esa-sceva/satcom-chunk-collection huggingface dataset:
# STN-Homography: Direct Estimation of Homography Parameters for Image Pairs
## 3 STN-Homography
### Comparison with Other Approaches  
We experimentally compared the mean corner error of our STN-Homography model with other reported approaches. The approaches used for comparison consisted of one traditional and two convolutional methods. The selected traditional approach was based on the ORB+RANSAC method and the reference deep convolutional approaches are the HomographyNet by [22] and the hierarchical  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model Name** & **L2 Loss Weight** & **L1 Loss Weight** & **Mean Corner Error [Pixel]** \\ \hline STN-Homography & 1.0 & 1.0 & 5.83 \\ STN-Homography & 1.0 & 10.0 & 21.86 \\ STN-Homography & 1.0 & 0.1 & 6.21 \\ STN-Homography & 10.0 & 1.0 & 4.85 \\ STN-Homography & 0.1 & 1.0 & 6.24 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Impact of loss weights on the performance of the STN-Homography model.  
method by [24]. As shown in Figure 3, our single STN-Homography model can achieve a mean corner error of 4.85 pixels, which is much smaller than the mean error of the ORB+RANSAC method which is equal to 11.5 pixels, the mean error of the HomographyNet [22] of 9.2 pixels, and the error of the single-stage hierarchical method [24] which is equal to 13.04 pixels.
# On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks
## 1 Introduction
### Related Work  
A survey on UAV positioning for throughput maximization using DL tools has been presented in [36]. Multi-layer perceptron (MLP) and long short term memory (LSTM) approaches are used to determine the UAV position maximizing the overall system performance and user throughput. Specific methods of ML and the hybrid of MLP-LSTM for classification/regression tasks and \(K\)-means algorithms are applied for automatic clustering of classes. Moreover, it is suggested to apply DL for indoor-outdoor UAV positioning in various environments, such as urban, suburban, and rural areas. Our paper is complementary with respect to [36], since we present additional U-RAN applications and discuss potential challenges by highlighting the possible solutions based on SL and RL methods.  
Survey [37] provides a detailed description of all relevant research, wherein ML techniques have been employed in U-RAN to improve different design and functional aspects, such as channel modeling, resource management, positioning, and security. Although [37] is a rather comprehensive survey, some U-RAN applications and challenges have not been discussed. Moreover, approaches relying on SL are not considered for several U-RAN scenarios and applications. In our paper, instead, we have considered all the main applications of U-RAN along with their challenges, by delineating how the available information can be used in SL and multi-objective SL approaches.  
ML applications in wireless sensor networks from networking and application perspectives have been discussed in the survey [25], which gives a good perspective of general applications in sensor networks that can be solved using ML methods. However, the use of UAVs as BSs (i.e., U-RANs) is not considered in this work.  
Several UAV applications have been discussed in [38]. A number of existing applications have been categorized into groups that have distinct qualitative and quantitative communication needs. However, Ref. [38] is mainly devoted to performance analysis, and recent solutions based on ML are not discussed.
# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
## 4 Experiments
### Ablation Study  
To evaluate the effectiveness of key components and feature types in the proposed method, we conducted two ablation studies. The first study isolates the contributions of SDF and SSC by systematically excluding them or using each component individually. The second study investigates the impact of feature types, including lane markings, poles, and traffic signs, by testing combinations of these features.  
Table 6 shows the results of the component ablation study. The \"base\" method, which combines SDF and SSC, achieves the lowest translation and rotation errors, demonstrating the importance of integrating both components. Excluding all components (\"exclude-all\") leads to the worst performance, particularly in elevation (z) and yaw, confirming the necessity of these spatial factors. SDF alone performs slightly better in z compared to SSC alone, while SSC shows better performance in rotation accuracy.  
Table 7 presents the results for feature type combinations. Lane markings alone deliver reasonable accuracy but are less robust in elevation (z). Adding poles improves accuracy across all dimensions due to their spatial consistency in urban scenes. Combining lane markings with traffic signs yields lower IoU and higher z error, highlighting that traffic signs alone are less effective than poles for precise registration. These findings emphasize the critical role of feature combinations in robust localization.
# Direct Selective Oxidation of Hydrogen Sulfide: Laboratory, Pilot and Industrial Tests
## 8 SuperClaus Process  
The main distinguishing feature of the modification of Claus technology known as the SuperClaus process is the supply of substoichiometric air in the thermal stage (Figure 4). Such a method results in the reaction mixture composition after the second catalytic converter containing predominantly hydrogen sulfide at a concentration of 0.8-3.0 vol.%, with trace amounts of sulfur dioxide. Such a mixture passes to the third sequential reactor filled with a catalyst for the direct catalytic oxidation of hydrogen sulfide [40; 41].  
