esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Distribution and Monitoring of Land Subsidence Using Sentinel-1 SAR Data in Java, Indonesia
## 2 Data and Methodology  
### Study Area  
Java refers to one of the largest and most populated islands in Indonesia. According to the 2020 census by the Central Agency on Statistics of Indonesia, there are more than 151 million people (about 56% of the total population) living on the island [16]. The island has also become the center of both government and business/industry in the country. Administratively, the island is divided into 6 provinces, namely, Banten, West Java, DKI Jakarta, Central Java, Yogyakarta, and East Java. Furthermore, the administrative region is divided into smaller regions called Kabupaten (regencies) and Kota (cities). There are 119 Kabupaten and Kota in Java Island.  
The physiography of Java is mainly divided into five elements, which are the southern mountains, Quarterrany volcanoes, Central Depression Zone, anticlinorium zone, and alluvial plains (Figure 1). These elements dissect the island in the north-south direction [17]. The southern part is indicated by the southern mountains. The Central Depression Zone of Java consists of alluvial deposits and Pleistocene sediments [18, 19, 20], alternating with Quaternary volcanoes, and the middle part of Java consists of the anticlinorium region. The alluvial deposit spreads along the northern part of Java, and the eastern part is dominated by anticlinorium formations. The width of the alluvial deposit in the northern part of Java varies between several km and 40 km, with a thickness of about 300 m [21]. A detailed geological map shows that alluvial deposits are found in several places, such as in the Bandung area.  
Hydroologically, the state of the groundwater in the lowlands in Java, such as along the northern coast of Java, is closely related to the overall stratification and silty sediment properties [21]. The annual precipitation of Java Island ranges from approximately 800 mm to 3200 mm, with a mean of 1800 mm [16]. In the northern part of Java, groundwater flow systems are driven by gravity, which is impelled by the flow from the higher elevation zone in the center arc of Java (e.g., anticlinorium and Quaternary volcano zones).
## 4 FACTS Controllers, Types, and Their Potential Role
In contrast, series-connected FACTS controllers regulate both active and reactive power by controlling reactance. Essentially, all series controllers inject a voltage in series with the line, as a variable impedance in series, multiplied by the current flow, represents a series voltage injected into the line. Therefore, series-connected controllers may comprise a variable impedance, such as a condenser, reactor, or static converter, operating at fundamental, subsynchronous, and harmonic frequencies [47]. Similarly, the other two types of FACTS controllers, namely, series-series or series-shunt, also regulate both active and reactive power. However, each of these FACTS devices possesses distinct advantages, disadvantages, or limitations, which are beyond the scope of this paper. As mentioned above, FACTSs are power-electronics-based technologies. This technology consists of thrustors and extends to contemporary advancements such as MOSFETs \begin{table}
\begin{tabular}{c c} \hline \hline
**Types** & **Functions** \\ \hline Series Controller & \begin{tabular}{c} Connect in series. \\ Inject voltage (V) in series with transmission line. \\ \end{tabular} \\ \hline Shunt Controller & \begin{tabular}{c} Parallel connected. \\ Inject current (I) to the transmission lines. \\ \end{tabular} \\ \hline Series–Series Controller & \
\begin{tabular}{c} Series connected. \\ Control more than one transmission line. \\ Inject V in series. \\ \end{tabular} \\ \hline Series–Shunt Controller &
\begin{tabular}{c} Connect both in series and parallel. \\ Control more than one transmission line. \\ Inject I in shunt and V in series. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification of FACTS controllers by type. Figure 1: Classification of FACTS controller with respect to generation. and IGBTs, which are the basic building block of FACTS controllers. Thyristors used in the FACTS devices are either self-commutated or line commutated [48]. This means that they differ on their turn-off capability. This capability of FACTS devices decides their fast response, reliability, and efficiency. With research and development (R & D) self-commutated controllers replace line-commutated controllers [49] that enhance the power system functionality. Based on turn-off capability, FACTS controllers are also classified as voltage source (VS) or current source (CS) converters [44]. Both VS converters and CS converters have their own merits and demerits [50], but choosing one or the other depends on the type of application and their role in power networks. Table A1 (given in Appendix B) shows applications of several FACTS controllers and their role in deregulated power networks.
# Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues
## 3. Maritime Federated Learning Use Cases
### Fault Detection  
Fault detection and predictive maintenance are crucial in the maritime industry, since the lifetime of vessels and port infrastructure and the security of assets and personnel are maximized. In this domain, FL has many benefits, as multiple vessels and assets participate in the training process and reduce the total execution time. Ref. [21] considered the problem of fault detection in the IoS, adopting FL to increase privacy, where the central server adjusted the aggregation interval based on the information from the shipping agents. Furthermore, to deal with the harsh maritime environment, a control algorithm was proposed to adapt the model aggregation interval during training, thus reducing cryptography, computation, and communication costs.  
In a similar setting, ref. [22] presented an interpretable multi-level FL network for ship fault diagnosis, dealing with non-i.i.d. data threatening FL performance. Specifically, this study focuses on the detection of rolling bearing faults in ships using an interpretable multi-level FL network. Rolling bearings are critical components in maritime machinery, and their failure can lead to significant operational issues. The types of faults detected in this context include (i) Outer Race Faults (i.e., damage to the outer raceway of the bearing), (ii) Inner Race Faults (i.e., damage to the inner raceway), (iii) Rolling Element Faults (i.e., defects in the rolling elements, balls or rollers), and (iv) Cage Faults (i.e., issues with the bearing cage that holds the rolling elements in place). These faults can result in vibrations, noise, and operational inefficiencies. The FL approach improves fault detection accuracy by addressing the challenges posed by non-i.i.d. (non-independent and identically distributed) data, ensuring robust and reliable diagnosis across different ships and operational conditions. To this end, the concept of multi-level sharing was introduced where in the first step, each client performed local training and in the second step, different parameters were sent to different layers according to calculated network values. Experimental evaluation revealed that the interpretable FL solution provided improved fault detection accuracy over other approaches, overcoming the issue of non-i.i.d. data.
## 3 Results  
### Gain Changes on Lyapunov Exponent Patterns  
In Figure 5a-c we calculate the LEs of AIII using different gains (\(C\) values), one value lower than \(C\) = 3.0 and the other value larger. The LE pattern, Figure 5a, at a lower value of \(C\) = 2.7 no longer exhibits a symmetric-like bubble; it is replaced by a curved trajectory of two equal LEs. A change to a higher value of \(C\) = 3.1 shows the LE replacement as the expected asymmetric bubble in Figure 5c, described in Reference [37]. The LE patterns of AIII at \(C\) = 2.7 resemble the LE patterns of AV and AVI at the standard (canonical) \(C\) = 3.0. Because of their resemblance, we then explored the LEs for AV and AVI to inspect their evolutionary LE pattern changes when \(C\) increases from 3.0, to 3.1 and 3.2 for AV, and \(C\) increases from 3.0, to 3.2, 3.25, 3.3, and 3.35 for AVI. The results are shown in Figures 5d-f and 6, respectively.  
Figure 5: Schematic of LEs vs \(Y\) graphed over the partial domains of AIII and AV at different values of the gain, \(C\). (**a–c**) AIII, the gain changes 2.7, 3.0, 3.1, respectively. (**d–f**) AV, the gain changes 3.0, 3.1, 3.2, respectively.  
Figure 6: Schematic of LEs vs \(Y\) graphed over the domains of AVI at different values of the gain, \(C\). (**a–e**) \(C\) = 3.0, 3.2, 3.25, 3.3, and 3.35, respectively.
## 2 Synthetic Aperture Radar Interferometric Processing
### Acquisition of SAR Images
#### 2.1.2 Acquiring SAR Images by Across-track  
Another approach to acquire SAR images is by across-track. Its imaging geometry is illustrated in Figure 3. In this approach, two SAR antennae, \(S_{1}\) and \(S_{2}\), are mounted on the same platform. The SAR data are acquired at the same time and the baseline, B, is perpendicular to the flight path. The system, therefore, records the master image and the slave image at the same time but from two slightly different locations.  
Figure 2: Imaging geometry of repeat-pass SAR interferometry.  
The main problem with the geometry of the across-track approach in the airborne configuration due to errors caused by the aircraft roll cannot be distinguished from the influence of the terrain slope. This problem is less critical in the spaceborne case, as a satellite track is more stable than an airborne flight path.  
