esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### The Block Structure  
On September 13, 2017, a photogrammetric survey was performed on the Santa Sofia farmstead, near Pavia, Northern Italy (Figure 2a). The test-site is a flat area totaling about 36 ha, used exclusively to cultivate rice. The whole acquisition was obtained by five flight missions, the planning for which is shown in Figure 2b, where the optical orthomosaic, which was used as background, was derived from a previous survey. In total, the project constituted about 1300 multispectral images, each composed of four bands. The AGL height was 70 m and image overlapping was 80% and 60% along- and across-track, respectively. The Sequoia camera was adopted, as previously mentioned. Twelve markers were placed on the ground and surveyed with the network real-time kinematic (NRTK) GPS mode. Virtual reference station (VRS) differential corrections were applied via connecting networked transport of RTCM (Radio Technical Commission for Maritime) via internet protocol (NTRIP - Networked Transport of RTCM via Internet Protocol) to the GNSS positioning service of \"Regione Piemonte and Regione Lombardia\" [34]. GCP coordinates have a planimetric and altimetric accuracy of 2-3 cm and 4-5 cm, respectively. GCPs were constituted by artificial markers with black and gray diamond shapes (Figure 1d); marker positions are illustrated in Figure 2b. At the beginning of each flight, the recommended radiometric calibration procedure was performed by acquiring the calibration target (Figure 1b).  
The present paper will only focus on flights 3 and 4, as these had a methodological purpose. The overlapping area allowed us to deeply analyze geometric and radiometric congruency under several processing scenarios (as described in Section 2.3), because it is quite wide (23 ha) and encompasses 4 GCPs.  
Figure 1: The equipment operated by the Laboratory of Geomatics of the University of Pavia: (**a**) the HEXA-PROTM unmanned aerial vehicle (UAV) used for the survey; (**b**) the Airinov calibration target supplied with the camera; (**c**) the Parrot Sequoia camera (the imaging and irradiance sensors are shown); (**d**) an example of the artificial markers used.
## 3 Results  
### Pulsed Terahertz (THz) Emission  
Figure 3a plots the average THz power vs. bias voltage at an incident 1550 fiber laser power of 65 mW. A maximum THz power of \(\sim\)117 \(\mu\)W was measured with a cross-calibrated Golay cell detector at room temperature [8]. The curve in Figure 3a is good fit by a power law: \(P_{THz}\)\(\alpha\) V\({}^{1.97}\). Figure 3b displays the power spectrum from the FFT of the interferogram obtained with a Michelson autocorrelator. It displays a maximum spectral density around 250 GHz.  
Figure 4a shows the THz time-domain waveform for 780-nm femtosecond laser pulses (obtained by frequency doubling of 1550-nm fiber laser), having an average pump power of \(\approx\)14 mW. Only one pronounced THz peak appears in the waveform as expected, indicating that the THz radiation is caused by the acceleration of the photocarriers in the large bias field along with their ultrafast recombination. This is typical behavior for cross-bandgap ultrafast photoconductive devices. In contrast, Figure 4b shows the THz waveform for the 1550-nm ultrafast pulses having an average power of \(\sim\)23 mW. Unlike the 780 nm waveform, a damped ringing occurs over four cycles, with the period increasing as the waveform evolves. The radiation field swings toward both positive and negative polarities, displaying a displacement-current-like alternation, which cannot be explained as normal photoconductive relaxation. And the fact that the same device pumped at 780 nm displayed no ringing rules out other possible effects, such as resonances in the spiral antenna. Furthermore, to check for possible non-linear optical effects in the detector, or self-interference along the THz beam path, we replaced the QD emitter with a commercial In\({}_{0.53}\)Ga\({}_{0.47}\)As cross-bandgap-excited PC switch with the same 1550 nm setup and the same receiver (Figure 5). No such ringing was observed. Figure 4d shows the power spectrum of 4b after FFT. Similar to Figure 2b, most of the power occurs in a band-limited range between \(\sim\)100 and 600 GHz. For the purpose of comparison, Figure 4c shows the power spectrum of 4a after FFT. As expected, the 780 nm power spectrum is broader than the 1550 nm power spectrum.  
Figure 3: (**a**) Terahertz (THz) power vs. bias voltage at a laser power of 65 mW. (**b**) Power spectrum obtained from auto-correlation measurement.
