esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Drought Severity and Vulnerability in the Lam Phaniang River Basin, Thailand
## 2 Materials and Methods  
### Study Area  
The Lam Phaniang River Basin, which is the main sub-basin of the Chi River Basin, is located in the northeastern part of Thailand. The Lam Phaniang River is the main river of the study area with the length of 150 km originating from Phu Khao Range at the eastern part of Loei Province, passing through Nong Bua Lamphu Province, and flowing into the Ubol Ratana Reservoir. The total area of the Lam Phaniang River Basin is approximately 1912 km\({}^{2}\) (3.9% of the entire Chi River Basin) with a long and narrow S-shape, which results in a relatively small peak discharge during floods [9]. The elevation along the flow path varies from 600 m above mean sea level (m.a.m.s.l.) at the upstream to approximately 100-200 m.a.m.s.l. at the downstream end of the targeted area (see Figure 1). The 2015 land use in the study area can be classified into agricultural land covering the area of 1302.0 km\({}^{2}\) (68.1%), forest covering 413.0 km\({}^{2}\) (21.6%), urban and built-up areas covering 98.6 km\({}^{2}\) (5.2%), water bodies covering 48.7 km\({}^{2}\) (2.5%), and miscellaneous covering 49.7 km\({}^{2}\) (2.6%). Referring to the information from Secretariat Office of the Chi River BasinCommittee [10], the climate of the study area is a tropical monsoon type with 1240.2 mm of mean annual rainfall, while the highest mean monthly rainfall amount was recorded in September (237.7 mm) and the lowest was found in January (5.7 mm). The mean annual temperature was reported to be about 26.7 \({}^{\circ}\)C, which varies from a maximum of 36.4 \({}^{\circ}\)C in April to a minimum of 16.7 \({}^{\circ}\)C in December. The mean annual runoff was estimated to be approximately 10.1 m\({}^{3}\)/s, in which the highest and lowest runoff were observed in September and April by 49.0 m\({}^{3}\)/s and 0.1 m\({}^{3}\)/s, respectively.
## 3 Results and Discussion
### Post Sunset Depletion Occurrence (18:00 LT to 21:00 LT)  
EUEL depletions below the night-time value that fulfills all predetermined CEJ criteria were detected between 18:00 LT and 21:00 LT, as illustrated in Figure 8a, where PSD occurred earlier at LKW station. PSD accounted for 28% of total EUEL depletions in the east (DAV) and 23% in the west (LKW). Overall, both parts were solar activity independent in a balanced way. As illustrated in Figure 8b, there was no correlation with the F10.7 index, as indicated by the low value of R = \(-\)0.14. Meanwhile, there was no significant difference in the percentage value between low and high solar activity levels in pie charts (c) and (d). Similarly, the correlation in Figure 8e was also weak, with R = \(-\)0.24, and both solar activity levels in (f) and (g) had no significant difference.  
Next, PSD was seasonally dependent, as shown in Figure 9. PSD varied symmetrically according to the season in both parts of Southeast Asia. However, most variations disagreed with the F10.7 index, except for the east during Equinox, which was inversely correlated (R = \(-\)0.64). Compared to other seasons, the percentage occurrence of PSD during the Equinox was also among the highest. Therefore, this season was dominant, as seen in (b) and (d), except in the west at the high solar activity level, where its percentage was almost equivalent to J-solstice. Furthermore, depletion before d  
Figure 8: **(a)** PSD recorded on 1 January 2016, in both parts of Southeast Asia, **(b)** percentage occurrence of PSD in the east (DAV) over solar cycle 24 (left \(y\)-axis) alongside F10.7 index (right \(y\)-axis) and (**c,d**) its dominance at low and high solar activity levels. Information in (**e–g**) is similar to (**b–d**) but for the west (LKW).  
occurrence was most remarkable at 05:00 LT and only represented 14% in the east and 12% in the west.  
Previous studies reported a relation between evening CEJ and the growth of equatorial plasma bubbles (EPBs) in the ionospheric F-region. It was found that the development of EPBs around sunset was stunted when evening CEJ was present [34, 35, 36, 37]. In other words, if EPBs existed, evening CEJ could not be observed. Even so, the EPB could still be generated under evening CEJ if the neutral wind blows eastward to generate the eastward electric field in the evening equator [38]. Thus, a further statistical study on the relation between evening CEJ (PSD) and EPB generation is needed, which had not previously been investigated because CEJ is a daytime phenomenon, and observation made was limited to 18:00 LT only.
# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 5 V2V Aids toward Sustainable Transportation Systems
### V2V Enabled Hybrid Cellular and Ad-Hoc Network Contributions in ITSs  
V2V cellular networks with ad-hoc networking are multifunctional, improving ITS sustainability by optimizing communication and combining the strengths of cellular infrastructure and peer-to-peer functionality [78]. Cellular networking is combined with ad-hoc networking to be able to switch automatically from a cellular network to a peer-to-peer one depending on factors such as network availability, bandwidth needs, and the distance to another vehicle. This flexibility guarantees reliable and resilient communication regardless of the nature of the network, whether it covers the entire cellular area or has high network congestion. Thus, the hybrid approach encompasses bigger data offloading capabilities, which allows vehicles to develop high-transport bandwidth applications such as software updates or video by utilizing the cellular network, and low latency, peer-to-peer real-time communication, which is primarily important to the issue of avoiding road collisions, are developed within the ad-hoc network. Furthermore, the inherent ad-hoc characteristic of ad-hoc networking ensures that the scalability and suitability of vehicles can be easily satisfied through the formation of communication links and data sharing between the vehicles, with no need for centralized infrastructure [44]. Besides that, V2V hybrid cellular networks, including ad-hoc networking, aid in the integration of advanced technologies such as edge computing and AI, which allow the vehicles to process and analyze the data on the vector itself for faster decision-making and recovery from external server failure. However, these networks form an integral part of ITSs, which boosts safety, mobility, and environmental sustainability by serving as a backbone for efficient and reliable vehicular communication.
# Anomaly Detection in Network Traffic via Cross-Domain Federated Graph Representation Learning
## 1 Introduction
These data often have obvious regional and local behavior characteristics, and it is difficult to cover complex and diverse attack patterns and communication structures. Therefore, when the model faces non-independent and identically distributed (non-IID) data, the features it learns have strong domain dependence and are difficult to adapt to the differences in traffic characteristics in other network environments, resulting in significantly limited overall generalization capabilities. To address the above challenges, this study proposes a cross-domain federated graph representation learning framework for network traffic anomaly detection. The goal is to address the limitations of traditional approaches in capturing structural dependencies and protecting sensitive information while enabling effective and privacy-preserving anomaly detection in heterogeneous, cross-domain environments. The proposed method achieves technical advancements through the development of a tripartite collaborative mechanism. First, a dual-channel GNN architecture is employed to process structural knowledge (federated-shared) and attribute knowledge (locally private) in parallel, sharing only low-sensitivity structural information to ensure privacy protection. Second, a cross-gating fusion module is designed to dynamically integrate features, thereby enhancing the representation capacity and improving classification accuracy. Finally, a cross-domainstructural guidance mechanism is introduced, wherein a global guidance model is trained on the server side by aggregating knowledge from public domains. This model provides global supervision for local client learning, enabling deep integration of structural information and effective knowledge transfer across domains.
## 2 UAV-Based Data Collections and Processing
### Data Collection Campaign  
The data collection flights were over different types of water surfaces (rivers, lakes, ponds, etc.) selected to test the sensor in detecting well known water surfaces. Nevertheless, these data were considered valuable by this study to challenge the sensor in detecting flood-similar water presence. The criteria for water detection were defined as being able to detect small water surfaces, distinguish narrow water flows and estimating water surfaces area. With that in mind, three test cases were chosen from the data campaign area:  
1. A 0.89 km\({}^{2}\) lake with a known basin where we could challenge the ability to estimate the area covered by water and its boundary.
2. A river stream to challenge detecting narrow water streams.
3. Small ponds of water to challenge the detection of small and unexpected water content on ground.  
Being not flood events, we were able to overlap the GNSS-R measurements on available orthophotos without the need for field measurements or satellite images taken on the same dates and time of the test flights.  
Figure 1: The global navigation satellite systems (GNSS)-R sensor used in this study, and a picture from one of the data collection campaigns.
## 3 Results  
### Proposed Architecture and Operation of Monitoring System  
The application of the above-mentioned monitoring system differs in the case of cold and hot rolling mills, respectively. The typical layout to be considered in cold rolling is the cluster mill with multiple rolls (up to 20, usually) (Figure 4). In this case, monitoring the pressure applied by each bearing of the backup roll on the cage pin is significant to detect any anomalous loading condition, dynamic response, temperature change, and even local damage evidence.  
