esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### General and Feasibility Results  
A total of 26 patients were contacted and screened for eligibility (enrollment rate: 1.44 patients per week). Of these, 20 participants presented for evaluation, met the eligibility criteria, and entered the study (recruitment rate: 76.92%). Finally, 11 participants who entered the study completed all 12 sessions of the experimental protocol at the scheduled frequency (adherence rate: 100%) and were included in the final analysis (retention rate: 55%). Ten participants were female (90.9%), and the mean age of the sample was 40.55 \(\pm\) 11.23 years (range 21 to 61 years). Except for one participant, all had a body mass index classified as overweight or higher.  
The intervention plan was adequately executed and well tolerated by the patients, so no adjustments to the protocol were necessary. All patients who did not complete the protocol (\(n=9\)) did so due to personal reasons unrelated to the study's assessments or intervention protocols. These reasons included scheduling conflicts with patients' work commitments (as the iVRE sessions were scheduled in the mornings, \(n=1\)), accessibility issues due to weather conditions (with half of the study conducted during winter, \(n=3\)), acute illnesses unrelated to FM (primarily seasonal respiratory conditions, \(n=3\)), unforeseen family obligations (\(n=1\)), and delays that could not be rescheduled (\(n=1\)). No dropouts occurred due to adverse events related to the iVRE program. Additionally, none of the adverse events investigated were reported during the interventions or after completing the 12 sessions of iVRE.
## 3 Drone Routing Algorithms  
In this section, we discuss algorithms for drone routing in drone-based delivery systems in terms of three different aspects, namely, trajectory planning, charging strategy, and security, which are crucial for designing effective and reliable drone-based delivery systems. Furthermore, we investigate and discuss each algorithm in terms of different aspects, including its main objective, operational characteristics, advantages, limitations, and performance.  
First, we classify the existing algorithms for drone routing in drone-based delivery systems. That is, we present a novel taxonomy emphasizing three major drone routing aspects for drone-based delivery systems: trajectory planning, charging, and security. The taxonomy is depicted in Figure 3.
## 4 Experimental Testing and Result Analysis  
### Dataset and Experimental Environment  
This study relies on an annotated dataset for model training and evaluation, which is derived from actual photos taken inside a substation electrical panel, with over 2500 connections between cables and terminals labeled in 350 images, and the input image size is 640 \(\times\) 640. The annotation details are shown in Figure 6, with each connection point clearly marked, ensuring consistent labeling and high quality during the training process. To achieve effective model training and fair evaluation, the dataset is divided into training, validation, and test sets in a 7:2:1 ratio.  
The experimental environment used in this paper is shown in Table 1.  
The basic model for this experiment is YOLOv8-pose, with a batch size set to 4 and an initial learning rate of 0.01.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Experimental Environment** \\ \hline Operating System & Windows 10, 64 bit \\ CPU & 13 th Gen Intel® CoreTM i7-13700K CPU @ \\
3.40 GHz (Intel, Santa Clara, CA, USA) \\ GPU & GeForce RTX 4060 (16.0 GB) \\ Memory & 32 GB \\ Python version & 3.11.8 \\ Deep Learning Framework & PyTorch 2.2.2, CUDA 12.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental environment.  
Figure 6: Data annotation pictures.
## 3 Data and Methodology
### Data Description  
The public dataset is provided by [3] which includes real-world data from a Portuguese bank industry that is covering the years from May 2008 to June 2013. This research considers the _Additional dataset_ file for model building and validation, which contains 4521 instances. There are 16 input variables divided into seven numeric and nine nominal attributes. The target variable named \"y\" is a binary class indicating whether a client applied for term deposit or not. All 17 variables are described in Tables 1 and 2. The dataset is highly imbalanced as only 11.5% of the instances indicate a positive label (i.e., a client has subscribed for a term deposit), which is the class of interest in this case.  
