esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Assessment of Balance (Static and Dynamic)  
Static balance was determined by assessing the performance on the Balance Error System Score (BESS). BESS is a clinical test used by clinicians and researchers to assess postural stability and balance [24,25]. Assessments were conducted with bare feet, allowing three practice trials before testing. The BESS consists of three stances: double-leg stance with hands on the iliac crests, single-leg stance (nondominant lower limb) with hands on the iliac crests, and tandem stance (nondominant foot behind the dominant foot) in a heel-to-toe fashion. Participants were asked to maintain static balance with eyes closed under 6 different conditions (3 stances on a firm surface and 3 stances on a foam surface) for 20 s each [26]. For scoring, an error is defined as: (1) hands lifted off the iliac crests; (2) opening eyes; (3) a step, stumble or fall; (4) moving the hip into more than 30\({}^{\circ}\) of abduction or flexion; (5) lifting the forefoot or heel or (6) remaining out of the test position for more than 5 s. The maximum possible number of errors in each trial was 10 and the maximum possible number of errors for the total test score was 60 [27]. Dynamic balance was assessed with the Y-balance test. The test was demonstrated and the participants were given four practice trials before the three assessment trials [28]. Each participant performed the Y-balance test barefoot to eliminate additional support. The testing was performed with the participant standing with the heel of the weight-bearing foot in the center of the grid, maintaining the base of support. The participant was instructed to slide the box, reaching as far as possible in the anterior, posteromedial, and posterolateral directions and to return to the initial upright position without losing balance. The reach distance was recorded (foot length was removed in the anterior direction since the heel was aligned with the middle of the grid) [29], and normalized to the participant's leg length (anterior superioriliac spine to inferior medial malleolus) providing a measure of performance [30]. The overall performance of the test, i.e., the composite reach distance, was computed using the greatest reach distance from each direction. The best trial of each direction was considered for analysis. Invalid attempts were considered when the participant: (1) removed his hands from the hip; (2) did not return to the initial position; (3) applied sufficient weight through the reaching foot to gain an increase in reach distance; (4) placed his foot on either side of the tube; (5) lifted or moved the support foot during the test; or (6) pushed or kicked the attachment with the reaching foot to achieve better results. In these cases, the test was discarded and the participant was asked to repeat the test [31].
## 1 Introduction
in depth. The results show that the ground track repeat period of the BDS IGSO (Inclined GeoSynchronous Orbit) and GEO (GEosynchronous Orbit) satellites is one day. As for the BDS MEO (MEdium Orbit) satellites, the ground track repeat period is seven days [17]. In addition, all the previous literature only presented the results of the repeat period, but did not present the results in skyplot. Based on the above, the reason why the MP and CNR are affected by floods is analyzed in theory. In addition, the reason why the satellite repeat period can be calculated for GLONASS satellite is demonstrated in theory and in skyplot. Data sets collected during two floods are used in the experiment. Combing the ground track repeat period of satellite, the influence of floods on the GPS satellite MP signal is analyzed. In addition, the CNR of GPS, GLONASS, and BDS is investigated in depth, and some meaningful conclusions are drawn.
## 2 Nyquist Analysis for Ideal FPR Controller on Voltage Applications
### Stability Analysis Based on a Conceptual Nyquist Trajectory
#### 2.2.6 Analysis  
The following sections collect all previous results (Sections 2.2.1-2.2.5) for the case of using the FPR controller for voltage applications. It should be noted that the analysis is valid when the voltage loop is not affected by any inner loop and the controller is implemented in the continuous-time domain.  
The conceptual Nyquist trajectories are illustrated in Figure 4. In this figure, the solid lines represent the conceptual trajectory of the Nyquist curve. In contrast, the dashed lines serve merely as supporting elements to aid in understanding the asymptotic behaviour of the resonance when the frequency \(\omega\) approaches \(\omega_{0}\).  
It can be seen that the stability is potentially ensured independently of the controller gains for any \(\alpha\) value above \(1\). This is because no encirclement of \((-1,0I)\) is present, and (3) does not have poles with positive real parts. On the contrary, below \(1\), there is a point where the system becomes unstable. In other words, according to the selected \(\alpha\) and system parameters (controllers gains and system values), the system stability is compromised. In fact, small \(\alpha\) values will tend to create a crossing point at the right of \((-1,0I)\), yielding to two clockwise encirclements of \((-1,0I)\). But, if the \(\alpha\) is big enough but still lower than \(1\)  
Figure 4: Conceptual Nyquist trajectories for \(\alpha\in[0,2]\) (solid lines define the Nyquist trajectory, and dashed lines describe asymptotic behaviours).  
the crosspoint will be moved to the left of (\(-1\),0I). This fact supposes one clockwise and one anti-clockwise encirclement of (\(-1\),0I). The result is a stable system.  
