esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dynamics Modeling of Shafting of Hydropower Units
\begin{array}{l}F_{x-ump}=\frac{RL\pi k_{1}^{2}l_{J}^{2}}{4\eta_{0}}(2 \Lambda_{0}\Lambda_{1}+\Lambda_{1}\Lambda_{2}+\Lambda_{2}\Lambda_{3})\cos\gamma \\ F_{y-ump}=\frac{RL\pi k_{1}^{2}l_{J}^{2}}{4\eta_{0}}(2\Lambda_{0}\Lambda_{1}+ \Lambda_{1}\Lambda_{2}+\Lambda_{2}\Lambda_{3})\sin\gamma\end{array} \right., \tag{15}\]  
where \(R\) is the radius of the generator rotor. \(L\) is the length of the generator rotor. \(k_{j}\) is the fundamental wave coefficient of the magnetomotive force. \(l_{j}\) is the generator excitation current. \(\mu_{0}\) is the permeability of air. \(\gamma\) is the rotation angle, and \(\cos\gamma=x_{1}/e,\sin\lambda=y_{1}/e\). \(e\) is the radial displacement of the generator rotor, \(e=\left(x_{1}^{2}+y_{1}^{2}\right)^{1/2}\). \(\varepsilon\) is relative rotor eccentricity. \(\delta_{0}\) is the average air gap length of the rotor. \(\Lambda_{n}\) is the air gap permeability, which can be expressed as:  
\[\Lambda_{n}= \begin{cases}\frac{n0}{\delta_{0}\sqrt{1-\varepsilon^{2}}}&(n=0) \\ \frac{2\mu_{0}}{\delta_{0}\sqrt{1-\varepsilon^{2}}}\Big{[}\frac{1-\sqrt{1- \varepsilon^{2}}}{\varepsilon}\Big{]}^{n}&(n\geq 1)\end{cases} , \tag{16}\]  
By substituting Equations (12) and (14) into the Lagrange equation, the differential equations of the dynamic motion of the spindle system can be obtained:  
\[\left\{ \begin{array}{l}m_{1}\ddot{x}_{1}+c_{1}\dot{x}_{1}+K_{11}x_{1}=m_{1} \epsilon_{1}\omega^{2}\cos\omega t+F_{x-ump}-k_{4}\theta_{x_{1}}\\ m_{1}\ddot{y}_{1}+c_{1}\dot{y}_{1}+K_{11}y_{1}=m_{1}\epsilon_{1}\omega^{2}\sin \omega t+F_{y-ump}+k_{4}\theta_{y_{1}}\\ m_{2}\ddot{x}_{2}+c_{2}\dot{x}_{2}+K_{22}x_{2}=m_{2}\epsilon_{2}\omega^{2}\cos \omega t+F_{x}-k_{4}\theta_{x_{2}}\\ m_{2}\ddot{y}_{2}+c_{2}\dot{y}_{2}+K_{22}y_{2}=m_{2}\epsilon_{2}\omega^{2}\sin \omega t+F_{y}+k_{4}\theta_{y_{2}}\\ J_{4_{1}}\ddot{\theta}_{x_{1}}+\left(J_{p_{1}}+m_{1}\epsilon_{1}^{2}\right) \omega\dot{\theta}_{y_{1}}=-c_{3}\ddot{\theta}_{x_{1}}+k_{4}y_{1}-k_{5}\theta _{x_{1}}\\ J_{4_{1}}\ddot{\theta}_{y_{1}}-\left(J_{p_{1}}+m_{1}\epsilon_{1}^{2}\right) \omega\dot{\theta}_{x_{1}}=-c_{3}\ddot{\theta}_{y_{1}}-k_{4}x_{1}-k_{5}\theta _{y_{1}}\\ J_{4_{2}}\ddot{\theta}_{x_{2}}+\left(J_{p_{2}}+m_{2}\epsilon_{2}^{2}\right) \omega\dot{\theta}_{y_{2}}=-c_{3}\ddot{\theta}_{x_{2}}+k_{4}y_{2}-k_{5}\theta _{x_{2}}\\ J_{4_{2}}\ddot{\theta}_{y_{2}}-\left(J_{p_{2}}+m_{2}\epsilon_{2}^{2}\right) \omega\dot{\theta}_{x_{2}}=-c_{3}\dot{\theta}_{y_{2}}-k_{4}x_{2}-k_{5}\theta _{y_{2}}\end{array} \right., \tag{17}\]  
where \(m_{i}\) is the rotor mass, \(x_{i}\) and \(y_{i}\) are the radial displacement coordinates, \(\theta_{i}\) is the inclination angle, \(j\) is the moment of inertia, \(K_{11}\) and \(K_{22}\) can be understood as the generalized stiffness, and the expressions are as follows:  
\[\left\{ \begin{array}{l}K_{11}=2K_{1}+K_{3}\frac{\sqrt{x_{1}^{2}+y_{1}^{2}}} {\sqrt{x_{1}^{2}+y_{1}^{2}}},\\ K_{22}=2K_{2}+K_{3}\frac{\sqrt{x_{1}^{2}+y_{1}^{2}}}{\sqrt{x_{1}^{2}+y_{2}^{2}}},\end{array} \right. \tag{18}\]