## 2 Zone-Based Registration Scheme
### Type I: Serial Shape
#### 2.1.3 Case 3: Moving Three Zones after Call Occurrence in the Second Zone  
After the UE moves from 3\(\rightarrow\)2\(\rightarrow\)1 and performs call processing in zone 2 while returning back to the zone, the Network_DB stores the middle zone 2 as the most recently registered area and the remaining zones at both ends in RA. In this case, four states are defined, as shown in Figure 7. Representatively, the state transition process for the state of \(C_{32}^{I}\) can be described as follows. The \(C_{32}^{I}\) indicates a state in which the Network_DB has \(\boxed{2|1|3}\) after implicit registration in zone 2. The UE then moves from zone 3 to zone 2.  
Figure 6: State transition probability for Case 2 of Type I.  
(1) \(I_{12}^{l}\): If a call occurs before the UE moves from zone 2 to another zone, it moves to state \(I_{12}^{l}\), and the probability is \(1-m\). After converting to state \(I_{12}^{l}\), the Network_DB will become \(\overline{2|3|1}\) and one paging will be performed when connecting UE in zone 2.  
(2) \(\overline{C_{23}^{l}}\): UE can transit to state \(C_{23}^{l}\) with a probability of \(\theta m\) when it returns to zone 3. The call connection in zone 3 requires three paging procedures (2\(\rightarrow\)1\(\rightarrow\)3).  
(3) \(E_{21}^{l}\): The probability of configuring the new RA of Type I is \(2(1-\theta)m/5\). One paging arises to connect a call in the new zone.  
(4) \(C_{21}^{l}\): When UE moves from zone 2 to zone 1, it transits to state \(C_{21}^{l}\). The probability is \((1-\theta)m/5\). Since the Network_DB is \(\overline{2|1|3}\), two paging operations are needed to find the UE staying in zone 1.  
Figure 7: State transition probability for Case 3 of Type I.  
(5) \(E_{21}^{II}\): UE can enter state \(E_{21}^{II}\) if it constructs a new RA corresponding to Type II, in which case the transition probability becomes \(2(1-\theta)m/5\). With the arrival of an incoming call in the new zone, one paging is processed.
## 3 Verification of the Model  
A number of field measurements can be found in the open literature, the aim of which were to identify the dynamic behavior of a railway turnout [10; 39; 40; 41]. It should be noted that most of them were conducted to verify numerical models. These measurements can be categorized into two groups: a measurement of accelerations [10; 40] and a measurement of contact forces [39; 41]. Validations by acceleration are commonly used as a direct method where the outputs produced by accelerometers were used. Nevertheless, it is difficult to determine contact force information directly from acceleration measurements. Therefore, validations by the measurement of contact forces are also preferred, where the contact forces were estimated by the output obtained from strain gauges.  
The numerical model used in this study has thus been verified by using the existing literature, which can be considered to be reasonable and feasible. The two methods in the previous paragraph are considered for validation purposes, to ensure the accuracy of the numerical model. To consider the two different cases, the input parameters such as speed, axle load, and stiffness values are adjusted to be similar to those of the reference cases [10; 39].  
The model in this study was compared with the field measurement in [10]. On one hand, the maximum acceleration is found to be 201.6 m/s\({}^{2}\), as shown in Figure 3a, which is consistent with the result in [10] where the maximum positive acceleration reaches 214.05 m/s\({}^{2}\). The discrepancy between two cases is around 6%. On the other hand, contact forces along a turnout can be presented in Figure 3b. It is noted that the maximum contact force occurs at the crossing panel and the magnitude can be up to 180 kN. These results are consistent with the field measurements in [39] where the impact forces can be measured between 150 and 200 kN. Here, it should be noted that exact conditions for field measurements in [10; 39] are unknown. Furthermore, the output of numerical simulations is deterministic whereas an element of uncertainty exists in the field measurements. Hence, discrepancies inherently and inevitably occur between the field measurements and the results of the simulation. Consequently, it can be concluded that the model carries similar attributes to the field measurement, and has sufficient accuracy, to represent the main dynamic characteristics of train-track interaction over a turnout.  
Figure 3: Multibody simulation and field data: (**a**) Vertical acceleration of the front wheel for comparison [10]; (**b**) normal contact forces along a turnout for comparison [39].
## 3 Numerical Results
### Link-Level Simulations
After studying the performance of the ADMM precoder considering the variation of the number of subcarriers, F, and RF chains, it is important to understand the main differences in terms of performance when considering architectures based on SPS, DPS, as well as the impact of quantized phase shifters (QPS). The results are shown in Figure 6. It can be seen that a better performance can be achieved when considering a design based on a DPS over SPS. It is important to remember, however, that the architecture based on DPS provides better performance but requires the use of twice the number of phase shifters in the implementation. In this same figure, we include QPS curves, which correspond to the SPS architecture with quantized phase shifters. It can be observed that, as expected, there is a performance loss when comparing the QPS curves against the ideal SPS, but this degradation is greatly reduced when using phase shifters with only 4 bits of quantization. Beyond, the choice of the parameters of the scenario, the BER performance can be improved by increasing the number of iterations of precoding algorithms.
