esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images
## 2 Materials and Methods
### Methodology  
#### 2.3.1 LULC Classification  
Figure 2 illustrates the methodological approach used in this study. The OLI Landsat-8 images were pre-processed using layer stacking and mosaicking. They were subject to atmospheric and radiometric correction with the sensor-specific information in the metadata file analyzed in ENVI v 5.4. In this process, spectral radiance was transformed to a planetary or exoatmospheric reflectance by normalizing solar irradiance to eliminate the influence of different solar zenith angles. The process accounted for different values of exoatmospheric solar irradiance resulting from the difference in a spectral band [33]. Atmospheric correction was also performed to remove the influence of ambient atmospheric conditions, including aerosols, thereby increasing the interpretability of the satellite image.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Serial Number** & **Year** & **Q\({}_{m}\) (m\({}^{3}\)s\({}^{-1}\))** & **Q\({}_{p}\) (m\({}^{3}\)s\({}^{-1}\))** & **Serial Number** & **Year** & **Q\({}_{m}\) (m\({}^{3}\)s\({}^{-1}\))** & **Q\({}_{p}\) (m\({}^{3}\)s\({}^{-1}\))** \\ \hline
1 & 1986 & 2530 & 2170 & 16 & 2001 & 3510 & 2456 \\
2 & 1987 &  
Radiometric correction is associated with a sensor that records the electromagnetic radiation intensity as a digital number (DN) for each pixel. These DNs may be translated to more practical real-life units such as radiance, reflectance, or temperature by using the sensor-specific information obtained from the metadata file of the Landsat image. Some software packages for image processing include radiometric calibration tools; for instance, Landsat-8 data can be translated to reflectance in ENVI v 5.4 without measuring the radiance first.  
After atmospheric and radiometric corrections, ArcGIS 10.6 was used to perform a supervised classification on the Landsat images obtained before, during, and after the occurrence of the 2014 flood to identify the LULC types during each period (Figure 3) following the example of [34; 35]. A maximum likelihood classification (MLC) [36; 37] was also applied on the outcomes of the supervised classification, which resulted in a LULC classification with six categories, i.e., crop/agricultural land, built-up area, barren land, sand, water/wetland, and deposited material.
## 3 Results
### Grip Strength
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Grip Span (mm)**} & \multirow{2}{*}{**Glove**} & \multicolumn{2}{c}{**Female**} & \multicolumn{2}{c}{**Male**} \\ \cline{3-6}  & & **Dominant** & **Non-Dominant** & **Dominant** & **Non-Dominant** \\ \hline \multirow{4}{*}{45} & 1 & 16.5 (\(\pm\)10.8) & 19.0 (\(\pm\)9.3) & 9.2 (\(\pm\)13.1) & 13.1 (\(\pm\)8.9) \\  & 2 & 15.0 (\(\pm\)8.3) & 11.6 (\(\pm\)13.2) & 0.7 (\(\pm\)20.4) & 8.1 (\(\pm\)13.3) \\  & 3 & 20.0 (\(\pm\)11.9) & 22.9 (\(\pm\)11.5) & 6.1 (\(\pm\)26.2) & 14.8 (\(\pm\)14.5) \\ \hline \multirow{4}{*}{55} & 1 & 13.7 (\(\pm\)10.8) & 10.6 (\(\pm\)8.0) & 7.5 (\(\pm\)18.4) & 7.3 (\(\pm\)12.5) \\  & 2 & 13.9 (\(\pm\)8.7) & 12.1 (\(\pm\)11.2) & 0.2 (\(\pm\)13.0) & 4.5 (\(\pm\)9.8) \\  & 3 & 15.3 (\(\pm\)8.5) & 12.6 (\(\pm\)8.3) & 7.4 (\(\pm\)23.2) & 12.2 (\(\pm\)12.7) \\ \hline \multirow{4}{*}{65} & 1 & 1.0 (\(\pm\)11.1) & \(-\)2.1 (\(\pm\)17.3) & 10.3 (\(\pm\)10.1) & 8.3 (\(\pm\)10.3) \\  & 2 & 3.7 (\(\pm\)14.2) & \(-\)3.3 (\(\pm\)15.4) & 8.5 (\(\pm\)10.0) & 7.0 (\(\pm\)13.7) \\  & 3 & 11.7 (\(\pm\)11.9) & 4.5 (\(\pm\)18.0) & 15.2 (\(\pm\)9.2) & 15.8 (\(\pm\)10.4) \\ \hline \multirow{4}{*}{75} & 1 & 8.4 (\(\pm\)9.9) & 5.4 (\(\pm\)13.6) & 9.9 (\(\pm\)8.2) & 12.6 (\(\pm\)9.6) \\  & 2 & 5.5 (\(\pm\)8.7) & 4.3 (\(\pm\)17.2) & 6.8 (\(\pm\)10.6) & 9.8 (\(\pm\)9.7) \\ \cline{1-1}  & 3 & 11.5 (\(\pm\)12.2) & 9.4 (\(\pm\)14.9) & 10.0 (\(\pm\)9.2) & 13.9 (\(\pm\)12.4) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RGS (%) under experimental conditions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Grip Span (mm)**} & \multirow{2}{*}{**Glove**} & \multicolumn{2}{c}{**Female**} & \multicolumn{2}{c}{**Male**} \\ \cline{3-6}  & & **Dominant** & **Non-Dominant** & **Dominant** & **Non-Dominant** \\ \hline \multirow{4}{*}{45} & 0 § & 23.9 (\(\pm\)4.8) & 22.3 (\(\pm\)3.8) & 34.6 (\(\pm\)10.0) & 33.5 (\(\pm\)8.9) \\  & 1 & 19.8 (\(\pm\)4.2) & 18.0 (\(\pm\)3.6) & 30.7 (\(\pm\)7.1) & 28.7 (\(\pm\)6.2) \\  & 2 & 20.4 (\(\pm\)5.0) & 20.0 (\(\pm\)5.6) & 33.2 (\(\pm\)7.3) & 30.2 (\(\pm\)6.9) \\  & 3 & 18.9 (\(\pm\)4.0) &  
Both the interaction effects between gender and grip span (\(p\) < 0.0001) and between gender and handedness (\(p\) < 0.01) were significant. Interaction effects between the gloved condition and grip span (\(p\) = 0.056) and between the gloved condition and gender (\(p\) = 0.48) were both insignificant. The three-way interaction effects of gender, grip span, and gloved condition were significant (\(p\) < 0.05). The regression analysis results of the grip strength over grip span are shown in Table 5.
## 3 Proposed Method
### Mixup Strategy for Regularization  
The diversity of the training samples is directly influenced to the generalization power of deep convolutional neural networks [49]. Many studies have demonstrated that constructing virtual samples can effectively improve the generalization ability of deep convolutional networks and prevent network overfitting [50; 51].  
However, due to the gap between natural images and remote sensing images, a regularization strategy that works on natural images may not be suitable for remote sensing images. Based on preliminary experiments, Mixup [52] is adopted as the regularization strategy in this paper.  
Mixup is used in the training phase and not in the testing phase. In each iteration of training phase, we randomly select two samples, \((x_{i},y_{i})\) and \((x_{j},y_{j})\). Then, we form a new sample by performing weighted linear interpolation of these two samples:  
\[\begin{split}\hat{x}&=\lambda x_{i}+(1-\lambda)x_{j }\\ \hat{y}&=\lambda y_{i}+(1-\lambda)y_{j}\end{split} \tag{3}\]  
where \(\lambda\) is a random number in \([0,1]\). \(\lambda\) is sampled from the \(Beta(\alpha,\alpha)\) distribution. This manner brings more randomness and makes the model more robust to such generated samples. In Mixup training phrase, we use only the new sample \((\hat{x},\hat{y})\).  
In each iteration of training phase, a mixed sample \((\hat{x},\hat{y})\) is generated by combining two randomly selected training samples in a mini-batch. This strategy can be utilized to efficiently train any network architecture.  
In addition to Mixup, we have also tested patch-based regularization strategies, such as CutMix [53]. Compared with objects in natural images, objects in remote sensing images often exist against more complex backgrounds. Consequently, patch-based methods often fail to construct meaningful samples and can even cause the accuracy to degrade.
