esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 WDM-FSO Architecture
The wavelength reuse scheme has been widely employed in WDM-PONs based on the RSOA, which can be used as an uplink colorless transmitter and modulator, thanks to its properties of optical gain, integration capability, and wide optical bandwidth [40]. Figure 2 depicts a bidirectional FSO WDM system based on a wavelength reuse scheme. It is similar to the original topology shown in Figure 1. The difference is that the demultiplexed WDM signals at the receiver side are passed through 3 dB optical splitters, where half of the WDM-OFDM modulated signal is fed to an OFDM receiver. For up-link, the other half of the downstream modulated signal is directly remodulated by the RSOA to generate the 10 Gbps NRZ OOK PRBS \(2^{23}-1\) upstream signal. As the seeding power of an RSOA has a significant effect on its modulation characteristics, a variable optical attenuator (VOA) is used to keep the power at the input of RSOA at a level in which it operates in the gain-saturation regime. The OOK signals generated are combined using a Mux and then launched back via an FSO channel to form upstream data, where they are separated by a DeMux, and each OOK signal is directly detected by a PIN receiver. Figure 1: The WDM FSO architecture. Figure 2: The bidirectional WDM FSO architecture.
## 5 Discussion
### Limitations  
The qualitative method of using semi-structured interviews with open-ended questions, as used in this research, allows for the opportunity for the interviewee to bring up subject-related issues that they deem pertinent [57]. As it is important to investigate the potential risks and benefits of autonomous shipping from the perspective of the Licensed Deck Officer, this research using semi-structured interviews and surveys is limited by its sample size. It would benefit this research to expand and include a larger sample size of Licensed Deck Officers and maritime and autonomous industry SMEs.
## 2 Materials and Methods
### Methodology
In subsequent steps, more vectors built on homologous points are iteratively discarded, until vectors built on the basis of 16 points (2 in each quadrant for positive and negative Z-coordinate values) are left. Then, scaling is performed in the auxiliary space according to the method of least squares (6), which can be written in the form of Equation (7)  
\[X=\left(A^{T}\cdot A\right)^{-1}\cdot A^{T}\cdot L \tag{6}\]  
where  
\(\bullet\)\(X\)--vector of unknown parameters;  
\(\bullet\)\(A\)--matrix of coefficients (partial derivatives --Jacobian transformation);  
\(\bullet\)\(L\)--constants vector. \[\lambda=\left(D_{TPC}^{T}\cdot D_{TPC}\right)^{-1}\cdot D_{TPC}^{T}\cdot D_{RPC} \tag{7}\]  
where  
\(D_{i}\)--length of the homologous vector in both point clouds--reference and transformed. In the next step, the quasi-rotation matrix is determined in the auxiliary space using Equations (5) and (7). \[\left[ \begin{array}{c}X_{rpc1}\\ Y_{rpc1}\\ Z_{rpc1}\\ X_{rpc2}\\ Y_{rpc2}\\ Z_{rpc2}\\ \ldots\\ X_{rpcN}\\ X_{rpcN}\\ X_{rpcN}\\ X_{rpcN}\\ X_{rpcN}\\ X_{rpcN}\\ \end{array} \right]=\left[ \begin{array}{cccccccc}X_{pc1}&Y_{pc1}&Z_{pc1}&0&0&0&0&0 &0&0\\ 0&0&X_{pc1}&Y_{pc1}&Z_{pc1}&0&0&0\\ 0&0&0&0&X_{pc1}&Y_{pc1}&Z_{pc1}\\ X_{pc2}&Y_{pc2}&Z_{pc2}&0&0&0&0\\ 0&0&X_{ipc2}&Y_{pc2}&Z_{pc2}&0&0&0\\ 0&0&0&0&X_{pc2}&Y_{pc2}&Z_{pc2}\\ \ldots\\ X_{pcN}&Y_{pcN}&Z_{pcN}&0&0&0&0&0&0\\ 0&0&0&X_{pcN}&Y_{pcN}&Z_{pcN}&0&0&0\\ 0&0&0&0&0&X_{pcN}&Y_{pcN}&Z_{pcN}\\ \end{array} \right]\cdot\left[ \begin{array}{c}m_{11}\\ m_{12}\\ m_{13}\\ m_{21}\\ m_{22}\\ m_{31}\\ m_{32}\\ m_{33}\end{array} \right] \tag{8}\]  
where  
\(rpc_{i},\ pc_{i}\)--indexes of the coordinates refer to subspace, with the centroid (centre of gravity) as the origin of the coordinate system. In the last step of the transformation, a return translation is performed from the auxiliary space to the real (measurement) space of the reference point cloud, which is written through the last component of the sum in Equation (5). Post-transformation deviations are calculated using relationship (9), and average errors m are calculated using deviations (9) and covariance matrix Q, using the Formula (10)  
\[V=A\cdot X-L \tag{9}\]  
\[Q=\left(A^{T}\cdot A\right)^{-1} \tag{10}\]  
with  
\[m_{0}=\pm\sqrt{\frac{V^{T}\cdot V}{n-k}} \tag{11}\]  
\[m_{X}=m_{0}\cdot\sqrt{Q_{XX}} \tag{12}\]  
\[m_{Y}=m_{0}\cdot\sqrt{Q_{YY}} \tag{13}\]  
\[m_{Z}=m_{0}\cdot\sqrt{Q_{ZZ}} \tag{14}\]  
The point cloud fusion is validated on the field control points, and accuracy is assessed by the mean deviation errors of the transformed points relative to the reference control points. The field control points in the form of targets were measured using Total Station and in the form of ground points using the DGNSS RTK technique.
