esa-sceva/satcom-chunk-collection huggingface dataset:
# Radioactivity in Soils of Kosovo and Radiological Implications
## 3 Results and Discussion
### Assessment of Radiological Health Risks  
Statistical descriptors of AGDE, ADR, AEDE, and ELCR radiological health risk levels were determined according to SGS-ANN simulation data for all of Kosovo. The smallest and highest of the mean levels of AGDE, ADR, AEDE, and ELCR were determined in the provinces of Gjilan (AGDE = 248 uSv/year, ADR = 37 nGy/h, AEDE = 46 uSv/year and ELCR = 0.16 \(\times\) 10\({}^{-3}\)) and Mitrovica (AGDE = 296 uSv/year, ADR = 43 nGy/h, AEDE = 53 uSv/year and ELCR = 0.18 \(\times\) 10\({}^{-3}\)), respectively.  
The mean AGDE value was found to be 269.8 uSv/year. These values are lower than the world mean (300 uSv/year) [49]. However, the maximum values calculated in all provinces except Gjilan were found to be higher than the global average. The mean of the absorbed dose rate (ADR) in the air, at 1 m above the ground, generated from gamma radiation of \({}^{226}\)Ra, \({}^{232}\)Th, \({}^{40}\)K, and \({}^{137}\)Cs in the soil samples of Kosovo resulted in 39.3 nGy/h, and this is 27% lower than the recommended values by UNSCEAR [28].  
In addition, the average AEDE of the soil samples that resulted was 48 uSv/y. It is lower than the average world value of 70 uSv/y [28]. The calculated values of the excess lifetime cancer risk (ELCR) for all soil samples ranged from 0.1 \(\times\) 10\({}^{-3}\) to 0.21 \(\times\) 10\({}^{-3}\), with a mean of 0.17 \(\times\) 10\({}^{-3}\). The average world value of ELCR is 0.29 \(\times\) 10\({}^{-3}\)[28]. The average ELCR acquired in this study was lower than the world's mean values. Furthermore, the distribution map of these parameters calculated for Kosovo is shown in Figure 7 and Table 3.  
Figure 6: The values of radiological hazard parameters, based on measured and simulated data from the soil.
# Application of External Torque Observer and Virtual Force Sensor for a 6-DOF Robot
## Materials and Methods
### Machine Learning-Based Robot Dynamic Model
#### 3.2.2 Robot Friction Model  
Through the mathematical equations of dynamics, the ideal torque of the arm can be calculated based on its current posture and velocity. However, friction is an inevitable factor in real environments. Therefore, an additional mathematical equation for friction needs to be established in order to accurately estimate the torque of each axis. The method for establishing the friction equation is to collect the actual torque of each axis during constant velocity motion and calculate it with the estimated values calculated by the dynamic model. Then, based on the calculated friction, a trend chart related to velocity can be drawn, such as in Figure 3. According to the trend chart, there is a correlation between the velocity of each axis and the friction value. By analyzing the relationship between the velocity and friction of each axis motor in both the forward and reverse directions, it is possible to deduce the linear equation for the friction of each axis motor. It was found that the friction levels in the forward and reverse directions of the motor were not equal.  
This paper aims to convert the approximate straight lines obtained from Figure 3 into mathematical formulas. These formulas will then be used as transformation formulas for calculating friction force and velocity. By combining them with the dynamics equation, it is possible to estimate the torque on each axis, including the friction force.
## 2 Materials and Methods
### Numerical Analysis  
A three-dimensional finite element model of IRE was developed using COMSOL Multiphysics 5.4 software (COMSOL Inc., Palo Alto, CA, USA), which was used to investigate the electric field distribution created by the voltage applied to the electrodes and the  
Figure 1: Model of gastrointestinal tract and triangular interdigitated electrodes (IDEs). The gastrointestinal track contains the mucosal layer, submucosal layer, and muscularis propria. Triangular IDEs are located on the outermost part of the balloon catheter and are in contact with the gastrointestinal track.  
resulting electromagnetic heating from current flow. The electric field was determined by solving the governing Laplace equation [14]:  
\[\
abla\cdot(\sigma\
abla\varphi)=0, \tag{1}\]  
where \(\varphi\) is the voltage (V) and \(\sigma\) is the electrical conductivity of the tissue (S/m).  