The across-track interferometer is also used for DEM generation [48]. In addition, it can also be available for sea surface mapping [49]. A system combining along-track and across track InSAR is carried out for measurement of ocean waves [50].
# Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication
## 2 Related Work
On one hand, implementing the spread spectrum into FGSM to add an additional index as a bear-information unit. On the other hand, proposing a new deep learning-based low complexity receiver for the new scheme avoiding the huge complexity of the ML decoder. This additional index transmits more data bits and enables transmitting few data bits physically and most of the bits are carried through the indices of the active antennas and spreading codes to conserve the energy of the underwater nodes and enhancing the utilization of the underwater channel bandwidth.
# A Lidar-Inertial Navigation System for UAVs in GNSS-Denied Environment with Spatial Grid Structures
## 1 Introduction
The spatial grid structures in industrial and living scenes are shown in Figure 1. When the UAV works in an environment full of hollowed cylindrical grid structures and curved lattice shell structures above the building, it is difficult for the traditional lidar navigation system to obtain sufficient and stable plane features and corner features. This is very challenging to UAVs using a lidar-based navigation system, which limits the use of UAVs in such an environment. Therefore, seeking to address the problems in applying lidar navigation systems in GNSS-denied environments with spatial grid structures, we propose a lidar-inertial navigation system for UAVs based on grid line features and grid surface features. The main contributions of this study are summarized below:  
1. Focusing on the spatial grid structure in the GNSS-denied environment, we designed a grid feature-extraction algorithm and a shell feature-extraction algorithm that only use logical judgment. 2. We implemented a lidar-inertial navigation system based on the assumption of local collinearity of grid features and local coplanarity of reticulated shell features. 3. We experimented with the navigation system in real application scenarios. Experiments undertaken show that, compared with other recently proposed lidar-based UAV navigation systems, the proposed system can achieve faster and more accurate UAV pose estimation in a GNSS-denied environment with spatial grid structures. The remainder of this paper is organized as follows: The second section introduces the fast extraction algorithm for grid and shell features and the implementation of the navigation system. The third section describes the experiments performed on the navigation system and compares the system with other systems with respect to the following: feature extraction, localization, and mapping. The last section presents the conclusions.
## 6 Description of the Developed Design
### Energy Storage  
For storing liquid hydrogen, specialised containers are considered, as sketched in Figure 12. Each container has the outer dimensions and corner fittings of a 40-ft container, which simplifies the logistics of the supply chain.--Because corner fittings are widely used at, for example, ports and train loading stations, existing transport infrastructure can be easily utilised. With the containerised solution, the liquid hydrogen can always stay in one tank. Thus, discharging and filling steps and the associated pumping losses are avoided.--Each of these containers incorporates a double-walled, vacuum-insulated stainless steel tank to store the liquid hydrogen. In Section 3, the storage efficiency of a truck tank comparable in size to the proposed container and a tank eight times larger, both double-walled and vacuum-insulated, is modelled. For the truck tank, the thermal efficiency is about 93% and, for the bigger tank, 97% for 60 days of storage. Given the advantage of simplifying the supply chain, the drawback of having 4% less thermal efficiency is seen as acceptable. Part of the container is also a small gaseous hydrogen tank to capture vaporised hydrogen due to boil-off. When the container is in the SailREC System, the boil-off is directly fed into the liquefaction plant or used for onboard fuel cells. Each container has an active cooling unit to reduce the boil-off. The SailREC System has an interface to power a container with electricity, to enable filling with liquid hydrogen and the withdrawal of vaporised hydrogen. During the supply chain, transport systems may support only a few of these interfaces or none of these.  
In a rough calculation, the weights of the liquid and gaseous hydrogen tanks are determined as 5465 kg and 822 kg, respectively. For the liquid hydrogen tank, the payload is 2358 kg of hydrogen. The additional structural weight of the container is assumed to be 3800 kg.--This is the empty weight of a standard 40-ft container [46]. For simplicity, the same weight is assumed to be the structural weight.--Another 3000 kg must be considered for extra hardware, such as an active cooling unit or photovoltaic modules. In total, this sums up to a weight of around 15.5 t for one storage container. One container has an energy capacity of 78.5 MWhH2 when considering the liquid hydrogen payload. For an energy harvesting cycle of 60 days and an assumed wind turbine capacity factor of 50%, about 14 containers are required to store the produced hydrogen of around 1100 MWh. Thus, the total weight of all hydrogen storage containers is approximately 217 t.  
