esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Finite Element Method Verification  
The Finite element modeling of the proposed system is performed using COMSOL Multiphysics software (5.2a, COMSOL Multiphysics Pvt. Ltd., Bengaluru, India) for five different scenarios. The AC/DC module, magnetic field, electric circuit and frequency domain study toolboxes are used to determine the magnetic field intensity on the Rx coils. The physics controlled mesh is used to reduce the computation time and memory usage during the analysis. The coil parameters of the designed system are given in Table 4. The Tx coil is excited with 10 V AC source with selective frequency sweep of 1 MHz, 1.5 MHz, 2 MHz and 3 MHz. First, all the Rx's are considered to be static, and the Tx frequency is switched to match the resonant frequency of each Rx without any cross-coupling compensation. The induced magnetic field on each Rx is shown in Figure 10. It is visible that the selected resonant coil is receiving the maximum field intensity. However, the non-resonant coils magnetic field strength also receiving 40% field strength of resonant coil. In the second case, the compensated capacitor is added to each load coil, and the Tx frequency is switched from 1 MHz, 1.5 MHz, 2 MHz, and 3 MHz to select Rx1 to Rx4 as shown in Figure 11. The magnetic field intensity of the non-resonant coils is less than 15% of the resonant coil, which indicates uniform power distribution to selected load with much reduction of cross coupling. In the third case, the Rx1, Rx2 and Rx3, Rx4 resonant frequency and the corresponding load coil resonant frequency are changed to 1 MHz and 2 MHz respectively. The analysis is performed for two set of the axial distance between the Rx coils as shown in Figure 12. When the distance between the Rx's are reduced to 3 c.m the field strength at non-selected load coil is close to 18% of the resonant coil field strength and when the distance between the Rx coil is increased to 6 c.m the field strength of the non-resonant coil is less than 15%. Finally, the lateral distance between the Tx and Rx, load coils are changed to 3 c.m, 6 c.m and the resonant frequency is switched from 1 MHz to 3 MHz, and the field strength is shown in Figures 13 and 14. In both, the cases the non-resonant coil field is minimum and the resonant coil field is maximum, which indicates the rejection of cross-coupling between the coil and elimination of frequency splitting under over coupled region. The high field strength at the
# Unconstrained Estimation of Multitype Car Rental Demand
## 2 Unconstrained Estimation  
### Hypothesis and Variable Definitions  
Before studying the forecasting method, the conditional hypothesis and variable definitions should be explained. This work is mainly aimed at the car rental industry, and the method is also applicable to the hotel and aviation industries, etc.  
**Hypothesis 1**.: _Unconstrained demand \(X\) of car type \(i\) in presell lead time meets normal distribution, and the lead time is seven days._  
**Hypothesis 2**.: _Demands of various car types are random and independent, and the numbers of car rentals are counted by day; the deadline is midnight._  
**Hypothesis 3**.: _Inventory quantity is constant without car dispatching among stations, taking no account of batch demand, overdue return, or oversold behaviors._  
**Hypothesis 4**.: _The customer's willingness to pay is from low to high._  
The variables are defined as follows:  
\(i\): index of car types, \(i\in I\). Indexes of \(I\) are listed in an increasing order of car rental price, corresponding to the gradually increased levels;  
\(j\): index of rental demand recording times, \(j=1,2,\cdots,\text{J}\);  
\(t\): review points of inventory control, \(t=0,1,\cdots,\text{T}\); presale system will open when \(t=0\), and it will be closed when \(t=\text{T}\).  
\(\Delta_{t}\): presale lead time interval between \(t\) and \(t-1\);  
\(B(i,j,t)\): order quantity limit of car type \(i\) in \(\Delta_{t}\) in the \(j\)th documented circle, namely the quota ceiling for rent;  
\(C(i,j,t)\): cumulative order quantity of car type \(i\) at review point \(t\) in the \(j\)th documented circle, particularly \(C(i,j,0)=0\);  
\(I(i,j,t)\): presale state of car type \(i\) in \(\Delta_{t}\) in the \(j\)th documented circle, \"1\" indicates that demand is not restricted, and \"0\" indicates that demand is limited.  