## 4 Discussion
### Limitations  
This study has limitations. In the first place, the existing heterogeneity in the equations for estimating body composition using the anthropometric method, moments of the season in which the assessment is performed, playing positions, and measuring instruments to evaluate the different body components make it difficult to compare the results. In addition, not all the studies that used the anthropometric method detail the measurement protocol applied, so the methodology and evaluators could be biased. On the other hand, although for the anthropometry section, we excluded certain measurement instruments that were not valid for the ISAK protocol, we did not apply any limiting criteria for the BIA and DXA instruments. This is important since, for example, there is a risk of unifying tetrapolar and octapolar BIA values, as well as monofrequency and multifrequency. In addition, it  
Figure 3: Body composition values differentiated by playing position (Weight, height, sum of six skinfolds (triceps, subscapular, supraspinal, abdominal, mid-thigh, and calf), fat mass percentage and kilograms, muscle mass percentage and kilograms, somatotype). End = endomorphy; Mes = me-somorphy; Etct = ectomorphy.  
would have been interesting to incorporate total body water data by playing position, but only one study or none described these body composition values. In a recent publication by the authors [17], the total body water values can be observed without differentiating by playing position. Another limitation is that, although data collection was limited to male professional soccer players, not all countries show the same soccer level, with some countries having higher Federation Internationale de Football Association (FIFA) rankings than others [66]. Although it is true that they are all professional soccer players, they do not show the same degree of professionalism in terms of economic level, sports facilities, and physical demands, which could affect many factors that could influence their body composition and sports performance. Finally, due to the limited number of studies included, the body composition values of anthropometry, BIA, and DXA were unified. This may affect the usefulness of FM, MM, and FFM values, as body composition values obtained through the different measurement instruments do not always show a high correlation [53; 57; 67; 68]. However, the usefulness of the sum of skinfolds and somatotype as values exclusive to anthropometry, BMC, and BMD as they are specific to DXA, and age, height, and total weight as parameters that do not require specific equations, do not show any limitation when using them as reference values.  
Despite all this, it is the only study currently found in the scientific literature that provides a complete description of body composition differentiated by the different playing positions. Based on the current situation, our work aims to be the first study to suggest indicative ranges of anthropometric values, FM, MM, and BM, in detail in the different playing positions of men's professional soccer.
# Enhancing CT Segmentation Security against Adversarial Attack: Most Activated Filter Approach
## 3 Experiments
### Experimental Results  
A range of experiments was conducted, exploring different adversarial attack settings and corresponding parameters, notably the step size (EPS) and the number of iterations (NITR). For each type of attack on CT segmentation, the EPS values are set to 0.01, 0.02, and 0.1. Moreover, For each type of attack on polyp segmentation, the EPS values are configured to 0.01, 0.1, and 0.5. The number of iterations is also adjusted to 5, 10, and 15, excluding FGSM, given its nature as a one-step attack. The results of these adversarial attacks for each setting, as tested on the test set, can be found in Tables 3 and 4. It is noteworthy that the genuine test samples achieved a dice score of 0.4524 and 0.8886 for CT segmentation and polyp segmentation performance, respectively. In the tables, a lower dice score resulting from an attack suggests that the attack is more effective.  
