esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Model Performance Evaluation  
The model simulation results were evaluated using the Pearson correlation coefficient, \(R^{2}\), relative error (RE) indices, root mean square error (RMSE), and percentage of bias (PBIAS). These parameters are used to measure the correlation between two variables, which provides a standard for evaluating the model. There is a fluorescence value at every wavelength from 650 to 800 nm, and each wavelength will provide a number, \(i\); \(n\) is the accumulated number of \(i\). A simulation fluorescence dataset has \(n\) values, marked as \(x_{1}...x_{n}\) (collectively known as \(x_{i}\)), each associated with a measured fluorescence \(f_{1}...f_{n}\) (known as \(f_{i}\), or sometimes \(\hat{x}_{i}\)). The most general definition of the coefficient of determination, \(R^{2}\), is:  
\[R^{2}=1-\frac{\sum_{i}\left(x_{i}-f_{i}\right)^{2}}{\sum_{i}\left(x_{i}- \overline{x}\right)^{2}}, \tag{10}\]  
where  
\[\overline{x}=\frac{1}{n}\sum_{i=1}^{n}  
Absolute error is the difference between the simulated value of x\({}_{0}\) and its actual value or the measured value of x, and it is given by:  
\[\Delta\mathrm{x}=\mathrm{x}_{0}\ \mathrm{-}\ \mathrm{x} \tag{13}\]  
In this study, the absolute error can represent a deviation between the simulated fluorescence and the measured fluorescence. The RE is the ratio of the absolute error to the measured error, given by:  
\[\delta\mathrm{x}=\frac{\Delta\mathrm{x}}{\mathrm{x}}\ \times\ 100\%. \tag{14}\]  
In the model simulation, the measured fluorescence data can be subtracted from the simulated fluorescence to calculate the absolute error. To better explain the results, the RE should be calculated.  
PBIAS and RMSE can be calculated as:  
\[PBIAS=\frac{\sum_{i=1}^{n}(y_{i}-x_{i})}{\sum_{i=1}^{n}x_{i}}\times 100\% \tag{15}\]  
\[\mathrm{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-x_{i})^{2}} \tag{16}\]  
where, \(x_{i}\) is the actual fluorescence data; \(y_{i}\) is the simulated fluorescence; \(n\) is the total number of data.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Exposed Workers**} & \multicolumn{3}{c}{**Effective Whole Body Dose in the**} & \multicolumn{3}{c}{**Cumulative Effective Whole Body Dose**} \\ \cline{3-6}  & & **Last Year (mSv) a** & & **over the Entire Working Life (mSv) b** \\ \cline{2-6}  & **N.** & **Median** & **Range** & **N.** & **Median** & **Range** \\ \hline Industrial Radiographers & 7 & 0.80 & \textless{}0.01–1.93 & 7 & 20.34 & 5.82–36.53 \\ \hline Hospital radiology workers & 20 & 0.24 & \textless{}0.01–1.57 & 20 & 1.07 & 0.20–38.26 \\ \hline Hospital surgery workers & 12 & 1.07 & 0.03–2.46 & 12 & 24.24 & 0.49–54.43 \\ \hline Other hospital workers & 4 & 0.18 & \textless{}0.01–0.67 & 4 & 7.08 & 0.20–15.63 \\ \hline Total & 43 & 0.27 & \textless{}0.01–2.46 & 43 & 5.31 & 0.20–54.43 \\ \hline \hline \end{tabular}  
* a \(p<0.05\); b \(p<0.01\).  
