (a) Oxygen Saturation, Respiratory & (a) Critical Event \\  & theatre & rate & \\  & (iii) OPD (iv) Cabin & (b) Heart Rate, Fever, Cough & (b) Relevant Event \\ \hline _Acute Gastritis_ & (i) Emergency & (a) Ultrasound, Endoscopy, & (a) Critical Event \\  & (ii) OPD (iii) Cabin & (b) Food Habit, Stress Measurement & (b) Relevant Event \\ \hline _Heart Disease_ & (i) Emergency (ii) Urgent Operation & (a) Blood Pressure, Creatinine test & (a) Critical Event \\  & Theatre & (iii) OPD, Cabin (iv) Remote Location & (b) Chest X-Ray, ECG, Echo & (b) Relevant Event \\  & & (i) OPD (ii) Cabin (iii) Remote & (a) Creatinine, Uric Acid, Ultrasound, & \\ _Liver_ & & location & Fever & \\ \hline _Regular Test for ENT_ & (i) OPD (ii) Cabin (iii) Remote & (a) X-Ray, Blood Test & (a) Normal Event \\  & location & & \\ \hline _Regular Test for EYE_ & (i) OPD (ii) Cabin (iii) Remote & (a) Opthalmology machine screening & (a) Normal Event \\ \hline _Regular Test for Gynae and Obs_ & (i) OPD (ii) Cabin (iii) Remote & (a) Blood Pressure, Movement of & (a) Normal Event \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Events Classification. ```
1:for each \(n\in N\)do
2:if (\(V_{n}<V_{th}^{low}||V_{n}>V_{th}^{high}\)) then
3:\(\partial_{C}\gets V_{n}\);
4:else
5:if (\(C_{v}\to R_{v}\) returns true) then
6:\(\partial_{R}\gets V_{n}\);
7:else
8:\(\partial_{N}\gets V_{n}\);
9:endif
10:endif
11:endfor
12:for each \(n\in N\)do
13:\(P(s_{n}\mid\partial_{C})=\frac{P(\partial_{C}|s_{n})P(s_{n})}{P\partial_{C}}\); \(P(s_{n}\mid\partial_{R})=\frac{P(\partial_{R}|s_{n})P(s_{n})}{P\partial_{R}}\) ; \(P(s_{n}\mid\partial_{N})=\frac{P(\partial_{N}|s_{n})P(s_{n})}{P\partial_{N}}\);
14:endfor
15:for each \(E\in\{\partial_{C},\partial_{R},\partial_{N}\}\)do
16:for each \(n\in N\)do
17:\(s_{n}=\underset{\forall s\in S}{\arg\max}(P(s\mid E))\);
18:endfor
19:endfor
20:return\(s_{n}\) ;
```  
**Algorithm 1** Probabilistic determination of Event Type.
## 2 Materials and Methods
### Experimental Procedure
#### 2.2.2 Preparation of Surfaces for Testing  
The contaminated fallout material was applied to three surfaces for the simulated vehicle-rolling and foot-traffic experiments. These surfaces included asphalt pavers (6'' \(\times\) 12'' \(\times\) 1.75'', Asphalt Products, Hanover, PA, USA), concrete pavers (12'' \(\times\) 12'' \(\times\) 1.75'', Pewter Square Concrete Step Stone, The Home Depot, Atlanta, GA, USA), and sand (Garick premium play sand, Garick LLC, Cleveland, OH, USA) loosely placed on vinyl fabric (80 Vinyl Military Spec Fabric, Herculite Products Inc., Emigsville, PA, USA) covering the floor of a tent. All the pavers were purchased in bulk and used in as-received condition. The soil bags were opened in the laboratory a few days before use; thus, the soil was equilibrated to laboratory conditions (T: 21.9 \(\pm\) 0.7 \({}^{\circ}\)C and RH: 54 \(\pm\) 7%).  
