esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
During the FSW process, the first evidence of plastic deformation and material flow needs to be explored in the entry zone. In the entry zone, even before entering the pin into the workpiece, the flow regime is affected by the rotating movement of the shoulder. Since the position of the shoulder edge is located ahead of the pin, therefore the first stage of the entry zone as the ejected spray is the consequence of the pure plastic flow imposed by the shoulder. It should be noted that the flow features in the spray zone outside of the workpiece, is totally different from the surface flow features of the weld track. Figure 9 shows the metallographic observations of the plastic internal flow patterns at the body of the weld with a focus on the tunnel void region. Figure 9a demonstrates the tunnel void occurred at the spray region at the beginning of the entry zone. The angle of view imaging can portray more details of the plasticized mass, accumulated outwards of the weld-seam. Because of the absence of the pin in the plastic deformation, spray region is sagging to the one side (bending towards the RS). As there is no backup material behind the movement direction, the mass is plasticized between two shoulders and then through the shoulders gap is ejected outside. Figure 8: The cross-sections of the stirring zone showing the internal flow details for the AA6082-T6 BFSW weld sample; (**a**,**b**) the entry zone; the AS and RS transverse sections, respectively. To be more focused on the flow history around the tunnel void, a 3D photomontage of the stereoscopic images, depicting the left-eye and right-eye views of the tunnel void position is shown in Figure 9b. According to the stereograph pictures of Figure 9b, the surface pitch ripples features are also observed as the curved flow induced by the shoulder rotation in a circle route between the AS and the RS. Regarding the weld cross-sections, the wavy flow layers accumulated at the circumferential edge of the tunnel void elucidates turbulent flow regimes strayed in random directions causing the flow discontinuity. Figure 9: Details of the apparent features of the tunnel void within the AA6082-T6 aluminum weld; (**a**) tunnel void emerged at the entry location of the weld, (**b**) photo-montage pattern of the tunnel void within the weld structure, revealed in cross-section and side view of the weld. The origins of these dispersed flow lines can be attributed to the plasticizing rotation of the shoulder in contrary with the pin-induced flow at the trailing edge of the tool, while the plasticized layers of the stirred mass are depositing to form the weld-seam. Therefore, incomplete refilling at the AS forms a kinked-shape discontinuity at the weld-track known as the tunnel void.
## 1 Introduction  
In general practice, numerical modeling, of the working process of centrifugal compressor (CC) stages, is usually conducted without taking into account the heat exchange between the flow and the flow part (FP) surfaces. In such cases, the flow part walls are considered to be adiabatic. In the case of medium and high consumable levels [1; 2; 3; 4; 5; 6], this approach is justified by the relatively low influence on the flow in the main path of the stage and the gas-dynamic characteristics (GDC) of heat dissipation from the flow by the flow part walls. In addition, experimental stands with model stages, which are most often used for numerical modeling, are usually well insulated and provide the most accurate measurement of the increase in flow temperature of the stage for calculating the internal pressure by the enthalpy method. The situation is somewhat different for modeling of low-flow stages, which have a number of physical features and calculation factors that must to be taken into account. The physical features are the width of the small channels, the closure of the boundary layers in such channels, the high relative disk friction losses and internal leaks, and the high compressible medium density. The calculation factors are the choice of computational domain [7], the quality of grid model, boundary conditions, turbulence models, friction loss calculations, and the effect of roughness on the calculated low-flow stage gas-dynamic characteristics. A separate effect on the low-flow stage characteristics is rendered by heat exchange between the flow and the flow part surfaces. This paper researches the calculated value of this heat exchange using computational gas-dynamics and the Numeca Fine/Turbo software package. On the basis of the modeling results, an analysis of the low-flow stage parameters, in the meridian section, with averaging over the circumferential coordinate for the practical operating modes allowed us to see the incorrect temperature distribution in the impeller gaps, when using adiabatic walls while setting boundary conditions. The main reason for this is the lack of heat dissipation from the flow, which is expressed as a significant overestimation of the gas temperature in the gaps and, in a principal manner, affects the flow parameters in the main path. The hot gas leakage, from the gap of the covering disk entering the inlet to the impeller, heats the main gas flow by increasing the temperature behind the impeller. Similarly, for leakage along the main disk, there is additional gas heating at the inlet to the diffuser.