\end{table} 
Table 3: Effective whole body dose in the last year (mSv) and over the entire working life (mSv) in exposed workers by job. _Appl. Sci._**2019**, \(9\), 5175 \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**DNA Damage Markers**} & \multirow{2}{*}{**Variables**} & \multicolumn{3}{c}{**Exposed**} & \multicolumn{3}{c}{**Controls**} & \multirow{2}{*}{**ANOVA**} \\ \cline{3-3} \cline{5-9}  & & **N** & **Median** & **Range** & **N** & **Median** & **Range** \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Spontaneous \\ chromosome breaks (\%) \\ \end{tabular} } & Male & 30 & 0.0 & 0.0-4.0 & 25 & 0.0 & 0.0–2.0 & Model: \(p=0.047\) \\ \cline{2-9}  & Female & 11 & 1.0 & 0.0-3.0 & 16 & 0.0 & 0.0-2.0 & Sex: NS \\ \cline{2-9}  & Smokers & 12 & 0.0 & 0.0-3.0 & 12 & 0.0 & 0.0–1.0 & Model: \(p=0.033\) \\ \cline{2-9}  & Non-smokers* & 29 & 1.0 & 0.0-4.0 & 29 & 0.0 & 0.0–2.0 & Exposure: \(p=0.010\) \\ \hline \multirow{2}{*}{TI (\%)} & Male & 32 & 3.4 & 0.9-4.3 & 27 & 2.7 & 0.9–4.6 & Model: \(p=0.034\) \\ \cline{2-9}  & Female & 11 & 3.4 & 1.9-4.9 & 16 & 3.8 & 2.4–4.7 & Exposure: NS \\ \hline \multirow{2}{*}{TI Endo III (\%)} & Male & 32 & 2.9 & 1.1-4.6 & 27 & 2.7 & 0.9–4.9 & Model: \(p=0.014\) \\ \cline{2-9}  & Female & 11 & 3.3 & 2.0-4.3 & 16 & 3.6 & 2.4–5.5 & Exposure: NS \\ \hline \hline \end{tabular}  
* Including ex-smokers; NS = not significant; model not significant for sex and smoking habits for total chromosome aberrations (%), chromatid breaks (%), chromatid breaks (%), micronucleid frequencies/1000 BN, TI Fpg (%), BLM-induced chromatid breaks (%), metaphases with chromatid breaks (%), chromatid breaks per cell; model not significant only for smoking habits for TI (%) and TI Endo III (%).  
\end{table} 
Table 6: DNA damage markers in workers subdivided by exposure and sex or smoking habit. \begin{table}
# Effect of Kinesio Taping on Hand Sensorimotor Control and Brain Activity
## 4 Discussion
For these participants, our results showed that Kinesio taping with neutral tension could reduce force sense errors by 50%, 78%, and 23%; under the taping with 20% additional tension, the force sense errors of these participants were reduced by 47%, 18%, and 59%. These observations suggested that there was a trend of improvement in force proprioception with Kinesio taping in individuals exhibiting poorer performances, and further investigation is required for confirmation. Contrary to previous studies, the current study did not observe changes in EEG with additional sensory afferent input induced by Kinesio taping. This discrepancy could be due to two factors. First, previous studies observed the immediate responses of EEG. They found that ERD and ERS were completed in less than 1 s after electrical or mechanical stimuli [35; 36]. However, due to the nature of taping application, we could only compare changes in EEG with and without taping. Second, previous studies conducted EEG recording during the resting state, while in the current study our participants performed a hand grip motor task. The EEG beta band power of the motor cortex would increase during a motor task compared with that under a resting condition [35; 37; 38]. The interaction or co-modulation between motor tasks and sensory input on EEG power is still unclear, but we think it could play a significant role in influencing the level of EEG beta power. Nevertheless, we observed a moderate correlation between the taping-induced improvement in force accuracy and the increase in the EEG (C3) beta band power. The EEG beta frequency band is related to the integration of afferent information in the sensorimotor cortex [39; 40], and the decrease in EEG beta frequency band (event-related desynchronization, ERD) is related to the excitability of the motor cortex [41]. We believe that for the participants who had significant change in EEG beta band power with Kinesio taping, sensory afferent input induced by taping might be better integrated and subsequently improve participants' performances.
# Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens
## 5 Data Searching and Browsing
### Geospatial Data Searching and Browsing
Two more examples of environmental data access infrastructures for environmental observation data related to water resources and ecological research, respectively, are the Hydrologic Information System (HIS) of the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) [32], and the Data Hub for the Virtual Observatory and Ecological Information System (VOEIS Data Hub) [33]. The platform most commonly used to distribute geoscientific data is the one based on the tools provided by UNIDATA. In particular, the THREDDS Data Server (TDS) ([[https://www.unidata.ucar.edu/software/tds/current/](https://www.unidata.ucar.edu/software/tds/current/)]([https://www.unidata.ucar.edu/software/tds/current/](https://www.unidata.ucar.edu/software/tds/current/))) provides metadata and data access for geoscientific arrays, which conform to the Common Data Model already described in Section 3, using different interfaces and protocols. Those interfaces include the OGC WCS and WMS standard interfaces, but also the NetCDF Subset Service (Ncess) that provides filtering functionality over CDM dimensions. The OGC has also a data access service specific for sensor observation data called Sensor Observation Service (SOS) [73], which provides searching functionality for datasets that conform to the OGC O&M data model already described in Section 3. Despite of spread spectrum of observation types supported by the O&M model, current SOS implementations ([https://52 north.org/software/software-projects/sos/](https://52 north.org/software/software-projects/sos/); [[https://sourceforge.net/projects/pysos/](https://sourceforge.net/projects/pysos/)]([https://sourceforge.net/projects/pysos/](https://sourceforge.net/projects/pysos/))) are based on spatial databases and mainly restrict to sampling methods based on in-situ observation. Therefore, remote sensing, which demands the recording of geospatial coverages in the result of the observations, is still completely ignored in SOS implementations. An approach that solves remote sensing data publishing based on OGC interfaces is described in [102]. More precisely, this solution combines a CSW catalog to discover SOS datasets, an SOS to publish observation metadata and a WCS to provide access to the result raster coverages.
