esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### The Study Area  
The Majiliang coal mine is located northwest of the Datong coalfield. The geographical coordinates are 112\({}^{\circ}\)53\({}^{\prime}\)32\({}^{\prime\prime}\)-113\({}^{\circ}\)00\({}^{\prime}\)16\({}^{\prime\prime}\) east longitude and 39\({}^{\circ}\)58\({}^{\prime}\)26\({}^{\prime\prime}\)-40\({}^{\circ}\)04\({}^{\prime}\)03\({}^{\prime\prime}\) north latitude. The bedrock with loess covered is only exposed on both sides of the valley. The exposed strata range from old to new: Ordovician, Carboniferous, Permian, Jurassic, and Quaternary, respectively. The Jurassic coal seam has been mined. Currently, coal seam No. 3 of the Upper Carboniferous Taiyuan Formation, a nearly horizontal coal seam, is the primary mineable coal seam. The thickness of coal seam No. 3 ranges from 5.10 to 7.63 m, with an average of 5.41 m, and the inclination angle of it ranges from 1\({}^{\circ}\) to 4\({}^{\circ}\), with an average of 2\({}^{\circ}\). The distance between coal seam No.3 and coal seam No. 14-2 overlying Jurassic coal seam ranges from 171.52 to 207.67 m, with an average distance of 189.66 m. The working face 8117 is located in the first level 301-2 panel, with 2377.4 m length and 239.4 width (Figure 2). The coal seam No. 3 is threatened seriously by water disaster because of complex strata conditions.  
Through the reasonable abstraction and simplification of the geological prototype, the engineering geomechanics model diagram (Figure 3) was established, which accurately reflects the objective reality of the geological body. Moreover, it is characterized by both the possibility of mechanical analysis and the feasibility of computer numerical simulation. In Figure 3, the electrode, DAM, and BS are added in order to better explain the layout of the electrical monitoring system in a similar material model, DAM refers to the data acquisition mainframe, and BS refers to base station.
# Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures
## 2 Model  
This section introduces the design principle, based on the theory of public goods and Voluntary Contribution Games [8], and it describes how it can be applied to the smart city scenarios of participatory sensing, with the example of traffic congestion [10], and smart grids, with the example of electric vehicle charging [11]. Voluntary contribution games involve the need of coordination between users to provide some public good, e.g., financing a playground with private donations. The contribution of this paper is to present Voluntary contribution games as a design principle for modeling generic smart city services, and not limited to modeling only a specific scenario, e.g., smart grids [12]. The rest of this section presents the details of our proposed design principle. Table 1 illustrates the notation of the Voluntary contribution games model; symbols are listed in order of appearance. \(V=\{1,\dots,n\}\) is the set of users and \(S\) is a service provider. In each round \(t\leq T\), each user produces a resource \(r_{i}\) with value \(v_{i}\). Users perform an action \(a_{i}\in\mathcal{A}=\{C,D\}\): \(C\) corresponds to contributing the resource and \(D\) to opt out from contribution. The set of contributors is denoted as \(A_{+}=\{i\in V:a_{i}=C\}\). Contribution is costly; thus, contributors pay a cost \(c_{i}\) that depends on the characteristics of resource and communication medium. Contribution might also entail a privacy cost \(p_{i}\), which models the risks of revealing private information to third parties. The service provider determines the service quality \(q=f(A_{+})=\sum_{i\in A_{+}}v_{i}\) based on all contributions received. A quality requirement \(\tau\), either global or per user, is generated at every timestep. Not all users are required to contribute in order to satisfy the requirement \(\tau\leq\sum_{i}v_{i}\). A round is successful, i.e., the service can be provided, if the quality is higher than the threshold: the success function is defined as \(S(q,\tau)=\{G\text{ if }q\geq\tau\text{ else }B\}\). Each user gets the same positive payoff \(G=G(\tau,q)\) from accessing the service, including those who did not contribute. If the quality threshold is not met, the service cannot be provided and every agent receives a large negative payoff \(B(\tau,q)>0\). Given that payoffs are distributed equally, there is no incentive for users to contribute unilaterally: the public goods theory predicts the existence of an equilibrium where nobody contributes.