# Proving Properties of [PERSON]'s Algorithm for Mutual Exclusion of N Processes
## 2 Modeling a Mutual Exclusion Algorithm
### Dekker's Algorithm for Two Processes  
As a concrete example, Algorithm 2 reproduces in pseudo-code Dekker's algorithm for N = 2 processes [1; 22], here referred to as Dekker-2. The algorithm is based on the following _output_ shared communication variables:  
bool flag[1..2] // all false initially  
int[1..2] turn //immaterial is the initial value  
There is one \(flag\) per process. The variable \(turn\) can be written by one process and read by the partner. A process communicates its willingness to enter its critical section through its Boolean \(flag\). However, a competing process (engaged in the entry part) gets the grant to enter the critical section provided its turn arrived, that is, its id is assigned to \(turn\). The process that achieved the permission to enter its critical section will be assigned to \(turn\) the identity of the partner process on exiting from the critical section. Process \(i\), \(1\leq i\leq 2\), introduces a local constant \(j=3-i\) to refer to the partner process. Busy waiting is expressed by the instruction: \"\(await(\)\(turn!=j\));\". This is equivalent to the following cycle: \"\(while(\)\(turn==j\));\". In other terms, process \(i\) spin-locks until the variable \(turn\) contains \(i\) as its value.  
```
Process(i): Local constant: pidj=3-i; //j is partner process repeat  NCS; //protocol-entry-part flag[i]=true; //request to enter while(flag[j]){//incompetition if(turn==j){//notmyturn flag[i]=false; //resetmyrequest await(turn!=j); //busy-waitinguntiltheturnismine flag[i]=true; //risemyrequestagain } } //competitionend:thepartnerhasnointerestinCSandtheturnismine CS; //protocol-exit-part turn=j; //turnassignedtopart flag[i]=false; //resetmyrequest forever
```  
**Algorithm 2** Dekker-2 mutual exclusion algorithm.  
The correct operation of Dekker-2 can be checked intuitively using Algorithm 2 with the help of comments. However, for a complete assessment of Dekker-2, our approach is to, preliminarily, reduce Algorithm 2 into the Timed Automata (TA) formalism of Uppaal [18; 19; 20].
# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## 2 Data
### Sentinel-1 Data  
Sentinel-1 is a two-satellite constellation whose payload is a SAR working at C band (5.4 GHz). It operates in the Interferometric Wide Swath (IWS) mode over land; in IWS mode, S1 acquires data with a 250 km swath at a spatial resolution of \(5\times 20\) m\({}^{2}\) (single look). S1 products are usually made available on the Copernicus Open Access Hub (also known as Sentinel Data Hub System--DHuS) Hub in 3-4 h. Hence, these products are suitable for a NRT service like that presented in this paper. AUTOWADE makes use of S1 Level-1 (L1) Ground Range Detected (GRD) products, whose spatial resolution is about \(20\times 20\) m\({}^{2}\); S1 data are available in dual polarization (VV + VH) for the IWS mode. Considering both satellites of the S1 constellation, the repeat cycle of S1 is 6 days. Considering both ascending and descending orbits and the overlap of the orbits, the average revisit time is 2-3 days.  