\begin{table}
\begin{tabular}{l l l c c} \hline
**Attribute** & **Description** & **Values** & **Count** & **Percent** \\ \hline \multirow{8}{*}{job} & & management & 969 & 21.4\% \\  & & blue-collar & 946 & 20.9\% \\  & & technician & 768 & 17.0\% \\  & & admin & 478 & 10.6\% \\  & & services & 417 & 9.2\% \\  & & retired & 230 & 5.1\% \\  & & self-employed & 183 & 4.0\% \\  & & entrepreneur & 168 & 3.7\% \\  & & unemployed & 128 & 2.8\% \\  & & housemaid & 112 & 2.5\% \\  & & student & 84 & 1.9\% \\  & & unknown & 38 & 0.8\% \\ \hline \multirow{2}{*}{marital} & & single & 1196 & 26.5\% \\  & & married & 2797 & 61.9\% \\  & & divorced & 528 & 11.7\% \\ \hline \multirow{2}{*}{education} & & primary & 678 & 15.0\% \\  & & secondary & 2306 & 51.0\% \\  & & tertiary & 1350 & 29.9\% \\  & & unknown & 187 & 4.1\% \\ \hline default & Has credit in default account & yes & 76 & 1.7\% \\  & & no & 4445 & 98.3\% \\ \hline housing & If there is a housing loan & yes & 2559 & 56.6\% \\  & & no & 1962 & 43.4\% \\ \hline \multirow{2}{*}{loan} & & yes & 691 & 15.3\% \\  & & no & 3830 & 84.7\% \\ \hline \multirow{2}{*}{contact} & & cellular & 2896 & 64.1\% \\  & & telephone & 301 & 6.7\% \\  & & unknown & 1324 & 29.3\% \\ \hline \multirow{6}{*}{month} & & jan & 148 & 3.3\% \\  & & feb & 222 & 4.9\% \\  & & mar & 49 & 1.1\% \\  & & apr & 293 & 6.5\% \\  & & may & 1398 & 30.9\% \\  & & jun & 531 & 11.7\% \\  & & jul & 706 & 15.6\% \\  & & aug & 633 & 14.0\% \\  & & sep & 52 & 1.2\% \\  & & oct & 80 & 1.8\% \\  & & nov & 389 & 8.6\% \\  & & dec & 20 & 4\% \\ \hline \multirow{2}{*}{poutcome} & & failure & 490 & 10.8\% \\  & & success & 129 & 2.9\% \\  & & other & 197 & 4.4\% \\  & & unknown & 3705 & 82.0\% \\ \hline \multirow{2}{*}{y: Target Class} & & yes & 521 & 11.5\% \\  & & no & 4000 & 88.5\% \\ \hline \end{tabular}
\end{table}
Table 1: Nominal Variables (Portuguese Bank dataset [3]).
## 5 Simulation Results
### Monte Carlo Simulations  
Under static conditions, we performed Monte Carlo experiments with 100 runs in five scenarios. The solutions drawn by GLNS are much better than those drawn by ACO. The number of sets varied from 5 to 25, average 15; the number of information spots varied from 20 to 587, average 336.  
With the unknown obstacles, we performed Monte Carlo experiments with 100 runs in five scenarios. The number of sets varied from 5 to 25, average 14.7100; the number of obstacle sets on each path averaged 5.8. The average radius of the collision-free spheres around obstacles when free spots were found was \(R=2.3 veh_{L}\). Over 90% percent of runs could figure out the free spots within the first iteration of the collision-free sphere. Each iteration of R 100 spots on the collision-free sphere was traversed. The number of obstacle spots in each set varied from 49 to 100. The results of static and unknown obstacle simulations are both illustrated in Figure 22. One-hundred runs with a current field are illustrated in Figure 23. We considered a scenario in which the currents were weak. The average velocity of currents in the air was 0.97 m/s, and underwater, 0.26 m/s.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline _Sets_ & \(Information\) _Spots_ & \(Origin\) _Path_ & \(Path\) _with Currents_ \\ \hline
5 & 25 & 209.48 & 212.3 \\
10 & 205 & 564.75966 & 541.3 \\
15 & 426 & 640.2459 & 650.2432 \\
20 & 421 & 197.42732 & 205.4523 \\
25 & 525 & 769.062302 & 777.8012 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The weighted distance costs of planned paths with and without current fields. There are small differences between the outputs of these two scenarios. A possible reason is the positive and negative influences of current fields offsetting each other.  