According to data provided by Jacobs Comprimo, the technology licensee from the beginning of the first industrial demonstration of the process in 1988, over 190 installations using the SuperClaus(r) process are currently operating or are under construction, with a total capacity of up to 1200 tons of sulfur per day.  
According to the authors, the catalyst provides the sulfur yield in the third converter at a level of 85%, and the total sulfur yield is 99-99.5% [42]. It should be noted that industrial experience shows the inconsistency of the real and expected results. Thus, in the SuperClaus(r) process, at the stage of the selective oxidation of hydrogen sulfide under industrial conditions, the sulfur yield does not exceed 80-83%, and the achieved total degree of sulfur extraction in SuperClaus(r) industrial installations is 98-98.6% [43], instead of the declared 99-99.5%. However, a sulfur yield of 85% at the stage of selective oxidation of hydrogen sulfide in treatment of tail gases of the Claus process is currently recognized as the best modern level for technologies using the direct heterogeneous catalytic oxidation of hydrogen sulfide.  
Figure 4: SuperClaus® process flow sheet diagram (adapted from [28]).
## 4 Results and Discussion
### Three-Dimensional Steady Simulation  
In the pipe system used in the offshore plant, high-temperature and high-pressure gas is transferred, and a pressure relief valve is used to prevent damage to the pipe which may be caused by strong pressure waves. However, the sudden pressure variation in the pressure relief valve emits high acoustic energy in the downstream direction of the pipe. To simulate this sudden pressure variation, the numerical simulations using the prescribed numerical methods were performed on the three-dimensional critical nozzle as shown in Figure 8. The computation grids are shown in Figure 9, and the boundary conditions were set to be the same as those used in the preceding two-dimensional axisymmetric problem. The grids used for the flow simulation were structured grids, the number of grids was 6,470,000, and the diameter of the nozzle throat was 8.5 mm. The overall grid quality was confirmed using the commercial grid generation tool ICEM CFD and the grid quality index was greater than 0.61, which confirms the good quality of the grids. In addition, y\({}^{+}\) based on the inflow condition was kept below 1. To construct the initial condition for the unsteady LES, three-dimensional steady-state compressible RANS simulation was performed beforehand. Figure 10 presents the numerical results of pressure and temperature distributions inside the pipe and the nozzle. It can be seen that the shock wave is formed inside the critical nozzle due to the high pressure difference in the pipe inlet and outlet, and the sudden pressure variation occurs before and after the shock wave. The shock inside the nozzle meets the boundary layer of the wall surface and the boundary layer separation occurs at this location. This complex interaction generates a strong coherent turbulent flow which in turn causes intense aerodynamic noise. The corresponding unsteady phenomena can be identified in Figure 11, which is discussed in detail in the next section. The similar phenomenon was also observed and discussed in the preceding study by [PERSON] et al. [8].  
Figure 8: Three-dimensional geometry of pipe with critical nozzle.  
Figure 7: Mass flow rate according to variation of exit pressure; **left**: d = 0.4 mm; **right**: d = 0.56 mm.
## 3 Proposed One Dimensional Atrous Convolution Nerual Network
### Optimizer and Loss Function  
The loss function represents the interval between the actual correct answer and the predicted value. In other words, the higher the loss, the greater the gap difference, and the network learns in the direction of reducing it.  
The proposed model uses Cross Entropy(CE). \(t_{i}\) is the ground truth (correct answer), and \(s_{i}\) is the \(i\)-th element of the score vector, which is the output of the last layer of CNN for each class \(i\).  
\[CE=-\sum_{i}^{C}t_{i}\log(s_{i}). \tag{6}\]  
Figure 5: Concept of spectral feature extraction using 1D-Atrous Convolution: Example has 3 kernel size and 3 rate size.  
Figure 6: Graph of ReLU6 and Hard-swish: (**a**) ReLU6 (**b**) Hard-swish.  
The optimizer is a function that finds the parameters that reduce the value of the loss function as much as possible in the network, that is, the weight and the bias. Optimizer functions include Batch Gradient Descent (BGD) [39] and Stochastic Gradient Descent (SGD) [40]. Currently, the most widely used optimizer is Adaptive Moment Estimation (Adam) [41]. Our proposed model also uses the Adam optimizer.
# Linked Open Government Data: Still a Viable Option for Sharing and Integrating Public Data?
## 6 What Factors Are Holding back the Spread of LOGD?
### Data Privacy and Security
#### 6.2.5 Economic/Financial Dimension  
In the economic/financial domain, emphasizing _financial commitment_ is crucial for the successful release of valuable data. The absence of a clear funding and governance model can render initiatives unsustainable. Coupled with a lack of ongoing commitment to maintaining LOGDs, this can lead to outdated or inaccessible data. Together, these factors play a key role in ensuring the successful implementation and long-term sustainability of LOGD projects.  