AUTOWADE has been conceived to work daily on the Italian territory, whose coverage is performed by means of 7 descending orbits (morning passes) and 6 ascending orbits (afternoon passes) as shown in Figure 1. For each orbit, the S1 GRD products are available as image slices for the purpose of data manageability. Each slice is a stand-alone product that can be processed independently. A total of 30 slices are included in the descending orbits covering Italy, while 34 slices are included in the ascending orbits.
## 4 Blockchain-Based Fine Dust Management System Model for Construction Sites
### Chaincode Design  
Chaincodes were designed to reduce oracle problems caused by human intervention and to automate systems. A chaincode consists of four functions that store and manage necessary data, excluding the functions essential for system operation. The proposed chaincode (smart contract) and the data to be managed on the blockchain are defined as illustrated in Table 2.  
Figure 2: The system architecture of the proposed fine dust management system.  
The subjects that utilize the chaincode are IoT, contractors, government inspection agencies, and systems. The methodology of using the chaincode according to each subject is discussed below.  
The IoT for measuring the concentration of fine dust generated at construction sites uses the chaincode of the system (smart contract) as an \"input argument.\" The main IoT data to be stored in the blockchain are IoT ID, Value, and IoTTime. The unique ID of the IoT (IoT ID) is used to identify in which construction site the fine dust concentration was measured to explain the key value of the sensor data. To determine the hourly fine dust concentration, IoTTime is recorded.  
For the contractor to upload a photo of the fine dust reduction measures using the application, the hash value of the original photo data is automatically extracted, and GC ID, Gctime, Hash Value, and Image Url are saved. The GC ID is used to determine which contractor uploaded the photo and whether the contractor was participating in the blockchain network. Because image data consume a large amount of capacity, the hash value corresponding to the photo is extracted using the SHA-256 algorithm. The original picture that was stored in the off-chain is linked by Image Url for open access.  
An inspection agency inputs the [regulation value] of fine dust concentration using the application. [Regulation ID] is used to prove that the inspection body is a reliable participant.  
If the fine dust concentration value at a construction site exceeds the set value, the system automatically alerts the construction company to take required fine dust reduction actions. GC ID is used to specify the contractor and Value and Regulation Value are used to prove that the set value has been exceeded.
## 2 Queue Model
### Numeric Example for the Queuing Model  
This section gives a simple example of the queuing model that is used for EV arriving in the charging station.  
Figure 3 presents the main inputs and outputs of the queuing model. Hence, the following numeric example demonstrates the application of this model in the EV-arriving process.  
Assume that a charging station has charging points \(N_{CP}=3\) and the arrival rate of the EV at this station \(\lambda=5\) EV/h. The charging point can charge the EV to be fully charged in \(T_{full}=30\) min hence, the service rate is 2 EV/h. Arriving EV balks when 6 EVs are in the system. Hence, to calculate the average waiting time and the overall time that the EV number 6 takes in the queue, the queue model is applied as follows:  
To calculate the stationary vector \(\pi\) for all state (EV = 6), \(\pi_{0}\) should be evaluated first by applying (9), hence \(\pi_{0}=0.068\). Then the \(\pi_{N_{EV}}\) from \(N_{EV}=1\)_to_ 6 can be calculated using (6). The first part of the Equation (6) will be applied as \(N_{EV}=1\)_to_ 2, which is \(N_{EV}<N_{CP}\) and the values will be as follows:  
\[\pi_{1}=\tfrac{1}{7!}\big{(}\tfrac{5}{2}\big{)}^{1}\times 0.068=0.170\]  
\[\pi_{2}=\tfrac{1}{2!}\big{(}\tfrac{5}{2}\big{)}^{2}\times 0.068=0.212\]  
However, when \(N_{EV}\geq N_{CP}\) which is \(N_{EV}=3\)_to_ 6  
\[\pi_{3}=\tfrac{1}{3!}\big{(}\tfrac{1}{3}\big{)}^{3-3}\big{(}\tfrac{5}{2}\big{)} ^{3}\times 0.068=0.177\]  
\[\vdots\]  
\[\pi_{6}=\tfrac{1}{6!}\big{(}\tfrac{1}{6}\big{)}^{6-3}\big{(}\tfrac{5}{2}\big{)} ^{6}\times 0.068=0.102\]  
The overall values of the stationary vector are calculated and arranged in Table 1.  