# Defect Detection on LED Chips Based on Position Pre-Estimation and Feature Enhancement
## 3 Proposed Method
### Rotation Correction  
After position pre-estimation, a single LED chip was acquired but it was usually inclined. To increase the accuracy of subsequent precise location and reduce misidentification at the same time, angle calculation and affine transformation were performed. By finding the smallest circumscribed rectangle, the inclination Theta angle was obtained according to the four sides of the rectangle. The transformation formula for rotation is defined as follows:  
\[\left[\begin{array}{c}x\prime\\ y\prime\end{array}\right]=\left[\begin{array}{cc}\cos\theta&-\sin\theta\\ \sin\theta&\cos\theta\end{array}\right]\left[\begin{array}{c}x\\ y\end{array}\right] \tag{13}\]  
where \(x\) and \(y\) are original coordinates, \(\theta\) is the angle of rotation, and so \(x^{\prime}\) and \(y^{\prime}\) are the coordinates after rotation. The corrected picture is more conducive to machine vision inspection, which can effectively reduce misjudgment and improve detection accuracy.
# A Systematic Approach to Studying Quark Energy Loss in Nuclei Using Positive Pions +
## 5 Conclusions  
We introduce a systematic way to study the quark energy loss in nuclear matter using positive pion (\(\pi^{+}\)) production data from SIDIS experiments. Using data from the EG2 experiment at Jefferson Lab, we explored the hadronization process in nuclear targets of varying sizes, such as deuterium, carbon, iron and lead. As a proof of concept, we first focus on a carbon target. The energy distribution of \(\pi^{+}\) reveals a consistent energy shift attributed to the quark energy loss. This shift was quantitatively assessed using statistical methods, including the Kolmogorov-Smirnov test, ensuring robust and reliable comparisons. The use of acceptance corrections, Coulomb corrections, and target-specific vertex cuts ensured that the observed energy distributions were free from detector biases and accurately reflected the underlying physical processes.  
Overall, this study demonstrates the utility of using the \(\pi^{+}\) production in SIDIS to probe quark energy loss mechanisms. The findings highlight the importance of nuclear size as a variable in understanding hadronization in cold matter, with implications for both theoretical models and experimental methodologies.  
All the authors contribute equally to the article. All authors have read and agreed to the published version of the manuscript.  
This work was funded by ANID PIA/APOYO AFB230003, Millennium Program--ICN2019_044.  
Data Availability Statement: Data is unavailable due to privacy restrictions.  
N.Z.-G. acknowledges the Doctoral scholarship of Pontificia Universidad Catolica de Valparaiso, granted by the Graduate Studies Directorate.  
The authors declare no conflicts of interest.
## 5 Experimental Evaluation  
To demonstrate the performance improvement of the proposed FT-CID model, the experimental model evaluates the proposed FT-CID model through the comparison of baseline models of different learning algorithms based on an IDS. It selects the baseline models, logistic regression (LR), multilayer perceptron (MLP), and CNN-based RPL-IIoT IDS due to the lack of existing research work in the area of FT-based RPL-IIoT IDSs.
