esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related work
### Overview of IEEE 802.15.4g
#### 2.1.1 Sun-FSK  
The SUN-FSK modulation has two main advantages: the first one is the good power efficiency, due to the constant envelope of the signal, and the low implementation complexity, and the second one is the compatibility with legacy systems. For example, most of the deployed systems for smart metering in the United States are based on FSK modulation schemes, mainly the ones that use the 902-928 MHz frequency band.  
SUN-FSK may be used in several frequency bands, which makes it suitable for different regions. In particular, the 2.4 GHz band is allowed worldwide, and the 863-870 MHz band is allowed in Europe. Three different operation modes are defined for each frequency band. The operation mode defines the modulation and channel parameters, such as the modulation type (2-FSK or 4-FSK), the channel spacing, and the modulation index.  
In the 863-870 MHz band, three data rates are supported depending on the operation mode selected: 50 kbps, 100 kbps, and 200 kbps. The higher data rate is achieved when using 4-FSK. However, only Mode 1, which uses 2-FSK and provides a data rate of 50 kbps, is mandatory. In this mode, the total number of channels is 34, with a channel spacing of 200 kHz.  
At the physical layer, the PSDU (PHY Service Data Unit) can be optionally processed by an FEC (Forward Error Correction) encoder. Two types of FEC may be applied: an RSC (Recursive and Systematic Code) or an NRNSC (Non-Recursive and Non-Systematic Code). FEC shall be employed on the PHR (PHY Header) and PSDU bits, applying a 1/2-rate coding with constraint length \(K=4\). Interleaving shall be employed in conjunction with NRNSC coding and may also be employed with RSC coding. The use of data whitening is optional.  
The SUN-FSK PPDU (Physical Protocol Data Unit) is composed of an SHR (Synchronization Header) with a preamble, a SFD (Start of Frame Delimiter), a 16 bit PHR, and a payload of up to 2047 bytes. In the PHR, the FCS (Frame Check Sequence) length is defined, as well as the frame length. There is one bit to indicate if data whitening is used, and another one to indicate the occurrence of mode switching. Mode switching is a mechanism that allows changing the symbol rate or modulation scheme used by the radios.
# Optimization of Distribution Network Current Protection for Inverter-Based Distributed Power Access
## 5 Current Protection Optimization Enhancement Program
### Judgment 1  
In distribution networks, access to distributed power sources changes the current characteristics during short circuit faults. Based on this phenomenon, an adaptive threshold \(I_{h1}\) can be introduced to optimize the protection mechanism. This approach can improve the response speed and accuracy of the protection system while reducing the risk of false operation.  
Through the real-time monitoring and dynamic adjustment of threshold \(I_{h1}\), the characteristics of the fault current can be captured more accurately, thus realizing the efficient protection of the distribution network. The criterion is considered to be satisfied if the line current exceeds \(I_{h1}\), as follows:  
\[I_{h1}=K_{rel}I_{load} \tag{17}\]  
where \(I_{load}\) is the real-time load current, which can be represented by a dynamic monitoring system that continuously tracks and reflects the current load flowing in the grid; \(K_{rel}\) is the reliability factor, \(I_{h1}\); and the initial value is \(K_{rel}I_{load}\) (note: \(I_{load-N}\) is the \(I_{load}\) rated value). In order to prevent transient fluctuations in the current from triggering unnecessary adaptive adjustments, a condition is set that the adaptive threshold is adjusted accordingly only if the load current varies by more than 5%.  
In summary, the adjustment of the adaptive threshold can be defined by the following expression:  
\[\frac{\left|I_{\text{load}-new}-I_{\text{load}-old}\right|}{\text{I}_{\text{ load}-old}}>5\% \tag{18}\]  
\[I_{h1}=K_{rel}I_{load-new} \tag{19}\]where \(\text{I}_{\text{load}-old}\) is the load current before the change, and \(\text{I}_{\text{load}-new}\) is the load current after the change.  
According to Equation (17), it is only 1.3 times the line current, so \(I_{h1}\) is significantly smaller than \(I_{set-c}\) and \(I_{set-z}\) from the sensitivity formula. When the line current decreases, the optimization method can improve its protection sensitivity.