\(IB(i,j,t)\): order quantity of car type \(i\) in \(\Delta_{t}\) in the \(j\)th documented circle, particularly \(IB(i,j,0)=0\), \(IB(i,j,t)=C(i,j,t)-C(i,j,t-1)\);  
\(U(i,j,t)\): spillage of car type \(i\) in \(\Delta_{t}\) in the \(j\)th documented circle, particularly \(U(i,j,0)=0\);  
\(CP(i,j,t)\): complete spillage of car type \(i\) in \(\Delta_{t}\) in the \(j\)th documented circle, particularly \(CP(i,j,0)=0\);\(SU(i,j,t)\): cumulative spillage of car type 1 to \(i\) at review point \(t\) in the \(j\)th documented circle, \(SU(i,j,0)=0\);  
\(IU(i,j,t)\): unconstrained estimation of car type \(i\) in \(\Delta_{t}\) in the \(j\)th documented circle, particularly \(IU(i,j,0)=0\);  
\(TU(i,j,t)\): real value of car type \(i\) in \(\Delta_{t}\) in the \(j\)th documented circle.
## 4 Analysis and Interpretation of Results
### Correlation Links Between Individual Production Sources and Residual Load
#### 4.3.3 Analysis of the Polish Market  
The Polish electricity system in the analysed period is characterised by a dynamic development of renewable energy sources, especially wind power. Nevertheless, market pricing remains closely linked to the development of residual load. The impact of solar generation has only become more significant in recent years, from 2020 onwards.  
The average correlation between wind and price was \(-\)0.135, indicating that as wind generation increases, prices fall--a typical effect for heavily subsidised and prioritised connected RES. However, this relationship is volatile over time--it was strongest between 2022 and 2024 (e.g., 2023: r = \(-\)0.468), which is related to the increase in installed wind capacity in northern regions and its more frequent spillover into surplus.  
Data on solar generation is only available from 2020 onwards, reflecting the later development of this technology in Poland. Since then, the Solar/Price and Solar/Residual Load correlations are slightly negative, but their value (e.g., in 2024: r = \(-\)0.32 to \(-\)0.47) shows that solar generation is starting to have a dampening effect on prices, especially in summer months with low demand.  
The Residual Load/Price correlation is strong throughout the period (r = +0.56 overall), peaking in 2019 and 2025 (r = +0.78 to +0.90). This consistent link reflects that electricity prices in Poland are still largely shaped by the classical equilibrium mechanism between available conventional generation and consumption, net of RES.  
The correlation Wind/Solar = +0.005 indicates that these sources are not seasonally or diurnally aligned--which is to be expected, given the different climate profiles.  
In Poland, wind power plays a key role and has already been a major price influencer, especially in recent years. Solar generation has been negligible for a long time, but after 2020 it starts to become statistically significant, especially in terms of the risk of negative prices in the summer months. Residual load remains the main determinant of market price, reflecting a market structure still oriented towards conventional resources.
# Power Profiling of Smart Grid Users Using Dynamic Time Warping +
###### Abstract  
Power consumption data play a crucial role in demand management and abnormality detection in smart grids. Despite its management benefits, analyzing power consumption data leads to profiling consumers and opens privacy issues. To demonstrate this, we present a power profiling model for smart grid consumers based on real-time load data acquired from smart meters. It profiles consumers' power consumption behavior by applying the daily load factor and the dynamic time warping (DTW) clustering algorithm. Due to the invariability of signal warping of this algorithm, time-disordered load data can be profiled and consumption features can be extracted. By this model, two load types are defined and the related load patterns are extracted for classifying consumption behavior by DTW. The classification methodology is discussed in detail. To evaluate the performance of the proposed model for profiling, we analyze the time-series load data measured by a smart meter in a real case. The results demonstrate the effectiveness of the proposed profiling method, achieving an F-score of 0.8372 for load type clustering in the best case and an overall accuracy of 77.17% for power profiling.  