# Reliability of Perceptual-Cognitive Skills in a Complex, Laboratory-Based Team-Sport Setting
## 3 Results
### Inter-Session Reliability  
Results for inter-session reliability (\(n=22\)) can be found in Table 2. Agreement in CoMR ranged from fair (left-handed jump throw) to moderate (right-handed breakthrough). Only two non-significant, slight levels of agreement were found (right-handed jump throw at t\({}_{3}\); right-handed standing throw at t\({}_{2}\)). Three left-handed attacks demonstrated substantial agreement in the latest occlusion points (pass at t\({}_{0}\); breakthrough at t\({}_{0}\)). Almost perfect agreement was found for the left-handed jump throw.  
Overall mean agreement of CoMR for individual right and left-handed attacks can be considered as fair to moderate. Note the between-hand differences in single kappa values at t\({}_{6}\) and t\({}_{0}\) in breakthrough, t\({}_{2}\)-t\({}_{0}\) in jump throw, t\({}_{3}\) in standing throw and t\({}_{0}\) in pass, and in the overall agreement in jump throw.  
A summarizing graphical overview of within and between-session reliability results is provided in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Right-Handed Attacks**} & \multicolumn{6}{c}{**Left-Handed Attacks**} \\ \hline
**Attack Action** & **Occlusion** & \(n\) & \(K\) & **95\% CI** & \(p\) & \(n\) & \(K\) & **95\% CI** & \(p\) \\ \hline Pass & & & & & & & & & & \\ \hline t6 (\(-1200\) ms) & 58 & 0.241 & 0.029 & - & 0.452 & 0.025 & 63 & 0.340 & 0.120 & - & 0.560 & 0.003 \\ t5 (\(-1000\) ms) & 57 & 0.344 & 0.112 & - & 0.575 & 0.004 & 63 & 0.593 & 0.399 & - & 0.788 & 0.000 \\ t4 (\(-800\) ms) & 65 & 0.428 & 0.221 & - & 0.635 & 0.000 & 65 & 0.378 & 0.156 & - & 0.599 & 0.001 \\ t3 (\(-600\) ms) & 61 & 0.355 & 0.122 & - & 0.587 & 0.004 & 65 & 0.437 & 0.204 & - & 0.670 & 0.000 \\ t2 (\(-400\) ms) & 63 & 0.336 & 0.094 & - & 0.578 & 0.005 & 62 & 0.455 & 0.221 & - & 0.688 & 0.000 \\ t1 (\(-200\) ms) & 61 & 0.414 & 0.223 & - & 0.605 & 0.000 & 60 & 0.485 & 0.301 & - & 0.668 & 0.000 \\ t0 (0 ms) & 60 & 0.527 & 0.351 & - & 0.703 & 0.000 & 59 & 0.423 & 0.237 & - & 0.609 & 0.000 \\ Overall & & 0.459 & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Right-Handed Attacks**} & \multicolumn{6}{c}{**Left-Handed Attacks**} \\ \hline
**Attack Action** & **Occlusion** & \(K\) & **95\% CI** & \(p\) & \(K\) & **95\% CI** & \(p\) \\ \hline Breakthrough & & & & & & & & & \\ \hline  & t6 (\(-1200\) ms) & 0.304 & 0.120 & - & 0.487 & 0.001 & 0.586 & 0.415 & - & 0.756 & 0.000 \\  & t5 (\(-1000\) ms) & 0.268 & 0.103 & - & 0.43
# Audio-Visual Action Recognition Using Transformer Fusion Network
## 4 Experimental Results
### Visual Interpretation with Grad-CAM  
Figures 6 and 7 present GRAD-CAM [37] visualizations for the UCF-sound and Kinetics-sound datasets. GRAD-CAM, which stands for gradient-weighted class activation mapping, leverages the gradient information from the last transformer layer of our feature extraction network. In each figure, alongside the original image, we provide two GRAD-CAM images. The first GRAD-CAM image is derived using only visual information, while the second incorporates data from all modalities. A notable observation is that the activations highlighted in the multi-modal model are more prominent and concentrated around areas of significant movement or sound generation. This contrast suggests that incorporating audio data alongside visual information enhances the model's ability to focus on relevant parts of the scene, leading to more accurate and interpretable results. The comparison between these two sets of GRAD-CAM images underscores the added value of audio data in enriching the model's understanding of the scene.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Method** & **Accuracy** \\ \hline \(L^{3}\)-Net [16] & 74.00\% \\ SlowFast [17], R50 & 80.50\% \\ AVSlowFast [17], R50 & 83.70\% \\ SlowFast [17], R101 & 82.70\% \\ MAFnet [35] & 83.94\% \\ AVSlowFast [17] & 85.00\% \\ MBT [24] & 85.00\% \\ Proposed methods (Swin-B, case (1)) & **89.34\%** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of multi-modal results on Kinetics-sound dataset. R50 denotes ResNet-50 [36], and R101 denotes ResNet-101 [36]. MAF denotes Multi-level Attention Fusion Network. The bold numbers represent the best results.  