###### Abstract  
This work considers the path planning problem of personal mobility vehicle (PMV) for indoor navigation using the Artificial Potential Field (APF) method. The APF method sometimes suffers from an infinite loop problem during the planning phase when the goal is blocked by obstacles with certain characteristics. To address the issue, this study deploys the map augmentation method for replanning. When infinite loop situations occur, the map is transformed and the search for drivable path is initiated. The method successfully generates a feasible trajectory when the map is rotated at a certain angle. The scenario of successful planning is shown in the result.  
map transformation; artificial potential field; lidar data +
Footnote †: journal: Applied Sciences
## 4 Discussion
### Recommendations for Veterarians  
* _Early development:_ Based on our findings, which indicated a higher fracture incidence in younger canines, veterinarians should advise owners of young dogs (aged 1.5 years and below) to regulate their physical activity to prevent bone and joint injuries. Emphasis should also be placed on providing a nutrient-rich diet to support optimal skeletal development.
* _Small-breed considerations:_ Given the significant prevalence of fractures in extra-small breeds observed in our study, it is advisable for owners of these breeds to implement safety precautions at home, such as using specific padding to mitigate risks from falls or jumps.
* _Domestic safety:_ Our analysis showed a notable number of fractures due to domestic accidents. Therefore, it is important for owners to minimize potential domestic hazards, ensuring a secure environment for their dogs.
* _Sporting dogs:_ There is a correlation between high activity levels and fracture risk. Veterarians should recommend appropriate warm-up and cool-down routines tailored to each breed to prevent muscle and joint injuries.
* _Elderly dog care:_ With a higher fracture incidence noted in older dogs, especially due to reduced sensory perception, modifying care routines to include safer walking paths and attentive supervision is essential.
* _Understanding canine body language:_ Considering the data showing fractures from dog-to-dog interactions, educating owners to understand canine body language can help prevent such incidents.
# Effect of Deacidification Treatment on the Flavor Quality of Zaosu Pear-Kiwifruit Wine
## 3 Results and Discussion
### Effect of Deacidification on Aromatic Profile of Pear-Kiwifruit Wine
Figure 4: Effects of deacidification treatment on aromatic profile of pear–kiwifruit wine: (**A**) commonly and uniquely aroma compounds in pear–kiwifruit juice and corresponding wines with and without deacidification treatment; (**B**) total concentration of aroma compounds; (**C**) esters; (**D**) alcohols; (**E**) volatile fatty acids; (**F**) terpenoids. Error bars indicate the SD (\(n=3\)). Values with different letters are significantly different (\(p<0.05\)) among the samples. The total concentrations of volatile fatty acids in all samples were below the off-flavor threshold of 20 mg/L, and no significant difference was observed between wines before and after MLF (Figure 4E). This indifference may be attributed to the different responses of individual compounds (Supplementary Table S5) to biosynthesis and esterification during MLF. Octanoic acid was the most abundant fatty acid in all wines, with the highest concentration (341 mg/L) in MLF wine (Table 4), in agreement with previous reports [12; 36]. Octanoic acid decreased by 79.2% after chemical deacidification and, consequently, the total fatty acid content decreased. We speculate that the CO\({}_{2}\) formed by deacidification agent treatment accelerates the volatilization loss of this compound. MLF significantly increased the total concentration of terpenoid alcohols (80.7 mg/L), whereas chemical deacidification decreased it (Figure 4F). Linalool, citronellol, and \(\alpha\)-terpineol are important contributors to the fruity aroma of pear-kiwifruit wine, accounting for 60% of the terpenoids after alcoholic fermentation (Supplementary Table S5). Presumably because of glycoconjugate hydrolysis, the content of the three main terpenoid alcohols increased significantly after MLF (Table 4), which is consistent with the high \(\beta\)-D-glucosidase and diglycosidase activities produced by \(O\). _oeni_ GF-2 [13]. In contrast, chemical deacidification decreased the concentrations of all terpenoids (Supplementary Table S5); the pH change after deacidification may have reduced the stability of terpenoids, such that degradation reduced their accumulation.
