esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
It should be noted that the parameters to be estimated in the combined ISL/SLR POD contain the state parameters and perturbation force parameters of the satellite, the state parameter of the SLR station, and some biases. To be specific, taking a satellite as an example, the state parameters of the satellite mean the initial 3D position and velocity parameters \(\delta x_{0}=\left[\delta x^{0},\delta y^{0},\delta z^{0},\delta v^{0}_{x}, \delta v^{0}_{y},\delta v^{0}_{z}\right]^{\mathsf{T}}\). The perturbation forces parameters \(\delta\mathbf{p}^{s}\) of the satellite usually include nine parameters of the light pressure model. The biases mainly comprise the antenna phase center offsets \(\delta x_{\text{pco\_isl}}=\left[\delta x_{\text{pco\_isl}},\delta y_{\text{ pco\_isl}},\delta z_{\text{pco\_isl}}\right]^{\mathsf{T}}\) and the hardware delay deviation \(\delta x_{\text{bias\_isl}}\) of the ISL observations. It should be noted that there may be some biases with unclear characteristic in the SLR observation, which be ignored in this study and will explored in the future. All parameters to be estimated are written in the following form:  
\[\mathbf{X}=\left[\delta x_{0},\delta\mathbf{p}^{s},\delta x_{str},\delta x_{\text {pco\_isl}},\delta x_{\text{bias\_isl}},\right]^{\mathsf{T}}. \tag{6}\]  
When expanding from a single satellite to multiple ones, observation equations can be formulated according to Equations (2) and (5), in conjunction with the parameters to be estimated listed in Equation (6). The observation equations in combined ISL/SLR POD can be expressed as  
\[ \begin{cases}\mathbf{L}_{isl}=\mathbf{F}_{isl}(\delta x_{o},\delta\mathbf{p}^{s},\delta x_ {\text{pco\_isl}},\delta x_{\text{bias\_isl}},\mathsf{f})+\varepsilon_{isl} \\ \mathbf{L}_{slr}^{bls}=\mathbf{F}_{str}^{bls}(\delta x_{o},\delta x_{str},\mathsf{f})+ \varepsilon_{slr}\end{cases} . \tag{7}\]  
By linearizing the observation equations, the error equations can be obtained as  
\[\left[ \begin{array}{c}v_{isl}\\ v_{slr}^{bls}\end{array} \right]=\left[ \begin{array}{ccc}\frac{\partial\mathbf{F} _{isl}}{\partial x_{0}}&\frac{\partial\mathbf{F}_{isl}}{\partial x_{\text{pco\_isl }}}&0&\frac{\partial\mathbf{F}_{isl}}{\partial x_{\text{bias\_isl}}}&\frac{ \partial\mathbf{F}_{isl}}{\partial x_{\text{bias\_isl}}}\\ \frac{\partial\mathbf{F}_{isl}^{bls}}{\partial x_{0}}&\frac{\partial\mathbf{F}_{slr}^{bls }}{\partial x_{str}}&0&0\end{array} \right]\left[ \begin{array}{c}\delta\mathbf{x} _{0}\\ \delta\mathbf{p}^{s}\\ \delta x_{\text{s}lr}\\ \delta x_{\text{pco\_isl}}\\ \delta x_{\text{pco\_isl}}\\ \delta x_{\text{bias\_isl}}\end{array} \right]-\left[ \begin{array}{c}l_{isl} \\ l_{bls}\\ slr\end{array} \right], \tag{8}\]  
where \(v_{isl}\) and \(v_{slr}^{bls}\) are the posteriori residual for the ISL and SLR observations, respectively. Correspondingly, \(l_{isl}\) and \(l_{slr}^{bls}\) denote the a priori residual. \(\partial()\) is the symbol of partial derivation. The remaining parameters have the same meaning as before. Subsequently, by employing the least squares method to solve these error equations, the combined ISL/SLR POD for BDS-3 can be realized.