From Table 1 it can be observed that:  
The probability that all charging points are idle is:  
\[Pr(all\ CP\ idle)=\pi_{0}=0.068.\]  
The probability that an EV will not have to wait is:  
\[Pr(no\ wait)=\pi_{0}+\pi_{1}+\pi_{2}=0.45\]  
The probability that an EV will have to wait is:  
\[Pr(\ wait)=1-Pr(no\ wait)=0.55\]  
The probability that an EV balk is:  
\[Pr(\ EV\ walks\ )=\pi_{6}=0.102\]  
The average number of EVs available in the system is computed using (10) is:  
\[L_{s}=2.944\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \(N_{EV}\) & **0** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline Component & \(\pi_{0}\) & \(\pi_{1}\) & \(\pi_{2}\) & \(\pi_{3}\) & \(\pi_{4}\) & \(\pi_{5}\) & \(\pi_{6}\) \\ Probability & 0.068 & 0.170 & 0.212 & 0.177 & 0.147 & 0.123 & 0.102 \\ \hline \end{tabular}
\end{table}
Table 1: Stationary vector.  
The number of EVs waiting in the queue based on (11) is  
\[L_{q}=0.700\]  
The overall time spent in the system can be calculated using (12) is:  
\[T_{s}=0.656\;\text{h}\]  
The average waiting time in the queue can be given by (13) is:  
\[T_{q}=0.156\;\text{h}\]
## 4 Discussion
Figure 9 shows that the maximum axial force in a sheet pile wall was constant across all scenarios, and the axial force in the pile diminished as Rx increased. This indicates that the addition of more piles could reduce the axial force experienced by the system. In the pile close to the building, Figure 9 depicts the changes in the axial force for Rx values of 0.25, 0.5, and 0.75. At Rx = 0.25, a sheet pile wall experienced its greatest axial force. The axial force in the pile diminished as Rx rose. In every instance, the location of the maximum axial force was the same. Figure 10 displays that the maximum shear force in a sheet pile wall could be observed at Rx = 0.25 and the shear force in the pile decreased as Rx increased, with all cases displaying the maximum shear force at the same location. This suggested that the shear force in the system was highest when the pile did not provide support and reduced as the number of piles increased. For Rx values of 0.25, 0.5, and 0.75, Figure 10 shows the variation in the shear force for the pile near the building. For a summary, it may be said that the highest shear force in a sheet pile wall was detected at Rx = 0.25 and that it declined as Rx rose, with the maximum shear force being present in every instance at the same location [32]. Lastly, Figure 11 shows that the bending moment in the pile decreased as Rx increased, and the location of the maximum bending moment remained the same across all cases. This indicates that the addition of more piles could reduce the bending moment experienced by the system. Rx = 0.25 depicts the maximum bending moment in a sheet pile wall, and Figure 11 shows the effects of bending moments at Rx values of 0.25, 0.5, and 0.75 on the pile next to the building. As Rx rose, the bending moment in the pile decreased, with the maximum bending moment occurring consistently in the same spot. The extreme axial, shear, and bending moment values in piles are shown in Table 6.  
Overall, these findings provide valuable insights into the behavior of a sewage pipe and sheet pile wall system and could inform the design and construction of similar systems in the future.