Regarding stability margins, it is complicated to provide a qualitative analysis due to the intrinsic asymptotic behaviour of the FPR controller. Thus, it will be treated in the following sections.
# CDL-GAN: Contrastive Distance Learning Generative Adversarial Network for Image Generation
## 3 Methodology: Contrastive Distance Learning
### Enhancement with the Siamese Modules  
Combining Contrastive Distance Learning (CDL) with the Siamese modules, we can make use of the same structure to share the weights and handle the relationship between paired data effectively. The method is shown in greater detail in Algorithm 1. On the one hand, Consistent Contrastive Distance (CoCD) offers a virtuous cycle for the generator exploiting more modes. On the other hand, Characteristic Contrastive Distance (ChCD) forces the discriminator to focus on meaningful visual information. The goal of the Siamese module, \(S_{1}\), is cooperative with the generator, \(G\). Meanwhile, the discriminator, \(D\), is constrained by the module \(S_{2}\), to make training more bidirectionally stable. In the training process, the generator creates two parts of fake data, while the discriminator needs to consider the difference of each real image with the two corresponding generated images. CDL can be balanced by adjusting the parameters of \(\lambda_{gen}\) and \(\lambda_{dis}\).  
```
0: parameters of generator \(\theta_{G}\) and discriminator \(\theta_{D}\), consistent contrastive distance coefficient \(\lambda_{gen}\) and characteristic contrastive distance coefficient \(\lambda_{dis}\), noise \(z\) and noise augment parameter \(\gamma\).
1:for number of training iterations do
2:for\(t=1\) to \(N_{D}\)do
3: sample batch \(Z\sim P(z)\), \(X\sim P_{real}(x)\)
4: sample augment noise \(\Delta z=\gamma*z\)
5: augment latent vector \(A(z)=z+\Delta z\)
6:\(L_{D}\leftarrow(G(z))-D(x)+D(G(A(z)))-D(x)\)
7:\(L_{dis}\gets L_{cod}^{D}(V_{G(z)},V_{x})+L_{cod}^{D}(V_{G(A(Z))},V_{x})\)
8:\(\theta_{D}\leftarrow\text{AdamOptimizer}(L_{D}+\lambda_{dis}L_{dis})\)
9:endfor
10: sample batch \(Z\sim P(z)\)
11: sample augment noise \(\Delta z=\gamma*z\)
12: augment latent vector \(A(z)=z+\Delta z\)
13:\(L_{G}\leftarrow-D(G(z))-D(G(A(z)))\)
14:\(L_{gen}\gets-L_{cod}^{G}\)
15:\(\theta_{G}\leftarrow AdamOptimizer(L_{G}-\lambda_{gen}L_{gen})\)
16:endfor
```  
**Algorithm 1** Contrastive Distance Learning (CDL)
# A Learned Reduced-Rank Sharpening Method for Multiresolution Satellite Imagery
## 3 Results
### Evaluation of Sharpening Quality  
Figure 4 shows the images used in these experiments. The images present a variety of geographic scenes, including coastal, agricultural, rural, urban, and mixed landscapes. The first four images were obtained from the dataset used in [30]. The ground truth images were generated hyperspectral Aviris data. The Aviris images of coastal and mountain areas were low-pass filtered and subsampled by a factor of 2 to achieve a 10 m pixel resolution. The Aviris images of urban and agricultural areas were also low-pass filtered and subsampled by a factor of 3 to achieve a 10 m pixel resolution. The S2 spectral responses and sensor PSFs were then applied to the subsampled Aviris images, and the  
image channels were further subsampled to match the actual pixel sizes of the S2 data. This process ensured that the ground truth images had a consistent 10 m pixel resolution across the different geographic regions and were spectrally and spatially representative of the actual S2 data. Although ground truth data is available for these images, it is solely used for the quantitative evaluation of the methods, not for training. The final image, used for qualitative evaluations, is an unprocessed 324 \(\times\) 324 pixel crop from S2 data captured by Sentinel-2B over Reykjavik, Iceland. The inset shows a closeup of mixed residential and commercial property. For regular S2 images, high-resolution ground truth data are not available.