## 1 Introduction
On this basis, in Section 3, the mathematical models of the proposed method are put forward, which mainly includes four parts: dynamical constraint equations for non-cooperative targets in cislunar space; BDS-based interplanetary link for irradiation of non-cooperative targets; transmission loss equation of BDS-3 inter-satellite link signal in cislunar space; CEI-based precision measurements of targets in cislunar space. 3. Based on the previous analysis, Section 4 will focus on the measurement errors of the inter-satellite link signals of Beidou-3 satellites in different orbits during the transmission process in the Earth-Moon space and the CEI observation process of the final received signals reflected by non-cooperative spacecraft. The error correction equation will be introduced to further correct the CEI observation equation of non-cooperative spacecraft in cislunar space. 4. Based on the above analysis, to give the feasibility demonstration of the proposed method, the link budget analysis and performance evaluation are presented in Sections 5 and 6, which focus on the carrier-to-noise ratio of the measurement of the target, and analysis of the pseudocode ranging and ranging rate of the GNSS-based interplanetary link signal, respectively. 5. The research results of this paper (which are demonstrated by the experiments shown in Section 7) can achieve effective and reliable tracking and measurement for non-cooperative targets in cislunar space, indicating a promising potential in the increasingly critical cislunar space situational awareness missions in the future.
## 5 Experiments and Analyses
### Video Delivery Experiment
#### 5.1.3 Ratio of the Three Types of Requests: 1:1:1 on the Random  
In the third part of this experiment, we redid the second part but the invoked algorithm was the Random. The experimental results are listed as the Y portion of X/Y in Table 14. The turnaround time of type-\(i\) was longer than that of the SPA-WP, \(i\) = 1, 2, 3 (see Turnaround-time row). The average turnaround time of a node, except that of node 2, was also longer than that of the node when the SPA-WP algorithm was invoked (see Average-turnaround-time column), because without load balancing, the number of requests/jobs that a lower performance node receives/executes was similar to the number of request/jobs received/executed by a high performance node, (see percentage-of-requests column which are 18, 18, 16, 16, 16 and 17). When given many more requests/jobs, the turnaround time of a low performance node will be longer. Furthermore, when the SPA-WP is employed, even nodes 1 and 4 execute less numbers of jobs, their turnaround times are similar to those of themselves since the queuing delay of a job in any one of the two nodes' queues is relatively shorter than the execution time of the job. The total average turnaround time was 170.58 s.
Furthermore, to the authors' knowledge, this study represents the first effort to establish a dynamic, data-driven tuning framework using deep reinforcement learning (DRL) for an asymmetric cost function. The proposed tuning scheme aims to optimize control objectives by balancing multiple competing criteria and enhancing control system performance. For comparative analysis, we propose a multiobjective GA (MOGA)-based neural network scheme that is employed to optimize the weighting factors. MOGA constructs a Pareto front of optimal weights [33] from which a dataset under varied operating conditions is established. These datasets are used as training data for a feedforward neural network (NN). This network provides a baseline data-driven approach for online tuning of the weighting factors across variable conditions. Test cases using a DRL-based dynamic tuning scheme and an NN-based static tuning approach are presented to validate the proposed method, demonstrating an improved weighting factor design in MPC for voltage source inverter (VSI)-fed PMSMs. The remainder of the paper is structured as follows. Section 2 presents the mathematical model of the considered electric drives. In addition, the general formulation of FCS-MPCC with an asymmetric cost function is presented. Then, Section 3 delves into a detailed explanation of the static and dynamic data-based training methods for the weighting factors' optimization strategies. Section 4 is dedicated to the evaluation of the effectiveness of the reinforcement learning approach, where we compare its performance under various test conditions with the baseline MOGA-NN-based method for the online selection of weighting factors in the asymmetric cost function. Finally, the significant findings of the proposed DRL online tuning scheme are summarized in Section 5.