# A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras
## 2 Related Work
### Head Tracking and Counting using RGB Datasets  
Many head tracking and counting systems that work on RGB datasets have been developed in recent years. Table 1 gives a detailed account of these systems.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Paper Name** & **RGB Datasets** & **Methodology** & **Classification/Regression Results** \\ \hline A people counting & & The foreground was extracted by some morphological operations. For head detection, an LBP based Adaboost classifier was used. MeanShift algorithm was used for head tracking. Finally, the heads were counted based on the Cross-Line Judgement technique. & People counting accuracy was 96\%. \\ \hline A people counting & & The system detects people from video sequences using HOG and Dalal HOG-SVM Bootstrapping framework. Each pedestrian is tracked using a Kalman filter and each pedestrians motion trajectory is calculated to track each individual. & The system uses three video sequences. Each video sequence is divided into two parts (in and out). Results show the detection accuracy achieved lies between 85–95\%. \\ \hline Vision-based People & & The system was developed for attendance monitoring. The system was divided into three parts. First, the people were detected using the trained deep learning model, i.e., Mobile-Net SSD. Secondly, the Tracking process was implemented using the Correlation Filter and SORT. Finally, people were counted based on reference lines and tracked path. & Accuracy can be measured using the COCO evaluation metric. They acheieved performance score of 83\% was achieved. \\ \hline \end{tabular}
\end{table}
Table 1: Detailed report of head tracking and counting systems that used RGB datasets.
# Deep Learning-Based Method for Classification and Ripeness Assessment of Fruits and Vegetables
## 2 Theoretical Background
### Convolutional Neural Networks  
Convolutional Neural Networks (CNNs) are a type of deep learning algorithm that is ideal for handling image and video processing tasks. They utilize a convolution operation to extract features from the input data. A CNN comprises an input layer, hidden layers, and an output layer. Furthermore, the input layer receives raw input data, such as an image or video, while the hidden layers execute the convolution operation. These layers contain filters, also referred to as kernels, which detect specific features in the input data. The convolution operation involves moving the filters over the input data and calculating the dot product between the filter weights and the input data at each position. The output is a new set of feature maps that display the features of the input data at varying levels of abstraction. One of CNNs' main benefits is their capacity to process spatial information and comprehend pixel relationships in an image. This is accomplished by scanning the entire image with the convolution operation and using pooling layers to efficiently down-sample the feature maps, enabling the network to concentrate on the most pertinent information. [24].
## 4 Discussion  
The SMA springs could be thermally elongated by using enameled wire energized with heat, which could effectively output the squeezing force. Active control of the SMA spring has been achieved. From the experiment, we found that the steady-state temperature of SMA grows rapidly with the increasing power supply, while the output force gradually increases. However, when the current is higher, the martensite phase transformation is too fast, which easily leads to the problem of unstable metallurgical transformation and weakened output force, so how to reasonably control the phase transformation temperature of the SMA will be the focus of further research.  
Furthermore, the shear yield stress of MRF is primarily caused by two parts: the dipole moment between magnetic dipoles and the friction of magnetic particles. According to the squeeze model established in this article, the shear yield stress was mainly related to  
Figure 14: Torque of the transmission device before and after squeeze.  
Figure 13: Squeeze characteristics of the MRF-J01T. (**a**) Deformation relationship of the MRF. (**b**) Relationship between squeeze stress and shear yield stress.  
the squeeze stress and shear strain, and the squeeze strengthening effect of MRF would disappear when the shear strain was too large. Nonetheless, as the squeeze stress increases, the percentage of shear stress due to particle friction increases, and the shear yield stress is further enhanced (in a small deformation range).  
By using electrothermal SMA spring pressurization, the performance of the transmission has risen by 4.88 times, solving the current problem that the torque of the MR transmission device is small. Moreover, when compared to the existing squeezed MR device, the composite device has a simpler structure and a high energy utilization, providing a new design idea for the squeezed MR transmission device.  
As the squeeze model used in this article was based on steady-state pressurization, it failed to consider the effect of transient shocks on the transmission torque. The actual process of transforming the single-chain model to the BCT model was more complicated, which limited the accuracy of the torque equation to a certain extent. In the future, the variation from a single-chain to a BCT structure on any short timescale could be simulated in detail, and the theoretical accuracy would be improved.