## 4 Simulation Modeling
### Reconfigurable Optical Add-Drop Multiplexer (ROADM) Simulation Model
To calculate the total optical losses of the ROADM, we considered that in TriPleX technology the interface loss between the fiber and the ROADM chip was between 0.5 dB and 1.25 dB, the TCs loss was 0 dB and the maximum propagation loss, corresponding to the longest length of 58.2 mm, was equal to 0.6 dB. Considering the above maximum loss values, the maximum losses of the ROADM were expected to be equal to 3.1 dB. Figure 5a illustrates the spectral response (SR) for the output ports 1 and 2 of the green block of MZIs having an FSR equal to 200 GHz. As it can be observed only wavelengths \(\lambda_{1}\) ( = 1536.9 nm) and \(\lambda_{3}\) ( = 1538.46 nm) were exiting the output port 1 of the green MZIs' block while wavelengths \(\lambda_{2}\) ( = 1537.67 nm) and \(\lambda_{4}\) ( = 1539.25 nm) were dropped at the output port 2, all placed at the flat-top area of the filters. These wavelengths were then fed into the brown cascades of MZIs. Figure 5b shows the respective SR of the brown cascade of MZIs that were incorporated in the upper arm of the ROADM and has and has a FSR equal to 400 GHz. Particularly, in this case, \(\lambda_{1}\) and \(\lambda_{3}\) were inserted in the input port 2 of the first brown MZIs' block and by properly adjusting the phase of the PSs, \(\lambda_{1}\) is dropped at the output port 1, while \(\lambda_{3}\) was dropped at the output port 2. Similarly, Figure 5c shows the SR of the brown cascaded MZIs (FSR = 400 GHz) employed in the lower arm of the ROADM. As it is shown, in this case the SR of the brown cascade was adjusted to add or drop \(\lambda_{2}\) at output port 2 and \(\lambda_{4}\) at output port 1. It should be noted, that in all cases flat-top filter shape was obtained by properly adjusting the coupling coefficients of the TCs. The present design has assumed TE waveguide, while as a next step, a polarization-insensitive Si\({}_{3}\)N\({}_{4}\)/SiO\({}_{2}\) waveguide and MZI filters should be employed [63; 64]. Figure 5: Simulated spectral response of the cascaded MZIs’ filters. (**a**) The filter with FSR = 200 GHz, passing \(\lambda_{1}\) and \(\lambda_{3}\) at its output port 1 and \(\lambda_{2}\) and \(\lambda_{4}\) at its output port 2. (**b**) The filter of the cascaded MZIs with FSR = 400 GHz properly phase adjusted to drop \(\lambda_{1}\) and \(\lambda_{3}\), and (**c**) the filter of the cascaded MZIs with FSR = 400 GHz properly phase adjusted to drop \(\lambda_{2}\) and \(\lambda_{4}\).
# Durability and Safety Performance of Pavements with Added Photocatalysts
## 3 Results
### Slip Resistance and Roughness  
The slip resistance (\(S_{R}\)) findings delivered by the pendulum tester are graphed in Figure 11. No meaningful differences were detected between the asphalt mix containing photocatalytic slurry S1 (S1-A) and the asphalt reference sample (Ref-A). In contrast, slip resistance declined significantly in asphalt mix S2-A relative to the reference. Resistance was slightly lower in emulsion-coated asphalts E1-A to E3-A than in the reference sample.  
Figure 10: (**a**) Effective porosity (\(\epsilon_{e}\)), and (**b**) capillary absorption coefficient (\(k\)) for all the samples studied.  
Figure 11: Slip resistance values (\(S_{R}\)) for the materials studied.  
Figure 12: 3D roughness profiles and images for (**a**) reference asphalt (Ref-A) and (**b**) photocatalytic slab S2-A.  
The profiles were subsequently translated into \(Ra\) (pixels), a parameter that characterises surface roughness. The respective values are shown in Figure 13 for all the materials studied. The most significant effect was the decline in roughness in the slurry-bearing porous asphalt materials. \(Ra\) is plotted against \(S_{R}\) in Figure 14. Further to the findings, slip resistance rose with roughness only in the samples with low roughness values (\(Ra<13\)), after which it remained flat. That apparent discrepancy may be attributable to the limited applicability of pendulum testers to rough surfaces [56]. The \(S_{R}\) values observed for the rougher samples (\(Ra>13\)) must therefore be interpreted with caution.
## 6 Security Analysis
### Mbsa  
Since the MBSA scheme uses a page-level hash chain, it can authenticate each page just as the SPA scheme authenticates each packet. However, this page-level authentication is vulnerable to DoS attacks because it has to keep all received packets to make up a page. In this scheme, DoS attacks can be circumvented by hop-by-hop authentication using MACs with a symmetric key. If any node without a symmetric key attempts to inject malicious packets, it will be detected by the MAC. Unfortunately, this scheme is susceptible to node capture attacks. Once a sensor node is compromised, all information, including all symmetric keys with neighbor nodes, is disclosed to the adversary. Therefore, the adversary can easily inject malicious packets to the neighbors. However, this can be prevented by the page-level hash (\(h_{i}\)) since the adversary cannot fabricate a whole page which has \(h_{i}\). In summary, the MBSA scheme provides an immediate authentication using MACs, thus it is robust to DoS attacks as long as sensor nodes are not compromised. However, the MBSA scheme is weak to node capture attacks since compromised nodes can send forged packets to their neighbors, but the page-level hash constrains the effect within the page.
