esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experiment and Methods
### Multilayer Structure Mold  
A multilayer structure mold was designed to apply CNT web film as a heater in the RHCM process, and rapid heating experiments were conducted. A computer-controlled DC supplier applies electricity (60 V) to the CNT web film and heats the mold cavity surface. The DC controller and infrared (IR) camera transmit the resistance change and temperature data to the computer, respectively (see Figure 2). As a heating source used in this rapid heating mold, the CNT web film generates Joule heat through direct electricity flows. Since electricity is directly applied to the CNT web film, the mold part in contact with the CNT web film is insulated. Therefore, the multilayer structure mold used in this heat-transfer experiment was electrically insulated and was stacked in the following order: mold metal, insulator, CNT web film, insulator, and mold metal, as shown in the cross-sectional view in Figure 3c. The mold was designed as a flat plate, and the CNT web film was also applied to the shape of the flat plate (see Figure 3d). A CNT web film layer exists along with the busbar, which is an electrode, to apply electricity to the CNT web film inside the multilayer structure mold. However, a flush can occur between the busbar and insulator during assembly and parts processing due to tolerances. Because of this flush, the CNT web film inside the multilayer mold cannot fully contact the insulator, resulting in non-contact areas. Hence, when heating is applied with electricity, the CNT web film is oxidized and damaged in non-contact areas, causing a short circuit and making stable heating difficult. To address this problem, an experimental mold set flush differences of 0 mm, 0.1 mm, and 0.3 mm between the busbar and insulator inside the multilayer mold. The damage to the CNT web  
Figure 1: Fabrication of CNT web film.  
film based on flush size was measured through Raman spectroscopy. The size of the mold made for the experiment was approximately 200 \(\times\) 100 \(\times\) 50 mm, and the main physical properties are listed in Table 1. The multilayer structure mold uses materials such as C1100 and Nak80, which have high thermal conductivity. CNT web film's thermal conductivity is relatively lower than that of these materials. However, the physical property provided in Table 1 is the out-of-plane direction (vertical) thermal conductivity. The in-plane direction thermal conductivity exceeds 600 W/m\(\cdot\)K, surpassing that of C1100. By this, when the Joule heat was generated by CNT web film with electricity, the entire film and the mold cavity surface were both heated uniformly.  
Figure 3: RHCM multilayer structure mold: (**a**) test mold, (**b**) inside of the test mold, (**c**) cross-sectional view of A-A’, (**d**) each layer of the multilayer structure mold.  
Figure 2: Schematic view of RHCM experiment.
## 4 Experimental Results of the Proposed Systems  
### Split the Two Datasets  
To achieve the aim of this study, the proposed systems in this study were evaluated through chest X-ray images from two datasets. The first dataset (Shenzhen) contains 662 chest X-rays divided into two classes: 326 normal X-rays and 336 tuberculosis X-rays. The second dataset contains 4200 chest X-rays divided into two classes: 3500 normal X-rays and 700 tuberculosis X-rays. The two datasets were divided into 80% for training and validation and 20% for the testing of the proposed systems. Table 1 summarizes the number of X-ray images for the two datasets during all phases.
## 4 Numerical Results and Discussion
### Comparison of Simulation and Test Data  
In order to verify the reliability of the rotor model established, the unbalance response obtained by simulation is compared with the measured data. According to the data of the residual unbalance displayed by the dynamic balancing machine, unbalances of 35 g\(\cdot\)mm and 25 g\(\cdot\)mm are applied to node 7 and node 15 of the dynamic model established, respectively. The amplitude-frequency response curves corresponding to node 13 obtained by the simulation and measurement are shown in Figure 23. The comparison analysis shows that the simulation and test results are basically consistent, which indicates that  
Figure 22: Transient response (\(0.05\leq\mathrm{t}\leq 0.12\) s) in frequency domain for the blade off occurring at two different locations: (**a**) 1\(\#\) bearing; (**b**) node 11 (C.G. position); (**c**) 2\(\#\) bearing.  
the model has enough accuracy. Because node 13 is close to the node position of the second-order mode shape (see Figure 8b), there is no second-order resonance peak in the unbalance response versus speed curves. The response error between simulation and measured data is mainly caused by machining and assembly. Overall, the dynamic model established in the study is effective and suitable.