# A Meta-Analysis on Dual Protocols for Chronic Stroke Motor Recovery: Robotic Training and tDCS
## 2 Materials and Methods
### Methodological Quality  
Three researchers ([PERSON], [PERSON], and NK) independently conducted methodological quality assessments on the 20 qualified studies using the Physiotherapy Evidence Database (PEDro) rating scale [67]. The PEDro scale for estimating study quality included a checklist of \(11\) items scored yes or no regarding group allocation, blinding, attrition, statistical analyses, and data variability. A fourth researcher ([PERSON]) additionally graded and confirmed final scores when any discrepancies in rating scores across the three researchers appeared. The range of PEDro scores from the 20 studies was 7 to 11 (mean PEDro score = 9.15). Based on previous studies [68; 69], these findings indicate a relatively high methodological quality in this meta-analysis: (1) excellent: 9-10, (2) good: 6-8, (3) fair: 4-5, and (4) poor: below 4. Table 4 shows specific details on all PEDro scores of each domain. \begin{table}
\begin{tabular}{l l l l l l l l} \hline Upper Limb Rehabilitation Criteria & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] \\  & 2015 [51] & 2020 [52] & 2022 [54] & 2018 [55] & 2019 [56] & 2013 [58] & 2011 [59] \\ \hline
1. eligibility criteria were specified & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) & \(1\) \\ \hline
2. subjects were randomly allocated to groups (in a & & & & & & & \\ crossover study, subjects were randomly allocated an order & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ in which treatments were received) & & & & & & & \\ \hline
3. allocation was concealed & 0 & 1 & 1 & 0 & 1 & 0 & 1 \\ \hline
4. the groups were similar at baseline regarding the most & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ important prognostic indicators & & & & & & & \\ \hline
5. there was blinding of all subjects & 1 & 1 & 1 & 1 & 1 & 0 & 1 \\ \hline
6. there was blinding of all therapists who administered & 0 & 1 & 0 & 0 & 1 & 0 & 1 \\ therapy & & & & & & & \\ \hline
7. there was blinding of all assessors who measured at least & 0 & 1 & 1 & 1 & 1 & 0 & 1 \\ one key outcome & & & & & & & \\ \hline
8. measures of at least one key outcome were obtained & & & & & & & \\ from more than 85\% of the subjects initially allocated & 1 & 1 & 1 & 1 & 0 & 1 & 1 \\ to groups & & & & & & & \\ \hline
9. all subjects for whom outcome measures were available & & & & & & & \\ received the treatment or control condition as allocated or, & & & & & & & \\ where this was not the case, data for at least one key & & & & & & \\ outcome were analyzed by “intention to treat” & & & & & & \\ \hline
10. the results of between-group statistical comparisons are & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ reported for at least one key outcome & & & & & & & \\ \hline
11. the study provides both point measures and measures & & & & & & & \\ of variability for at least one key outcome & & & & & & & \\ \hline Total & 8 & 11 & 10 & 9 & 10 & 7 & 11 \\ \hline \end{tabular}
\end{table} 
Table 4: Methodological quality assessment using PEDro Score.
## Materials and Methods
### SURR Model Calibration and Validation  
In the SURR model, the parameters are divided into two types, the physical and conceptual parameters. The physical parameters can be estimated based on the basin and channel characteristics; however, for determining the conceptual model, a calibration and validation process has to be undertaken. There are twelve conceptual parameters in the SURR model, in which only five parameters are sensitive in rainfall runoff simulation [24; 25]. The SURR model was manually calibrated and validated using observed discharge at 3 flow stations, namely Gunnam, Jeonkok, and Jeogseong (Figure 1b). The efficiency criteria used to evaluate the model performance include the root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (CC) and relative error in volume (REV). The details of the model calibration and validation procedures and results of model performance evaluation were presented by [PERSON] et al. [24].  
The evaluation results show that the performance of the model is satisfactory for use in the simulation of streamflow in the study catchment, except for Gunnam station. There are two major reasons for the low performance of the model for Gunnam station. The first one is the high uncertainty of the data interpolated using Thiessen polygon method, as all observed data were only obtained for the Southern area, and the second one is the impact of the Northern upstream dam on the discharge at the station, which is discussed in the present study. According to [PERSON] et al. [28], the average width of the uncertainty interval at Gunnam station was found to be significantly greater than those of the other stations due to the large bias between the mean rainfall and the corresponding interpolated MAP, contributing the high uncertainty intervals of discharge. The large difference in simulated and observed discharge in high flow events due to such reasons causes the high value of REV and the negative value of NSE.  