According to [PERSON] et al. [2], one of the major challenges for OGD lies in \"the costly integration of government data across domains and political boundaries\". This is attributed to datasets being published in various formats, utilizing different vocabularies, and accompanied by varying-quality metadata. [PERSON] et al. [43] propose a \"self-service approach\" to address the cost problem of LOGD, shifting the burden of data conversion towards the data consumer. However, realizing this potential comes with its own set of costs, as observed in the pioneering efforts in the US and UK, where creating high-quality LD requires considerable investment. [PERSON] et al. [97] acknowledge the potential barrier to improving OGD provision in Greece due to the _reduced budgets of government agencies_ amid an economic crisis. Despite the acknowledged benefits of opening government data,agencies might prioritize other ICT projects deemed to be of higher visibility and priority. The economic constraints pose a challenge to realizing the full potential of OGD.  
Maintenance also emerges as a significant concern. [PERSON] et al. [33] highlight maintenance tasks such as updating information in the graph, preserving links, and checking service availability. The critical nature of the maintenance phase is underscored, requiring further development to ensure the validity of produced data in a decentralized context. [PERSON] [9] emphasizes the importance of data stewardship and curation for ensuring the effectiveness of data. Addressing the governance aspect, [PERSON] et al. [88] emphasize the challenges of managing large and diverse datasets from government agencies, requiring significant time and effort. The difficulty in using human effort for governing such datasets, especially when they are distributed across multiple agencies, poses a substantial hurdle.
## 3 Proposed Collaborative FL Framework
### Basic Workflow  
Figure 2 illustrates our basic workflow in a collaborative FL framework. Initially, the central server accepts the registration of all edge clients through the secure channel. All legitimate edge clients receive their ID assignment from the server. No ID information will be stored anywhere except for each edge client storing their own ID. Then, the server awaits the edge clients' grouping requests. Upon receiving the grouping requests, the server collects the participating clients' ID and divides them into corresponding groups through a grouping scheme. Although malicious users do not join the initial registration, they can forge a client's profile with a randomly chosen ID. In order to avoid ID leakage, each edge client has to encrypt a message with the public key sent by the server before sending out ID  
Figure 1: The workflow in a collaborative FL framework.  
information. Hence, the server can decrypt ID messages and execute the grouping scheme. Based on our adversary model, the adversary can overhear this encrypted message but cannot decrypt it in time. According to the grouping scheme, the groups are created and the edge client with the largest ID number is selected as the group leader in a given group. Then, all groups execute the FL mechanism. In each group, each edge client receives the initial model from the group leader and trains this local learning model with the local dataset. According to the principle of FL, all edge clients send their trained model to the corresponding group leader which aggregates all local models to generate a global model using a base FL algorithm. By now, all group leaders have their corresponding global models. Next, all group models are sent out to all participating clients. All participating clients can make output predictions through the received global models, and the output predicted by the majority of global models is considered the final output. This is the essence of the voting mechanism. Of course, these global models include normal and malicious ones. Through our proposed collaborative FL framework, the edge client is capable of making accurate output predictions even in the presence of numerous malicious clients.  
The following subsections describe the working steps in the whole workflow.
# Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines
## 2 Material and Methods
### Hardware Design  
A drone with six rotors was designed, implemented, and tested for the autonomous inspection of transmission lines. The robotic platform can be described in general terms as follows:  
* The drone deployed in real applications has a flight controller (Type Erle Brain 2 with a 900 MHz quad-core ARM Cortex-A7 CPU processor), which has a flight control unit (a computer that provides basic flight controls) and a companion computer (computational system in charge of image processing and image broadcasting). Additionally, the controller has an inertial measurement unit (IMU), an integrated altimeter and an embedded Kalman Filter for the treatment of signals. The UAV also has a GNSS antenna with an absolute error of 1 m.
* Visual data are acquired with the SJ4000 Turnigy HD ActionCam 1080P Full HD video camera. According to the manufacturer, the visual camera in TV mode has a resolution of 1920 \(\times\) 1080 pixels.
* The monocular camera has been previously calibrated to find the focal point and to estimate its parameters. Additionally, the camera is aligned with a gimbal that compensates the fast dynamic rotation of the hexacopter and controls the image plane to stay horizontal and parallel to power-lines. This process is essential for smooth target tracking in the image. The visual information is sent to a computational device that is in charge of higher-level behaviors, in an embedded form, such as the image processing and image broadcasting.
