esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantifying Sub-Elite Youth Football Weekly Training Load and Recovery Variation
## 2 Materials and Methods
### Methodology  
The outfields players were monitored, resorting to a portable GPS throughout the whole training session (STATSports Apex(r), Newry, Northern Ireland). The GPS device provides raw position velocity and distance at 18 Hz sampling frequencies, including an accelerometer (100 Hz), a magnetometer (10 Hz) and a gyroscope (100 Hz). Each player kept this micro-technology inside a mini pocket of a custom-made vest supplied by the manufacturer, which was placed on the upper back between both scapulae. All devices were activated 30 min before the training data collection to allow for an acceptable clear reception of the satellite signal. Concerning the optimal signal for the measurement of human movement, the match data considered eight available satellite signals as the minimum for the observations [35]. The validity and reliability of the global navigation satellite systems (GNSS), such as the GPS tracking, have been well established in the literature [36; 37; 38]. The current variables and thresholds should consider a small error of around 1-2% reported in the 10 Hz STATSports Apex(r) units [36].
# Detecting Suicidal Ideations in Online Forums with Textual and Psycholinguistic Features
## 4 Results
### Classification Performance
#### 4.1.2 Experiment 2 (Feature Selection)  
In Experiment 2, we investigated the impact of Boruta feature selection on the classification performance. The aim was to ascertain whether models trained with selected features achieved higher performance compared to models trained with all features. Figure 3 illustrates a comparison between the mean performance metrics of the models trained using all features and those trained with Boruta-selected features. Each subplot within the figure represents a distinct feature type.  
Figure 3: Average performance scores of the models trained with different types of features: (**a**) TF-IDF; (**b**) LIWC; (**c**) NRC.  
Figure 2: Performance scores of models trained with different types of features: (**a**) logistic regression; (**b**) naive Bayes; (**c**) random forest; (**d**) support vector machines.  
As shown in Figure 2(a), when feature selection was applied to TF-IDF features, it resulted in an increased performance across all metrics. Specifically, the average accuracy increased from 73.76% to 83.54% and precision from 62.05% to 69.85%. In particular, the highest increments were observed in recall and F1 scores. Recall increased by 29.14%, from 38.66% to 67.80%, while F1 score grew by 25.52% from 42.59% to 68.11%. Out of all TF-IDF-trained models, RF experienced the largest F1 score gain of 37.68% from 28.12% to 65.80%. [PERSON] reduced the TF-IDF features from 5902 to 32, accounting for about 0.54% of the original features. Moreover, feature selection led to a 14-fold reduction in the TF-IDF-trained models' average training time.  
Similarly, when feature selection is applied to LIWC features, improvements in accuracy, precision, recall, and F1 score were observed, although to a lesser extent, as shown in Figure 2(b). The average accuracy increased from 75.16% to 77.90%, precision from 59.98% to 62.71%, recall from 60.49% to 60.98%, and F1 score from 56.46% to 59.06%. Among the LIWC-trained models, NB showed the highest F1 score increase of 5.99%. The number of LIWC features was reduced from 108 to 22 (approximately 20% of the original features), while the LIWC-trained models' training time decreased by 2.43-fold.  
In contrast, applying feature selection to NRC features led to a decrease in all performance metrics, as illustrated in Figure 2(c). There was a slight decline in accuracy by 0.35%. However, a more prominent reduction was observed in precision, decreasing by 12.21% from 56.33% to 44.12%. Recall and F1 score dropped by 4.51% and 6.76%, respectively. Boruta reduced the NRC features by half, from 22 to 11. The average training time decreased by 20% from 53.65 ms to 43.02 ms. In summary, Boruta feature selection improved the classification performance of models trained with TF-IDF and LIWC features but resulted in a decreased performance among NRC-trained models.