Figure 21: The replanned path and trajectory in current fields. The weighted costs of recorded strong currents fields are illustrated. The original path and trajectory passed through or quite near to (distance ¡ vehicle length) strong current fields, which would be unsafe and involves a high energy cost. The replanned path and trajectory avoided such areas. The weighted cost of avoiding the current was 770.2315, and to not avoid it was 810.1669 in this case.  
The original trajectory drawn by heuristic \(GLNS+KMEANS+KDtree\) was better than ACO 88 times out of 100 runs. The average weighted cost of the former was 279.4952, and that of the latter was 432.5474. However, when it comes to the regeneration, heuristic \(GLNS+KMEANS+KDtree\) performed better 72 times out of 100 runs, especially when the number of sets was enormous. The average weighted cost of the heuristic GLNS's regenerated trajectory was 585.8950, and that of ACO's trajectory was 674.8518.
# Pile Base and Shaft Capacity under Various Types of Loading
## 4 Numerical Simulations
### Model Parameters  
Numerical simulations performed by [PERSON] and [PERSON] [36] proved that the Hardening Soil model allows for better modelling of the pile performance due to the hyperbolic load-strain relationship and the linear relationship between unloading and reloading. Each simulation stage was performed in a homogeneous subsoil for three different subsoil models, representing coarse, medium, and fine sands. Based on the soil parameters determined from the actual soil tests, the parameters for the Hardening Soil model were derived. They were determined using the assumptions and correlations collected in [34; 37]. The soil parameters for each soil model are presented in Table 2.  
The pipe pile and steel shoe were modelled as beam elastic elements with E = 210.0 GPa and \(\
u\) = 0.3. The width of the elements was assumed to be 0.05 m for the pipes and 0.1 m for the shoe. On this basis, the parameters of the beam elements were determined (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**LP**} & \multirow{2}{*}{**Soil**} & \multirow{2}{*}{**I\({}_{\text{D}}\)**} & \(\boldsymbol{\gamma}\) & \(\boldsymbol{\Phi^{\prime}}\) & \(\boldsymbol{\psi}\) & \(\boldsymbol{\text{c}^{\prime}}\) & \(\boldsymbol{K_{0}}^{\text{NC}}\) & \(\boldsymbol{\sigma_{\text{\bf{o}ed}}}^{\text{ref}}\) & \(\boldsymbol{m}\) & \(\boldsymbol{\text{E}_{\text{\bf{o}ed}}}^{\text{ref}}\) & \(\boldsymbol{\text{E}_{\text{\bf{o}ed}}}^{\text{ref}}\) & \(\boldsymbol{\text{E}_{\text{\bf{o}ed}}}^{\text{ref}}\) \\ \cline{3-11}  & & & **kN/m3** & \({}^{\text{o}}\) & \({}^{\text{o}}\) & \({}^{\text{o}}\) & **kPa** & - & **kPa** & - & **MPa** & **MPa** & **MPa** \\ \hline
**1** & FSa & 0.35 & 18.5 & 30.5 & 0.5 & 1.0 & 0.5 & 100 & 0.591 & 15.4 & 23.4 & 70.2 \\
**2** & MSa & 0.60 & 19.0 & 34.7 & 4.7 & 1.0 & 0.4 & 100 & 0.513 & 53.1 & 81.7 & 245.2 \\
**3** & CSa & 0.75 & 19.5 & 36.2 & 6.2 & 1.0 & 0.4 & 100 & 0.466 & 92.2 & 139.7 & 419.1 \\ \hline \hline \end{tabular} \(\gamma\)–unit weight; \(\varphi\)–friction angle; \(\psi\)–angle of dilatancy; \(\phi\)–c-cohesion; \(\text{K}_{0}^{\text{NC}}\)–coefficient of lateral earth pressure for a normally consolidated stress state; \(\sigma_{\text{\bf{o}ed}}^{\text{ref}}\)–reference confining pressure; m–power in stress-dependent relation; \(\text{E}_{\text{\bf{o}ed}}^{\text{ref}}\)–reference oedometer loading stiffness; \(\text{E}_{\text{\bf{o}ed}}\)–reference triaxial loading stiffness; \(\text{E}_{\text{\bf{u}ed}}\)–reference triaxial unloading stiffness.  