## 2 Materials and Methods
### Image Acquisition  
The samples of composted material were subjected to image acquisition in a specialised photographic chamber illuminated with visible light (Figure 1). The construction of this chamber was described in detail by [PERSON] et al. [43]. As a light source, fluorescent lamps, Sylvania Luxline Plus F15W/865 (Feilo Sylvania, Budapest, Hungary), were used. The images were acquired using a Nikon D80 digital single-lens reflex camera (DX format sensor), equipped with a fixed lens Nikkor 35 mm f/1.8G AF-S DX (Nikon Corporation, Tokyo, Japan). The exposure parameters were set according to the rules of photography, taking into account the intensity of the light illuminating the photographed sample. The ISO number of the camera's sensor was set to ISO100, the aperture was set to f/5.6, and the shutter speed was set to 1/25 s. A total of 1536 images of composted material were acquired with a resolution of 968 \(\times\) 648 pixels covering an area of 98 \(\times\) 65 mm. The 576 images were of a composted mixture with untreated straw and 960 with ensiled straw. At the same time, 865 images were of immature material, while 672 were of material that had reached the early maturity stage. Example images taken at the beginning and the end of each compostion experiment are shown in Figure 2. It is worth noting that the composted material filled the entire frame of the photograph. Therefore, there was no problem of separating the material from the background at a later stage of the research.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment** & **Sludge** & **Straw** & **Straw Type** & **Experiment** & **Sampling** \\
**Number** & **Content (\%)** & **Content (\%)** & & **(Days)** & **Days** \\ \hline
1 & 30 & 70 & untreated & 36 & 1, 10, 20, 36 \\
2 & 45 & 55 & untreated & 36 & 1, 10, 20, 36 \\
3 & 60 & 40 & untreated & 36 & 1, 10, 20, 36 \\
4 & 30 & 70 & ensiled & 29 & 1, 6, 14, 29 \\
5 & 40 & 60 & ensiled & 29 & 1, 6, 14, 29 \\
6 & 50 & 50 & ensiled & 29 & 1, 6, 14, 29 \\
7 & 45 & 55 & ensiled & 39 & 1, 7, 14, 25 \\
8 & 55 & 45 & ensiled & 39 & 1, 7, 14, 25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Plan of experiments.  
Figure 1: The photographic chambers used in the study.  
Figure 2: _Cont._
# Evaluation of Multi-Stream Fusion for Multi-View Image Set Comparison
## 2 Problem and Related Work
### Related Work  
The primary problem of matching two image sets can be divided into subproblems such as:  
* semantic segmentation (detection of the foreground object's ROI),
* feature extraction,
* image-to-image similarity evaluation, and
* image set-to-image set similarity evaluation.  
The literature doesn't provide a complete solution to this problem, but we can find several papers related to these subproblems. The \"DeepRanking\" approach [8] is a comprehensive solution based on deep learning (DL) technology that provides both feature extraction and similarity evaluation of two images. We shall describe it in-detail in Section 4. The alternatives for the DL-based features can be hand-crafted keypoint descriptors, such as SIFT, speeded-up robust features (SURF), or \"oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary features (BRIEF)\" (ORB) [10]. Several methods of this kind are compared in [11], paying attention to their performance for distorted images. Nowadays, computationally efficient binary keypoint descriptors dominate in real-time image analysis applications [12].  
The next subproblem, how to measure a distance between images (or feature vectors), can be solved by the use of obvious metrics such as Euclidean, Manhattan, or Cosine distance or another, more specific one, such as the Hausdorff metric [1].  
The evaluation of two image sets reminded us of the evaluation of blind source separation (BSS) algorithms as applied to the image sources [9]. There we faced a similar problem--every estimated output must have been uniquely assigned to a single source and,in an ideal case, should not contain any cross-correlation with other sources. We proposed an error index for both sets (separated outputs and reference sources) which handles the possible differences in permutation (ordering of images in a set) and amplitude scales, and is normalized by the number of set items.  
Thus, our approach applies the DeepRanking-based DL stream of image pair comparison, the SIFT descriptors as ROI features, and the Euclidean distance in a conventional stream for image-to-image similarity evaluation. We also adapted the error index from BSS research to estimate the image sets dissimilarity score.  