Figure 6: GRAD-CAM visualization results of UCF-sound using Swin-L as a feature extractor. For each original image, its subsequent two GRAD-CAM images correspond to the outcomes using solely visual information and using all modalities, respectively.
Multi-Temporal Remote Sensing for Forest Conservation and Management: A Case Study of the Gran Chaco in Central Argentina  
[PERSON]  
1 Instituto Nacional de Tecnologia Agropecuaria, Estacion Forest Villa Dolores (EEA Manfredi), Las Encrucijadas, Camino Viejo a San Jose, Km 1 Villa Dolores, Cordoba 85070, Argentina; [EMAIL_ADDRESS] (F.G.A.); [EMAIL_ADDRESS] (L.C.); [EMAIL_ADDRESS] (D.R.L.)  
[PERSON]  
2 EnvixLab, Department of Biosciences and Territory, University of Molise, Contrada Fonte Lappone, 86090 Psche, Italy; [EMAIL_ADDRESS] (F.P.); [EMAIL_ADDRESS] (R.R.-T.); [EMAIL_ADDRESS] (ML.C.)  
[PERSON]  
1 Instituto Nacional de Tecnologia Agropecuaria, Estacion Forest Villa Dolores (EEA Manfredi), Las Encrucijadas, Camino Viejo a San Jose, Km 1 Villa Dolores, Cordoba 85070, Argentina; [EMAIL_ADDRESS] (F.G.A.); [EMAIL_ADDRESS] (L.C.); [EMAIL_ADDRESS] (D.R.L.)  
[PERSON]  
1 Instituto Nacional de Tecnologia Agropecuaria, Estacion Forest Villa Dolores (EEA Manfredi), Las Encrucijadas, Camino Viejo a San Jose, Km 1 Villa Dolores, Cordoba 85070, Argentina; [EMAIL_ADDRESS] (F.G.A.); [EMAIL_ADDRESS] (L.C.); [EMAIL_ADDRESS] (D.R.L.)  
[PERSON]  
2 EnvixLab, Department of Biosciences and Territory, University of Molise, Contrada Fonte Lappone, 86090 Psche, Italy; [EMAIL_ADDRESS] (F.P.); [EMAIL_ADDRESS] (R.R.-T.); [EMAIL_ADDRESS] (ML.C.)  
[PERSON]  
4 Antimorogenic alteration of tropical and subtropical forests is a major driver of biodiversity loss; notably, the Chaco Forest, which is the largest dry forest in the Americas, is among the most impacted regions. Sustainable forest management, a key objective of the UN's 15 th Sustainable Development Goal (SDG), underscores the need for advanced monitoring tools. This study integrates Sentinel-2 remote sensing (RS) spectral indices with field data to analyze forests under varying management regimes and levels of alteration in a representative area of the Chaco region (Chancani Provincial Reserve and surrounding areas of the West Arid Chaco). Forest structure types and conservation levels were linked to monthly spectral index behavior using linear mixed models. Spectral indices such as the BI (Brightness Index), NDWI\({}_{\text{Cao}}\) (Normalized Difference Water Index), and MCARI\({}_{\text{Sent}}\) (Modified Chlorophyll Absorption in Reflectance Index) effectively differentiated forest stands by conservation status and structural alteration. This combined RS and field data approach proved highly effective for detecting and characterizing forests with diverse conservation and sustainability conditions. The methodology demonstrates significant potential as a reliable RS-based tool for monitoring forest health and supporting progress toward SDG targets, particularly in regions like the Chaco Forest, which face extensive anthropogenic pressures.