The fiber optic sensor can be deployed along the fixed pin (Figure 5) by eventually exploiting the lubricant feed channels 60a, 60b, and 60c in Figure 2, or similar since the massive constitution of the pin allows the creation of some additional channels, even smaller to embed the fiber optics sensors. The external head 42 (Figure 2) allows locating the required connections to the interrogator and demodulator devices, as connectors 84 do for the lubrication system. Exploiting separated channels, being eventually filled by liquid, reduce the risk of early deterioration of fiber coatings, particularly if lubricant feed channels were preliminarily selected to guest fiber optics. Measurements are performed at dedicated locations as 64a and 70 or similar. One sensor can reach the inner ring of the backup bearing to detect the local pressure along an arc of the circumferential deployment of fiber optic installed inside the pin, just below the inner ring, as it was even performed in the case of bearing housing in the work of [62]. Measurement of local temperature can be performed at a dedicated cavity produced on the pin surface in correspondence to the inner ring.  
Similarly, a double set of fiber optic sensors can be applied to rolls of the hot rolling mill by resorting to the structure of the vertical positioning system, which directly controls the outer ring of main bearings (Figure 6). In the case of roller bearings, pressure and temperature monitored are those closest to the main body of rolls. In addition, optical measurements assure monitoring of the strip temperature, while some load cells help in controlling the load applied to the work roll. Eventually, the local measurements can be applied to several points to have at least some discretized access to pressure and temperature distributions with the bearing. A similar arrangement could even be conceived for lubricated bearings and aims at detecting the effect of load on the elastic deformation of rings to improve the prediction by modeling of the gap inside the bearing layout.  
Figure 4: Layout of cluster mill with inner fixed pin: picture (**a**) [60], sketch (**b**), and rendering (e) [61].
## 2 Literature Review
### Spatiotemporal Learning Data Visualization
#### 2.2.1 Eye-Tracking Data Visualization  
Eye-tracking data visualization techniques can be categorized into two main types: AOI (area of interest)-based visualization methods and point-based visualization methods ([PERSON] et al., 2016). AOI-based visualization methods divide the space into defined areas and aggregate data, such as the number of fixations and fixation duration within each area. Then, these aggregated data are linked to the AOIs for display. Notable methods in this category include AOI River (2010) and Timeline Visualization (2011). Although this approach effectively illustrates how eye-tracking data change over time, it simplifies spatial information and limits the representation of detailed spatial features. On the other hand, point-based visualization methods directly display data points within a graphical interface without aggregation. This category includes methods like attention maps ([PERSON] et al., 2016), scan path visualizations ([PERSON] et al., 2016), and space-time cubes ([PERSON] et al., 2016). Attention maps and scan path visualizations present detailed data, allowing for a complete understanding of the information. However, displaying excessive details directly on a two-dimensional interface without any aggregation can make it challenging to present the data clearly and may lead to visual clutter. Space-time cubes employ a three-dimensional space to represent spatial information on the Z-axis, thereby extending the two-dimensional interface.
## 5 Conclusions  
We have studied effects of thermal stress and nanoscale forces on pull-in instability and resonant behavior of electrostatically actuated microplates. Mechanical nonlinearities are accounted for by modeling the deformable microplate using the von Karman plate theory. The thermal stress is modeled as a homogenous residual stress depending on the microplate temperature. Nanoscale surface forces are described using the van der Waals force. We have derived a simple and tractable mass spring single degree of freedom model for analyzing the behavior of the considered device. The reduced-order model is derived by using a single basis function for the transverse displacement and eight basis functions to describe strain hardening due to membrane stretching.  
Results show that the pull-in voltage and the pull-in deflection are strongly affected by thermal stresses and the van der Waals force. More specifically, as the temperature of the MEMS increases the pull-in voltage decreases. Moreover, the van der Waals force becomes more relevant as the MEMS size is reduced and can potentially lead to the spontaneous collapse of the system in absence of applied voltage.
## 4 Experimental Results  
The performance of the proposed technique was estimated with existing routing and clustering techniques, such as LEACH [29], EECRP [30], FEEC-IIR [24], and CL-IoT [31]. Compared to the previous approaches, 500 nodes were used to calculate the performance characteristics for energy consumption, network life, end-to-end latency (E2 ED), PDR, communication cost, and communication overhead. In these simulations, 100 homogenous sensor nodes and nine primary cluster nodes with infinite battery energy were positioned in 1000 m\({}^{2}\). The parameters used for the experimental setup are given in Table 1.