## 4 Experiments and Results
### Evaluation Metrics  
For the CrowdHuman dataset, we used average precision (AP), recall, log-average miss rate (denoted MR\({}^{-2}\)), floating-point operations per second (FLOPS), and frames per second (FPS) for performance evaluation. For the WiderPerson dataset, the evaluation was conducted using the official metric AP and MR\({}^{-2}\). AP represents the average detection accuracy for all images belonging to a specific category, and a higher value indicates better detection performance. Recall measures how many positive samples are correctly predicted, reflecting the network's ability to detect positive samples, and a higher value is better. MR\({}^{-2}\) is derived by plotting a curve with FPPI on the horizontal axis and log (MR) on the vertical axis [40]. Nine FPPI values are uniformly sampled within the range [0.01,1], and the corresponding logarithmic miss rate values were calculated. The average of these values was then computed, and an exponential transformation applied to express it as a percentage, representing the miss rate (MR\({}^{-2}\)). Lower MR\({}^{-2}\) values indicate superior detector performance. The formulas for precision (P), recall (R), and average precision (AP) are as follows:  
\[P=\frac{TP}{TP+FP} \tag{19}\]  
\[R=\frac{TP}{TP+FN} \tag{20}\]  
\[AP=\frac{\sum_{n}Precision}{n} \tag{21}\]  
where TP represents the number of true-positive detections, FP denotes the number of false positives (background regions incorrectly identified as targets), FN indicates the number of false negatives (actual targets misclassified as background) and n represents the total number of images belonging to a certain class.  
MR\({}^{-2}\) is an evaluation metric optimized for pedestrian detection tasks, particularly suitable for small-target detection in dense scenarios. The calculation formula is:  
\[\text{MR}^{-2}=\text{exp}(\frac{\sum_{i=1}^{\text{N}}(\text{MR}(\text{FPPI}_{i })}{\text{N}}) \tag{22}\]  
where FPPIi denotes the FPPI value at the i-th sampling point within the interval [10\({}^{-2}\),10\({}^{0}\)] (i.e., FPPI ranging from 0.01 to 1) sampled at logarithmic intervals of 10\({}^{0.25}\). N represents the total number of sampling points.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline CPU & Intel® Xeon® Platinum 8373 (36 cores @ 2.6 GHz) \\ RAM & 64 GB \\ Image Resolution & 640 \(\times\) 640 pixels \\ Batch Size & 32 \\ Training Epochs & 300 \\ Optimizer Method & Adam \\ Learning Rate & 0.01 (initial) \\ Weight Decay & 0.0005 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hardware and implementation details.
###### Abstract  
Monitoring training load, recovery, and wellness in maturation ages is even more relevant than in adulthood, especially during congested schedules. The objective of this study was to quantify and describe the differences in external and internal load, recovery, and perceived wellness in elite under-16 female players during a period of three competitions in seven days based on their participation during matches. An observational design was used to quantify the external load (total distance, high-speed distance, and PlayerLoad) and internal load (differential sRPE), as well as the recovery and wellness of 18 international under-16 players during a development tournament. The external load pattern of the starting players was undulating, with matches showing the highest internal load values compared to training sessions and non-starters. Non-starter players (whether training or matches) showed similar external load and differential sRPE values throughout the tournament (\(p>0.05\)). Starting players experienced higher internal load than non-starters during matches (\(p<0.037\)), without differences between training sessions (\(p>0.05\)). No changes were seen in wellness or recovery (all \(p\)-values \(>0.05\)), except for non-starters in Session 3 compared to Session 7 (\(p=0.024\)). Starting players had more weekly total minutes (\(p=0.018\)), differential sRPE (breath: \(p=0.002\); leg: \(p=0.002\); cognitive: \(p=0.003\)), total distance (\(p=0.003\)), and PlayerLoad (\(p=0.008\)) than non-starters. Our results indicate differing load patterns between starters and non-starters, which do not affect wellness and recovery differently. Starters accumulate more weekly total load. Coaches can use these findings to guide decisions and load compensations in high competition contexts for young female soccer players.  