# Fuzzy Multi-Criteria Based Trust Management in Heterogeneous Federated Future Internet Testbeds
## 3 Proposed Fuzzy VIKOR Reputation System
### User's Credibility
```
1:Inputs:\(UO,\ SD,\ MD\)
2:Outputs:\(CR,\ \widehat{UIO}\)
3:for\(\forall UO_{i}\in UO\)do
4:\(CASE1\equiv(SD_{i}\geq UO_{i})\ \land\ (SD_{i}\geq MD_{i})\)
5:\(CASE2\equiv(SD_{i}\leq UO_{i})\ \land\ (SD_{i}\leq MD_{i})\)
6:\(CASE3\equiv(SD_{i}>UO_{i})\ \land\ (SD_{i}\leq MD_{i})\)
7:\(CASE4\equiv(SD_{i}\leq UO_{i})\ \land\ (SD_{i}>MD_{i})\)
8:\(e_{M}=\frac{|MD_{i}-SD_{i}|}{SD_{i}},\ i=1,\ldots,k\), Monitoring Relative Error
9:\(e_{O}=\frac{|UO_{i}-SD_{i}|}{SD_{i}},\ i=1,\ldots,k\), Opinion Relative Error
10:\(e_{D}=\frac{|UO_{i}-MD_{i}|}{SD_{i}},\ i=1,\ldots,k\), Relative Distance
11:\(C=[c_{i}]^{\top},\ i=1,\ldots,k\), Correction Vector
12:if\(CASE1\lor CASE2\)then
13:\(c_{i}=1-e_{O}\)
14:elseif\(CASE3\lor CASE4\)then
15:\(c_{i}=1-(e_{Mi}+e_{Oi})\)
16:endif
17:\(c_{i}=max(c_{i},0)\)
18:endfor
19:\(\hat{c}=avg(c_{i})\)
20:\(CR_{n}=\frac{(n-1)CR_{n-1}+\hat{c}}{n}\)
21:for\(\forall UO_{i}\in UO\)do
22:\(\widehat{UO}_{i}=UO_{i}\)
23:if\(CASE1\lor CASE2\)then
24:if\(UO_{i}\leq MD_{i}\)then
25:\(\widehat{UO}_{i}=MD_{i}+e_{Oi}CR_{n}\)
26:elseif\(UO_{i}<MD_{i}\)then
27:\(\hat{UO}_{i}=MD_{i}-e_{Oi}CR_{n}\)
28:endif
29:endif
30:if CASE3 then
31:\(\widehat{UO}_{i}=MD_{i}-min(e_{Mi},e_{Oi})CR_{n}\)
32:endif
33:endif
34:if CASE4 then
35:\(\widehat{UO}_{i}=MD_{i}+min(e_{Mi},e_{Oi})CR_{n}\)
36:endif
37:endif
38:endfor
```  
**Algorithm 1** User credibility mechanism.
# Q-LBR: Q-Learning Based Load Balancing Routing for UAV-Assisted VANET
## 5 Simulation Results and Analysis
### Performance Analysis
utilization of 76.1% and a latency of 2.12 s. As the amount of traffic increases, the overall performance is lowered compared to the previous experiment, but still shows a stable performance based on dynamic load balancing.
## 2 Materials and Methods
### Method of Collection of Data  
The selected intrabony defects were allocated to one of the three experimental groups using a computer-generated randomisation (random allocation software) as:  
Group A (\(n=7\))--EnHA graft and periosteal pedicle as barrier membrane.  
Group B (\(n=7\))--Only EnHA as graft.  
Group C (\(n=7\))--An open flap debridement procedure only.  
Patients between 25 to 55 years age with chronic periodontitis having intrabony defects \(\geq\) 6 mm probing depth (PD), 2 or 3 wall defects detected using CBCT, gingival biotype >1.5 mm and systemically healthy compliant subjects who had not received any periodontal treatment in the past six months were included.  
Teeth with pulpal/periapical involvement or having poor prognosis, smokers, pregnant/lactating females and patients with known systemic diseases/conditions precluding any elective surgery were excluded from the study.  