# Learning Spatial-Temporal Background-Aware Based Tracking
## 3 Spatial-Temporal Regularization Background-Aware Correlation Filter  
### Background-Aware Correlation Filters Framework  
In this work, the proposed algorithm mainly chose the background-aware filter as the baseline tracker, and then briefly reviews the basic principles of BACF.  
Denote by \(x\in R^{D}\) a vectorized image with \(D\) channels, \(y\) is the ideal correlation response map \(y\in R^{D}\), the main objective function of BACF is to reduce the value of the objective function to the minimum, that is, minimize it:  
\[E(w)=\frac{1}{2}\left\|y-\sum_{d=1}^{D}Bx_{d}+w_{t}\right\|_{2}^{2}+\frac{\mu} {2}\sum_{d=1}^{D}\left\|w_{d}\right\|_{2}^{2} \tag{1}\]  
The letter \(B\) represents a clipping matrix in the formula, and it is binary. Input \(D\) elements through matrix clipping, and \(w_{\text{d}}\) is the representative of the \(D\) channel in the channel learning filter.  
BACF mainly exploits negative training in real sense samples densely pick up from the background area to learn or update the filter, which can deal effectively with appearance changes, but there still existing some imperfections in addressing boundary effect. First, due to the BACF tracker mainly learns and trains the classifier through current frame, it does not take into account the spatial-temporal information of the historical frame, which affects the discriminative ability of the filter so that it cannot distinguish the target from similar backgrounds. Secondly, in terms of feature representation, BACF mainly employs 31-channel HOG features for representing the target. Limited traditional hand-crafted features are difficult to capture abstract semantic information, which immediate affects the accuracy of tracking equipment. From the above analysis, it is obvious that the BACF tracker has some deficiency in dealing with the appearance change problem, and then degrades its performance in dealing with boundary effects. In this work, to solve the impact of the above problems, a spatial-temporal regularization background-aware DCF based algorithm is proposed; the framework of the proposed method is shown in Figure 1.
## 4 Simulation Results
### Calibration Performance with Different Numbers of Channels  
Firstly, the calibration performance of the proposed method is verified in a 4-TIADC and an 8-TIADC to demonstrate that the proposed method works well in TIADCs with different numbers of channels. The polynomial coefficients for the 4-TIADC and the 8-TIADC are set as:  
\[\begin{split}\widetilde{\alpha}_{2}&=[0.001\ -0.003\ \ \ 0.004\ \ \ 0.001]^{T}\\ \widetilde{\alpha}_{3}&=[0.002\ -0.005\ -0.001\ \ \ 0.004]^{T} \end{split} \tag{28}\]  
and:  
\[\begin{split}\widetilde{\alpha}_{2}&=[0.001\ -0.002\ \ \ 0.005\ -0.005\ -0.002\ \ \ 0.003\ \ \ 0.004\ \ \ 0.002]^{T}\\ \widetilde{\alpha}_{3}&=[0.001\ -0.003\ -0.002\ \ \ 0.0 \ \ 0.001\ -0.004\ \ \ 0.002\ \ \ 0.005]^{T}\end{split} \tag{29}\]  
respectively. A multi-tone signal composed of 19 sinusoids from DC to \(0.745\pi\) with a uniform amplitude is adopted as the input. The step size \(\mu\) is set to 0.1.  
**Example 1**.: The spectra of 4-TIADC output without and with calibration are shown in Figure 7. As can be seen from the spectrum before calibration, the nonlinearity-induced error spurs spread over the entire Nyquist frequency band, although the input is band-limited to \(0.745\pi\). The SNDR and spurious-free dynamic range (SFDR) before calibration are 43.26 and 46.73 dB, while after calibration, the SNDR and SFDR increase to 75.48 and 80 dB, respectively.  
The corresponding convergence curves are shown in Figure 8. The ideal nonlinear parameters calculated exploiting Equation (13) are \(\vec{c}_{2}=[0.00075\ \ 0.00175\ -0.00175\ \ 0.00025]^{T}\) and \(\vec{c}_{3}=[0\ \ 0.0005\ \ -0.0015\ \ 0.003]^{T}\). The estimated nonlinear parameters converge to the ideal parameters after about \(1.0\times 10^{4}\) samples as the figures show.  