# Investigations on Factors Affecting 3D-Printed Holes Dimensional Accuracy and Repeatability
###### Abstract  
This paper investigates the impact of several factors related to manufacturing, design, and post-processing on the dimensional accuracy of holes built in the additively manufactured parts obtained by material extrusion process (MEX). Directly fabricated holes in the 3D prints are commonly used for joining with other parts by means of mechanical fasteners, thus producing assemblies or larger parts, or have other functional purposes such as guiding the drill in the case of patient-personalized surgical guides. However, despite their spread use and importance, the relationship between the 3D-printed holes' accuracy and printing settings is not well documented in the literature. Therefore, in this research, test parts were manufactured by varying the number of shells, printing speed, layer thickness, and axis orientation angles for evaluating their effect on the dimensional accuracy of holes of different diameters. In the same context of limited existing information, the influence of material, 3D printer, and slicing software is also investigated for determining the dimensional accuracy of hole-type features across different manufacturing sites, a highly relevant aspect when using MEX to produce spare or end-use parts in a delocalized production paradigm. The results of this study indicated that the layer thickness is the most relevant influence factor for the diameter accuracy, followed by the number of shells around the holes. Considering the tested values, the optimal set of values found as optimizing the accuracy and printing time was 0.2 mm layer thickness, two shells, and 50 mm/s printing speed for the straight holes. Data on the prints manufactured on different MEX equipment and slicers indicated no statistically significant difference between the diameters of the holes. The evaluation of 3D-printed polylactic acid test parts mimicking a surgical template device with inclined holes showed that the medical decontamination process had more impact on the holes' dimensional variability than on their dimensional accuracy.  
3D printing; holes; process parameters; medical decontamination; dimensional accuracy; dimensional repeatability 2022
# FAS-Res2 net: An Improved Res2 net-Based Script Identification Method for Natural Scenes
## 4 Experiments
### Results of the Method in This Paper
#### 4.3.6 Experimental Results under Different Parameters  
In order to further confirm the superiority of the method proposed in this paper and find out the best experimental results, we carried out related experiments on the SIW-13 dataset under different parameter conditions. The experimental results are shown in Table 5. The basic network structure of Res2 Net50 and Res2 Net101 was used for improvement, and experiments were carried out under different batch sizes. In general, the effect of extracting features based on the network structure of Res2 Net50 was better than that of Res2 Net101. Because the Res2 Net101 network has too many layers, the extracted feature information is relatively redundant, and the problem of over-fitting has occurred. It can be seen from the experimental results that the identification rate does not necessarily strictly follow the linear growth relationship with the increase of the batch size, but may decrease. When the value of Batchsize is 32, the effect of improving the network based on Res2 Net50 is the best to achieve an identification rate of 94.7%.  
To demonstrate the effectiveness and generalization of our proposed method, we also conducted experiments on the publicly available CVSI-2015 dataset. Compared with the SIW-13 dataset, the CVSI-2015 dataset contains fewer samples, which may hinder the training convergence of deep learning networks. To address this issue, we employed a novel data augmentation technique to increase the number of training sets by generating edge flow images from raw images. Our experimental results show that the script identification rate increases to 96.0%, which is better after applying edge flow data augmentation.