A sender transmits the packets to the relays and destination. The BER is calculated at the destination. If the data is errorless than a specific point, they are forwarded further. Otherwise, retransmission is considered. Moreover, mobile sinks that move either in a vertical or a horizontal direction to cover the entire region of the network have been used. The protocol has good results for the packet drop, alive nodes, and throughput. However, mobile sinks need to update geographical information, which increases the complexity of the operation. In [18], the depth threshold is determined for the destination, which is also known as the master node and is selected as on the lowest deepness and highest residual energy. Furthermore, the parameters used for the master node are also used for the two cooperative relays. The master node gets the data from a source node. The BER is computed at the master node. When the BER is an acceptable predefined limit, data are accepted. Otherwise, it considers retransmission. This protocol has a high throughput and network lifetime. However, it has high latency since the holding time is predefined for the relays. Secondly, when the source node transfers data, it uses three paths, which causes an extra delay. The optimized depth-based routing (ODBR) is presented in [27]. This technique is actually a single-path routing protocol and only considers the information of depth for the data forwarding. The ODBR protocol has efficient energy consumption and network lifetime, because of the single-path routing technique. However, this single-path routing technique reduces throughput efficiency and network reliability. In depth-based routing (DBR) [12], a source node detects the packet, it sends data in the way of the sink surface. The pressure sensor is used in order to choose the best forwarder. The sender picks the forwarding node as on the lowest depth. The DBR protocol has good throughput and latency. However, the node having low depth is used multiple times during packet delivery. In this way, a die quickly having the lowest depth due to high load. Therefore, the void zone is created in the network. [PERSON] et al. in [28] propose a depth and noise-aware routing (DNAR) scheme in which a sender picks the closer node as a relay that has the smallest path from the surface and smallest noise over the source-relay link. This reduces the corruption of information packets by channel noise, which increases reliability in the information. The DNAR does not need the geographical location of each node. The DNAR has good throughout, residual energy and lifetime of the network. But it has high latency due to frequent checking of the channel conditions.
## 3 Results and Discussion
### Multivariate Data Analysis
#### 3.2.2 Comparisons Between Groups B and C
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** &
\begin{tabular}{c} **Feature** \\ **Selection Mode** \\ \end{tabular} & **Feature Subsets** & **AUC** & **Accuracy** & **Precision** & **Recall** & **Specificity** \\ \hline \multirow{8}{*}{**Features**} & Full dataset & All 9 laboratory biomarkers & 0.489 & 0.475 & 0.472 & 0.475 & 0.475 \\ \cline{2-9}  & & Lymphocytes, platelets, RBCs, D-dimers, eosinophils, LDH, creatinine, CRP & 0.606 & 0.575 & 0.585 & 0.575 & 0.575 \\ \cline{2-9}  & & Lymphocytes, platelets, RBCs, D-dimers, eosinophils, LDH, creatinine & 0.634 & 0.650 & 0.656 & 0.650 & 0.650 \\ \cline{2-9}  & & Lymphocytes, platelets, RBCs, D-dimers, eosinophils, LDH & 0.613 & 0.600 & 0.610 & 0.600 & 0.600 \\ \cline{2-9}  & & Lymphocytes, platelets, RBCs, D-dimers, eosinophils & 0.634 & 0.600 & 0.610 & 0.600 & 0.600 \\ \cline{2-9}  & & Lymphocytes, platelets, RBCs, D-dimers & 0.591 & 0.525 & 0.525 & 0.525 & 0.525 \\ \cline{2-9}  & & Lymphocytes, platelets, RBCs & 0.690 & 0.625 & 0.633 & 0.625 & 0.625 \\ \cline{2-9}  & & Lymphocytes, platelets & 0.684 & 0.650 & 0.679 & 0.650 & 0.650 \\ \cline{2-9}  & & Lymphocytes & **0.708** & **0.725** & **0.730** & **0.725** & **0.725** \\ \hline \multirow{8}{*}{**Features**} & Full dataset & All 34 variables & 0.448 & 0.450 & 0.450 & 0.450 & 0.450 \\ \cline{2-9}  & & Lymphocytes, IL-2R, HGF, platelets, IL-10, eosinophils, IL-7, CXCL10, PARK7, CXCL10, PARK7 & 0.765 & 0.650 & 0.650 & 0.650 \\ \cline{2-9}  & & Lymphocytes, IL-2R, HGF, platelets, IL-10, eosinophils, IL-7, CXCL10 & 0.768 & 0.650 & 0.656 & 0.650 \\ \cline{2-9}  & & Lymphocytes, IL-2R, HGF, platelets, IL-10, eosinophils, IL-7, CXCL10 & 0.765 & 0.675 & 0.675 & 0.675 \\ \cline{2-9}  & & Lymphocytes, IL-2R, HGF, platelets, IL-10, eosinophils, IL-7, CXCL10 & 0.765 & 0.675 & 0.675 & 0.675 \\ \cline{2-9}  & & Lymphocytes, IL-2R, HGF, platelets, IL-10 & 0.765 & 0.675 & 0.675 & 0.675 \\ \cline{2-9}  & & Lymphocytes, IL-2R, HGF, HGF, platelets, IL-10 & 0.774 & **0.725** & **0.730** & **0.725** & **0.725** \\ \cline{2-9}  & & Lymphocytes, IL-2R & 0.725 & 0.675 & 0.687 & 0.675 & 0.675 \\ \cline{2-9}  & & Lymphocytes & 0.708 & **0.725** & **0.730** & **0.725** & **0.725** \\ \cline{2-9}  & & Univariate & VEGF; Lymphocytes & 0.636 & 0.625 & 0.633 & 0.625 & 0.625 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._emphasizing once again the ability of these three biomarkers to differentiate between the two groups of patients. Once more, these results highlight the potential of t-SNE as a screening technique to define precise feature subsets, considering a specific target.