# UCMAC: A Cooperative MAC Protocol for Underwater Wireless Sensor Networks
## 2 Related Work
Then a cooperator creates a network coded-packet with the source's and destination's data packets and then transmits it to the source and the destination, which allows them to obtain the respective data quickly. Taking into account the impact of PHY layer, the performance of NCCARQ-MAC is further investigated and analyzed under realistic channel conditions, especially for correlated shadowing [20; 21]. In that context, network coding technique contributes to increase in network throughput when bidirectional traffic is dominantly generated. Such technique may be inefficient in sensor networks where unidirectional traffic dominates. Reference [16] proposed a _hybrid_-type cooperative MAC protocol where both of the proactive and the reactive mechanisms are applied. Graded back-off time that depends on a maximum achievable rate makes only the highest-rate cooperator cooperate alone (proactive). If data packet decoding is unsuccessful, the destination sends a negative ACK (NACK) packet to its source and/or the cooperator to request data retransmission (reactive). Prior to the retransmission, handshake of request-to-send (RTS) and CTS packets is performed again. In the UCMAC, a cooperator directly retransmits a data packet eliminating this handshaking procedure to reduce delay. Meanwhile, existing related work for the underwater wireless networks has dealt with several important issues other than MAC layer ones. In [23], [PERSON] et al. proposed a new signaling method called wave cooperative (WC) transmission where a cooperator immediately amplifies and forwards source signals whenever their multipath components pass through. The WC method can outperform existing methods such as the amplify-and-forward (AF) and the decode-and-forward (DF) because a destination can receive high strength multipath components in less time. Reference [24] developed a best-relay (or cooperator) selection criterion, called cooperative best relay assessment (COBRA) to select best cooperators under the varying underwater channel. Rather than using the CSI, the COBRA relies on propagation delays between any pair of nodes in the network and statistical channel parameters when selecting the best cooperator. The researchers in [24] briefly mentioned a methodology for MAC based on RTS-CTS handshaking and the COBRA criterion. Clearly, it is necessary for the cooperative MAC protocols to utilize the RTS-CTS handshaking procedure in acquiring and/or sharing cooperator information.
## 3 Results and Discussion
### Principal Component and Interaction Network Analysis of Different Indexes  
The results of a principal component analysis based on soil chemical indexes, key aroma compounds and their odor characteristics showed (Figure 3) that different indexes could be divided into two principal components, and principal component 1 had the largest contribution rate, ranging from 66.5% to 70.2%, and could effectively distinguish different samples. Secondly, it was found that P1 (soil pH of 3.29) and P2 (soil pH of 4.13) were acidified soils and were mainly distributed at the negative end of principal component 1, while P3 (soil pH of 4.74) and P4 (soil pH of 5.32) were suitable soils for tea tree planting and were mainly distributed at the positive end of principal component 1. A further analysis revealed that all the soil chemical indexes except TP were distributed at the positive end of principal component 1, and eight key compounds and six odor characteristics were distributed at the positive end of principal component 1. The above indexes played a major role in distinguishing acidified soils from non-acidified soils. The results of the interaction network analysis between pH and the chemical indexes of the tea rhizosphere soil showed (Figure 4) that pH was significantly and positively correlated with the CEC and the AN and AK contents; the OM content was significantly and positively correlated with the AP content; the CEC was significantly and positively correlated with the AK content; and there was a significant and positive correlation between the AN, AK and AP contents. It was reported that when the soil pH was below 8.0, the CEC showed an upward trend with the increase in the soil pH, and the larger the CEC, the more beneficial the decomposition of the soil organic matter, the more beneficial the increase in the available nutrient content in the soil and the greater the improvement in the soil's fertility [52; 53; 54]. It was evident that in this study, as the pH of the tea rhizosphere soil increased, the CEC of the soil increased, which promoted the decomposition of the soil organic matter, and then increased the contents of AN, AP and AK in the soil and enhanced the soil's fertility. Figure 3: Principal component analysis of soil chemical indexes, key compounds and the odor characteristics of different samples. Note: P1—soil pH of 3.29; P2—soil pH of 4.13; P3—soil pH of 4.74; P4—soil pH of 5.32. pH—soil pH. OM—organic matter content; CEC—cation exchange capacity; TN—total nitrogen; TP—total phosphorus; TK—total potassium; AN—available nitrogen; AP—available phosphorus; AK—available potassium. Figure 4: Interaction network of soil chemical indexes, key compounds and their odor characteristics based on correlation analysis. Note \(-\) Significant positive correlation; significant negative correlations \(-\) the correlation is not significant. \(-\) pH: soil pH: OM: organic matter content; CBC: cation exchange capacity; TN: total nitrogen; TP: total phosphorus; TX: total potassium; AN: available nitrogen; AP: available phosphorus; AC: available potassium.