The parameters recorded were defect density and defect fill in CBCT [9; 10] (at baseline and 6 months), Pocket probing depth (PPD), Relative attachment level (RAL), Plaque index (PI) [11], Gingival index (GI) [12], Gingival Bleeding Index (GBI) [13] at baseline, 1, 3 and 6 months.  
Bone fill is calculated using CBCT in the following manner:  
Cone beam computed tomography (CBCT) was used for radiographic analysis at base line and at 6 month follow-up. CBCT with slice thickness of 0.2 mm, exposure of 90 kV, 10 mA with a scan time of 0.018 s /slice was used. Using the software, points were marked on the cementoenamel junction (CE)), base of the defect (BD) and alveolar crest (AC) and the defect depth was measured using (CE) to BD)-(CE) to AC) (Figure 1).  
Intra-bony defect (IBD) measurements were taken using the following:  
* Distance from CEJ to the base of the defect (CEJ-BD).
* Distance from the CEJ to the most coronal extension of alveolar bone crest (CEJ-AC). The intrabony component of the defects was measured as (CEJ-BD)-(CE)-AC). Angle was defined as the angle between lines 1 to 2 and 2 to 3 (Refer Figure 1).  
Bone fill is not the same as regeneration and can be defined as the restoration of tissue in a treated periodontal alveolar defect. In the present study, the bone fill is calculated by taking two measurements, namely,  
* Distance from CEJ-BD (base of the defect).
* Distance from CEJ-AC (most coronal extent of alveolar crest).  
Thus, the intrabony component of defect is measured as (CEJ-BD)-(CEJ-AC) (Refer Table 2). Therefore, the bone fill can be measured using these two parameters, i.e., (CEJ-BD)-(CEJ-AC) change from baseline to 6 months.  
Bone density is measured using CBCT-HU:  
The term \"bone quality\", however, is not so simple to define. There is no clear consensus on the definition of bone quality; however, in general, it encompasses multiple aspects of bone physiology, the degree of mineralization, the morphology and type of trabecular pattern.  
When evaluating or classifying the density of the bone, Grey Values GVs obtained from 3D CT images are quantified as Hounsfield units.
## 2 Related Work and Technologies
### TV Whitespace
Fixed and mobile measurements in rural, suburban and urban settings showed that the model presented is appropriate to describe the path loss over distances of up to a few kilometers and that they can be used in the process of filling a geolocation database. This work had the contribution of the European project ICT-COGEU (COgnitive radio systems for efficient sharing of TV white spaces in EUropean context), whose website is currently offline. In Portugal, the process of converting analog TV to digital TV started in 2012, and the Portuguese entities recently changed the frequencies of digital terrestrial television broadcasters to new frequencies in order to free up the space previously occupied for future 5G networks. This process is expected to be completed by the end of December 2020 [35; 36]. The use of TVWS is of great interest to the scientific community, namely for communications on LPWAN and long-range networks, but there seems to be a lack of investment in this technology, namely by the current players in the telecommunications market. As an example, the most recent document on this subject published by the authority responsible for the regulation of communication policies in Portugal, ANACOM (Autoridade Nacional de Comunicacoes), is dated August 2016 [37].
## 6 Simulation Results
### Performance of Reconstruction Signal  
We implement our experiment using reconstruction algorithm gOMP. In our experiment, we choose modified diffusion wavelets based on distance and sparse matrix as our represent basis and measurement matrix, respectively. In order to show the recovery quality of the proposed algorithm, we plot Figure 13, which illustrates the error of the reconstruction signal decoding at the sink node. In our experiment, we define the reconstruction error as in Equation (32):  
\[\varepsilon_{1}=\|\mathbf{x}-\hat{\mathbf{x}}\|_{2}\cdot\|\mathbf{x}\|_{2}^{- 1}. \tag{32}\]  
We select 750 humidity readings, which are described in Figure 13a. Then, Figure 13b shows the recovery performance of sensor node readings with 256 CS measurements. This also demonstrates that 256 CS measurements can reconstruct the original signal with a relative recovery error of \(\varepsilon_{1}=0.0306\)  
Figure 12: Comparison of different represent basis (**a**) DFT coefficients (**b**) coefficients based on degree (**c**) coefficients based on distance.