# Optimal Particle Filter Weight for Bayesian Direct Position Estimation in a GNSS Receiver
## 8 Simulations and Real-World Results
### Impact of Different Code Delay Bias Variances  
Basically, the expected measurement accuracy \(\sigma_{w}\) strongly depends on the amplitude of the correlation value \(|P|\) and thus on the coherent integration time \(T_{coh}\) and the GNSS signal strength. However, with the introduction of a theoretical lower limit of the measurement accuracy in Equation (40) defined by \(\sigma_{\Delta\tau}\), the estimated accuracy \(\sigma_{w}\) must approach the lower limit \(\sigma_{\Delta\tau}\) with an increasing correlation value \(|P|\). This behaviour is shown in three consecutive plots in Figures 6-8 with an increasing coherent integration time \(T_{coh}=[1,10,100]\) ms. From this series, it is clearly visible that \(\sigma_{w}\) also approaches a very small \(\sigma_{\Delta\tau}=0.1\) m in the case of a long coherent integration time \(T_{coh}=100\) ms.  
Figure 5: Impact of different GNSS signal amplitudes on the correlation values \(|P|\) and resulting probability function for a constant coherent integration time \(T_{coh}\) and constant code delay bias standard deviation \(\sigma_{\Delta\tau}\). Higher signal strengths result in a smaller weighted standard deviation \(\sigma_{w}\) of the probability function.  
The magnitude of \(\sigma_{\Delta\tau}\) should cover residual user range errors (coming from orbit, satellite clock, multipath or ionospheric errors), which can be in the range of several meters.  
Figure 6: Impact of different code delay bias standard deviations \(\sigma_{\Delta\tau}\) on the weights \(w_{k}^{i}\) for a coherent integration time \(T_{coh}=1\) ms. For the given conditions, only the weighted standard deviation \(\sigma_{w}\) for \(\sigma_{\Delta\tau}=6\) seems to approach the theoretical lower limit of \(\sigma_{\Delta\tau}\). In particular, \(\sigma_{\Delta\tau}=0.1\) can not be reached due to the influence of the low correlation time \(T_{coh}\) at given \(C/N0\).  
Figure 7: Impact of different code delay bias standard deviations \(\sigma_{\Delta\tau}\) on the weights \(w_{k}^{i}\) for a coherent integration time \(T_{coh}=10\) ms. It can be seen that \(\sigma_{w}\) approaches the theoretical limits for \(\sigma_{\Delta\tau}=3\) and \(\sigma_{\Delta\tau}=6\).
# Extreme Droughs in the Peruvian Amazon Region (2000-2024)
## 3 Results  
### Annual Precipitation and Evaporation in the Study Areas of the Peruvian Amazon  
The first approximation to the problem of droughs in the Peruvian Amazon is the analysis of the interannual precipitation variability in the study period. Figure 2 shows the records of annual precipitation and evaporation for the six study areas from each dataset. A general feature is the relatively low interannual variability of the evaporation record, whose variations are hardly noticed in the figure, except for the decreasing trend in the last two years of the record in all of the study areas, which has been identified as a characteristic for the entire Amazon basin in 2024 [3]. The interannual variability is mainly dominated by precipitation, with differences across datasets, particularly the low precipitation values estimated by CHIRPS for the LOR3 and MOY areas in the first half of the period (2000-2009). The annual precipitation maxima correspond to HVs 2007-2008, 2011-2012, and 2014-2015 for most areas, and the minima were found in 2005-2006, 2009-2010, 2015-2016, and 2023-24. In general, over almost the entire period, precipitation in the areas of the Loreto department was greater than in the rest of the areas, particularly in LOR1 and LOR2, which will be shown to be consistent with fewer drought conditions. Annual precipitation is almost always well above annual evaporation, but it must be emphasized that this is not held for monthly precipitation, which undergoes periods where evaporation surpasses precipitation, mainly in the dry seasons, which can be associated with drought and cause the cumulative water deficit.  
The average annual cycle of the water deficit in the different study areas and estimated by different precipitation datasets is shown in Figure 3.  
Figure 2: Interannual variability of precipitation (Pr) and evaporation (Et) records for the six study areas, obtained from ERA5 (precipitation and evapotranspiration) and the precipitation datasets CHIRPS and MSWEP for the period 2000–2024. The color lines in the legend refer to the different data sources.  
Most of the water deficit is obtained for the dry season, including the June-August period, which is the end of the hydrological year, and particularly in August, which is its last month. This is especially evident in the observational datasets MSWEP and CHIRPS (Figure 3B,C), while the semi-observational ERA5 shows more dispersion but with a clear mode in August (Figure 3A). This behavior differs from the Brazilian Amazon region, for which the hydrological year ends in September [1].