# Dynamic Downsampling Algorithm for 3D Point Cloud Map Based on Voxel Filtering
## Experiments  
### Evaluation Metrics  
The point cloud simplification rate is a well-known metric to measure the goodness of point cloud map downsampling. This simplification rate is calculated by (9).  
\[R=\frac{\text{NUM}-\text{num}_{d}}{\text{NUM}} \tag{9}\]  
where NUM is the total number of points in the original point cloud, \(\text{num}_{d}\) is the total number of points in the downsampled point cloud, and \(R\) is the point cloud simplification ratio.
## 4 Case Studies
### Wind Power, Photovoltaic Power and Load Power Forecasting
#### 4.1.4 The Results of Feature Selection  
For wind power forecasting, photovoltaic power forecasting and load power forecasting, MICs between features and true value are calculated respectively, and the features with larger MICs are selected as the input features. The optimal number of input features is determined by an exhaustive search.  
It can be seen from Figure 5 that different numbers of input features will lead to different forecasting results. For wind power forecasting, photovoltaic power forecasting and load power forecasting separately select the number of input features that can minimize the RMSE, which are 13, 8 and 33 respectively, as shown by the red dot in Figure 5.
# Enhanced IoT-Based End-To-End Emergency and Disaster Relief System
## 5 Experimentation
### Results and Discussion
#### 5.2.2 End-To-End Delay and Link Quality Estimation  
Figure 8 depicts the average round trip time delay (\(E2E_{delay}\)) recorded from the OMD to the Labeeb-IoT platform versus \(E2E_{LQE}\). It can be seen that the \(E2E_{LQE}\) decreases with the rise of \(E2E_{delay}\)  
Figure 7: Average TCP and UDP throughput (Mbit/s) and jitter (ms) per hop count.  
Indeed, \(E2E_{delay}\) exceeds 1 s when \(E2E_{LQE}\) reaches less than 0.7 between 1030 and 1070 s. The same behavior appears between 1155 and 1175 s. \(E2E_{LQE}\) and \(E2E_{delay}\) are proportional. An \(E2E_{LQE}\) equal to zero means that the link is disconnected; the same link shows an infinite \(E2E_{delay}\). Figure 8 shows also the effectiveness of the metric used in the ORACE-Net routing protocol (i.e., \(E2E_{LQE}\)). The route update mechanism based on the optimal \(E2E_{LQE}\) then is validated by our experiment. Indeed, ORACE-Net prevents the link quality degradation, then looks for a better route with optimized link quality, delay and disconnection avoidance.
# Design and Implementation of a System-on-Chip for Self-Calibration of an Angular Position Sensor
## 3 Calibration Method Design
### Theoretical Analysis and Implementation Details of the LMSED Module
The operands and some parameters are designed to be read and written by the APB bus. This means that the user can modify the parameter configuration by modifying the algorithm running in the processor. Figure 5: Working principle of the Least Mean Square Error Demodulation (LMSED) algorithm.
## 3 Experimental Section
### Synthesis of Compounds **11-14** from Styrene Derivatives and 1H-Indene
The residue was dried in vacuum, giving the product (0.445 g, quantitative yield) in the form of a yellow powder, mp 73-75 \({}^{\circ}\)C.  