# Atomicity Violation in Multithreaded Applications and Its Detection in Static Code Analysis Process
## 3 Existing Solutions
The fact that the phenomenon of atomicity violation is language-independent and accounts for 70% of all multithreading errors [7] (without deadlock) shows how dangerous this phenomenon is. In [16], a paper was published describing the Kivati tool for Linux and x86 platform, which aims to detect atomicity violations in applications written using C language. This tool interferes with the source code of the system kernel in order to use so-called watchpoints to observe resources that have been used in atomic regions. These regions are obtained during the analysis of the program whose operation is observed by Kivati. The fact of observing the operation of the program (as well asinfluencing its operation) classifies the method used as a mixed one, which contradicts the information provided by the authors that the method used belongs to static methods [16]. Java grammar allowed [26] to develop a method of static analysis of the Java code allowing one to detect the phenomenon of atomicity violation. Their method described in the work entitled [26] is based on locating in the definition of classes of methods with the keyword _synchronized_ in the declaration, whose structure is then analyzed for the occurrence of patterns that guarantee the phenomenon of atomicity violation [26]. The disadvantage of this method is that it cannot be translated into languages differing in similarity to Java, including the C language. The method called \"Hybrid Atomicity Violation Explorer\" (HAVE) [27] is based on the use of the so-called \"Static Summary Tree\", created during static analysis of Java code. Then, based on the created trees, the work of selected fragments of the application is simulated. The effects of the simulation and the created trees are the basis for building the so-called hybrid trees, which are then analyzed using the author's algorithm, which results in information about potential conflicts that cause the atomicity violation and race condition. The authors of the method mention the occurrence of _false-positive error_ in the results of their method and further work on their elimination. They also indicate further development of static code analysis as one of the most important elements of the developed method. All described methods today can be included into few groups. The first group contains applications which are not available publicly from different reasons. The second group contains applications for other languages or for application which are not considered in the scope because they used libraries other than pthread. The third group contains methods which were developed for needs of specific languages and cannot be implemented in C language easily. For these reasons, there is no any other static analysis method which can be compared directly with the method proposed later in this paper. There is also no other known database with well described known bugs where C applications with pthreads library can be found.
## 4 Experiment
### Results
In addition, we further visualize the reconstruction results of the 3D scene, including point clouds and meshes, in Figure 11, further demonstrating the potential of our method in practical applications. Figure 11: Point cloud and mesh models of an example object. (**a**) shows the point cloud model generated by our method, highlighting the detailed representation of the object’s surface as a collection of points. (**b**) illustrates the mesh model derived from the point cloud, demonstrating how the points are interconnected to form a continuous surface. Our method supports exporting both point clouds and mesh models for various applications, facilitating flexible use in different domains. Figure 10: 3D visualization of mesh models generated by LiDAR, S2P, and SatelliteRF. Compared with S2P, SatelliteRF delivers more intricate details and crisper edges, yet displays some local inconsistencies. Figure 9: Visualization of new view synthesis results for DFC 2019 dataset 260 scenes. We zoom in on local areas to demonstrate the ability of our method to reconstruct scene details. The red boxes indicate the local areas. A comparative analysis of training times for multi-view satellite image reconstruction methods based on neural radiance fields reveals the temporal demands of various scene representation techniques across a range of application scenarios, as summarized in Table 4. Notably, our method requires a mere 6.1 min of training time, yet it sustains competitive accuracy and efficiency in scene reconstruction tasks. This observation is particularly salient, underscoring the optimization and effectiveness of our approach, especially pertinent in contexts necessitating rapid model development. Given the extensive training durations typically required by conventional methods, our approach marks a significant advancement, facilitating quicker iterations and deployment in time-sensitive applications. This advantage is especially pertinent in dynamic settings where swift model updates are crucial for maintaining operational accuracy and relevance.
# Prediction of Curing Time/Shear Strength of Non-Conductive Adhesives Using a Neural Network Model
## 2 Materials and Methods
### Neural Network Model  
In this study, an artificial neural network (ANN) black-box model was selected to consider complex nonlinear relationships among numerous variables [25; 26; 27]. To build a prediction model, experimental datapoints for a curing time at 280 \({}^{\circ}\)C and shear strength at room temperature were predicted as stated in the previous section.  