Figure 12: Sketch of the liquid hydrogen storage container (view from the top).
# Safety Risk Assessment of Highway Bridge Construction Based on Cloud Entropy Power Method
## 3 Evaluation Method
### Entropy Method  
The entropy weight method is an objective weighting method that determines the weight of decision indicators according to the amount of information contained in each indicator value [26]. Generally, if the entropy corresponding to the index value is smaller, it indicates that the degree of variation of the index value, the impact on the comprehensive evaluation, and the weight value are all greater; otherwise, the weight value is smaller.  
There are \(m\) evaluation objects, denoted as \(M=(M_{1},M_{2},\cdots,M_{m})\), \(n\) evaluation indicators, denoted as \(N=(N_{1},N_{2},\cdots,N_{n})\), and the value of evaluation object \(M_{i}\) on evaluation indicator \(N_{j}\), denoted as \(r_{ij}(i=1,2,\cdots,m;j=1,2,\cdots,n)\). The original data matrix R is formed by \(r_{ij}\), where \(r_{ij}\) is the evaluation value of the \(i\)th object under \(j\)th evaluation indicators.  
The steps for calculating the weights of each evaluation index using the entropy weighting method are as follows [27]:  
1. Data standardization  
The difference in the dimensional units of the evaluation indicators will affect the evaluation results during the calculation process. Therefore, it is necessary to perform dimensionless processing on the original data and obtain the dimensionless matrix \(B=\left(b_{ij}\right)_{m\times n}\)  
Figure 4: Cloud generator operation schematic.  
Figure 3: Schematic diagram of the digital features of the cloud model.  
For the benefit-type index with a more favorable value, the dimensionless processing expression is:  
\[b_{ij}=\frac{r_{ij}-\min_{j}(r_{ij})}{\max_{j}(r_{ij})-\min_{j}(r_{ij})} \tag{2}\]  
For the cost index, for which the smaller the value, the more favorable it is, the dimensionless processing expression is:  
\[b_{ij}=\frac{\max_{j}(r_{ij})-r_{ij}}{\max_{j}(r_{ij})-\min_{j}(r_{ij})} \tag{3}\]  
where \(\max_{j}(r_{ij})\) is the maximum value of \(r_{ij}\) under the \(j\)th evaluation index, and \(\max_{j}(r_{ij})\) is the minimum value of \(r_{ij}\) under the \(j\)th evaluation index.  
Calculating the proportion of the \(i\)th evaluation object under the \(j\)th indicator  
\[p_{ij}=\frac{b_{ij}}{\sum\limits_{i=1}^{m}b_{ij}} \tag{4}\]  
Calculating the entropy value of the \(j\)th indicator  
\[e_{j}=-k\sum\limits_{i=1}^{m}p_{ij}\ln p_{ij} \tag{5}\]  
\[K=\frac{1}{\ln m} \tag{6}\]  
The greater the difference in a certain index in the evaluation system, the smaller \(e_{j}\); the smaller the difference, the greater \(e_{j}\). If \(e_{j}=1\), this means that the index \(i\) has no influence on the evaluation system at this time.  
Calculating the difference coefficient of the information entropy of the \(j\)th indicator  
\[d_{j}=1-e_{j} \tag{7}\]  
Determining the weight of each evaluation indicator  
\[\gamma_{j}=\frac{d_{j}}{\sum\limits_{j=1}^{n}d_{j}} \tag{8}\]
## 2 Materials and Methods
### Variational Autoencoders  
Variational autoencoders represent an expansion of conventional AEs [12; 13]. In conventional autoencoders, the encoder acquires a latent representation of the data, which is subsequently utilized by the decoder to reconstruct the initial input data. The process is executed deterministically, indicating that identical input will yield identical output. In contrast to other methods, VAEs aim to establish a mapping between the input data and a probability distribution across the latent space. Specifically, this distribution is represented by the mean and variance of a Gaussian distribution, which is typically utilized. The ability to perform random sampling from the latent space is a valuable technique as it enables the subsequent utilization of this output as an input for the decoder component, thereby facilitating the generation of new data.  