\end{table}
Table 2: Soil parameters for the Hardening Soil model.  
Figure 7: Calculation area for numerical simulations.  
The test simulations were carried out for different values of the interface parameter \(R\) between the pile and soil (\(R\) = 0.900, \(R\) = 0.750, \(R\) = 0.666)
# Study on Low Temperature Drying Characteristics of Chicken Manure in a Humid Environment
## 3 Results and Discussion
### Calculation of Effective Diffusivity  
The effective diffusivity was an important parameter for the drying kinetics study of chicken manure in a low-temperature and high-humidity environment. The drying process of chicken manure could be simplified as the diffusion process of water from inside the manure to the outside. [PERSON]'s law describing material diffusion could be used to calculate the diffusion coefficient of water in the chicken manure [24; 25; 26]. The relationship between the diffusion coefficient and moisture ratio is calculated with Equation (9).  
\[MR=\frac{8}{\pi^{2}}\sum_{n=0}^{\infty}\frac{1}{\left(2n+1\right)^{2}}\mathbf{ exp}\Bigg{[}-\frac{\left(2n+1\right)^{2}\pi^{2}t}{4L^{2}}D\Bigg{]} \tag{9}\]  
where, \(L\) is the half-thickness of the manure sample, \(D\) is the effective diffusivity in m\({}^{2}\)/s, \(t\) is the drying time, and \(n\) is the number of samples.  
For the long drying process of chicken manure, Equation (9) could be further simplified to only the first term of the series, and could be written in logarithmic form:  
\[\mathbf{ln}(MR)=\mathbf{ln}\frac{8}{\pi^{2}}-\frac{\pi^{2}}{4L^{2}}Dt \tag{10}\]  
The diffusion coefficients were typically calculated by plotting experimental data in terms of ln (_MR_) versus drying time. From Equation (10), a plot of ln (_MR_) versus drying time gave a straight line with slope and intercept of:  
\[Slope=-\frac{\pi^{2}}{4L^{2}}D \tag{11}\]  
\[Intercept=\mathbf{ln}\frac{8}{\pi^{2}} \tag{12}\]  
The effective diffusivity was calculated with Equation (11), using slopes derived from the linear regression of ln (_MR_) versus drying time shown in Figure 7. Generally, an effective diffusivity was used due to the complexity of the drying process and limited information available on the mechanism of moisture movement during the drying process. The effective diffusivity could also provide parameters for the mass transfer study of chicken manure. The values of effective diffusivity for different conditions are presented in Table 6. They varied from 6.37 \(\times\) 10\({}^{-7}\) to 2.17 \(\times\) 10\({}^{-6}\) m\({}^{2}\)/h. In addition, it can be seen from the table that the values of effective diffusivity were proportional to the temperature and air velocity.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Air Velocity (m/s)** & **Thickness (mm)** & **Temperature/(\({}^{\circ}\)C)** & **Slope** & **D/(m\({}^{2}\)\(\cdot\)h\({}^{-1}\))** \\ \hline
1.2 & 40 & 15 & \(-\)0.04795 & 8.31 \(\times\) 10\({}^{-7}\) \\
1.2 & 40 & 25 & \(-\)0.06591 & 1.31 \(\times\) 10\({}^{-6}\) \\
1.2 & 40 & 35 & \(-\)0.10742 & 2.13 \(\times\) 10\({}^{-6}\) \\
1.2 & 20 & 25 & \(-\)0.09809 & 6.37 \(\times\) 10\({}^{-7}\) \\
1.2 & 60 & 25 & \(-\)0.05328 & 2.17 \(\times\) 10\({}^{-6}\) \\
0.6 & 40 & 25 & \(-\)0.05435 & 1.08 \(\times\) 10\({}^{-6}\) \\
1.8 & 40 & 25 & \(-\)0.07983 & 1.59 \(\times\) 10\({}^{-6}\) \\ \hline \end{tabular}
\end{table}
Table 6: Effective diffusion coefficients during drying process of chicken manure.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