## 3 Results and Discussion
### Measurement of Physical Property Changes for the Sandstone Cores
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sandstone**} & \multicolumn{5}{c}{**Reaction Time (day)**} & \multicolumn{2}{c}{**Porosity Change Rate**} \\ \cline{2-7}  & **0** & **10** & **30** & **60** & **90** & **150** & **(\%) during 150 Days (1)** \\ \hline  & 8.183 & 8.321 & 8.464 & 8.569 & 8.553 & 8.649 & 5.7 \\ S1 & 8.121 & 8.149 & 8.293 & 8.299 & 8.350 & 8.39 & 3.3 \\  & 7.986 & 8.298 & 8.705 & 8.767 & 8.791 & 8.888 & 11.3 \\ S1 average \(\pm\) std & 8.097 \(\pm\) 0.10 & 8.256 \(\pm\) 0.09 & 8.487 \(\pm\) 0.21 & 8.545 \(\pm\) 0.23 & 8.565 \(\pm\) 0.22 & 8.642 \(\pm\) 0.25 & 6.7 \\ \hline  & 8.760 & 8.991 & 9.360 & 9.460 & 9.462 & 9.459 & 8.0 \\ S2 & 8.889 & 9.334 & 9.673 & 9.857 & 9.811 & 9.859 & 10.9 \\  & 8.680 & 8.713 & 8.919 & 9.148 & 9.163 & 9.160 & 5.5 \\ S2 average \(\pm\) std & 8.776 \(\pm\) 0.11 & 9.013 \(\pm\) 0.31 & 9.318 \(\pm\) 0.38 & 9.488 \(\pm\) 0.36 & 9.479 \(\pm\) 0.32 & 9.493 \(\pm\) 0.35 & 8.2 \\ \hline  & 3.121 & 3.278 & 3.221 & 3.305 & 3.353 & 3.412 & 9.3 \\ S3 & 3.056 & 3.282 & 3.33 & 3.38 & 3.336 & 3.358 & 9.9 \\  & 3.093 & 3.349 & 3.337 & 3.361 & 3.402 & 3.419 & 10.5 \\ S3 average \(\pm\) std & 3.090 \(\pm\) 0.03 & 3.303 \(\pm\) 0.04 & 3.296 \(\pm\) 0.07 & 3.349 \(\pm\) 0.04 & 3.364 \(\pm\) 0.03 & 3.396 \(\pm\) 0.03 & 9.9 \\ \hline  & \multicolumn{5}{c}{value for 150 day – value for 0 day} & \multicolumn{1}{c}{\(\times\) 100.} \\ \cline{2-7}  & & & & & & \\ \end{tabular}
\end{table} 
Table 4: Porosity change for the three sandstone cores for 150 reaction days. Figure 10: Average porosity change (**a**) and average seismic wave velocity change (**b**) for the sandstone cores during the scCO\({}_{2}\)-sandstone-groundwater reaction. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Property**} & \multicolumn{5}{c}{**Reaction Time (day)**} & \multicolumn{2}{c}{**Property Change Rate (\%)**} \\ \cline{2-7}  & **0** & **30** & **60** & **90** & **150** & **during 150 Days (1)** \\ \hline Dry density (g/cm\({}^{3}\)) & 2.390 \(\pm\) 0.0003 & 2.387 \(\pm\) 0.0007 & 2.386 \(\pm\) 0.0003 & 2.384 \(\pm\) 0.0004 & 2.384 \(\pm\) 0.0008 & 0.25 \\ P-wave velocity (m/s) & 3.578 \(\pm\) 31 & 3.467 \(\pm\) 25 & 3.412 \(\pm\) 62 & 3.410 \(\pm\) 12 & 3.375 \(\pm\) 45 & 5.67 \\ S-wave velocity (m/s) & 1.288 \(\pm\) 65 & 1.252 \(\pm\) 45 & 1.226 \(\pm\) 23 & 1.231 \(\pm\) 57 & 1.214 \(\pm\) 55 & 5.75 \\ \hline  & \multicolumn{5}{c}{\(\star\) (1); \(\left|\) value for 150 day – value for 0 day} & \multicolumn{1}{c}{\(\times\) 100.} \\ \cline{2-7}  & & & & & & \\ \end{tabular}
\end{table} 
Table 5: Average density and seismic wave velocity changes for the three sandstones during the reaction.