In addition, information of the periods of releasing water from the dam in the Northern part was collected based on the sudden change of river water level and flow downstream of the dam. However, information of periods of storing water in the dam is difficult to define exactly; hence, to consider this kind of dam impact, flow rate at each station was calculated using Equation (3) and compared between observation and simulation for the same rainfall event after the model was calibrated.  
\[\textit{Flow rate}\left(\%\right)=\frac{\sum_{l=1}^{T}\text{Runoff}}{\sum_{l=1 }^{T}\text{Rainfall}}\times 100\% \tag{3}\]  
where Runoff = Q/A (discharge/area) and Rainfall is mean areal precipitation (MAP) in the same period, and \(T\) is total simulation time in hour.
###### Abstract
The horn aperture actually plays the role of the vibrators in a Yagi-Uda array. Circular polarization is eventually achieved by appropriately adding the transversal field components directly originating from the horn antenna, and from the passive elements, respectively. To test our concept, ten FSS configurations were investigated on the basis of simulation, and the best four were fabricated and successfully validated by means of measurements. Our antenna was designed for applications operating in the X-band around 12 GHz, or in the lower Ku band, typically below 13 GHz. It can be used as a feed element for a parabolic reflector antenna for maritime satellite communication systems, for fixed Earth-to-satellite communications (the uplink is typically performed using the frequency band from 11.45 GHz to 11.7 GHz), or as part of a sensor for finding the direction of a wave in order to point an antenna system in that direction and therefore to ensure maximum power transfer. This paper is organized as follows. Section 2 presents the method for obtaining a dual polarization by using passive radiators, Section 3 presents the proposed radiating system consisting of a pyramidal horn antenna with a layered FSS structure placed in front of the aperture, and Section 4 presents the conclusions.
# Micro-Location Temperature Prediction Leveraging Deep Learning Approaches
## 4 Experiments and Results
### Discussion
* Strong Statistical Evaluation: Model performance was evaluated using MAE, MSE, and Pearson correlation coefficient, ensuring accuracy and consistency were assessed. On the other hand, the method's limitations need to be summarized as follows:
* Limited Number of Input Parameters: The LSTM network uses a restricted set of input features, which may omit critical meteorological factors affecting temperature prediction, increasing the risk of Type II errors. * Temporal Resolution Gap Between Datasets: Differences in sampling frequency (e.g., hourly for CERRA and hourly for ICON-D2, but refreshed on a 3 hourly basis) complicate synchronization and model integration. * High Computational Complexity: Extensive model training and hyper-parameter tuning require significant computational resources and time (approx. 3 h per training phase). * Model Overfitting in Some Configurations: Certain LSTM configurations showed overfitting and poor generalization, especially when the number of parameters outstripped the volume of training data. * Lack of Broader Applicability: The model is tailored to a specific micro-location in Slovenia, limiting its direct applicability to other geographical areas without re-training and recalibration. It is clear that a lot of improvements could be made to better grasp all the details of the micro-location into the model learning process as well as prediction process. We could argue that we don't explicitly define the physics of the model and model resolution but it is all already integrated into the model in the learning phase, as well as the prediction phase. We just recognize the patterns to get as close as possible to the micro-location, where the weather station, as source of reliable data, helps a lot to achieve that goal.
# Remediation of Metal/Metalloid-Polluted Soils: A Short Review
## 4 Removal Techniques
### Phytoextraction  
Phytoextraction is an absorption process of heavy metals and metalloids from soil through the plant roots. Then, the pollutants are transported and accumulated in the aboveground biomass, such as shoots. Bioaccumulation in shoots is advantageous for harvest. [PERSON] et al. [112] studied different non-eatable floriculture plants, i.e., antirrhinum, pansy, calendula, and marigold, in soil contaminated by cadmium, chromium, nickel, and lead. They added a bacterial inoculum and EDTA, finding that both promoted the growth of biomass and the accumulation of contaminants in the roots, and this limited the transfer of contaminants to the shoots.  