The subsequent sections explore both the detection efficacy of the adversarial attacks discussed in this study and provide a visual analysis to assist medical professionals. For each evaluation scenario concerning attack detection, adversarial samples are generated according to specific attack settings, matched one-to-one with the genuine samples in the test set. This results in an equal number of genuine and adversarial samples.
## 2 Literature Review and Hypotheses
### Creativity  
Some studies [37; 38; 39] suggest that creativity derives from the structure of intelligence that includes cognition, memory, divergent thinking, convergent thinking, and assessment, but other researchers [8; 40; 41; 42; 43; 44; 45] take on a broader definition. Creativity can be perceived as the ability to generate new viewpoints, relationships, or meanings to produce an innovative idea that is different from the original by reprocessing, amending, integrating or recombining linkable factors based on the original knowledge structure that satisfies a special need or meaningful purpose and exhibits a new originality which has its own unique values [37; 46; 47]. While studies [48] indicate that knowledge sharing can limit creativity, other researchers [49] argue that the sharing of such knowledge enhances creation skill, openness, and independence which are key characteristics of creativity. Therefore, creativity is the foundation of innovative knowledge; and innovation is the result of expressed creativity.  
Creativity is the process of developing innovative solutions to solve problems, but such a process usually differs as a result of differing contexts [50; 51; 52; 53]. Accordingly, the need for assessing creativity is well supported by several studies [54; 55], and one of the different techniques used is cognitive assessment [56]. The most commonly used cognitive assessment examines the level of fluency, flexibility, elaboration, and originality of the thought process as an assessment index [37; 57]. The Torrance Tests of Creative Thinking (TTCT), developed by Torrance [58] adopt a more stringent graphic and language written testing that measures creativity potential.  
Prior studies [59; 60; 61; 62] have indicated that through well-designed training, creativity can be elevated and further enhances the skill and efficacy of individual employee, which will have a flow on effect to the collective innovation process. This can significantly contribute to the overall effectiveness and growth of sustainable operations in a corporation [4; 5; 6]. This study will focus on investigating the impact of communication formats (specifically CMC and FtF) in creativity training on potential creativity, which seek to address the two proposed research questions identified earlier.
## 2 Materials and Methods
### Comparison of Sample Purification Conditions  
A 5 g sample was moistened and spiked with the pesticide working solution to achieve a concentration of 0.1 mg/kg. The sample was subsequently extracted using 10 mL of a 1:1 (\(v/v\)) mixture of MeCN and EA, containing 0.1% formic acid. The extraction was then partitioned using the Original QuEChERS salts. The following dSPE methods were employed for the purification of the extracts: (1) PSA purification, utilizing 25 mg of PSA and 150 mg of MgSO\({}_{4}\); (2) C18 purification, using 25 mg C18 and 150 mg of MgSO\({}_{4}\); (3) a combined PSA and C18 purification, with 25 mg of each PSA and C18, and 150 mg of MgSO\({}_{4}\); (4) a larger combined PSA and C18 purification, with 50 mg of each PSA and C18, and 150 mg of MgSO\({}_{4}\). For each purification method, 1 mL of the supernatant was transferred into a dSPE tube, mixed, and subsequently, the purified extract was matrix-matched with MeCN in a 1:1 ratio. The recovery rates and matrix effects were compared using three replicates (\(n=3\)).
# 5G-NR Physical Layer-Based Solutions to Support High Mobility in 6G Non-Terrestrial Networks
## 1 Introduction
* This paper summarizes the work the 3 GPP is currently conducting to enable NTN as a component of NR technology and to identify remaining issues and potential research directions. * With these considerations, the 5G-NR numerologies are optimized to support high mobility. * We analyze the link-level performance of a 5G-NR physical channel for downlink user data transmission, known as a physical downlink shared channel (PDSCH) under a 3 GPP NR-NTN channel model, such as single-path Rayleigh, Rician, and multi-path tapped delay line (TDL) channel models, in terms of the bit error rate (BER), maximum achievable throughput, and spectral efficiency. * We identify the impact of various numerology and modulation and coding schemes (MCSs), pilot-aided perfect and DM-RS-based practical channel estimation, and MIMO/beamforming schemes at the stationary UE mobility and 500 km/h. * Based on the link-level simulation results, we propose 5G-NR-based potential solutions for NTN to support high mobility in the 6G technology evolution. The structure of this paper is as follows. Section 2 presents a literature review of related work in NTN. Section 3 provides an overview of NTN with its structure in 6G, scenarios, use cases, major challenges and solutions, and the channel models proposed by 3 GPP to support NR-NTN. In Section 4, 5G-NR-based solutions are proposed for high mobility. These solutions are analyzed using the link-level simulation in Section 5, where the simulation results and analysis are discussed. The conclusions are given in Section 6.