## 4 Discussion
### Advantages and Disadvantages of Wearable Devices  
Like most other equipment or software, wearable devices/sensors have advantages and disadvantages. The main advantage of these devices/sensors is that they can be easily used in any field environment (increasing the ecological validity); thus, coaches/teachers can obtain feedback (positive or negative) on the measured variables in real time and report the performance level of players/athletes/students, whether in training, game or class [36]. Furthermore, the data obtained through these devices/sensors can serve a multitude of purposes, always based on the objectives outlined at the beginning of each study, such as: (i) measuring, controlling, and increasing the physical performance of players/athletes/students; (ii) preventing possible injuries caused by excessive overload (in training or game); (iii) preventing the early return to training/game of injured players/athletes; (iv) monitoring and predicting the performance development of younger players/athletes. In addition, these devices/sensors are programmed/manufactured to operate in any sports environment (outdoor or indoor venues) because they are small, light, wireless, and easy to transport [36]. Finally, some devices/sensors have additional features, such as waterproofing or memorizing data at low temperatures [37; 38].  
These wearable devices/sensors also have disadvantages that researchers and coaches should consider when using. For example, [PERSON] et al. [38] concluded in their study that only 5% of wearable technologies (mobile apps and equipment designed to provide biofeed-back to players/athletes/students) used in various studies were officially validated through independent research and that the institutions/companies that manufacture this equipment should invest more to prove the reliability/validity/effectiveness of their products [38].
# Cross-Layer-Aided Opportunistic Routing for Sparse Underwater Wireless Sensor Networks
## 3 System Model
### Channel Model  
#### 3.2.1 Energy Propagation Model  
In this paper, two propagation models are exploited. First, a theoretical model is used to analyze the effectiveness of control packets. Second, we use the Bellhop model in our simulation, because it has a good approximation of an actual underwater channel environment [32].  
Theoretical model: The path loss is related to the distance \(d\) and frequency \(f\)[33], which can be represented as  
\[A(f,d)=A_{p}d^{k}\alpha(f)^{d} \tag{1}\]  
where \(A_{p}\) is the transmission anomaly that represents the degradation of the acoustic intensity and scattering of sound [34; 35; 36]. Generally, \(A_{p}\) is a log-normally distribution for time-varying shallow water. The parameter \(k\) denotes the spreading factor that describes the extended geometric shape (\(k=2\) for spherical spreading, \(k=1\) for cylindrical spreading), the absorption coefficient \(\alpha(f)\) can be empirically expressed as below [33].  
\[10\log_{10}\alpha(f)=0.11\frac{f^{2}}{1+f^{2}}+44\frac{f^{2}}{4100+f}+2.75 \cdot 10^{-4}f^{2}+0.003 \tag{2}\]  
Bellhop model: Bellhop software, based on ray theory, can accurately reflect the attenuation of the underwater channel. We exploit this software to compute the path loss of information and spatially varying interference [32; 37]. When using Bellhop, some specific sea information is required, such as SSP, the bathymetric profile, and the type of bottom sediments, which can be obtained from the World Ocean Atlas [38], General Bathymetric Chart of the Oceans [39], and National Geophysical Data Center Deck41 database [40], respectively.  
Figure 1: Network architecture.
# Research on Demand-Based Scheduling Scheme of Urban Low-Altitude Logistics UAVs
## 5 Conclusions  
Based on the improved simulated annealing algorithm and considering time cost, risk cost, maneuvering cost, and other related factors, this paper constructs a de-mand-based scheduling scheme model for urban low-altitude logistics drones. This model cannot only make the drone requirements reach the ideal stage but also calculate the drone requirements according to changes in relevant parameters.  
This paper uses the RN/BN network to construct the scheduling scheme model and compare it with other algorithms. The experiments show that the model proposed in this paper outperforms other models in terms of distribution cost and distribution time and can better find out the optimal distribution route and optimal distribution cost. The improved simulated annealing in this paper improves the solution by 5-9% compared with the standard simulated annealing, which shows the rationality of the improved simulated annealing and also provides a reference basis for the logistics drone configuration in distribution centers and a reasonable scheduling method for future drone logistics, facilitating its normalization.  