# How Should the Transition from Underwater to Surface Swimming Be Performed by Competitive Swimmers?
## 4 Discussion
### Study Limitations and Future Research  
The present results provide interesting insights on how swimmers could organize their movements when transiting from underwater to surface swimming. The underwater kicking execution seems to be the priority for swimmers before transiting to surface swimming. However, during the first arm stroke cycle, swimmers should carefully control their body inclination and depth as well as the time gap between propulsive arm or leg movements. At this point, the stroking parameters are still the most correlated variables with average velocity, but some other kinematic parameters can represent meaningful improvements for competitive swimmers. It should be also acknowledged that further research is still needed for kinematics parameters during transition as all measurements in the present study were discrete values. Continuous analysis of swimmers' inclination and depth when transiting from underwater would provide greater information relating to their body positioning. This is especially relevant for alternate strokes, where inclinations and body depth were part of the regression model.
## 2 Literature Review
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Paper** & **Methods** & **Data** & **Advantages** & **Limitations** \\ \hline This paper & MLP, RBF & 15,000 clients & High accuracy, captures complex patterns & Black-box interpretability issues \\ [PERSON] et al. (2023) [29] & Ensemble methods & 900,000 records & Robust against over-fitting & Increased computational complexity \\ [PERSON] et al. (2023) [30] & CNNs & Benchmark & High accuracy & High resource requirements, not tested in real-case \\ [PERSON] \& [PERSON] (2023) [26] & SVM, PCA & Benchmark & High accuracy with fewer parameters & SVM struggles with large data sets \\ [PERSON] \& [PERSON] (2024) [28] & Random forest, gradient boosting & Company data & High accuracy and robustness & Complexity in training \\ [PERSON] et al. (2024) [27] & Graph convolution network (GCN) and graph theory & 100,000 game users & Using social activity data and advanced graph convolution networks & Limits the precision of predicting specific churn timelines \\ \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of methods from literature. networks in capturing complex, non-linear relationships but also provides insights into their limitations, particularly regarding interpretability. This dual focus on performance and interpretability is often underexplored in the existing literature. The main contributions of the paper are as follows:  
* The paper provides a detailed comparative analysis of various churn prediction models, including traditional methods like fuzzy logic and rough sets, and advanced machine learning models such as MLP and RBF networks. This comparison highlights the strengths and weaknesses of each approach in the context of the telecommunications industry. * The research emphasizes the importance of meticulous data preprocessing, including attribute selection, the handling of missing data, and the encoding of categorical variables. These steps are shown to significantly improve the performance of the predictive models. * The paper utilizes real-world data from a Polish mobile telecommunications provider, ensuring that the findings are directly applicable to industry scenarios. This adds practical value to the research, making the results relevant for telecom companies looking to implement churn prediction systems.
# Effect of Soil Washing with Ferric Chloride on Cadmium Removal and Soil Structure
## 2 Materials and Methods  
### Soil Properties  
Soil samples were collected from the plow layers (0-15 cm) of a Cd-contaminated paddy field in Maoming City (21\({}^{\circ}\)44' N, 110\({}^{\circ}\)46' E), Guangdong Province, China. Prior to chemical analysis, the samples were air-dried and passed through a 2 mm sieve. Soil texture was as follows: sand 17.3% (0.05-0.60 mm), silt 73% (0.002-0.05 mm), and clay 9.7% (<0.002 mm), with a pH of 4.57 (1:5.0, CaCl\({}_{2}\)), 22.5 g\(\cdot\)kg\({}^{-1}\) of total C, and 4.3 cmol\({}_{c}\cdot\)kg\({}^{-1}\) of CEC. The total Cd concentration of the paddy soil was 2.10 mg\(\cdot\)kg\({}^{-1}\), which was substantially higher than the background value (average 0.20 mg\(\cdot\)kg\({}^{-1}\)) in uncontaminated soils in China [2].