power profiling; user privacy; smart grid; smart home; dynamic time warping (DTW); time-series analysis Article
# Hide45: A Method for Optimal Payload Data Hiding in Base45 Encoded Strings
## 6 Empirical Analysis
Considering the fact that this optimization, in our testing environment, requires in all cases between 4 s and 8 s with a median of 5 s, it has no effective impact over the whole procedure. \begin{table}
\begin{tabular}{c c c c c} \hline
**File Format** & **Avg Payload** & **Avg Payload** & **Th. Gain** & **Emp. Gain** \\
**\(\bullet\)** & **[23] [bps]** & **Hide45 [bps]** & **[\%]** & **[\%]** \\ \hline JPEG & \(0.17025\pm 0.01270\) & \(0.17807\pm 0.00494\) & 4.6 & 4.6 \\ \hline TIFF & \(0.26252\pm 0.04132\) & \(0.31376\pm 0.01758\) & 22.0 & 19.5 \\ \hline PNG & \(0.17091\pm 0.01251\) & \(0.17235\pm 0.00503\) & 1.4 & 0.8 \\ \hline ZIP & \(0.14177\pm 0.01708\) & \(0.14251\pm 0.00325\) & 2.5 & 0.5 \\ \hline BZ2 & \(0.13959\pm 0.01207\) & \(0.14140\pm 0.00895\) & 3.4 & 1.3 \\ \hline GZ & \(0.13404\pm 0.00540\) & \(0.13410\pm 0.00203\) & 0.07 & 0.04 \\ \hline PDF & \(0.16998\pm 0.02877\) & \(0.26002\pm 0.04044\) & 57.4 & 53.0 \\ \hline MP3 & \(0.16989\pm 0.01656\) & \(0.24231\pm 0.03960\) & 45.2 & 42.6 \\ \hline \end{tabular}
\end{table} 
Table 2: Set of files in JPEG, TIFF, and PNG; ZIP, BZ2, and GZ; PDF and MP3 formats: average payloads per output symbol and gains computed for [23] and the proposed method Hide45.
# Glove Prototype for Feature Extraction Applied to Learning by Demonstration Purposes
## 3 Theoretical Concepts
### Quaternion-Based Complementary Filter
#### 3.5.1 Accelerometer-Based Correction  
To find the accelerometer delta quaternion, the measured gravity vector is rotated from the local frame to the global frame by the inverse predicted quaternion of the gyroscope:  
\[\mathbf{R}(^{\text{G}}_{L}\mathbf{q}_{\omega})\,^{L}\mathbf{a}=\,^{\text{G}} \mathbf{g}_{p} \tag{12}\]  
where \(^{\text{G}}_{L}\mathbf{q}_{\omega}\) is the conjugate of gyroscope normalized predicted quaternion \(\mathbf{q}_{\omega}\), and \({}^{\text{G}}\mathbf{g}_{p}=\begin{bmatrix}g_{x}&g_{y}&g_{z}\end{bmatrix}^{T}\) is the predicted gravity which has a small deviation from real gravity vector \({}^{\text{G}}\mathbf{g}=\begin{bmatrix}0&0&1\end{bmatrix}^{T}\). The accelerometer delta quaternion is computed from the rotation of \({}^{\text{G}}\mathbf{g}\) into \({}^{\text{G}}\mathbf{g}_{p}\):  
\[\mathbf{R}(\Delta\mathbf{q}_{\text{acc}})\,^{\text{G}}\mathbf{g}=\,^{\text{G}} \mathbf{g}_{p} \tag{13}\]  
The delta quaternion is used interpolated via LERP with identity quaternion \(\mathbf{q}_{I}=\begin{bmatrix}1&0&0&0\end{bmatrix}\) by the gain \(\alpha\) with value 0.005:  
\[\overline{\Delta\mathbf{q}}_{\text{acc}}=(1-\alpha)\mathbf{q}_{I}+\alpha \Delta\mathbf{q}_{\text{acc}} \tag{14}\]  
Normalizing the interpolated quaternion:  
\[\widehat{\Delta\mathbf{q}}_{\text{acc}}=\frac{\overline{\Delta\mathbf{q}}_{ \text{acc}}}{\|\overline{\Delta\mathbf{q}}_{\text{acc}}\|} \tag{15}\]  
and finally correcting the orientation by multiplying the gyroscope predicted quaternion by the delta quaternion:  
\[\mathbf{q}=\,\mathbf{q}_{\omega}\,\widehat{\Delta\mathbf{q}}_{\text{acc}} \tag{16}\]  
This way, the roll and pitch are corrected, but the heading angle is not affected and totally estimated by the gyroscope. If there was magnetometer data available, a correction for the heading could be computed with the respective delta quaternion.
## 4 Materials and Methods
### RNA-Seq Data Processing  
The quality of sequencing reads was estimated using FastQC (v0.11.3) ([[http://www.bioinformatics.babraham.ac.uk/projects/fastqc/](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/)]([http://www.bioinformatics.babraham.ac.uk/projects/fastqc/](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/))). Then adaptors and low quality bases were removed using Trimmomatic (v0.33) with parameters set as \"ILLUMINACLIP:adaptor.fa:2:30:10 LEADING:5 TRAILING:5 SLIDINGWINDOW:4:5 MINLEN:25\" [53]. Clean reads from all samples were pooledtogether and fed to de novo RNA-seq assembly software Trinity (v2.2.0) with default parameters [54]. Then assembled all transcripts were used as reference and raw sequencing reads were mapped to these references using Bowtie2 with default parameters [55]. Those transcripts containing mapped reads were kept and the longest transcripts of each 'gene' were extracted as Unigenes. The normalized transcript expression in FPKM (Fragments Per Kilobase of transcript per Million mapped reads) were determined by the RSEM program (v1.2.19) [56]. Two replicates of each sample were treated separately and used to estimate dispersions by edgeR when performing sample normalization and differential expression genes selection. The hot map of gene expression data was generated in the MultiExperiment Viewer (MeV) 4.9.0 software [57]. The average FPKM value of the two biological replicates were log-2 transformed, hierarchical clustered with Euclidean distance.