The primary aim of VAEs is to reduce the reconstruction loss, which is similar to that of a conventional AE. However, VAEs also strive to minimize the KL differences between the acquired distribution and a prior distribution across the latent space. The KL divergence quantifies the degree of resemblance between two probability distributions, especially the extent to which distribution Q provides an adequate approximation of distribution P. Assuming that x refers to the input data and z represents the latent variables or the encodedrepresentation of the input data, the objective in the context of VAE is to approximate the posterior distribution \(\mathrm{P}(z\,|\,x)\), which facilitates the projection of data into the latent space. Due to the unknown nature of \(\mathrm{P}(z\,|\,x)\), a simplified estimation of \(\mathrm{Q}(z\,|\,x)\) is utilized. In the process of training a VAE, the encoder module is trained to minimize the discrepancy between the posterior distribution \(\mathrm{Q}(z\,|\,x)\) and the prior distribution \(\mathrm{P}(z\,|\,x)\) by optimizing the KL divergence between the two distributions. Consequently, the objective function of the VAE comprises the divergence of the KL term, necessitating its minimization.
## 3 Results and Discussion
### Air Velocity at the Animal Height and Just above the Emission Surface  
Figure 11a shows air velocities at the cow height in different locations for Cases 1 to 9. Large variations in local air velocities were observed with different opening combinations. In most areas, the highest air velocities at the cow height occurred in Case 8 SWCD, where both sidewalls and all gates were completely open. In locations near the windward side i.e., R2, W2, FA, the highest air velocities were observed in Case 3 SMB, where the practical opening configuration is used in summer. By contrast, Case 2 SMT gave the lowest air velocities at the cow height except in the region near the leeward side where the lowest air velocities happened in Case 6 SMTD and Case 7 SMBD. This was probably caused by the up-jet airflow pattern with air recirculation zone positioned near the floor, as seen in Figure 9. In addition, it was found that the closer to the windward side, the higher the air velocities at the cow height. Similar results were found in [PERSON] et al. [14] 1:40 scale model study. It indicated non-uniform airflow distributions in the animal occupied zone with likely better ventilation in the windward side than in the leeward side.  
The air velocities close to the emission surface in different locations and different opening combinations are compared in Figure 11b since the ammonia release is closely related to the air velocity over the manure surface [35]. Generally, the highest air velocities occurred at the locations W1 and R2, from where the most ammonia emissions would be released because of an increased convective mass transfer coefficient, but the amount of manure soiling the floor is an important factor of ammonia emission. Case 3 SMB and Case 8 SWCD contributed to higher air velocities above the emission surface near the windward side and near the leeward side, respectively, and thus higher ammonia emissions from the barn. Increasing air velocity effects on increasing ammonia emissions [1; 36; 37] because ammonia evaporation losses increased linearly with the increasing airflow velocities at the floor level [38; 39]. By contrast, Case 1 SM opening configuration tended to give lower near floor air velocities which may result lower emissions, especially in the middle and near the leeward side of the barn. The results indicated that the opening combination may affects cow comfort depending on the location of the cows, and the emission. Therefore, the seasonal opening combinations can be controlled depending on the outside weather conditions.
# Prioritization of Drugs for Fungal Keratitis Eye Infections: An In-Silico Analysis
## 2 Result and Discussion
### Target-Template Alignment for Homology Modeling  
The RNAP protein sequence of _A. flavus_ and _A. fumigatus_ was used as the target sequence, and a similarity search was performed against the protein databank (PDB) for a suitable template by using Basic Local Alignment Search Tool for Protein (BLASTP). The obtained X-ray crystal structures were considered as templates and were further analyzed for their resolution, sequence similarity, and secondary structure similarity (SOPMA), covering the maximum range of the target sequence. The X-ray crystal structures from the PDB database, the 3h0g_B chain, shared a sequence similarity of 63.64% and 67.80% with the RNAP from _A. flavus_ and _A. fumigatus_, respectively. Previous studies demonstrated that a sequence identity higher than 25% between the two proteins is similar in 3D structures [26]. Hence, the 3D structures of the respective template were considered to be suitable for modeling through homology modeling.