* The information extracted is stored locally in a 16 GB internal memory and sent as a data packet at regular intervals to a companion computer to prevent problems occurrence. To perform this process, the drone is equipped with a communication system based on a transmitter/receiver 433 MHz and WIFI connection employed for telemetry operations, and transmitter/receiver 5.8 GHz employed for image broadcasting.
* The UAV works with the Robot Operating System (ROS-Indigo) [50], adapted to the specifications of this problem.  
The general scheme of the sensory and processing system embedded in the UAV is shown in Figure 3.  
Figure 2: UAV inspection in transmission lines.
## 5 Data Analysis and Main Findings
### Exploratory Factor Analysis (EFA)  
Exploratory factor analysis (EFA) helps determine the strength as well as the correlations between the items in a study [59]. Tests included in an EFA, namely, the Kaiser-Meyer-Olkin (KMO), Bartlett's B, and eigenvalue, determine how the elements of a construct correlate to each other, eliminate weak elements, and build an integrated model.  
The KMO of this present study was 88.37%, which exceeds the recommended 68.13% for a sample test. The Bartlett's B appropriate level of correlation was also statistically significant at 0.001. Therefore, all 24 factors satisfied the original EFA tests. As seen in Table 3, which illustrates the structures, the next stage included internal rounds to extensively test the theoretical framework of the present study.
## 8 Simulation
### Experimental Results
#### 8.2.2 Average Exchanged Messages and Consumed Energy  
As wireless communication is the most expensive operation in wireless networks and depletes most of nodes energy, the number of exchanged messages and the con  
Figure 12: Average cluster head cardinality using the k-hop intra-clustering (**a**) \(k=1\), (**b**) \(k=2\), (**c**) \(k=3\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Clustering Algorithm** & \multicolumn{3}{c}{**Intra-Cluster Topology**} \\ \hline  & Single-hop & Two-hop & Three-hop \\ \cline{2-4} Mezghani & 6.9\% & 21.1\% & 27\% \\ MH-LEACH & 55.3\% & 64.2\% & 67.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Synthesis of the average CH’s cardinality gains compared with Mezghani and MH-LEACH.  
sumed energy are strongly associated and have a significant impact on devices lifespan. Figures 13 and 14, respectively, show the average energy consumed by network nodes and the average number of exchanged messages during the clustering process with according to nodes density increase and considering \(k\in\{1,2,3\}\). The curves' shape show that the energy consumed by network devices is proportional to the number of exchanged messages. In general, as the density increases the energy consumed and the number of messages, generated by the three approaches, increase as well. The multi-hop communications reduce the energy consumption by reducing the communication distance from CMs to their CH, especially when the size of the network scales. Figure 13 demonstrates that whatever the number of deployed nodes, DC2 HC consumes less energy than the other protocols. This improvement can be attributed to the small number of elected CHs and reduced CH rotation that decreases the number of clustering messages exchanged. The number of exchanged messages is reduced by 36.5% and 6.9% compared to Mezghani protocol and MH-LEACH protocol respectively. The energy consumption is also reduced by an average of 21.7%, which is suitable for increasing the network lifetime. Table 6 summarizes the gains obtained by DC2 HC when compared to Mezghani and MH-LEACH in terms of energy consumption.
# Chromium(III) Removal from Wastewater by Chitosan Flakes
## 1 Introduction
Considering that equilibrium analysis is the most important fundamental study required to evaluate the affinity of a sorbent, the ability of chitosan to remove chromium(III) by adsorption was studied in the present work to evaluate its potential application in the field of tannery wastewater treatment [1]. Numerous adsorption tests of chromium(III) on chitosan flakes were conducted to investigate the effects of contact time, pH, initial Cr(III) concentration, and, using real wastewater, ion competition. The thermodynamic behavior was assessed using the well-known Langmuir and Freundlich isotherm models.
## 4 Results and Discussions
### Impact of Automatic Hybridized K-Means with MOA  
Hybridization of K-means with MOA for automatic clustering has been found to improve the performance of these algorithms in handling cluster analysis. Automatic determination of cluster numbers assists in avoiding the sensitivity of initial seeds in the initial population [45]. In most cases, it helps select near optimum initial cluster centroids for the clustering process instead of the usual random selection of the initial cluster centroids.  
Determining the number of clusters automatically also enhances the convergence speed of the resultant hybridized clustering algorithm due to fewer iterations required to obtain the optimal cluster result. The impact of automatic hybridized algorithms is more pronounced when handling real-life datasets. An accurate guess of the correct number of clusters in real-life datasets is an assiduous task, if not impossible, due to its high dimensionality and density. Improving traditional K-means to solve real-life automatic clustering problems through hybridization is of great impact in cluster analysis.