The temperature distribution was solved using the modified [PERSON] bioheat transfer equation [14; 28]:  
\[\
abla\cdot(k\
abla T)+\omega_{b}c_{b}(T_{a}-T)+Q_{m}+\sigma|E|^{2}=\rho c_{t} \frac{\partial T}{\partial t}, \tag{2}\]  
where \(k\) is the thermal conductivity of the tissue (W/m K), \(T\) is the temperature (K), \(\omega_{b}\) is the blood perfusion rate (1/s), \(c_{b}\) is the specific heat capacity of blood (J/kg K), \(T_{a}\) is the arterial temperature (K), \(Q_{m}\) is the metabolic heat generation in the tissue (W/m\({}^{3}\)), \(\rho\) is the tissue density (kg/m\({}^{3}\)), and \(c_{t}\) is the specific heat capacity of the tissue (J/kg K). The joule heating term (\(\sigma|E|^{2}\)) was averaged over the entire duration of the treatment instead of calculating the heating from each individual pulse.  
The tissue electrical conductivity was determined using the following equation to incorporate changes occurring due to electroporation and the thermal effect [14]:  
\[\sigma_{d}(E,T)=\sigma_{0}\big{[}1+Aflc2 hs(normE-E_{delta},E_{range})+\alpha(T- T_{0})\big{]}, \tag{3}\]  
where \(\sigma_{0}\) is the baseline electrical conductivity, \(A\) is the multiplication factor, \(\alpha\) is the conductivity-temperature coefficient, \(T_{0}\) is the physiological temperature, and \(E_{delta}\) is the threshold electric field in electroporation. \(flc2 hs\) is a smoothed Heaviside function defined in COMSOL software, which changes from zero to one over the range of \(\pm E_{range}\). \(A\), \(E_{range}\) and \(E_{delta}\) are given in Table 1. The conductivity-temperature coefficient was taken as 0.02 (1/K) in this study. The value of 0.02 (1/K) was chosen from the literature [29; 30].
## 3 Quality Control of Silicon Carbide Printed Part Formation
### Densification Post-Processing Techniques
Through continued optimization of carbon control strategies (such as PRBI) and the development of advanced polymer-derived precursors, BJ-printed SiC components have demonstrated flexural strengths of 257 \(\pm\) 14.26 MPa at room temperature and 380 MPa at 1673 K, fully satisfying the mechanical performance requirements for nuclear fuel cladding and non-load-bearing structural parts in aerospace applications. With further advances in this field, both the mechanical and thermal properties of BJ-fabricated SiC components are expected to continue improving.
# Effectiveness of High-Intensity Therapy in Rehabilitation of Parkinson's Disease Patients
## 2 Methods
### Outcomes
#### 2.2.2 Balance and Posturography Outcomes  
Improvements in balance and gait performance were measured using standardized tools such as the Berg Balance Scale (BBS), Dynamic Gait Index (DGI), Balance Evaluation Systems Test (BESTest), and the Tinetti Assessment Tool (TAT). Posturography metrics were analyzed to evaluate center of pressure (COP) displacement, sway area, and velocity changes, providing insights into the participants' balance stability.
## 5 Simulation Results and Discussions
### Impact of Node Mobility Speed
Overall, Traj-Q-GPSR consistently demonstrates superior link maintenance and transmission reliability across the entire speed range, significantly enhancing system throughput in dynamic network conditions.
# Cooperative Downlink Listening for Low-Power Long-Range Wide-Area Network
## 4 IoT Query Dissemination Based on CDL  
CDL is capable of enabling not only downlink broadcasting by sharing cooperatively a downlink slot among devices, but also data-centric communication utilizing group address field efficiently. In this section, we introduce groupcasting and geocasting in association with CDL.