# Unsupervised Domain Adaptation with Coupled Generative Adversarial Autoencoders
## 3 Proposed Approach
### Domain Adapted Classifier  
The focus of the unsupervised domain adaptation method described in this paper is to extend a classifier's generalization ability on two domains, originally trained on the source domain that generalizes to the unlabeled target domain. To this end, we train a classifier \(C\) with the source domain images and the synthetic target domain images generated by \(\{ENC_{S},DEC_{T}\}\). Unlike some other domain adaptation works where the discriminator is modified as classifier, our classifier has a stand-alone structure, shown in Figure 1, which is easy to be detached from the whole network for future training. We do not describe the detailed architecture of the classifier in Figure 2 because it is task specific. During training, we use the typical cross-entropy loss to optimize \(C\):  
\[\mathcal{L}_{C}(ENC_{S},DEC_{T},C)=\mathbb{E}_{x^{S},y^{S}}[-y^{S}\log C(DEC_{ T}(ENC_{S}(x^{S})))-y^{S}\log C(x^{S})] \tag{6}\]  
In addition, the classifier \(C\) has another function in CGAA, that is being a part of the optimization of the generator \(\{ENC_{S},DEC_{T}\}\) with a class-consistency loss. When training the generator, \(C\) assigns a label \(\hat{y}\) the generated image \(x^{S\to T}\), and the class-consistency loss is defined as:  
\[\mathcal{L}_{CC}(ENC_{S},DEC_{T},C)=\mathbb{E}_{x^{S},y^{S}}[-y^{S}\log C(DEC_{ T}(ENC_{S}(x^{S})))] \tag{7}\]  
where \(y^{S}\) is the class label of the input \(x^{S}\). The class-consistency loss makes sure the output image \(x^{S\to T}\) remains class-invariant, which is essential for the classification task in domain adaptation. With \(\mathcal{L}_{C}\) and \(\mathcal{L}_{CC}\), our final optimization object becomes:  
\[\begin{array}{ll}\min_{ENC_{S},ENC_{T},DEC_{S},DEC_{T},C}&\max_{D_{S},D_{T}}& \alpha\mathcal{L}_{GAN_{S}}(ENC_{T},DEC_{S},D_{S})+\beta\mathcal{L}_{REC_{S}} (ENC_{S},DEC_{S})\\ &\alpha\mathcal{L}_{GAN_{T}}(ENC_{S},DEC_{T},D_{T})+\beta\mathcal{L}_{REC_{T} }(ENC_{T},DEC_{T})+\gamma\mathcal{L}_{CC}(ENC_{S},DEC_{T},C)\\ &\mathcal{L}_{C}(ENC_{S},DEC_{T},C)\end{array} \tag{8}\]  
The minmax optimization of Equation (8) is achieved by two alternative steps. During the first step, we keep the discriminators and the classifier fixed, optimize the generators and at the same time, minimize the reconstruction losses and the class consistent loss. During the second step, we keep the generators fixed and optimize the discriminators and the classifier.  
Figure 2: The network architecture. The \(ENC_{S}\) and \(ENC_{T}\) are of the same structure as the encoder shown in this figure, so are the two decoders and the two discriminators. The convolution layer is denoted as conv, the transposed convolution layer (deconvolution layer) is denoted as dconv and the residual block is denoted as ResBlock. N means neurons (channels), S means stride, and LReLU means leaky ReLU. BN stands for batch normalization layer and Fc stands for fully connected layer. We share the weights of the dark-color layers in the coupled models during training.
# Evaluation of VoIP QoS Performance in Wireless Mesh Networks
## 4 Results and Analysis
### First Responders with Random Mobility  
In this scenario, it is assumed that the first responders are approaching a disaster-affected site using a random waypoint mobility model as shown in Figure 7. The dotted lines connected with a cloud show nodes association with an IPv4 subnet and the solid lines show VoIP peers. In this model, all nodes are configured with a minimum mobility speed of 1.1 m/s and a maximum mobility speed of 1.5 m/s, which is a typical mobility speed for pedestrian traffic.  
The achieved MOS values in all the sub-scenarios are presented in Figure 8a for VoIP initiators and Figure 9b for receivers. The analysis shows that FRW4 is the best performing sub-scenario in which the IEEE 802.11g, IEEE 802.11, G.729, and AODV have been integrated. The FRW5 is the worst performing sub-scenario in which the IEEE 802.11n, IEEE 802.11e, G.711, and AODV have been integrated.  
Figure 6: First responders with waypoint mobility MOS values for VoIP initiators and receivers: (**a**) VoIP initiator; (**b**) VoIP receiver.  