# Deep Learning for Neuromuscular Control of Vocal Source for Voice Production
## 3 Results
### Control System
For the muscle parameters, the order in error is \(P_{L}<a_{CT}<a_{TA}\) < \(a_{LC}\) according to each box's interquartile range and the extent of whiskers. Among the somatosensory features, the error in % is moderately high for \(\sigma_{lig}\) (\(\pm\)30%), followed by \(\mathrm{L_{g}}\) (\(\pm\)21%), excluding the outliers. For the other two features (\(\sigma_{muc}\) and \(\sigma_{mus}\)), the error in % is low (\(\pm\)11%) across all the test signals, excluding the outliers. For the acoustic features, the error in % is moderately high in the prediction of SNR (\(\pm\)30%), and very low in the prediction of \(\mathrm{f_{o}}\) (\(\pm\)7%), excluding the outliers. For the SPL and NSC, the error is relatively low (\(\pm\)12%). \begin{table}
\begin{tabular}{l l c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Control Parameters**} & \multicolumn{4}{c}{**Somatosensory Features**} & \multicolumn{4}{c}{**Acoustic Features**} \\ \hline
**Row** & \(\mathbf{P_{L}}\) & \(\boldsymbol{a_{CT}}\) & \(\boldsymbol{a_{TA}}\) & \(\boldsymbol{a_{LC}}\) & \(\mathbf{I_{g}}\) & \(\sigma_{muc}\) & \(\sigma_{lig}\) & \(\sigma_{mus}\) & \(\mathbf{f_{o}}\) & **SPL** & **NSC** & **SNR** \\ \hline First & Targets & 0.20 & 0.19 & 0.50 & 0.46 & 1.48 & 12.7 & 8.13 & 12.9 & 92 & 10.5 & \(-\)3.12 \\ \hline  & Predictions & 0.22 & 0.26 & 0.62 & 0.44 & 1.51 & 12.9 & 8.18 & 13.2 &  
Figure 11 shows the difference between the targets and predictions in actual units for somatosensory and acoustic features. The control parameters were not included because they are non-dimensional, ranging between 0 and 1. The error is between \(\pm\)0.2 cm for \(L_{g}\), \(\pm\)100 kPa for \(\sigma_{muc}\), and \(\pm\)20 kPa for both \(\sigma_{lig}\) and \(\sigma_{mus}\). For the acoustic features, the error is between \(\pm\)15 Hz for f\({}_{o}\), \(\pm\)2 dB for SPL, \(\pm\)3 for NSC, and \(\pm\)4 dB for SNR, excluding the outliers. Figure 12 shows the boxplot of outputs generated by each controller for the test data. It can be observed that the acoustic feedforward controller (afc) is the dominant controller after training. The range of values generated by the affc is higher than the range of values generated by the feedback controllers except for TA muscle activation. Values generated by affc ranged between 0 and 1 except for \(a_{LC}\). The values produced by affc for \(a_{LC}\) varied only in a small range around 0.5. Among the feedback controller outputs, only \(a_{TA}\) varied significantly, suggesting that more than one controller was necessary to learn the TA muscle control's complexity. Figure 10: Boxplots showing error in % for the twelve features/parameters from the control system test data.
# Check for special characters and punctuation
## 5 Discussion
### Detection Resistance  
The steganalysis results, visualized in Figure 4, show consistently lower detection rates across all tested methods. Our system achieves a detection accuracy of 0.635 against CNN-based detection, compared to 0.911 for RNN-Stega. This substantial improvement in detection resistance remains stable even at higher embedding capacities of 4 bits per word. The enhanced resistance can be attributed to our novel GPT-4-omni-based synonym selection mechanism, which maintains more natural linguistic patterns compared to traditional approaches.