# Association between Motivational Climate, Emotional Intelligence, and Bicycle Use in Schoolchildren
###### Abstract  
(1) Background: The psychological benefits of cycling have been identified such as the maintenance of low-stress levels. However, no studies have been found addressing the benefits of cycling on variables such as emotional intelligence (EI) and motivational climate (MC), which are important for holistic development in children. This study aimed to investigate the interrelationships between MC, EI, and cycling habits in schoolchildren. (2) Methods: A descriptive, comparative, cross-sectional study was conducted in a sample of 347 Spanish schoolchildren (46.4% boys; 53.6% girls; Mage = 10.55, S.D. = 0.97). A sociodemographic questionnaire, the Trait Meta-Mood Scale (TMMS-24), and the Perceived Motivational Climate in Sport Questionnaire (PMCSQ-2) were applied for data collection. (3) Results: Boys use bicycles more frequently than girls; task-oriented motivational climate (TC) is observed in girls and schoolchildren with moderate cycling habits; ego-involving motivational climate (EC) prevails in boys and students who cycle more than four times per week; no statistical association was found between EI and cycling use habits; and EI and its dimensions correlate with TC and some categories of EC. (4) Conclusions: Moderate cycling habits are linked to a task-oriented MC and have slightly higher scores on general EI and its dimensions.  
cycling; emotional intelligence; motivational climate; schoolchildren; physical education Article
## 3 Classification Method  
The steps taken by the classification system used in this study are summarized in Figure 3. The ROIs for the sEMG signals were determined using a window detection algorithm applied to the audio signals (top portion of Figure 3). Then, the system extracted seven different time-domain features from the observed sEMG ROIs, in addition to the GUSSS ratios [13], which measure the degree of presence of previously learned sEMG patterns within the observed sEMG signal. Then, a support vector machine (SVM) was employed for classification due to its elevated performance in non-linearly separable 2-class problems. The remaining steps of the system, such as signal normalization and imbalanced data training, are also represented in Figure 3 and will be further discussed in Section 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Task** & **Description** & **Reps** & **Time a ** \\ \hline baseline & Neutral with no movements for collecting & & \\  & pure noise & & \\ syllable1 & “afa afa afa if iii if ufu ufu “ [16] & 1 & 6 s \\ /a/ normal & /a/ as in honest & 55 & 2 s \\ /u/ normal & /u/ as in you & 55 & 2 s \\ /i/ normal & /i/ as in reel & 55 & 2 s \\ sentence1 & “The dew shimmed over my shiny blue & & 55 & 4 s \\ sentence2 & “Only we feel you do fail in new fallen dew” & & 55 & 4 s \\ syllable2 & “afa afa afa if iii if ufu ufu ufu” [16] & 1 & 6 s \\ /a/ pressed & /a/ simulated pressed & 55 & 2 s \\ throat clear & Single throat clear & 55 & 1.5 s \\ /u/ pressed & /u/ simulated pressed & 55 & 2 s \\ cough & Single cough & 55 & 1.5 s \\ /i/ pressed & /i/ simulated pressed & 55 & 2 s \\ syllable3 & “afa afa afa if iii if ufu ufu ufu” [16] & 1 & 6 s \\
100\% MVC & Maximum voluntary contraction & 3 & 8 s \\
50\% MVC & Submaximal voluntary contraction & 3 & 15 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: The complete sequence of utterances, their repetitions and durations for sEMG and acoustic data collection. Subjects in study 2 did not complete their simulated pressed phonations and non-speech gestures.