# Calculation of Nonlimit Active Earth Pressure against Rigid Retaining Wall Rotating about Base
## 5 Verification by Comparison  
A model test (Figure 4a-c) was conducted by [PERSON] et al. [19], simulating sandy soil under semi-infinite conditions. The model test parameters were wall height H = 1.0 m, soil weight \(\gamma=15.34\text{kN}/\text{m}^{3}\), soil friction angle \(\varphi=33.4^{\circ}\), interface friction angle \(\delta=2/3\varphi\), and void ratio \(e=0.706\). The experimental values were compared for three different wall rotations of \(\varepsilon_{1}\), \(\varepsilon_{2}\), or \(\varepsilon_{3}\). Figure 4d shows the experimental soil pressure values obtained by [PERSON] et al. [15] for a retaining wall with sand backfill soil behind the wall in RB obtained via model box experiments. The test parameters included wall height H = 0.6 m, soil weight \(\gamma=15.92\text{kN}/\text{m}^{3}\), soil friction angle \(\varphi=36^{\circ}\), interface friction angle \(\delta=22^{\circ}\), void ratio \(e_{max}=0.740\) and \(e_{\text{min}}=0.500\), relative density of the soil \(D_{r}=45\%\), and wall rotation \(\varepsilon_{4}=10\times 10^{-4}\) rad.  
Figure 4: Comparisons between existing theoretical solutions and experimental data with varying rotation angles: (**a**) \(\varepsilon_{1}=1\times 10^{-4}\) rad of Fang; (**b**) \(\varepsilon_{2}=2\times 10^{-4}\) rad of Fang; (**c**) \(\varepsilon_{3}=5\times 10^{-4}\) rad of [PERSON] [19]; (**d**) \(\varepsilon_{4}=5\times 10^{-4}\) rad of [PERSON] [15].  
The soil along the wall height is in a nonlimiting state from [PERSON]'s data when the wall rotation angle is \(\varepsilon_{1}\), \(\varepsilon_{2}\), or \(\varepsilon_{3}\) by calculating the soil displacement Sz along wall depth. It can be observed in Figure 4a-c that from the surface to a depth of 0.5 m, the earth pressure distribution curve was linear. At depths greater than 0.5 m, the earth pressure distribution curve exhibited clear nonlinearity and a tendency to diverge outwards. As the depth increased (from the wall top), the rate of increasing earth pressure increased. The results of the theoretical approach used in this paper were in good agreement with [PERSON]'s [19] experimental values. When the wall rotation was \(\varepsilon_{4}\), the upper half of the soil in the height of the wall was in ultimate equilibrium, whereas the lower half part of the soil remained in a nonlimiting state. Figure 4d compares the theoretical estimated values from this research against [PERSON]'s experimental data [15]. In Figure 4d, unlike in Figure 4a-c, an inflection point in the earth pressure distribution curve was observed at a depth of 0.5 m because of the transition from a nonlimiting state to an active limit state of the soil behind the wall in this area. The theoretical method presented in this study is corroborated by [PERSON]'s experimental data [15], and the earth pressure distribution curve exhibited an inflection point.
# Stream-DBSCAN: A Streaming Distributed Clustering Model for Water Quality Monitoring
## 1 Introduction
[14] presented a method combining expected maximum value (EM) and partitioning around medoids (PAM), and several detection methods, such as Welch \(t\)-test, Wilcoxon test, and Spearman correlation, to analyze the water quality of the Yangtze River. [PERSON] et al. [15] used a density algorithm to sort probes and achieve intelligent management of large-scale water distribution networks (WDN). [PERSON] et al. [16] tested three machine learning methods, namely, day dependent support vector regression (SVR) models, adaptive orthogonal projection, and unsupervised clustering technology, for anomaly detection of flow, pressure, conductivity, and temperature in water quality. The experimental results showed that these methods had limited effectiveness when dealing with significant data volumes and complex data attributes. While machine learning and other methods have been employed for intelligent water quality management, the challenge of real-time data representation for massive data streams remains an urgent issue to be addressed. Based on the previously mentioned research idea, this paper proposes a distributed clustering modelling framework called \"stream-DBSCAN\". The framework leverages the Flink distributed parallel computing model as the data processing platform to optimize the classical density clustering DBSCAN algorithm. The improved DBSCAN algorithm is deployed to the Flink distribution as the core algorithm of the computing node, thus enabling the density clustering DBSCAN algorithm to have the ability of distributed parallel computing when combined with the streaming data processing mode. Using the indices of ammonia nitrogen (NH4N), pH value, and turbidity of Menlou Reservoir from May to August in 2019, water quality indices were clustered into several categories. The relationship between water quality and time was then analyzed based on the clustering results.