The architecture of multi-stream fusion of data analysis streams that covers all of the aforementioned subproblems into one final decision was chosen. The idea of such architecture was previously proposed in [13]. Different works [14; 15; 16] deal with multi-view image fusion to improve the results of single-view image analysis. The problems solved in these papers are different from our problem, yet the idea of using different views to obtain better results is close to our objective.  
An overview of related papers, in accordance with a methodology to literature survey and evaluation developed in [17; 18;
# Steel Surface Defect Detection Based on YOLOv8-TLC
## 4 Experimental Results and Analysis
### Analysis of Realization Results
#### 4.3.4 Comparative Experiments with Detection Algorithms  
To verify the superiority of the proposed algorithm, we selected several detection algorithms for comparison, including YOLOv3, YOLOv5n, YOLOv5s, YOLOv6n, YOLOv6s, YOLOv9s, YOLOv10n, YOLOv11n, as well as methods from references [24,30], and also included YOLOv8n. The comparison results are presented in Table 8.  
Figure 16: Normalized confusion matrix of YOLOv8-TLC.  
Figure 15: Normalized confusion matrix of YOLOv8.  
From Table 8, it can be seen that the improved model has the highest mAP value compared to other models. Although the latest YOLOv11n achieves higher accuracy than the baseline YOLOv8n model, the improvement is minimal and does not surpass the enhancements made in this study. Furthermore, the improved algorithm meets the requirements for real-time detection, balancing both detection speed and accuracy, demonstrating superiority in steel surface defect detection.
## 2 Materials and Methods
### Ongoing Sampling Campaign with Low-Cost Sensors and Reference Pumps
Measuring site 2 (MS2, Lat/Long: 45.27573\({}^{\circ}\), 19.80066\({}^{\circ}\)) was located in the Industrial Zone South, at the very border of the residential area in the Rumenacka 20 street. Measuring instruments were located at the height of about 2.5 m next to a very busy roundabout within the courtyard of the Veterinary Institute in Novi Sad. The distance between the roundabout and measuring site was about 30 m. The traffic at MS2 consists mainly of light and heavy trucks and buses since the roundabout represents the main way that leads to the highway in that city area. The rest of the Industrial zone consists of a couple of industrial plants whose production capacities are very low, so the main source of suspended particles at this location is traffic. The third measuring site, (MS3, Lat/Long: 45.2494\({}^{\circ}\), 19.87729\({}^{\circ}\)), was situated in Petrovaradin, associated municipality of Novi Sad. Instruments were placed at the height of 3 m, near the intersection of Reljkoviceva and Preradoviceva Street, which is one of the busiest intersections in Petrovaradin municipality. The distance between the measuring site and Reljkoviceva Street was 5 m, and the distance to the intersection was 60 m. Reljkoviceva  
Figure 1: Map with location of measuring sites (MS1, MS2, MS3, MS5) and monitoring stations at national (NN), regional (RN) and local level (LN). Street is the main street with heavy traffic that passes through Petrovaradin from Novi Sad. Traffic mainly consists of trucks and cars and, to a lesser extent, of bus traffic. Sources of PM\({}_{2.5}\) at the measuring site beside traffic are residential heating. The fourth site (MS5, Lat/Long: 45.23406\({}^{\circ}\), 19.88453\({}^{\circ}\)), was also situated in Petrovaradin, as was MS3. Rackog Street serves as a transportation hub and is in the vicinity of the intersections of regional roads. It is the busiest street in Petrovaradin, with high intensity of both heavy vehicle and light vehicle traffic. The majority of heavy transportation from Reljkoviceva Street passes through this street. The measuring devices were set at the height of 3 m, at a distance of 12 m from the street. At this site, particulate matter can be emitted mainly by traffic and by residential heating boilers. Considering MS positions, possible sources of PM\({}_{2.5}\) in the ambient air at all four localities include traffic, along with households and facilities in the vicinity that are using natural gas and other fossil fuels as a heating source.