Figure 8: Schematic diagram of relationship between drone quantity/charging time and delivery cost, where (**a**) represents the RN route and (**b**) represents the BN route.  
The drawback of this article is that it did not fully consider safety factors and did not dynamically consider factors such as the safety interval and collision risk of drones. Although the two routes in this article consider relevant factors in path planning, specifically within the route, only the ideal situation is considered in this article. In future research, we will attempt to study the impact of other complex parameters on logistics drones by including safety considerations.  
At present, due to the lack of relevant data on urban low-altitude drone logistics distribution, actual drone distribution data are lacking. Particularly, there is little research on the air distribution network of urban low-altitude logistics, and the relevant data are insufficient. We can only assume the drone air distribution network through a few channels and simulate and estimate the drone distribution data. The next step will be to study the drone scheduling scheme combined with actual drone data and then verify the accuracy of the scheme.  
Conceptualization, H.Z.; Investigation, O.F.; Data curation, T.T.; Writing--original draft, S.W.; Visualization, Y.H.; Supervision, G.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (71971114) and the Fundamental Research Funds for the Central Universities (No. NQ2022012).  
Not applicable.  
The authors acknowledge with thanks the support for this work by the College of Civil Aviation at Nanjing University of Aeronautics and Astronautics.  
The authors declare no conflict of interest.
# End-to-End Light Field Image Compression with Multi-Domain Feature Learning
## 3 Method  
### Architecture of MFLFIC-Net  
To enhance the compression efficiency of light field images, a MFLFIC-Net is proposed, which exploits multi-domain features and their correlation to obtain a complete angle feature and reduce the redundancy among multi-domain features. Specifically, the large disparity angle information contained in the EPI domain is explored to obtain a complete angle feature, which contains angle information on the complete disparity range. In addition, the intrinsic correlation between spatial and complete angle features is further exploited to reduce redundant information in features.  
As shown in Figure 1, the proposed network consists of three parts: an encoder, an entropy coding, and a decoder. The encoder is employed to generate a latent representation from the original image. In the entropy coding phase, the latent representation is compressed into a bitstream for transmission. Finally, the decoder, which consists of a spatial-angle joint transform module and reconstruction module, is utilized to reconstruct the image from the reconstructed latent representation. In detail, the feature extractors are first used to map the original light field image \(x\) to the feature space for obtaining spatial feature \(f_{S}\), angle feature \(f_{A}\), and EPI feature \(f_{E}\). Subsequently, the E-ACM is designed to obtain the complete angle feature \(f_{A^{*}}\). Then, the SAJTM is designed to effectively reduce the redundant information between \(f_{S}\) and \(f_{A^{*}}\), and obtain the compact spatial feature \(f_{S}^{c_{2}}\) and the compact angle feature \(f_{A^{*}}^{c_{2}}\). After that, \(f_{S}^{c_{2}}\) and \(f_{A^{*}}^{c_{2}}\), are channel-concatenated and pass through a convolution layer to obtain the latent representation \(y\). Then, \(y\) is entropy encoded and decoded by the entropy coding. Finally, the reconstructed latent representation \(\widehat{y}\) passes through the decoder to obtain the reconstructed light field image \(\widehat{x}\).
# Optimal Non-Integer Sliding Mode Control for Frequency Regulation in Stand-Alone Modern Power Grids
## 8 Conclusions  
This work investigates a hybrid microgrid, in which the DGs and RESs are integrated to construct a complex plant with advanced functionality. A novel model-based FOSMC technique is designed for the LFC of the concerned MG with high penetration of renewable systems. Since the effectiveness of the model-based technique depends on the controller's coefficients, a hybrid SCA-HS algorithm is suggested and implemented to tune the coefficients optimally. In the sense of the Lyapunov criterion, the theoretical analysis is conducted to guarantee the stability of the suggested LFC-based MG. Furthermore, hardware-in-the-loop experiments have been carried out in this study to justify the feasibility of the FOSMC controller in a real-time environment. To show the supremacy of the suggested technique, the dynamic behavior of the test MG with the FOSMC controller is compared with the MPC and SMC approaches. Experimental outcomes confirm that the suggested technique successfully handles the aforementioned challenges of the MG, and outperforms the considered techniques.  