# A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks
## 4 Hybrid Training
### Deployment  
In the deployment phase, the pre-trained models acquired from the training phase are applied in real-time scenarios. During each timestep \(t\), individual Vehicle-to-Vehicle (V2V) agent \(k\) estimate their local channel state. They then leverage their observations of the surrounding environment, incorporating the knowledge gained through the training process, to select actions based on their trained Q networks. These models encapsulate learned behaviors and optimized decision-making strategies cultivated during the training phase. The deployment procedure in Algorithm 2 is executed online and, based on environment conditions, the trained DQN of the agent only updates when it experiences significant changes in the environment.  
In the context of live communication instances, V2V agents employ these pre-trained models to estimate local channel characteristics in real time. Leveraging these observations and the acquired decision-making strategies helps agents make informed decisions to optimize communication links. The overarching goal is to ensure efficient spectrum sharing among V2V entities, thereby contributing to the overall effectiveness of the communication network.
# Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones
## 6 Experimental Results
### Setup and Datasets
#### 6.1.1 Camera-IMU Calibration/Evaluation Dataset  
The dataset of BoardF consists of multi-view checkerboard data, in which the checkerboard is always laid horizontally with the X or Y axis pointing directly north and multi-view data are captured around the checkerboard about every 15 degrees. The specific placement of the checkerboard satisfies the needs of [63], which requires an ideally vertical or horizontal checkerboard, and the needs of [50], which requires a checkerboard aligned to the north. We organized BoardF into two sub-datasets: originally, the data were captured in a hall and a balcony separately and were therefore divided into two subsets named hall and balcony. Either hall or balcony can be used to perform the camera-IMU calibration; however, we prefer to use them together (i.e., the dataset of BoardF) to avoid potential overfitting.  
BoardL was collected for calibration using our method. The restrictions on the checkerboard orientation applied in BoardF are removed, and the downtilt and azimuth angles of the checkerboard are treated as the ground truth.  
Both BoardF (including its two subsets) and BoardL are further split in half, with one half serving as the calibration set and the other serving as the evaluation set.  
Motion is a dedicated set for use in camera-to-IMU calibration and synchronization toolbox(CRISP) [44], which requires inputs consisting of video data and gyroscope readings. We note that the implementation of CRISP as provided by its author is built using fixed-ratio gyroscope data; however, because the gyroscope in an Android phone works in an event-based manner, we resampled the gyroscope readings as suggested by the author.
# Difference DV_Distance Localization Algorithm Using Correction Coefficients of Unknown Nodes
## 6 Conclusions  
In order to reduce the sensitivity of DV_Distance algorithms to ranging error and further improve the localization accuracy, a difference DV_Distance localization algorithm using correction coefficients of unknown nodes is proposed, and the proposed algorithm use the correction model based on  
Figure 10: The change curve of mean square deviation with maximum communication radius.  
Figure 9: The change curve of average localization error with maximum communication radius.  
unknown nodes, and the information from unknown nodes to anchor nodes is incomplete, so it cannot calculate the correction coefficient directly. The proposed algorithm adopts the difference of the accumulated hop distance and the actual Euclidean distance between anchor nodes to indirectly calculate the correction coefficients for different direction anchor nodes, and the final correction coefficients of unknown nodes can be obtained after weight processing according to their actual contribution, reflecting the difference of correction coefficients from different unknown nodes. The final simulation experiments demonstrate that the proposed algorithm has higher localization accuracy and the localization stability is also improved.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper and [PERSON] helped to revise the paper.  
This research was funded partly by [National Natural Science Foundation of China] grant number [U1604151], [Outstanding Talent Program of Science and Technology Innovation in Henan Province] grant number [174200510008], [Program for Scientific and Technological Innovation Team in Universities of Henan Province] grant number [16 IRTSTHN029], [Science and Technology Project of Henan Province] grant number [182102210094], and [Natural Science Project of Education Department of Henan Province] grant number [18A510001].  
The authors declare no conflict of interest.