The Garick sand utilized was selected as follows: Two (2) Israeli sandy soil samples, including \"Ze'elim\" and \"Rotem\", were received and characterized. Two (2) local soil samples, including Ohio mulch (Ohio Mulch, Columbus, OH, USA) and the Garick sand with similar particle sizes, were characterized and compared at the same time. The test results are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Particle Size Analysis** & **Wetted** & **Nonwetted** \\ \hline Clay (\%) & 24.00 & 28.00 \\ \hline Silt (\%) & 59.90 & 56.70 \\ \hline Sand (\%) & 16.10 & 15.30 \\ \hline Organic Matter (\%) & 0.43 & 0.25 \\ \hline Fine Gravel (\%) & 0 & 0 \\ \hline
**Sand Fractions (\%)** & \\ \hline
1 mm & 0.10 & 0.00 \\ \hline
0.25 mm & 1.10 & 0.00 \\ \hline
0.15 mm & 0.60 & 0.20 \\ \hline
0.05 mm & 14.30 & 15.10 \\ \hline \end{tabular}
\end{table}
Table 1: Particle size distribution of wetted and nonwetted simulated fallout material.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Particle Size Analysis**} & \multicolumn{2}{c}{**Israel Soil Samples**} & \multicolumn{2}{c}{**Experimental Bulk Soil**} \\ \cline{2-5}  & **Ze’elim** & **Rotem** & **Ohio Mulch** & **Garick** \\ \hline Clay (\%) & 4 & 2 & 1.5 & 2 \\ \hline Silt (\%) & 4 & 1.2 & 1.6 & 0.8 \\ \hline Sand (\%) & 92 & 96.8 & 74.4 & 97.2 \\ \hline Organic Matter (\%) & 0.08 & 0 & 0.87 & 0.05 \\ \hline Fine Gravel (\%) & 0 & 0 & 22.5 & 0 \\ \hline
**Sand Fractions (\%)** & & & & \\ \hline
1 mm & 0.1 & 1 & 12.6 & 1.4 \\ \hline
0.25 mm & 28.9 & 87.3 & 59.6 & 74 \\ \hline
0.15 mm & 10.6 & 5.8 & 1.4 & 12.3 \\ \hline
0.05 mm & 52.4 & 2.7 & 0.08 & 9.5 \\ \hline
**Texture Class** & Sand & Sand & Sand & Sand \\ \hline \end{tabular}
\end{table}
Table 2: Particle Size Distribution of Different Soils.  
Garick sand was selected for the study because of its particle sizes in the sand fraction, similar to Rotem particle sizes. While this sand is technically a \"soil\", the material will be referred to throughout this document as a \"sand surface\" because the surface is primarily sand and also because one of the stabilization products is called \"Soil2O\" which tends to cause some linguistic confusion.
## 2 Mathematical Model of Reservoir Heating by E-CSS in Horizontal Wells
### Reservoir Single Heat Source Heat Transfer Model  
When any point on any cross-section of the horizontal section of an ordinary horizontal well is heated by a single heat source on the cross-section (the temperature at this point is \(T\), the initial temperature of the reservoir is \(T_{i}\), and the heat inflow speed is \(\phi\)), the following formula can be used to represent the change in temperature \(T\) over time during the heating process of the heat source at this point [21]:  
\[T=T_{i}-\left(\frac{\phi}{4\pi k}\right)\text{Ei}\left(-\frac{r^{2}}{4\lambda \alpha t}\right) \tag{1}\]  
where \(\text{Ei}(x)\) is an exponential integral function, and its expression is  
\[\text{Ei}(x)=\int_{-\infty}^{x}\frac{e^{t}}{t}dt\]
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 1 Introduction
### Taxonomy and Requirements
#### 1.2.4 Security and Privacy  
The transmission of health-related information between on-body sensors and monitoring devices in WBAN systems and subsequently over the internet to central controllers in hospitals is strictly private and confidential. Health-related information must be encrypted so that the patient's privacy is protected. Healthcare professionals who have access to information must be confident that the patient's vital information is not tampered with or altered and did truly originate from the monitored individual. Furthermore, an overly secure system might disallow healthcare professionals from accessing vital health-related information in certain emergency events and thus jeopardize patient's life. Moreover, enriching the current systems with security and privacy mechanisms significantly increases the cost of energy for communication which results in more power drain from small batteries [52].  
The rest of this paper is organized into six sections. Section 2 reviews a number of existing low-power communication technologies that are appropriate candidates for remote health monitoring applications. Section 3 compares and discusses the advantage and disadvantage of using the existing low-power technologies. Section 4 discusses the future prospects of remote health monitoring systems. Section 5 provides a brief overview of some of the most recent research articles published in the area of telemonitoring systems and finally Section 6 provides a conclusion to this article.
# Small-Sample Bearings Fault Diagnosis Based on ResNet18 with Pre-Trained and Fine-Tuned Method
## 2 Relevant Theoretical Approaches  
### Vmd  
VMD is a method for signal processing that decomposes signals by non-recursive variational methods [33]. Unlike Empirical-Mode Decomposition (EMD), VMD can address the issue of frequency aliasing. VMD can break down time-series data into a series of band-limited IMFs and adjust the optimal center frequency and bandwidth of each IMF. In VMD, the goal is to decompose a set of modal components so that the sum of the band widths of these components is minimum, ensuring that the aggregate of all modal components can reproduce the original signal. The calculation equation is as follows [34].  
\[\left\{\begin{aligned} \underset{\{u_{k}\},\{u_{k}\},\{\sum_{k}\}}{ \left\|\partial_{t}\right[\left(\delta(t)+\frac{j}{\pi t}\right)^{*}u_{k}(t) \right]}e^{-j\omega_{k}t}\left\|_{2}^{2}\right\}\\ s.t\sum_{k=1}^{k}u_{k}=f(t)\end{aligned}\right. \tag{1}\]  
where \(u_{k}=\{u_{1}\), \(u_{2}\),..., \(u_{k}\}\) represents the \(k\) modal components derived from the decomposition, \(w_{k}=\{w_{1}\), \(w_{2}\),..., \(w_{k}\}\) indicates the central frequency of each component, \(*\) denotes the convolution operator, \(f(t)\) indicates the signal to be decomposed, i.e., the input signal.  