The input parameters are 4 types of resins, 2 types of hardeners, 8 types of catalysts, and a coupling agent, and 65 datapoints were used for the prediction of the curing time with regression models. 4 types of resins, 4 types of hardeners, 6 types of catalysts, and a coupling agent were used as input parameters, and 44 datapoints were used for the prediction of shear strength with regression models. The data sets for the curing time and the shear strength of NCA were listed in Appendix A.  
The ANN model, as a multilayer perceptron (MLP) network, with one hidden layer with 3 nodes was used. In the hidden layer nodes, a rectified linear unit (ReLU) activation function was employed. An MSE loss function and adaptive moment estimation (ADAM) optimizer, which had a learning rate of 0.001, \(\beta\)1 = 0.9, \(\beta\)2 = 0.999, and \(\varepsilon\) = 10\({}^{-8}\), were selected in model training and optimization. Modeling was implemented by Python and Keras application programming interfaces [28].  
65 datapoints for the prediction of the curing time of NCA and 44 datapoints for the prediction of the shear strength of NCA were randomly divided into training and validation datasets at a ratio of 80 % and 20 %. Since the dataset is small and overfitting was not found in the model training, the model test step was omitted, and the model was verified using the third-party data which was not engaged in the training. The model was trained for 1000 epochs with minibatches of 10.
# Linking Singular Spectrum Analysis and Machine Learning for Monthly Rainfall Forecasting
## 3 Methodologies
### Forecast Verification  
The performances of the forecasting models are analyzed using the two criteria, root mean square error (RMSE) and the Nash-Sutcliffe model efficiency coefficient (NSE), for the calibration and validation periods. The definitions of the different criteria are presented below:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{11}\]  
where \(y_{i}\) and \(\hat{y}_{i}\) are the observed and estimated values, respectively. Smaller values of RMSE suggest higher accuracy.  
\[\text{NSE}=1-\frac{\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}}{\sum_{i=1 }^{n}\left(y_{i}-\overline{\hat{y}_{i}}\right)^{2}}\]  
An NSE of 0.75-1.0 corresponds to a \"very good\" performance, 0.65-0.75 to a \"good\" performance, and 0.5-0.65 to a \"reasonable performance\", while values below 0.5 reflect unsatisfactory performance [94]. In essence, the closer NSE is to 1, the more accurate is the forecast.
## 7 Conclusions  
In this paper we addressed the problem of finding the optimal cache deployment in terms of number of caches, their location and their size (in terms of storage capacity) in a hierarchical, optical metro network, which minimizes the overall network resource occupation under limited storage capacity. We provide an analytical model that serves as a tool to calculate the optimal storage distribution according to several features, such as the dimension of the metro network and the size and the Zipf popularity distribution of the VoD-content catalog. The model, given a fixed budget in terms of storage capacity, finds the number of caches to deploy at each network level and distributes the storage capacity available to minimize the overall network resources used for VoD-content delivery. To cross-validate our analytical model and to evaluate VoD-content delivery under dynamic traffic, we use a discrete-event simulator for dynamic VoD-content caching and distribution. Numerical results show that in a case where the storage capacity is limited, the optimal cache deployment requires an intelligent distribution of the the available storage capacity among caches of various network segments. Moreover, we also show that the optimal cache deployment depends on the location of the metro cache (how far it is from end-users and how wide is the metro network) and the skewness of the popularity distribution. Our model also helps with identifying when deploying an excessive amount of storage capacity in the network does not further improve network resource utilization.  
**Author Contributions:** conceptualization, O.A. and F.M.; methodology, O.A., F.M. and M.T.; software, O.A. and D.A.; validation, O.A., F.M. and M.T.; formal analysis, O.A. and D.A.; investigation, O.A.; resources, D.A.; data curation, D.A.; writing--original draft preparation, O.A.; writing--review and editing, F.M.; visualization, D.A.; supervision, M.T.; project administration, M.T. and A.P.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the European Community grant number 761727, _Metro-Haul_ project.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.