## 3 Results
### DEMATEL Analysis of Expert Opinions  
The DEMATEL method was applied to the questionnaire in stages to analyze the impact relationships of defects. First, for the collected questionnaire values, the arithmetic mean was calculated using equation (1), and this was used to generate the generalized matrix (_A_; Table 10). Second, to normalize the generalized matrix (_A_), the maximum value was calculated using equation (2) and applied to equation (3), thus deriving a normalized matrix (_N_). Third, the TRM (_T_) was calculated (Table 11) by multiplying \(N\) by the inverse matrix of the unit matrix (equation 4). Fourth, this study calculated (equation 5) for the sum of columns (D) and the sum of the rows (R), (D+R), and (D-R) factors in the TRM (_T_) of Table 12. Fifth, the threshold value was calculated using equation (7) to get a value of 0.4928. Values smaller than the threshold values in matrix (_T_) were identified as having no  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**No.**} & \multicolumn{3}{c}{**Embedded Expert Respondents**} & \multicolumn{3}{c}{**Survey Analysis Result of Cronbach’s \(\alpha\)**} \\ \cline{2-9}  & \multirow{2}{*}{**Field of Embedded Software Development**} & **Number of Years of Embedded Software Experience** & **Respondent Class or Certification** & **Scale Average Class or Life This Item** & **Scale Distribution if This Item was Deleted** & **Scale** & **Cronbach’s \(\alpha\) if This Item was Deleted** \\ \hline R1 & IP CCTV, IoT, Device driver & 10 & PE. * & 26.639 & 98.358 & 0.765 & 0.895 \\ R2 & Device driver & 6.5 & PE. * & 26.882 & 99.615 & 0.736 & 0.897 \\ R3 & Industrial device & 6 & PE. * & 27.778 & 103.559 & 0.312 & 0.911 \\ R4 & Mobile, IoT & 20 & IT auditor & 27.333 & 101.231 & 0.534 & 0.902 \\ R5 & Mobile, IoT & 9.5 & IT auditor & 27.222 & 99.447 & 0.676 & 0.898 \\ R6 & Mobile & 10.5 & IT auditor & 27.132 & 97.346 & 0.671 & 0.897 \\ R7 & Mobile & 14 & PE. * & 27.681 & 102.680 & 0.584 & 0.901 \\ R8 & Mobile, IoT & 12 & PE. * & 27.208 & 99.649 & 0.546 & 0.902 \\ R9 & IoT & 8 & Top engineer & 27.111 & 98.155 & 0.632 & 0.899 \\ R10 & Network cam, & 7 & PE. * & 26.757 & 98.843 & 0.717 & 0.897 \\ R11 & Mobile, industrial device control & 8 & Top engineer & 27.819 & 101.743 & 0.478 & 0.904 \\ R12 & Industrial control & 8.5 & PE. * & 27.882 & 98.748 & 0.569 & 0.901 \\ R13 & Mobile & 10 & PE. * & 27.882 & 98.748 & 0.569 & 0.901 \\ R14 & Intrusion Prevention & 6 & PE. * & 27.569 & 97.939 & 0.477 & 0.906 \\ R15 & IoT & 5 & Top engineer & 26.604 & 98.507 & 0.693 & 0.897 \\ R16 & Home automation & 5 & Top engineer & 27.167 & 96.252 & 0.616 & 0.900 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Summary of [PERSON]’s \(\alpha\) analysis results for survey of embedded expert respondents.  
impact, whereas larger values have an impact. Finally, the factor (D-R) in Table 12 is set on the \(y\)-axis, while the factor (D+R) is set on the \(x\)-axis. These are then used to draw the impact relationship map.