###### Abstract  
The Building Safe Water Use Plan promoted by the Macao Marine and Water Bureau aims to encourage property management entities to regularly maintain building water supply systems to ensure the safety and stability of drinking water. However, traditional laboratory testing methods are often time-consuming and labor-intensive, making real-time and efficient water quality monitoring challenging. To address this issue, this study proposes a Raspberry Pi-based multi-sensor system for rapid water quality detection and improved monitoring efficiency. This system integrates multiple sensors to measure key water quality parameters, such as pH, total dissolved solids (TDSs), temperature, and turbidity, while recording data in real-time. The data were continuously collected over a period of five months (July to November 2024). The collected data were analyzed and validated using machine learning algorithms, including Isolation Forest, Random Forest, Logistic Regression, and Local Outlier Factor. Among these models, Random Forest exhibited the best overall performance, achieving an accuracy of 98.10% and an F1 score of 98.99%. These results show that the dataset demonstrates high reliability in anomaly detection and classification tasks, accurately identifying deviations in water quality. This approach not only enhances the efficiency of water quality monitoring but also provides technological support for urban drinking water safety management.  
water quality monitoring; Macao; anomaly detection; dataset 2025  
3
## 2 Measurement of Torsional Vibrations in ICEs  
Early fault detection and diagnosis for diesel engines is essential to ensure reliable operation during their service life. The reciprocating motion of ICEs always induces some degree of torsional vibration during their operation. The engine piston experiences different strikes with the rotation of the crankshaft. Any increase in the rotation speed of the crankshaft leads to a pressure rise in the cylinder as the piston reaches the top dead center (TDC) on the compression stroke. The pressure is further increased at the ignition and combustion stages, immediately after TDC. This pressure is released as soon as gases are expanded when the piston approaches bottom dead center (BDC). The pressure is transferred to the piston through the connecting rod, generating tangential forces that are useful work. While the useful work increases, the rotation speed of the crankshaft augments during the combustion stroke. That is while, the compression stroke, in turn, reduces engine's angular velocity. These fluctuations in the crankshaft speed make the detection of the compression stroke of the firing cylinder very difficult [3].  
For successful fault detection, it is vital to model the dynamics of the engine components, especially the torsional vibration of the crankshaft. Lagrange rule is a reliable method for such modeling [3]:  
\[\frac{d}{dt}\left(\frac{\partial T}{\partial\dot{\theta}_{i}}\right)-\frac{ \partial T}{\partial\dot{\theta}_{i}}+\frac{\partial U}{\partial\dot{\theta} _{i}}+\frac{\partial D}{\partial\dot{\theta}_{i}}=Q_{i},\ i=0,\ 1,\ \ldots,\ n, \tag{1}\]  
where \(T\) is the kinetic energy and \(U\) is the potential energy of the crankshaft, defined based on degrees of freedom (df) of the diesel engine. Also, \(D\) shows the dissipative energy which depends on the inertia, torsional stiffness, damping, and number of mass, as well as angular displacement of each mass.  