microcycle; periodization; GPS; RPE; sport; soccer; youth; women; maturation; training load +
Footnote †: journal: Applied Sciences  
1 Exercise and Sport Sciences, Faculty of Health Science, Universidad Francisco de Viloria, 28223 Pozuelo, Spain 12 Royal Spanish Football Federation, Las Rozas de Madrid, 28232 Madrid, Spain; [EMAIL_ADDRESS]  
2
# Siberian Pine and Larch Response to Warming-Drying Climate in the Southern Boundary of Their Range
## Materials and Methods
### Satellite Data Analysis  
#### 2.5.1 Landsat-Based Analysis along the Elevation Transset  
Landsat time series data (1988-2023) were analyzed to detect changes in trees' and shrubs' cover along the elevation transect (Figure 1). For this purpose, Landsat-5 scene (19 September 1988) and Landsat-9 scene (27 August 2023) were used. A topo normalization was applied for both scenes using a C-correction [38]. We removed burned forest areas using the GFLDF product (2001-2022) [39]. We used a support vector machine algorithm to generate a land cover classification map. We generated a training sample set based on our ground survey data and freely available high-resolution scenes (Google Maps, ArcGIS World Imagery, and Yandex Maps). We focused on the determination of the forested and shrulband areas. We estimated the classification accuracy based on the error matrix and KHAT (\(\kappa\))-statistics [40]. We used classification maps to find the zones of forest and shrulband gains and losses. We considered the following classes: (1) forest gain, (2) shrulband gain, (3) forest loss, and (4) shrulband loss. We estimated shifts of the upper boundaries of forests and shrubs as differences between 99 th percentiles of the area distributions concerning elevations.
## 2 Method
### Calibration Model
#### 2.3.2 Non-Linear Method  
The variables required to predict the sensor's wrench include a total of 11 parameters: the CoM vector of the end tool, the incline angle of the base, and the elements of the crosstalk matrix. To determine these variables, we design an objective function as shown in Equation (10) and aim to minimize it using a non-linear optimization technique.  
\[w^{err}=\left\|\mathbf{w}_{f,i}^{sensor}-\left(\left[\begin{array}{c}\mathbf{ f}_{f}^{est}\\ \mathbf{\tau}_{f}^{est}\end{array}\right]_{i}+\mathbf{w}_{f}^{bias}+\mathbf{C} \begin{bmatrix}\mathbf{\tau}_{f}^{est}\\ \mathbf{0}_{3\times 1}\end{bmatrix}_{i}\right)\right\|^{2}, \tag{9}\]  
\[H(\mathbf{X})=\sum_{i=1}^{n}w_{i}^{err},\quad\text{where}\quad\mathbf{X}= \left[\begin{array}{cccccccccccccccc}d_{x}&d_{y}&d_{z}&\alpha&\beta&c_{1}&c_{2 }&c_{3}&c_{4}&c_{5}&c_{6}\end{array}\right] \tag{10}\]  
By using the predicted values obtained through Equations (3) and (4), along with the bias values from Equation (5), we minimize the error with respect to the sensor data. Defining \(w^{err}\) as in Equation (9), we set the objective function \(H\) as the summation of the \(w_{i}^{err}\) values across \(n\) specified postures, allowing us to find the variables through gradient descent [18]. In general, performing gradient descent on \(n\) data points for an \(m\)-dimensional vector over \(k\) iterations is known to have a time complexity of \(O(n\cdot m\cdot k)\). In our case, the optimization process involves 60 data points and an 11-dimensional vector. Since the optimization was completed within fewer than 1000 iterations during the experiment, the computational cost was not a significant burden. The cases where CoM, crosstalk, and inclination are calibrated using Equation (10) are labeled as _Full Calibration_ for the following experiments.
###### Abstract  
This paper presents a two-level hierarchical control method for the power distribution between the hybrid energy storage system (HESS) and the main dc bus of a microgrid for ultrafast charging of electric vehicles (EVs). The HESS is composed of a supercapacitor and a battery and is an essential part to fulfill the charging demand of EVs in a microgrid made up of a 220 V\({}_{\text{RMS}}\) ac bus, two dc buses of 600 V and 1500 V, respectively, and four charging points. A state machine defines the four operating modes of the HESS and establishes the conditions for the corresponding transitions among them, namely, charging the battery and the supercapacitor from the bus, injecting the current from the HESS into the 1500 V dc bus to ensure the power balance in the microgrid, regulating the bus voltage, and establishing the disconnection mode. The primary level of the control system regulates the current and voltage of the battery, supercapacitor, and dc bus, while the secondary level establishes the operating mode of the HESS and provides the appropriate references to the primary level. In the primary level, sliding mode control (SMC) is used in both the battery and supercapacitor in the inner loop of a cascade control that implements the standard constant current-constant voltage (CC-CV) charging protocol. In the same level, linear control is applied in the CV phase of the protocol and for bus voltage regulation or the current injection into the bus. PSIM simulations of the operating modes and their corresponding transitions verify the theoretical predictions.  
address=Astridge, Martin-Salamero, LaTeX,  
variation in the electrical architecture of the EV and in the levels of current and voltage handled inside, and therefore it precluded the adoption of many production standards.  