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 3 Common Classification of Photocatalysts
### Non-Oxide Photocatalyst
#### 3.2.1 CdS Series Photocatalyst  
CdS is a semiconductor material, having a bandgap of about 2.42 eV and a maximum absorption peak of 514 nm. Therefore, CdS can absorb visible light or ultraviolet light with a wavelength of less than 514 nm, which makes CdS more efficient for visible light photocatalysis [100; 101]. In addition, the bandgap position of CdS semiconductors is well suited for many photocatalytic reactions, such as water decomposition [100] and CO\({}_{2}\) reduction [102]. More importantly, the position of the CdS conduction band edge is lower than the position of other common semiconductors (such as TiO\({}_{2}\), SrTiO\({}_{3}\), and ZnO) [103]; this means that in the photocatalytic reaction, photoelectrons of CdS have a stronger reducing power. Therefore, CdS has been intensively investigated as a photocatalyst. However, the CdS material is prone to light corrosion, which results in severe limitations on the number of recoverable photocatalysts. To solve this fundamental problem, researchers have proposed some of measures to improve the use ratio of CdS. The most common method is to prepare CdS composite materials usingdifferent materials or ion doping methods. The CdS composite also enhances the absorption of long wavelength light, thereby achieving the full utilization of the visible part of natural light.
## 4 Discussion  
In this work, we have shown that deep learning can be used for SDB using wave kinematics information as well as color. We have evaluated the performance of a deep learning SDB approach (DSPEB) on real data using Sentinel-2 satellite imagery. We propose two different variants of DSPEB based on wave kinematics (W-DSPEB) and color (C-DSPEB; Section 2.3) and we compare them to a state-of-the-art physics-based SDB method, S2 Shores [24, 43], on two different sites. We show in Section 3.3 that the use of composite estimates over multiple dates compared to single-date estimates improves the performance of all methods tested. We show that the performance of the deep learning-based model (W-DSPEB) exceeds that of the physical method (S2 Shores) and the deep learning color-based method (C-DSPEB) in correlation, RMSE, and temporal standard deviation on two test sites. W-DSPEB achieves an RMSE of 3.26 m in French Guiana and of 5.12 m in Gironde. While this does not yet meet international standards for bathymetry surveys, it demonstrates that deep learning can be applied to both spectral and wave kinematic information for coastal SDB, rivaling existing physics-based models.  
Figure 11: Reconstruction of a full Sentinel-2 tile in Gironde using composite estimates from C-DSPEB (**a**) and W-DSPEB (**b**). The bathymetry survey data used for Gironde is presented in Figure 3.  
Figure 10: Sensitivity of C-DSPEB to background color. (**a**) C-DSPEB estimation standard deviation over six dates in French Guiana. (**b**) Example image of the test area in French Guiana showing high water turbidity which causes the large temporal variance in the C-DSPEB model estimates.
# Synthesis of Four-Link Initial Kinematic Chains with Spherical Pairs for Spatial Mechanisms
## 1 Introduction
In this work, we not only display the graph of individual points but also study the visualization of vectors defining the displacement between two points, such as \(X_{Di}-X_{A}\), \(Y_{Di}-Y_{A}\), \(Z_{Di}-Z_{A}\), as well as the components of transformation matrices calculated from the rotation parameters. Moreover, we illustrate the visualization of composite transformations obtained through matrix multiplication. These visualizations provide a powerful tool for interpreting the intricate geometric relationships inherent in kinematic chain synthesis and robotic system design. By integrating these visualization techniques into a cohesive framework, our methodology enables users to interactively analyze and validate the performance of complex spatial transformations [11]. This work aims to contribute to the field by offering a flexible and accessible approach to 3D visualization, ultimately enhancing the understanding and debugging of multi-dimensional transformation processes in various engineering applications. In the sections that follow, we detail the mathematical foundations underlying the transformation matrices, describe the implementation of the visualization routines in Python, and present several case studies that demonstrate the practical utility of our approach [12]. The methodology described below focuses on the synthesis of initial kinematic chains for spatial mechanisms, specifically those with spherical kinematic pairs. The main goal of this methodology is to optimize the positions of points \(A\), \(B\), and \(C\) in two solids, \(Q_{1}\) and \(Q_{2}\), ensuring that the distance between points \(B\) and \(C\) remains as close as possible to a constant value \(R\) across all positions of the solids. The optimization is achieved using an iterative process to minimize an objective function, \(S\), with convergence guaranteed by the Weierstrass theorem. The process begins by choosing initial reference points \(B^{(0)}\in Q_{1}\) and \(C^{(0)}\in Q_{2}\). These points are selected arbitrarily within the solids. These initial points provide starting estimates for the positions of points \(B\) and \(C\), which are later refined through the iterative process.