[PERSON] and [PERSON] conceived and designed the controllers; [PERSON] wrote the paper and analyzed the data; [PERSON] contributed materials/analysis tools; [PERSON]-H.K. performed the experiments.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 10: Percentage of improvement of the suggested technique over the MPC and conventional SMC controllers.  
Figure 9: Peak overshoot and undershoot of frequency deviation using various control strategies.
## 3 System Design
### Haptic Vest  
#### 3.1.1 Design of Haptic Vest and Driving Method of Voice Coil Type Vibrators  
Figure 2 shows the hardware of the prototype haptic vest. 80 haptic reactors (Alps Alpine), which are voice-coil type vibrators with high response, are used in the prototype, and they are spaced evenly at 50 mm intervals. The two-point discrimination threshold for the back was set at 50 mm, and the area where the vibration stimulus was presented was set at a distance equivalent to this two-point discrimination threshold [1].  
The vibrators are connected to 10 cascaded 8-bit shift registers (TPIC6B595) through a microcontroller (ESP32) by serial connection, enabling the 80 vibrators to be driven independently (Figure 3). The data from the PC to the microcontroller include the output of the 80-stage shift register and the vibration intensity parameter. The data received in the microcontroller is projected onto the spatial arrangement of the vibrators built into the haptic vest. When the output of 10 bytes to all the shift registers is completed, the outputs of all shift registers are updated.  
As a voice coil vibrator is used instead of an eccentric motor, it is necessary to generate an appropriate AC signal as a pulse. For example, when stimulating a vibration frequency of 400 Hz, it is necessary to generate a 400 Hz square wave, and it is necessary to switch the output of the shift register at least 800 times per second. At least a 64 kHz clock is required to switch the 80-stage shift register. Furthermore, in order to control the strength of vibration by means of pulse width modulation (PWM), it is considered necessary to transmit the data at approximately 2 MHz in order to fine-tune the pulse width with 32 gradations. The shift registers and the SPI communication output of the microcontroller used here can transmit and receive signals at a much faster rate. In the developed system, the voice coil type vibrator can express 32 gradations, and the response speed is 1 ms. The experiments with various users confirmed that when the 80 vibrators were driven independently, all the vibrators could be distinguished and perceived.  
Figure 3: Hardware configuration of the haptics vest.  
Figure 2: (a) 1 Shift Register for each unit drives 8 actuators, (b) 80 voice coil type actuators are built in with 50 mm interval.
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 1 Introduction
### Contributions  
In this paper we study for the first time the coexistence between URLLC and eMBB services in the uplink of a C-RAN system with analog fronthauling in which the URLLC signals are still decoded locally at the EN, while the eMBB signals are forwarded to the BBU over analog fronthaul links.  
Figure 1: C-RAN architecture overview for uplink direction: (**a**) Digital Radio-over-Fiber, (**b**) Analog Radio-over-Fiber, (**c**) Analog Radio-over-Radio, (**d**) Analog Radio-over-Copper.  
In particular, the main contributions of this paper are three-fold:  
* We extend the uplink C-RAN theoretic model proposed in [32] to the case of analog fronthaul, assuming that the fronthaul links are characterized by multiple, generally interfering, channels that carry the received radio signals;
* By leveraging information theoretical tools, we investigate the performance trade-offs between URLLC and eMBB services under both OMA and NOMA, by considering different interference management strategies such as puncturing, considered for the standardization of 5G New Radio [33; 34], Treating Interference as Noise (TIN), and Successive Interference Cancellation (SIC);
* The analysis demonstrates that NOMA allows for higher eMBB information rates with respect to OMA, while guaranteeing a reliable low-rate URLLC communication with minimal access latency. Moreover, differently from the case of conventional digital C-RAN architecture based on limited-capacity fronthaul links [32], in analog C-RAN, TIN always outperforms puncturing, while the best performance is still achieved by NOMA with SIC.