# A Linkage Representation of the Human Hand Skeletal System Using CT Hand Scan Images
## 2 Materials and Methods
### Joint Centers Estimation  
The wrist joint center was estimated as the intersection of the distal wrist crease and the centerline of the third metacarpal [18]. The remaining finger joint centers were estimated by a novel method developed in the present study. The method consists of two steps: (1) estimation of finite joint centers between different hand postures and (2) estimation of joint centers as the centroid of the finite joint centers. Finite joint centers are estimated in three steps: (1) alignment of proximal bone segments of a joint at different postures to measure the relative motions of the distal bone segments of the joint from one posture to another, (2) identification of centerlines of the distal bone segments of the joint at different postures, and (3) determination of finite joint centers as the intersection points of the identified centerlines at different postures, as shown in Figure 2. The alignment of the proximal bone segments of the joint was performed using the abovementioned three-point alignment method in RapidForm 2006. The centerlines of the distal bone segments were identified in two steps: (1) acquisition of a preliminary centerline using the principal component analysis (PCA) method and (2) refinement of the preliminary centerline to obtain the fine centerline by performing a linear fit of the centroids of the surface vertices at the perpendicular planes to the preliminary centerline along the distal bone shaft, the part of a bone excluding the two ends of the bone. More detailed information on the centerline identification method can be found in our previous paper [15]. After all the finite joint centers of a joint were determined, the joint center of that joint was derived as the centroid of the finite joint centers using a k-means clustering method [19], as shown in Figure 3.  
Figure 3: Estimation of the rotation center (**red**) of a joint as the centroid of finite joint centers (**green**).  
Figure 2: Estimation of a finite center of rotation of a joint between two different postures: (**a**) alignment of proximal bone segments of the joint at different postures; (**b**) identification of the centerline of the distal bone segment of the joint; (**c**) determination of a finite joint center (**red**) as the intersection point of the identified centerlines at different postures.
## 1 Introduction
This, correspondingly begs the questions: 'what could be achieved with an ideal, bespoke remote sensing system?' and 'what would this ideal system comprise?'. The analysis presented below attempts to explore the parameters for a constellation of optical satellites against this background, using Scotland as a case study area to define the mission requirements. The design primarily focuses on the number of spacecraft and orbits required to provide the necessary temporal and spatial resolution. This design is presented not so much in the expectation that a satellite system for archaeology will be launched, but, by outlining the high-level features of such an ideal system, to contribute to the further development of this data source for the discipline.
# Analysis of SNR for High-Orbit Target Detected by Ground-Based Photoelectric System
###### Abstract  
To determine the feasibility of observing high-orbit targets with a large aperture telescope, we created a simulation based on electronics to evaluate the signal-to-noise ratio (SNR) model for an infrared ground-based photoelectric system. Atmosphere transmission and sky background radiation data were obtained using MODTRAN software, then the SNRs of the high-orbit target (HOT) in different temperatures and orbit heights were calculated separately. The results showed that the observation of the HOT in a short band was possible, and the effect of short-wave was excellent at low temperatures. On the basis of this model, some space targets were observed by a K-band photoelectric telescope for verification and had constructive results. Thus, the model can be used as a basis for whether a HOT can be detected.  
SNR; high-orbit; MODTRAN; simulation; infrared detection +
Footnote †: journal: Applied Physics Letters
## 3 Methodology
### Data Recording and Analysis  
Google has recently unveiled a set of valuable resources aimed at fostering developer engagement with RGNSSM. These resources include the GNSS Logger v3.0.6.4, MATLAB Mobile v6.3.0, and GPS Test v3.10.3 applications, which are conveniently available on the Android application store, enabling developers to efficiently gather data for analysis. Subsequently, Windows-based programs such as Emlid Studio and GNSS Analysis v4.6.0.1 were employed to analyze the data acquired during the initial stage.