In a clear-cut contrast, the charging of EVs has remained in the same categorization envisaged in the first years, i.e., domestic charging, fast charging, and ultrafast charging. Domestic charging uses an on-board battery charger that is connected to the ac mains and requires several hours to complete the charging process. Fast charging is based on a dc voltage supply handling power between 50 kW and 150 kW, and it results in charging times between 10 and 30 min. Ultrafast charging is performed from a dc bus providing a minimum value of power of 350 kW, reducing the charging time to less than 10 min [2].
# Game Algorithm for Resource Allocation Based on Intelligent Gradient in HetNet
###### Abstract  
In order to improve system performance such as throughput, heterogeneous network (HetNet) has become an effective solution in Long Term Evolution-Advanced (LET-A). However, co-channel interference leads to degradation of the HetNet throughput, because femtocells are always arranged to share the spectrum with the macro base station. In this paper, in view of the serious cross-layer interference in double layer HetNet, the Stackelberg game model is adopted to analyze the resource allocation methods of the network. Unlike the traditional system models only focusing on macro base station performance improvement, we take into account the overall system performance and build a revenue function with convexity. System utility functions are defined as the average throughput, which does not adopt frequency spectrum trading method, so as to avoid excessive signaling overhead. Due to the value scope of continuous Nash equilibrium of the built game model, the gradient iterative algorithm is introduced to reduce the computational complexity. As for the solution of Nash equilibrium, one kind of gradient iterative algorithm is proposed, which is able to intelligently choose adjustment factors. The Nash equilibrium can be quickly solved; meanwhile, the step of presetting adjustment factors is avoided according to network parameters in traditional linear iterative model. Simulation results show that the proposed algorithm enhances the overall performance of the system.  
heterogeneous network (HetNet); co-channel interference; Stackelberg game; spectrum resource allocation; gradient algorithm +
Footnote †: journal: Computer Phys. Commun.  
16 December 2016 27 February 2017 28 February 2017
# Elevation Angle Characterization for LEO Satellites: First and Second Order Statistics
## 3 Methodology for the Elevation Angle Characterization
### Results Validity
#### 4.1.4 Elevation Angle CDF for Different Satellite Characteristics  
Simulations for this paper where performed for a satellite with the mass and drag areas listed in Table 1, nonetheless, satellites with different values in those characteristics will share very similar functions \(F_{\Theta}(\theta)\). Figure 14 shows the empirical CDF's for two satellites with the same orbital parameters but with different mass and drag areas. For both satellites, the orbit altitude, \(h\), was set to 1800 km and the orbit inclination, \(i\), to 60\({}^{\circ}\), and both ES's were located at 25\({}^{\circ}\) N. As shown in Figure 14, variations in the satellite mass and drag area characteristics will have a very little impact in the elevation angle CDF's. For lower altitudes the effect is greater, but not significant enough for the altitudes covered in this paper. Table 10 show the mass and areas for satellites shown in Figure 14.
# Effect of Hydrogen Ion Presence in Adsorbent and Solution to Enhance Phosphate Adsorption
## 2 Material and Methods
### Fixed Column Tests  
The pH effect on the phosphate was tested by comparing the breakthrough behavior for the GAC and the GACA using a fixed-bed column (3 cm in diameter, 7 cm in bed height, and 49.6 mL in bed volume) in the down-flow mode. The influent was set to 1 mg/L of phosphate and an initial pH of 7.2, operated with an empty bed contact time (EBCT) of 49.6 min and a superficial liquid velocity (SLV) of 0.09 m/h. The effluent was automatically collected and the pH was also measured to verify the pH shift during adsorption.