## 4 Results and Discussions
The main difference of UV- and VIS-MFRSR AODs from other AODs is the consideration of the ozone and NO\({}_{2}\) optical depth. Namely, the variation in Rayleigh scattering by ozone and NO\({}_{2}\) is accurately considered in the retrieval of UV- and VIS-MFRSR AOD following the methodology in [PERSON] et al. (2018) [19], whereas the climatology of ozone and NO\({}_{2}\) is only considered in the retrieval of AERONET and SKYNET AODs. It appears that the AOD performance itself is better for the AERONET and SKYNET AODs, probably due to the better detection of direct sun (the AERONET and SKYNET instruments follow the diurnal cycle of the solar position), resulting in more accurate PM\({}_{2.5}\) estimation using the M1 model. However, in the M3 model where the variation of meteorological and chemical (ozone and NO\({}_{2}\)) factors is considered, accurate consideration of ozone and NO\({}_{2}\) optical depth in UV- and VIS-MFRSR AOD appears to be more influential on the estimation of qualified PM\({}_{2.5}\). The study site, Yonsei University, is located near a road where high traffic generally occurs [26]. We note that the usage of local air pollutants in the MLR model also appears to be significant for the PM\({}_{2.5}\) estimation, particularly for the polluted region. Finally, we examined the seasonal variation in PM\({}_{2.5}\) estimation using ground-based AODs in multiple channels. Table 5 shows a comparison of correlation coefficients, RMSE, and slope between the observed PM\({}_{2.5}\) and PM\({}_{2.5}\) estimated from the usage of AERONET AODs in the M3-1 model, as a representative example, during each season. Similar to  
Figure 4: Wavelength dependence of (**a**) R, (**b**) RMSE, and (**c**) slope values of the M1 model, and that of (**d**) R, (**e**) RMSE, and (**f**) slope values of the M3-1 model using multiple ground-based AODs. The unit of RMSE is \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\). the usage of satellite AODs (Table 4), the performance of PM\({}_{2.5}\) estimation is the best in autumn, which obtained the highest correlation coefficient, lowest RMSE, and highest slope. Although the quality of PM\({}_{2.5}\) estimation appears to be relatively lower in spring, probably due to the large reflection of Asian dust events near the surface, PM\({}_{2.5}\) estimation appears to be reliable for all seasons, even winter. This difference between the usage of satellite and ground-based AODs implies that the inconsistency of PM\({}_{2.5}\) estimation in winter may be attributed to the surface conditions, because the AOD retrieval from the ground-based measurement is not significantly associated with the surface reflectance. In addition, no wavelength dependence of AOD was found for each season, meaning that the channel selection of AOD is not of concern if the statistical approaches are applied for PM\({}_{2.5}\) estimation.
## 4 Simulation Experiment and Analysis  
### Performance Evaluation of Multipath Routing Algorithms  
In order to verify that the multi-path routing algorithm of the inter-satellite link system proposed in this paper can realize shunting and improve system performance, we carried out simulation experiments on waiting delay, packet loss rate, and network throughput indicators. In the simulation experiment, the sending rate of business data packets increased from 10 packets/s to 100 packets/s, and the simulation time was set to 1 h. Taking the 16-node inter-satellite network shown in Figure 7 as an example, assuming the following topology, it is required to calculate the routing table from node V7 to other nodes, and select the optimal path according to the path load.  
According to the above inter-satellite network connection conditions, a total of 16 paths from the source node V7 to destination nodes V0, V9, V11, V3, V12, and V13, were selected for service transmission testing. All the paths obtained by the algorithm in this paper are as follows: [V7->V1->V0], [V7->V2->V0], [V7->V8->V9], [V7->V10->V9], [V7->V2->V11], [V7->V10->V11], [V7->V1->V0->V3], [V7->V2->V0->V3], [V7->V2->V6->V3], [V7->V8->V9->V12], [V7->V2->V11->V12], [V7->V10->V11->V12], [V7->V2->V6->V13], [V7->V2->V11->V13], [V7->V10->V11->V13].  
The paths obtained by the breadth-first search algorithm were [V7->V1->V0], [V7->V8->V9], [V7->V2->V11], [V7->V1->V0->V3], [V7->V8->V9->V12], [V7->V2->V6->V13]. There is only one path to the destination node V0, V9, V11, V3, V12, and V13.  