## 4 Characterization of Real-Life Signals
### Feature Space Design
#### 4.3.2 Features from RSSI Time Series
The condition \(\lambda=-65\) dB is applied to the RSSI traces to derive \(\overrightarrow{T_{P}}\), and the normalized histograms of packet duration (denoted as \(H(\overrightarrow{T_{P}})\)) of the Bluetooth and Wi-Fi are displayed side by side in Figure 10. The Bluetooth's \(H(\overrightarrow{T_{P}})\) shows a peak at about 120 us, and this corresponds to the duration of the POLL packet; while Wi-Fi's \(H(\overrightarrow{T_{P}})\) shows a peak around 20 to 40 us, which corresponds to the duration of an acknowledgment packet [27]. Hence, the two technologies can be told apart by observing the peaks' location in \(H(\overrightarrow{T_{P}})\). As demonstrated in this case study, the time domain features such as packet duration \(T_{p}\) can be readily extracted from narrowband RSSI; Furthermore, these features are determined by the specific wireless standard, which are immune to the change of measurement scenarios, hence being reliable for classification of non-streaming technologies.
## 5 Discussion
### Reliability of Downscaled SIF  
Since the RF algorithm introduction, it has garnered significant acclaim and found extensive applications across diverse domains [63; 64]. Noted for its remarkable predictive  
Figure 11: The change in SIF anomaly index from 2001 to 2020.  
Figure 10: The spatial distribution of drought in Henan Province from 2001 to 2020.  
accuracy, efficient training speed, and robustness, RF stands out as a preferred option for analysing and optimising models. Therefore, this study utilized the RF approach to build the downscaled SIF model.  
The relationship between SIF and GPP has been consistently demonstrated in previous studies [2]. In this study, the downscaled SIF exhibited a stronger positive correlation with MODIS GPP compared to GOSIF, with correlation coefficients of 0.74 and 0.68, respectively (Figure 5). This finding further validates the reliability of the downscaled results. Meanwhile, to demonstrate the superiority of the RF-based downscaled method, this study conducted a comparative analysis between the downscaled SIF and direct resampling results (Figure 6). The downscaled SIF result obtained through the RF method exhibited enhanced spatial resolution compared to that achieved by direct resampling methods. The downscaled SIF not only preserves the information of the original GOSIF, but more significantly enhances spatial resolution through the research conducted.
## 4 C-ITS Services within CAMINO  
As part of the ETSI C-ITS specifications, several facility services have been standardized to be used by ITS applications, such as CA, DEN, IVI, Traffic Light Maneuvering (TLM), Traffic Light Control (TLC) and more [16]. CAMINO-Core currently provides a basic implementation of CA, DEN and IVI, that allows transmission and reception of CAM, DENM and IVM respectively. The services are built based on the Vanetza library [17] that is an an open-source implementation of the ETSI C-ITS protocol suite.  
Depending on the configuration, one or more of these services will automatically run at startup of the CAMINO-Core. Each running service will initiate message transmission with a pre-configured sending interval. The CAM, DENM and IVIM messages are ASN.1 encoded as per the ETSI and ISO standards. An overview of the supported specifications are given in Table 2. When a message is sent towards the southbound interface via the Comm Controller and Transceiver modules, a header containing a so-called \"service identifier\" is added if the message is to be transmitted using one of the short range communication modules. When such messages are received from the southbound interface, they are directed towards the correct service based on this service identifier. For long-range communication CAMINO does not add any headers to the messages and instead uses separate MQTT topics to differentiate between different services.  
The content of the messages can be constructed based on static configuration parameters and dynamic GNSS information, but can also be fed in real-time by the external northbound applications (sensor input, external services) via the DUST-Core. Inversely, the CAMINO-Core services can push information to the northbound services and actuators through the DUST-Core. This allows for a flexible and modular programming environment to create tailored C-ITS services based on standardized ETSI specifications that can be easily updated and extended.  
In addition to the C-ITS and logging services, CAMINO-Core also contains a GNSS service, which retrieves positioning data with a configurable interval from an external GNSS device via the GPS daemon (GPSd). These positioning data can then be used in the C-ITS services and added to the messages that are being transmitted. Moreover, the GNSS device is used for timing synchronization through the use of the Network Time Protocol (NTP) [18].