Figure 7: First responders with random waypoint mobility (captured during simulation).
# Long-Term Creep Mechanical and Acoustic Emission Characteristics of Water-Immersed Coal Pillar Dam
###### Abstract  
This study conducted uniaxial creep tests on coal samples under both natural and water-saturated conditions for durations of about 180 days per sample to study the stability of coal pillar dams of the Daliuta Coal Mine underground reservoir. Combined with synchronized acoustic emission (AE) monitoring, the research systematically revealed the time-dependent deformation mechanisms and damage evolution laws of coal under prolonged water immersion and natural conditions. The results indicate that water-immersed coal exhibits a unique negative creep phenomenon at the initial stage, with the strain rate down to \(-0.00086\%/\)d, attributed to non-uniform pore compaction and elastic rebound effects. During the steady-state creep phase, the creep rates under water-immersed and natural conditions were comparable. However, water immersion led to an 11.4% attenuation in elastic modulus, decreasing from 2300 MPa to 2037 MPa. Water immersion would also suppress AE activity, leading to the average daily AE events of 128, which is only 25% of that under natural conditions. In the accelerating creep stage, the AE event rate surged abruptly, validating its potential as an early warning indicator for coal pillar instability. Based on the identified long-term strength of the coal sample, it is recommended to maintain operational loads below the threshold of 9 MPa. This research provides crucial theoretical foundations and experimental data for optimizing the design and safety monitoring of coal pillar dams in CMURs.  
c 2025  
6 July 2025  
14 July 2025  
**Keywords:** coal mine underground reservoir; long-term creep behavior; acoustic emission; water immersion; stability analysis
# The Challenges of Advanced CMOS Process from 2D to 3D
## 3 Process Integration of New Transistor Architecture
### Stress Measurements in Nano-Scaled Transistors
This shift is due to strain compensation in presence of B atoms in Si matrix and it corresponds to the number of substitutional boron concentration. By applying the contraction coefficient of boron in Si ((6.3 \(\pm\) 0.1) \(\times\) 10\({}^{-24}\) cm\({}^{3}\)/atom) and the amount of the strain compensation, the boron concentration is estimated to 2 \(\times\) 10\({}^{20}\) cm\({}^{-3}\). Further investigations about the effect of NiSiGe formation on the SiGe strain were performed. In Figure 9c, the SiGe peak is at the same position as in Figure 4b indicating the strain in SiGe is preserved during the Ni silicide formation process [49]. Although HRRLMs is a powerful technique for strain measurement and they can be easily obtained by conventional high-resolution X-ray apparatus, they cannot measure strain in ultra-small SiGe crystals grown on Si fins in 3D FinFETs due to the limited signal acquisition. Therefore, the strain amount in the processed FinFET structures was determined by using X-ray beam in a Synchrotron facility as shown in Figure 10[50]. The high intensity beam offers the chance to determine the misfit parameters and the strain relaxation condition for small SiGe crystals. In Figure 10, the SiGe peak is aligned with Si peak along \(K_{\perp}\) indicating a small strain relaxation amount. Figure 9: high-resolution reciprocal lattice maps (HRRLMs) around (113) reflection from 22 nm metal oxide field effect transistors (MOSFETs) with Si\({}_{0.65}\)Ge\({}_{0.35}\) in S/D areas as follows (**a**) an intrinsic layer and (**b**) B-doped layer (**c**) as previous sample but with a SiGe sacrificial layer which was consumed in NiSiGe cap layer. Reproduced with permission from [54]. Copyright Journal of Applied Physics, 2013.
## 1 Introduction
### Sensorless Control of BLDCMs
State observers, including Luenberger observers and advanced disturbance observers [37; 38], use feedback mechanisms to estimate rotor position while compensating for parameter variations. Despite their simplicity and ease of implementation, these methods are limited in handling high-frequency disturbances or dynamic parameter changes. Saliency-based observers exploit the inherent rotor saliency and typically require high-frequency (HF) signal injection for position estimation at low speeds [39; 40; 41]. These methods are particularly effective where back-EMF methods fail, but prolonged HF excitation can degrade performance, leading to increased torque ripple, mechanical noise, and vibration. As mentioned before, nonlinear-model-based methods perform well at medium to high speeds, while high-frequency signal injection techniques dominate at low speeds, leveraging rotor anisotropy for position estimation. The trade-offs among accuracy, computational complexity, and robustness remain a central challenge in observer design. Comparative analyses of existing methods, as summarized in Table 1, highlight the need for further innovation to achieve seamless, high-performance sensorless control across all operating conditions.