# Embedded PSO for Solving FJSP on Embedded Environment (Industry 4.0 Era)
## 3 Flexible Job Shop Scheduling Problem
### Definition  
A five-tuple \((J,O,M,a,d)\) defines the FJSP, where  
* \(J=\{I_{1},I_{2},\ldots,J_{n}\}\) is a series of \(n\) distinct jobs with no precedence relationship between them.
* \(O=\{\{O_{11},O_{12},\ldots\}\cup\{O_{21},O_{22},\ldots\}\ldots\cup\{O_{n1},O_{n2 },\ldots\}\}\), is the series of operations, where \(O_{ji}\) represents operation \(i\); job \(j\);
* \(M=\{M_{1},M_{2},\ldots,M_{k}\}\) is a series of machines;
* \(a:O\times M\rightarrow\{0,1\},\ a(O_{ji},M_{l})=1\) if \(O_{ji}\) can be processed by \(M_{l}\); and
* \(d:O\times M\to N,\ d(O_{ji},M_{l})\) defines the standing of \(O_{ji}\) on \(M_{l}\).  
There are two types of FJSP instances: total and partial [31]. If any machine processes any action, an FJSP instance is total (noted as \(FJSP^{T}\)); otherwise, it is partial (noted as \(FJSP^{P}\)):  
\[FJSP=\left\{\begin{array}{ll}FJSP^{T}\,&if\ \ a(O_{ji},M_{l})=1;\ \ \forall i,j,l\\ FJSP^{P}\,&otherwise\end{array}\right. \tag{1}\]  
An FJSP instance's flexibility degree (FD) is defined as  
\[\sum_{\begin{subarray}{c}j=1\ldots|J|\\ i=1\ldots|J|\\ l=1\ldots|M|\end{subarray}}a(O_{ji},M_{l})\] \[FD=\frac{l=1\ldots|M|}{|M|.|O|} \tag{2}\]  
where \(|J_{j}\) denotes the number of operations performed in job \(j\). The makespan (\(MS\)) in Equation (3), which measures the completion time of all jobs, the robustness measure (\(RM\)) in Equation (4), which measures the schedule insensitivity to disruptions, the sum of delays (SD) in Equation (5), and the work load (\(WL\)) of all machines in Equation (6) can all be set as optimization criteria for the \(FJSP\):  
\[MS=max\{C_{1},C_{2},\ldots,C_{n}\} \tag{3}\]  
\[RM=ST-MS \tag{4}\]  
\[SD=\sum_{j=1}^{n}(C_{j}-E_{j}) \tag{5}\]  
\[WL=\sum_{l=1}^{k}d(O_{ji},M_{l})\ |\ O_{ji}\ \text{is affected to}\ M_{l} \tag{6}\]  
where \(C_{j}\) is the completion time of job \(j\), \(E_{j}\) is the minimum duration of job \(j\), and \(ST\) is all machines' rest time (also called slack time).  
Various assumptions for the \(FJSP\) might be made depending on the problem scenario. The following are the most commonly held assumptions in this paper: all machines are available at time zero, and all jobs are released; the machine setup time and transportation time between operations are minor; and operations are not preemptive. For demonstration, the \(FJSP^{T}\) instance from Table 2 is utilized. This example consists of three jobs (\(J_{1}\), \(J_{2}\), \(J_{3}\)) with three, three and four operations, respectively, and four machines (\(M_{1}\), \(M_{2}\), \(M_{3}\), \(M_{4}\)). The duration of the operations on the various machines is given in the last four columns of Table 2.