The quadratic penalty operator \(\alpha\) is employed to guarantee the precision of the signal reconstruction, whereas the Lagrange multiplication operator \(\lambda(t)\) is utilized to uphold the stringency of the constraint.  
\[\mathrm{L}(\{u_{k}\},\{\omega_{k}\},\lambda)=\alpha\sum\!\left\|\partial_{t} \right[\left(\delta(t)+\frac{j}{\pi t}\right)u_{k}(t)\right]\!e^{-j\omega t^{ k}}\!\left\|_{2}^{2}a_{k}^{n+1}+\left\|f(t)-\sum_{k}u_{k}(t)_{2}^{2}+ \lambda(t)\right\|\!,\left\langle f(t)-\sum_{k}u_{k}(t)\right\rangle^{k} \tag{2}\]  
where \(\alpha\) is the control parameter of frequency bandwidth or penalty factor; \(\lambda(t)\) means Lagrange multiplier.  
The alternating direction technique for multipliers is used to alternatively update \(\hat{u}_{k}^{n+1}\), \(\hat{\omega}_{k}^{n+1}\) is employed to obtain the optimal solution, which is the saddle point of the Lagrangian function in Equation (2). The Fourier transform of \(\hat{u}_{k}^{n+1}\) corresponds to the intrinsic mode functions \(\hat{\lambda}^{n+1}\), allowing the set of IMFs \(\{u_{k}\}\) and central frequency \(\{\omega_{k}\}\) to be expressed as follows.  
\[\hat{u}_{k}^{n+1}(\omega)=\frac{\hat{f}(\omega)-\sum_{\ell\
eq k}\hat{u}_{i}( \omega)+\frac{\hat{\lambda}(\omega)}{2}}{1+2\alpha(\omega-\omega_{k})^{2}} \tag{3}\]  
\[{\omega_{k}}^{n+1}=\frac{\int_{0}^{\infty}\omega|\hat{u}_{k}(\omega)|^{2}d \omega}{\int_{0}^{\infty}|\hat{u}_{k}(\omega)|^{2}d\omega} \tag{4}\]  
where \(\hat{u}_{k}(\omega)\) and \(\hat{f}(\omega)\) are the Fourier transform of the \(k\)th mode and the original signal, respectively, \(\omega_{k}\) is the center frequency of the \(k\)th mode, \(n\) is the number of iterations.
## 6 Worst-Case Latency Analysis of the FWOS Model
### Length of the Contention-Free Interval
#### 6.1.3 Block Intervals  
Servicing the \(Q_{k}^{h}\) frame at the corresponding window cannot be guaranteed when the remaining time is insufficient to transmit the entire frame. Accordingly, at the end of each open window, there is a guard band interval (\(d_{k}^{h,gb}\)), named here as block interval. This interval equals the time required to transmit the largest size of the \(Q_{k}^{h}\) frame, \(f_{k}^{h,max}\)[12], i.e.,  
\[d_{k}^{h,gb}=\frac{f_{k}^{h,max}}{C_{h}} \tag{18}\]  
Then, under the effect of this guard band interval, the contention-free window ends with  
\[t_{k}^{h,gb,i}=t_{k}^{h,c,i}-d_{k}^{h,gb} \tag{19}\]
## 5 Experimental Evaluation
### Results and Discussion
#### 5.4.4 Performance Evaluation under Zipf Distribution of Content Files  
The Zipfian distribution shows the closeness of the request probability of contents. A lower Zipf parameter indicates that only a few contents are likely to be requested many times while a higher Zipf parameter shows many content files having relatively same request probabilities, making all of the content files highly probable in the event of content requesting. Figure 6 presents the hit rate, latency, and backhaul traffic performances under  
Figure 5: Performance of the number of users variation on hit rate, latency, and backhaul traffic.  
the varying Zipfian parameter. The cache hit rate plot shows a very close performance by the proposed, Greedy, and LFRU, but the best among them is the proposed. A lower Zipf parameter is good for the edge network because only a few contents are regularly requested. The most requested files can be comfortably cached to service users. On the other hand, if the parameter is high, the edge network needs to cache as many of the probable files to meet users' requests.  
The latency and backhaul traffic records steady progressions of all the schemes with the increasing Zipf parameter. From the latency sub-figure of Figure 6, it can be deduced that DPoM outperforms the baseline schemes. However, the backhaul traffic performance show FIFO and the TLRU schemes outperforming the proposed, Greedy, and LFRU schemes.