In real-world applications, engines are subjected to numerous external excitations during their operation. An external excitation torque of a single cylinder includes both the combustion gas torque (\(M_{p}\)) and reciprocating inertia torque (\(M_{j}\)):  
\[M_{s}=M_{p}+M_{j} \tag{2}\]  
By applying fast Fourier transform (FFT) to \(M_{s}\) and assuming uniform amplitude for the excitation torque of each cylinder, the harmonic torque of different engine cylinders can be obtained [3].
## 3 The Proposed Optimization Algorithm
### Receive Beamforming  
With the given transmit power \(p_{m,k}\) and UE association indicator \(\alpha_{m,k}\), the receive beamforming vectors \(\mathbf{v}_{m,k}\), \(\forall m,k\), can be obtained based on the optimal MMSE criteria to cope with the multiuser interference, which is obtained by  
\[\mathbf{v}_{m,k}^{*}=\arg\max SINR_{m,k}, \tag{23}\]  
which can be further represented as  
\[\mathbf{v}_{m,k}^{*}=\arg\max\frac{\mathbf{v}_{m,k}\mathbf{Q}_{1}\mathbf{v}_{m,k}^{H}}{\mathbf{v}_{m,k}\mathbf{Q}_{2}\mathbf{v}_{m,k}^{H}}, \tag{24}\]  
where \(\mathbf{Q}_{1}=\mathbf{h}_{m,k}p_{m,k}\mathbf{h}_{m,k}^{H}\), \(\mathbf{Q}_{2}=\sum\limits_{j\in\mathcal{U}_{m}\setminus\mathcal{S}_{m,k}}{ \mathbf{h}_{m,j}p_{m,j}\mathbf{h}_{m,j}^{H}}+\sum\limits_{i=1,t\
eq m}^{M+1} \sum\limits_{j\in\mathcal{U}_{l}}{\mathbf{h}_{m,j}p_{i,j}\mathbf{h}_{m,j}^{H} }+\sigma_{m,k}^{2}\mathbf{I}\). It can be seen that (24) is a generalized Rayleigh quotient from [22]. Thus, the optimal solution is the eigenvector corresponding to the maximum generalized eigenvalue of matrix \(\mathbf{Q}_{2}^{-1}\mathbf{Q}_{1}\), which is written as  
\[\mathbf{v}_{m,k}^{*}=\lambda_{max}(\mathbf{Q}_{2}^{-1}\mathbf{Q}_{1}) \tag{25}\]
## 3 Theoretical Analysis and Experiment
### Theoretical Analysis  
It is generally considered that the magnetic interference of airborne equipment is mainly caused by current. Previous papers show that the electronic equipment on a UAV can radiate complex electromagnetic fields to the surrounding area during operation and two kinds of airborne electronic equipment interference can affect detection: (1) The interference magnetic field generated by the UAV's power source in the process of supplying power. This is related to the current and has a wide frequency spectrum and large amplitude. (2) The interference magnetic field generated by the control circuit's changing current, such as the switching circuit and pulse digital circuit. This changes suddenly and has small amplitude [8].  
According to electromagnetic field theory, the current can generate a magnetic field. In the low-frequency band, the magnetic interference generated by the current can be calculated by using the Biot-Savart law, which is shown in the following equation [24]:  
\[d\overset{\rightarrow}{B}=\frac{\mu_{0}}{4\pi}\frac{Id\l\times\overset{ \rightarrow}{r}}{r^{3}} \tag{7}\]  
\[\overset{\rightarrow}{B}=\oint_{L}\frac{\mu_{0}I}{4\pi}\frac{dl\l\times \overset{\rightarrow}{r}\overset{\rightarrow}{e}_{r}}{r^{3}} \tag{8}\]  
where \(I\) is the source current and \(L\) is the integration path, \(dl\) is a source current differentiation, \(\overset{\rightarrow}{e}_{r}\) is the unit vector that source current differentiation points to the field point to be solved, \(r\) is the distance of the current element from the field point to be solved, \(\mu_{0}\) is permeability of vacuum. The magnetic interference caused by current is related to source current, wire shape and observation distance.