# An Optimal Power Control Strategy for Grid-Following Inverters in a Synchronous Frame
## 3 Discrete LQR-ORT Controller Design
### Main Grid Power Contribution Calculation Using Superposition Principle  
As \(V_{\mathrm{gdq}}\) is not considered in (12) to compute the LQR-ORT controller, its effects on the injected power must be subtracted from the raw reference signal \(\left[\begin{array}{cc}P_{ref}&Q_{ref}\end{array}\right]^{T}\) to obtain the net power reference \(r[k]\) as shown in Figure 3. Thus, the superposition principle must be applied to the closed-loop system:  
\[X[k+1]=\left(\overline{A}_{T}-\overline{B}_{T}K_{d}\right)X[k]+\overline{B}_{1 T}K_{\
u}\mathrm{vr}[k]+\overline{B}_{2T}V_{\mathrm{gdq}}. \tag{19}\]To analyze the power contribution of \(\rm V_{gdq}\), the closed-loop system (19) must be simulated with \(\rm r[k]=\left[\begin{array}{cc}0&0\\ \end{array}\right]^{T}\). The power contribution of \(\rm V_{gdq}\) is computed using the expression  
\[\rm Y_{V}=\left[\begin{array}{c}\rm P_{V}\\ \rm Q_{V}\end{array}\right]=\left[\begin{array}{cc}\rm V_{gd}&0\\ 0&-\rm V_{gd}\end{array}\right]\left[\begin{array}{c}\rm\stackrel{{ =}}{{I}_{\rm od}}\\ \rm\stackrel{{=}}{{I}_{\rm oq}}\end{array}\right] \tag{20}\]  
where \(\rm\stackrel{{=}}{{I}_{\rm od}}\) and \(\rm\stackrel{{=}}{{I}_{\rm oq}}\) are the output current values when (19) reaches steady state with nominal values of \(\rm V_{gdq}\). Since \(\rm Y_{V}\) and \(\rm K_{v}\
u\) are computed offline based on ideal components, there may be slight deviations in the experimental steady-state output values. To avoid these deviations, the integral of the power error is added to \(\rm r[k]\) as in Figure 3. This integral is calculated using a low-gain \(\rm K_{s}\) to not affect stability margins nor transient response. The net reference \(\rm R[z]\) is then defined as follows:  
\[\rm R(z)=\left[\begin{array}{c}\rm P_{ref}\\ \rm Q_{ref}\end{array}\right]-\left[\begin{array}{c}\rm P_{V}\\ \rm Q_{V}\end{array}\right]+\frac{\rm K_{s}T_{s}}{z-1}\left[\begin{array}{c} \rm P-P_{ref}\\ \rm Q-Q_{ref}\end{array}\right] \tag{21}\]  
where \(\rm R(z)=\ Z[r[k]]\), with \(\rm Z[\cdot]\) being the \(z\)-transform operator. Terms \(\rm P\) and \(\rm Q\) represent the unfiltered measured power injected to the main grid.
## 2 Materials and Methods
### Moisture Ratio, Drying Rate and Mathematical Model  
Having the initial moisture content of the product and its weight loss, the moisture and mass transport during the drying process can be determined by Equation (1) [15]:  
\[MR=\frac{M_{t}-M_{e}}{M_{b}-M_{e}} \tag{1}\]  
Furthermore, the drying rate of the carrot slices was calculated by Equation (2) [5,16]:  
\[DR=\frac{M_{t+\Delta t}-M_{t}}{\Delta t} \tag{2}\]  
where \(MR\) is the moisture ratio (dimensionless), while \(M_{t}\) and \(M_{e}\) are the instant sample moisture and the equilibrium moisture (d.b.), respectively. \(M_{b}\) also denotes the initial moisture of the samples (d.b.). \(M_{t+\Delta t}\) is the moisture at t + \(\Delta t\) (g-water/g-dry-matter). \(\Delta t\) represents the time interval (min) and \(DR\) indicates the drying rate (g-water/g-dry-matter per min).  