Phytoremediation allows to have a cheap and ecologically sustainable approach, but it is slow and depends on plant growth, seasonality, and environmental conditions. For these reasons, phytoremediation is often combined with other removal techniques. Combination is useful for improving removal efficiency, for example, using:  
* phytoremediation + bioaugmentation;
* phytoremediation + EDTA amended soil [112];
* electrokinetic remediation + phytoremediation [113].  
[PERSON] et al. [114] compared biological processes such as natural attenuation, bioaugmentation, and phytoremediation. They found that bioaugmentation-assisted phytoremediation optimizes the synergy effect of plants and microorganisms, improving the removal of heavy metals and metalloids [115].
## 3 ITS Standards and Projects
### ITS Key Enabling Standards
The ETSI ITS standard defines a reference architecture for cooperative vehicular communications, including six main layers [10]: (i) the application layer for the general management of ITS applications (e.g., prioritization, classification, _etc._); (ii) the facilities layer for the support of sessions and data presentation; (iii) the networking and transport layer, including the support of GeoNetworking, IPv6 networking, TCP/UDP transport protocol, _etc._; (iv) the medium access layer with the support of various communication technologies (e.g., IEEE 802.11p, Wi-Fi, 2/3G, LTE, _etc._), as shown in Table 2; (v) the management entity for managing the features of the whole ITS architecture layers; and (vi) the security entity which provides security services. A detailed overview of the ETSI TC ITS standard [10], including its security layer [24], will be provided in Section 5 and analyzed in the context of an ITS safety application use case.
## 2 Methodology
### New Control Methodology
However, the frequency tolerance for the Ecuadorian insular grid is more lenient due to the significant challenges associated with managing massively integrated renewable resources in a low-inertia power system. This scenario has necessitated the exploration of solutions like the one proposed in this study. Figure 2: Schematic diagram of the proposed Fuzzy Logic Controller. Reproduced with permission from [33]. To be conservative, a broad frequency variation margin was considered when defining the numerical values for the membership function illustrated in Figure 3c, set at \(\pm\)1.5%. This selection is tailored specifically for this system; nevertheless, it is crucial to highlight that the actual system's conditions where this solution is to be implemented should be taken into account to adjust this threshold appropriately. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & & VH & H & M & VL & L \\  & M & MS & S & S & MS & S \\ SoC & VL & VHS & HS & MS & NS & S \\ [\%] & VH & VHS & VHS & MS & VHS & HS \\  & H & MS & HS & S & HS & MS \\  & L & HS & MS & S & S & NS \\ \hline  & VH & VHS & VHS & MS & VHS & HS \\ Frequency & H & MS & HS & S & HS & MS \\  & M & MS & S & S & MS & S \\  & VL & VHS & HS & MS & NS & S \\  & L & HS & MS & S & S & NS \\ \hline \end{tabular}
\end{table} 
Table 1: FLC rules for power concerning the SoC of the BESS and grid frequency. Figure 3: Proposed FLC membership functions for power smoothing control: (**a**) Input 1: Power (**P**), (**b**) Input 2: SoC, (**c**) Frequency, and (**d**) Output: data time window size (WS). Categories include NS (no smoothing), LS (low smoothing), S (smoothing), MS (moderate smoothing), and HS (high smoothing). Input 1 values are represented as VL (very low), L (low), M (medium), H (high), and VH (very high). Input 2 values denote percentages: very low = 10%, low = 30%, medium = 50%, high = 70%, and very high = 90%. Reproduced with permission from [33]. The membership function's chosen frequency values at VL (Very Low) and VH (Very High) reflect this broader margin to accommodate potential fluctuations. These values are not necessarily the maximum standard frequency tolerances set by the electricity provider but are rather selected to ensure robustness in the face of variable operating conditions. The FLC rules define the relationship between power (\(P\)), frequency (\(f\)), and the SoC, determining the time window size (WS). This ensures that the smoothing process adapts to the current conditions of the PV system and the Li-Ion BESS, maintaining optimal power smoothing and grid stability. Figure 4 illustrates the changes in the three inputs and the output during FLC evaluation. The surface is obtained by defuzzifying the tabulated membership functions using the centroid method, which calculates the output by finding the center of the area under the curve of the aggregated fuzzy set. This method provides a precise control action and is ideal for applications requiring fine-tuned and responsive control, such as the proposed power smoothing technique.