# Dosimetric Optimization of a Laser-Driven Irradiation Facility Using the G4-ELIMED Application
## 2 Materials and Methods  
### The G4-ELIMED Application  
A dedicated Monte Carlo application has been developed to simulate the full ELIMED beamline and, in particular, to assess the dosimetric features of the ion beam on the user sample [16]. The Geant4 (GEometry ANd Tracking) toolkit [19; 20; 21], version 10.03, was selected as the most appropriate code for the ELIMED transport and dosimetry beamline simulation for its robustness, versatility, and reliability of the implemented physical processes.  
The G4-ELIMED application realistically reproduces each element of the beamline, both in terms of geometry and magnetic features; it includes the detectors for the beam diagnostics and dosimetry (e.g., the SEM detector and the Faraday cup) and allows to retrieve complementary key information, such as secondary radiation emission along the beam transport section, ion dose distributions at the irradiated sample, and many others.  
Since the ELIMED beamline was designed and realized to work in a wide range of applications (e.g., radiation chemistry like pulsed radiolysis of water [22; 23], nuclear physics for generation of isotopes for Positron Emission Tomography (PET) [24; 25], cultural heritage using proton activation analysis (PAA) techniques [26], and material science through radiation stress-tests, including electronics for space application [27; 28]), the beamline setup can be easily modified, thus the simulation tools should support the exploitation of such beamline modularity. The user-friendly interface of the code allows its simple use also by non-expert users.  
In this work, all the simulations were carried out with \(10^{5}\) initial particles and a maximum simulation step of 50 \(\upmu\)m. These values were chosen to obtain high-quality results while maintaining a reasonable computational time.
# Retrieval of Nearshore Bathymetry around Ganquan Island from LiDAR Waveform and QuickBird Image
## 3 Materials and Methods
### Research Area and Data
#### 3.1.4 Auxiliary Data  
The auxiliary data used in this study mainly included tide tables for the period covering 2012 and 2013 published by National Marine Scientific Data Center. The time period of the LiDAR data measured onsite was approximately 3 h, and the tides changed considerably during those periods. Therefore, performing a tidal correction on each water depth value converted from the waveform data was necessary to ensure the accurate use of control points and validation points.
## 5 Research Challenges and Future Directions
### Latency Minimization  
As the number of BSs increases, the possibility of transmission delay increases. Real-time processing capabilities of suggested methods may be significantly assisted by understanding the effect of transmission and scheduling delays. Further research is required in this area to analyze the theoretical implications of delay-sensitive traffic for hybrid coordinated multipoint in a HetNet. The performance-delay tradeoff due to coding across several fades should also be investigated. Offloading tasks and resources to the edge of the network using appropriate edge computing and proactive caching technologies is an attractive approach to reducing network latency in a HetNet [162]. Furthermore, collaborative user processing may reap the benefits of the Bs processing and the user processing, allowing for the real-time operation of delay-sensitive applications while also managing the enormous volume of data at the HetNet.  
One of the biggest obstacles for mission-critical communications is the propagation delay, which has a major effect on the system performance in HetNet communications. The time it takes for a signal to travel from a BS to a user is known as the propagation delay. In addition, while deciding on transmission settings, the propagation delay must be considered. As the user radio channel in HetNet communications is subject to fast oscillations over time, the user may be unable to decode the sent data or may experience an undesirable QoS after the propagation time has gone [163]. Imperfect channel estimation over cellular networks has been the subject of numerous recent publications. The effects of these variables on the user channel and strategies for dealing with sudden changes in the channel by taking propagation delay into account may need to be explored in future studies to guarantee uninterrupted service.
## 5 Conclusions
Therefore, we must note that while there are similarities between dam crest surveys and other infrastructure surveys (like roads and tunnels), there are also significant differences that must be accounted for at every step, from acquisition to processing and interpretation. While the 200 MHz antenna was successful in gathering sufficient and varied information for both Gura Apelor and Herculane dams, this does not imply that it will be equally effective for other types of dams; embankment dams are made of local materials, an aspect which implies the variety in terms of permittivity values of the constituent materials, whereas for concrete dams, these may pose different metallic materials on the crest and would interfere with the data collection [45] even more than was the case at the Herculane dam. It can be said that the GPR method proves to be fast, non-invasive, and effective in providing dam crest conditions and important information about the internal structure of both types of dams built in Romania, concrete arch dams and embankment dams, which can aid at better understanding the behaviors of these types of constructions. We note an area of potential subsidence, also highlighted in previous topo-geodetic surveys, as well as one area of potential rebar damage. We highlight several areas where a more detailed monitoring is suggested and note this as a potential contribution of GPR surveys in this type of environment.