We tested the business packets from node V7 to node V0, V9, V11, V3, V12, and V13, respectively, and recorded the arithmetic mean value of waiting delay, packet loss rate, and network throughput of each pair of nodes using traditional routing and the routing algorithm proposed in this paper.
## 2 Materials and Methods
### Isolation of Immune Cells and Flow Cytometry  
Organs were collected immediately after euthanasia. Mouse blood was collected by cardiac puncture immediately after the mice were euthanized and collected in EDTA-coated tubes. Immune cells were obtained after performing two erythrocyte lysis steps. Hepatic immune cells were isolated using a Percoll gradient. Homogenized liver was suspended in 80% Percoll (GE Healthcare) and overlaid with 40% Percoll diluted in complete RPMI 1640 (Gibco) containing 10% fetal calf serum, L glutamine, and penicillin/streptomycin (cRPMI). After a centrifugation step without brake (800\(\times\)\(g\); 25 min; 21 \({}^{\circ}\)C), cells localized in the interphase of the two Percoll layers were transferred to a new collection tube for further washing steps with PBS and erythrocyte cell lysis. The obtained single cell suspension was washed twice with cRPMI. Spleen cells were separated with a cell strainer (70 \(\upmu\)m), centrifuged (300\(\times\)\(g\)) and washed with PBS before two erythrocyte lysis steps. Bone marrow-derived immune cells were collected by flushing out the bone marrow from isolated thighs with a cannula and 2 mL PBS, followed by separation of cells with a cell strainer. Monocytesfrom spleen and bone marrow were purified using the EasySeptTM Monocyte Isolation Kit (StemCell Technologies) according to the manufacturer's instructions.  
Flow cytometry analysis was performed on immune cells (2 \(\times\) 10\({}^{6}\)) from liver (Supplementary: blood, bone marrow, spleen). Staining was performed using murine Fc-Blocking solution, followed by fixation of cells in 1% paraformaldehyde. The following antibodies were used:  
CD45.1 BUV395 (A20) (Becton Dickinson); CD11b BV510 (M1/70), CD14 BV421 (1 Sa14-2), CD38 PeCy7 (90), CD45.2 PeCy5 (30-F11), CD74 AF647 (In1/CD74), CD86 AF700 (GL-1), Ly6C FITC/PE (HK1-4), Ly6G BV785 (1A8) (BioLegend); IRF2 AF488 (sc-374327) and mafb PerCP (OTI1E9) (Novus Biologicals). Analysis was performed on a BD LSR Fortssa and a Cytek Aurora, and data were analyzed using FlowJo software V10.7.1.
# A PINN-Based Nonlinear PMSM Electromagnetic Model Using Differential Inductance Theory
## 5 Conclusions  
This paper derives a nonlinear model of the PMSM based on differential inductance in both the ABC and dq0 frames. By treating current excitation and electrical angle as independent variables, the model establishes analytical expressions for inductance, magnetic flux linkage, magnetic energy, and torque, though iron losses and high-frequency eddy current effects remain unmodeled limitations.  
To address the limitations of classical torque models and local linearization approaches, this paper develops a Fourier coefficient PINN for flux linkage based on FEA data and periodic boundary conditions of PMSM physical quantities. This network not only fits numerical relationships of given sample points but also ensures specific differential relationships and boundary conditions between inputs and outputs, enabling gradient descent-based active optimization. Benefiting from the independence of Fourier coefficients, the network allows parallel training/computation and facilitates angular frequency domain analysis of the PMSM.  
To verify the accuracy of the differential inductance-based nonlinear model, this paper compares PINN and FEA torque prediction results under both SE and USE conditions with local linearization schemes. Results demonstrate that the proposed nonlinear torque model achieves precise torque and torque ripple calculation. With the same number of FEA sampling points, the PINN model shows higher accuracy in torque prediction for non-FEA sampled operating points and under USE conditions.  
Figure 11: Current waveform under USE condition.  
Figure 12: Torque and armature flux linkage under the USE condition computed by the FEA, PINN, OLAM, and GLM methods: (**a**) torque waveform; (**b**) torque FFT.  
**Author Contributions:** Methodology, X.W.; modeling, S.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Key Research and Development Program of China (Grant No. 2024 YFB2504900, Subproject No. 2024 YFB2504903). The project \"Intrinsic Wide-Temperature-Range High-Specific-Energy Power Battery Technology\" is administered by the Ministry of Science and Technology of China.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.