## 3 Modeling Approach
### Load Profiles
#### 3.1.1 Electric Load Curves  
Electric load profiles are generated based on four different sets of input data, reproducing four specific seasonal behaviors. The data were collected during a field campaign conducted by the authors in the study area (2017), in which specific questionnaires to investigate the ownership and usage patterns of electrical and thermal appliances were administered to the population. The seasonality of the loads refers to dawn and dusk times, which influence the working hours of street lighting, outdoor, and indoor lights. Also, the school follows seasonal behavior and the electric load is considered null during the holiday period (December-February). Moreover, the loads are modeled depending on the day of the week, and, for example, the school is not considered to have any load during the weekends.  
Figure 2: Schematic summary of the four scenarios considered: (**a**) traditional energy system; (**b**) conventional micro-grid; (**c**) multi-good micro-grid; (**d**) integrated multi-energy system.  
The collected data are used to build a table of electricity behavior of the different identified categories of users inside the village, reported in Tables S1 and S2 of the Supplementary Information. For each user, the type and number of owned appliances are identified, and for each appliance its (i) power, (ii) number of daily functioning time windows, (iii) time windows start and end times, (iv) percentage of variability of time windows start and end times, (v) total daily functioning time, (vi) percentage of variability of total daily functioning time, (vii) minimum time the appliance is kept on after a switch-on event, (ix) percentage of occasional use, and (x) weekend or weekday use. Stochastically simulating the energy consumption of each user inside the village based on this information, the aggregated electric load profile is generated. The interested reader can refer to the original work describing the RAMP tool [41] and to a previous work in which the described methodology is adopted and coupled with a mini-grid sizing tool for a case study in Bolivia [40].
# A QoS-Enabled Medium-Transparent MAC Protocol for Fiber-Wireless 5G RAN Transport Networks
## 3 The QoS-Aware MT-MAC Protocol
### State-of-the-Art MT-MAC Protocol Operational Rules
Note that, because of the explicit polling nature, no inter-frame spaces are assumed to exist between the transmitted packets, leading to higher medium utilization efficiency, while the DFs and RRFs are properly ordered by the CO in the time domain to be of equal duration. Each DF comprises the exchange of DATA_POLL, DATA, and ACK packets as depicted in Figure 3b. DATA_POLL packets denote which MT-MAC client has authorization to transmit immediately after the reception of the DATA_POLL message. Following the DATA_POLL reception, the MT-MAC node transmits the data packets as instructed and, finally, the CO transmits an ACK packet to the node to acknowledge the correct data reception. DATA_POLL packets are created as per the Polling Sequence (PS) that is constructed by the CO at the end of the second CP, defining the transmission schedule of each SF. The algorithm that creates the PS depends on the specific version of the MT-MAC protocol. For instance, in the original MT-MAC protocol [12], the PS has a predefined size, which is divided equally amongst the active RAUs. In the CW-MT-MAC protocol [19], each active user recognized by means of the second CP is assigned a predefined number of packets in  
Figure 3: Operation of the legacy gMT-MAC protocol. (**a**) An example of the 2 nd Contention Period operation for an RAU with 3 connected nodes. Each node chooses randomly the slot to transmit its MAC address (ID) and Buffer Status (BuS). If there are no collisions, the CO transmits an ACK packet acknowledging the correct reception of the ID + BuS packets. Based on the latter, the CO constructs the Polling Sequence (PS) of the next SF. (**b**) DATA_TX period example for an RAU with 3 nodes, with N1 having 1 packet to transmit, N2 having two packets to transmit, and N3 having 3 packets to transmit. the PS. Finally, in the gMT-MAC protocol [20], the PS is constructed based on the number of packets the nodes have reported during the first CP, and each client receives a transmission opportunity window that is equal to the time necessitated for transmitting the number of reported packets. The PS creation algorithm for the proposed qMT-MAC is described in Section 3.2. Nodes that have successfully advanced to the DATA_TX period can piggyback their BuS at the end of their final DATA packet, thus avoiding the necessity to partake in a subsequent second CP.