\({}^{1}\)H-NMR (400 MHz, D\({}_{2}\)O): \(\delta\) 2.21 (s, 3H, CH\({}_{3}\)), 3.67 (dd, \(J\) = 14.2, 3.2 Hz, 1H, SCH\({}_{2}\)), 3.89 (dd, \(J\) = 14.2, 2.4 Hz, 11, SCH\({}_{2}\)), 6.72 (m, 3H, NCH, Ar), 7.10 (d, \(J\) = 8.0 Hz, 2H, Ar), 7.84-7.88 (m, 1H, H\({}_{\rm quino}\)), 7.98-8.00 (m, 1H, H\({}_{\rm quino}\)), 8.04-8.08 (m, 1H, H\({}_{\rm quino}\)), 8.19-8.21 (m, 1H, H\({}_{\rm quino}\)), 9.15-9.16 (m, 1H, H\({}_{\rm quino}\)), 9.22-9.24 (m, 1H, H\({}_{\rm quino}\)). \({}^{13}\)C-NMR (101 MHz, D\({}_{2}\)O): \(\delta\) 20.11 (CH\({}_{3}\)), 29.67 (SCH\({}_{2}\)), 69.05 (NCH), 121.93, 125.57, 125.97, 127.93, 129.38, 129.41, 129.57, 131.37, 133.73, 133.90, 139.64, 149.48, 150.16. Anal. Calcd for C\({}_{18}\)H\({}_{16}\)NClS: C 68.89, H 5.14, N 4.46, Cl 11.30, S 10.22. Found: C 69.00, H 5.21, N 4.54, Cl 11.56, S 10.68. _3-Methyl-3-phenyl-2H,3H-(1,4)thiazol[2,3,4-ij]quinolin-4-ium chloride_ (**14**). A solution of sulfuryl chloride (0.108 g, 0.8 mmol) in methylene chloride (10 mL) was added dropwise to a solution of di(8-quinolinyl) disulfide (0.256 g, 0.8 mmol) in methylene chloride (10 mL), and the mixture was stirred for 10 min at room temperature. A solution of \(\alpha\)-methylstyrene (0.189 g, 1.6 mmol) in methylene chloride (10 mL) was added dropwise, and the reaction mixture was stirred for 24 h at room temperature. The mixture was filtered, and the solvent was removed by rotary evaporator. The residue was washed with cold hexane and dried in vacuum, giving the product (0.436 g, 87% yield) in the form of a yellow oil. \({}^{1}\)H-NMR (400 MHz, D\({}_{2}\)O): \(\delta\) 1.58 (s, 3H, CH\({}_{3}\)), 3.57 (d, \(J\) = 14.6, 1H, SCH\({}_{2}\)), 3.78 (d, \(J\) = 14.6 1H, SCH\({}_{2}\)), 6.58-6.64 (m, 1H, Ar), 6.66-6.72 (m, 2H, Ar), 7.00-7.05 (m, 2H, Ar), 7.77-7.62 (m, 1H, H\({}_{\rm quino}\)), 7.98-8.03 (m, 1H, H\({}_{\rm quino}\)), 8.07-8.13 (m, 1H, H\({}_{\rm quino}\)), 8.21-8.25 (m, 1H, H\({}_{\rm quino}\)), 8.90-8.94 (m, 1H, H\({}_{\rm quino}\)), 9.00-9.04 (m, 1H, H\({}_{\rm quino}\)). \({}^{13}\)C-NMR (101 MHz, D\({}_{2}\)O): \(\delta\) 28.80 (CH\({}_{3}\)), 50.91 (SCH\({}_{2}\)), 74.85 (NCH), 122.00, 124.79, 126.84, 127.22, 127.86, 129.28, 129.41, 130.14, 136.99, 141.46, 143.67, 144.20, 148.31. Anal. Calcd for C\({}_{18}\)H\({}_{16}\)NClS: C 68.89, H 5.14, N 4.46, Cl 11.30, S 10.22. Found: C 69.12, H 4.99, N 4.27, Cl 11.19, S 9.96.
# Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data
## 5 Discussion  
The causes of the errors in determining the harvest dates are considered below and a way of possible improvement to the algorithms is specified.
## 2 Acquisition and Characterization of Pore Structure
### Mercury Intrusion Porosimetry  
Mercury intrusion porosimetry is a technique for characterizing pore structure by injecting a non-wetting phase fluid (mercury) into a porous medium under applied pressure, overcoming surface tension. The mercury intrusion method characterizes pore structure features primarily by recording the relationship between the intrusion pressure and the  
Figure 2: Comparison of different methods for pore structure characterization [26; 27].  
volume of mercury injected, which in turn reflects the characteristics of the pore capillary pressure curve [28]. This method can be used to calculate a series of parameters related to the pore throat distribution and size, such as displacement pressure, median saturation pressure, and mercury injection/withdrawal efficiency, as well as indicators that measure the spatial heterogeneity of the pore structure, such as skewness, mean coefficient, and sorting coefficient. Based on different techniques, the mercury intrusion method can be further divided into high-pressure mercury intrusion [29] and constant-speed mercury intrusion [30].  
This method can calculate parameters such as the size of the core's pore throats and specific surface area. However, it does not allow for direct observation of the sample, nor can it detect isolated pores. Furthermore, once used, the cores cannot be reused.