The majority of the thin-layer drying models of the agricultural, food, and pharmaceutical products rely on \(MR\). \(MR\) can result in more uniform data and prevent their scattering [17]. Among the various drying models, five drying models shown in Table 2 were employed to model the agricultural product drying process whose data were compared with the experimental results of the present study. The thin-layer drying model was then selected as it exhibited a better coefficient of determination (\(R^{2}\)) and root mean square error (RMSE) values according to Equations (3) and (4):  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{N}\left[MR_{\text{exp},i}-MR_{\text{pre},i} \right]^{2}}{\sum\limits_{k=1}^{N}\left[\frac{\sum\limits_{k=1}^{n}MR_{\text{ pre},i}}{N}-MR_{\text{pre},i}\right]^{2}} \tag{3}\]  
\[RMSE=\left[\frac{1}{N}\sum\limits_{i=1}^{N}\left(MR_{\text{pre},i}-MR_{\text{ exp},i}\right)^{2}\right]^{\frac{1}{2}} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Acronym**} & \multirow{2}{*}{**Description**} & \multicolumn{3}{c}{**Air**} & \multicolumn{3}{c}{**Power (W)**} \\ \cline{3-8}  & & & **T (\({}^{\circ}\)C)** & **V (m/s)** & **MW** & **INF** & **US** \\ \hline
1 & HD & Hot air dryer & 65 & 1 & 0 & 0 & 0 \\
2 & INFD & Infrared dryer & 0 & 0 & 0 & 500 & 0 \\
3 & MWD & Microwave dryer & 0 & 0 & 450 & 0 & 0 \\
4 & INF–HD & Infrared-assisted hot air dryer & 65 & 1 & 0 & 500 & 0 \\
5 & MW–HD & Microwave-assisted hot air dryer & 65 & 1 & 450 & 0 & 0 \\
6 & US+HD & Ultrasound-assisted hot air dryer & 65 & 1 & 0 & 0 & 70 \\
7 & US+INFD & Ultrasound-assisted infrared dryer & 0 & 0 & 500 & 70 \\
8 & US+IMWD & Ultrasound-assisted microwave dryer & 0 & 0 & 450 & 0 & 70 \\
9 & US+INF–HD & Ultrasound-assisted infrared-hot air dryer & 65 & 1 & 450 & 0 & 70 \\
10 & US+MW–HD & Ultrasound-assisted microwave-hot air dryer & 65 & 1 & 0 & 500 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the drying method.  
where \(N\), \(z\), \(MR_{\text{exp,i}}\) and \(MR_{\text{pre,i}}\) denote the number of observations, the number of the dryer constants, and the predicted and experimentally obtained \(MR\) of the i\({}^{\text{th}}\) sample, respectively.
## 3 Proposed Multiple Stationary Human Targets Detection Method
### Step A: Pre-Processing
#### 3.1.3 Advance Normalization  
Because the distance between the target and the radar antenna is generally very long, the energy scattered by the target is weak. In addition, the low reflectivity of the human body and the high signal attenuation of obstacles make the energy of the respiration signal recorded by the radar weak.  
The advance normalization (AN) method is used to enhance the weak respiration signal [47]. The basic idea of the method is to search the maximum in the interval \((t_{\text{Lmax}},t_{\text{end}}]\) serially and normalize the current signal in the interval \((t_{\text{Lmax}},t_{\text{Nmax}}]\), where \(t_{\text{Lmax}}\) represents the fast-time of the maximum value found last time, \(t_{\text{Nmax}}\) is the fast-time of the maximum value found this time, and \(t_{\text{end}}\) is the last fast-time of the measured impulse response.
## 3 Results and Discussion
### Dissolved Trace Elements
#### 3.2.4 Zinc  
A similar, yet less pronounced trend was evident for Zn in previously published literature [18; 19; 20] for site 1: the values in those earlier studies ranged between 0.19 and 6.8 mg/L of dissolved Zn compared to the mean concentration of 0.11 mg/L in this study. Analogous to Ni, Zn potentially co-precipitates with Fe, which would explain the comparatively lower concentrations in our post-treatment study. In all other sampling sites, significantly lower mean concentrations were measured compared to site 1. The SA-WQG report for aquatic ecosystems proposed a TWQR of \(\leq\)0.002 mg/L and a CEV of 0.0036 mg/L dissolved Zn, which were surpassed in every measurement at all sampling sites. The AEV of 0.036 mg/L was also exceeded at least once at every sampling site and in the mean content of sites 1 and 3. This indicates that, according to the guidelines, the system and especially site 1 is still heavily polluted with Zn, potentially causing adverse chronic and acute effects.