**Example 2**.: The input signal and simulation setting in this example are the same as in Example 1. The output spectra with and without calibration are shown in Figure 9. The error spurs are greatly suppressed after calibration as the figures show. The SNDR and SFDR before calibration are 38.51 and 40.82 dB, respectively, while after calibration, the SNDR and SFDR improve to 67.87 and 70.98 dB, respectively. The performance after calibration in this example is worse than the performance after calibration in Example 1. The reason is the SNDR before compensation is lower in this example, which means the difference between \(y[n]\) and \(x[n]\) is larger. Thus, the accuracy of error reconstruction decreases, and the calibration performance degrades.  
Figure 7: The spectra of 4-TIADC output (**a**) before and (**b**) after calibration.
# Postharvest Reduction of _Salmonella enterica_ on Tomatoes Using a Flagmonic Acid Emulsion
## 3 Results
### Effect of Sanitizers on Fruit Texture Over Time  
At 0 d, there was no significant difference in tomato skin strength (measured by penetration) among the four sanitizers or water (\(p\geq\) 0.05; Table 3).  
The 50 mM PEL was the only treatment for which skin strength at 0 d was significantly lower than that of NR (\(p\leq\) 0.05; Table 3). Skin strength at 0 d ranged from 7.23 (50 mM PEL) to 9.82 N (NR). There were no significant differences in skin strength between NR, water, chlorine or PAA at 0, 1 or 7 d. However, the skin strength of tomatoes treated with 30 mM or 50 mM PEL decreased by 3.83 and 2.62 N, respectively, from 0 d to 1 d. No treatments exhibited significant differences in tomato skin strength between 1 d and 7 d. Skin strength at 0 d and 7 d were significantly different for both 30 mM and 50 mM PEL (mean decrease: 4.57 and 3.72 N, respectively).  
At 0 d, there was no significant difference in firmness measured by compression among the four sanitizers, water or NR (\(p\leq\) 0.05), with an average firmness of 16.15 N (Table 4).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Treatments**} & \multicolumn{5}{c}{**Storage Time (d)**} \\ \cline{2-6}  & \multicolumn{2}{c}{**0**} & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**7**} \\ \cline{2-6}  & \multicolumn{5}{c}{**Force (N)**} \\ \hline Chlorine & 9.04 \(\pm\) 0.55 & ab 1A 2 & 9.00 \(\pm\) 0.45 & aA & 8.68 \(\pm\) 0.69 & aA \\ PAA & 8.81 \(\pm\) 0.66 & abA & 9.85 \(\pm\) 0.66 & aA & 8.41 \(\pm\) 0.45 & aA \\
30 mM PEL & 8.51 \(\pm\) 0.83 & abA & 4.68 \(\pm\) 0.19 & bB & 3.94 \(\pm\) 0.15 & bB \\
50 mM PEL & 7.23 \(\pm\) 0.29 & bA & 4.61 \(\pm\) 0.34 & bB & 3.51 \(\pm\) 0.21 & bB \\ Water & 9.02 \(\pm\) 0.48 & abA & 9.93 \(\pm\) 0.57 & aA & 9.21 \(\pm\) 0.40 & aA \\ NR & 9.82 \(\pm\) 0.72 & aA & 9.80 \(\pm\) 0.49 & aA & 8.68 \(\pm\) 0.59 & aA \\ \hline \hline \end{tabular}  
* Values followed by similar lowercase letter indicate no significant differences (\(p\geq\) 0.05) among treatments (rows) within storage time (columns). 2 Values followed by similar uppercase letter indicate no significant differences (\(p\geq\) 0.05) among storage time (columns) within treatments (rows). PAA, peroxyacetic acid; PEL, plargonic acid; NR, no rinse.  
\end{table}
Table 3: Effect of the interactions among sanitizer treatments (including water and no rinse (NR) controls) and storage time on tomato skin penetration (N; \(n=8\)).  
The 30 mM and 50 mM PEL treatments were the only ones for which there was a significant difference in firmness from 0 d to 1 d. For example, the maximum force of the 50 mM PEL sample decreased from 15.30 N at 0 d to 7.17 N at 1 d. NR, water and PAA exhibited significant increases in firmness from 1 d to 7 d. Chlorine was the only treatment for which no significant change in tomato firmness occurred over time.