esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 System Analysis
### Existence of Solutions and Optimal Solution Problems
\tag{6}\]  
In System of Linear equations (6), \(\frac{k}{s}\) and \(\frac{k}{M}\) are rounded up for the rigor of logic; \(\sum_{i=1}^{s}(\lceil\frac{k}{s}\rceil-\eta_{i})=\sum_{j=1}^{M}(\lceil\frac{k }{M}\rceil-\mu_{j})=k\) ensures the number of dummy locations and dummy query content selected are both \(k\); \(\sum_{j=1}^{M}x_{ij}^{(z)}=\lceil\frac{k}{s}\rceil-\eta_{i}\) indicates the number of locations belonging to the \(i\)th organization in the \(z\)th times query; \(\sum_{i=1}^{s}x_{ij}^{(z)}=\lceil\frac{k}{M}\rceil-\mu_{j}\) represents the number of times that the service category \(C_{j}\) is selected in the \(z\)th times query. \(\sum_{z=1}^{n}\sum_{j=1}^{M}x_{ij}^{(z)}-\sum_{z=1}^{n}\sum_{j=1}^{M}x_{fj}^{ (z)}=0\) denotes that the number of times that any of the basic service categories is selected in the times queries are equal, which can be derived from \(\sum_{i=1}^{s}x_{ij}^{(z)}=\lceil\frac{k}{M}\rceil-\mu_{j}\). The objective function having a solution means that given \(k\), when \(s\) changes from \(2\) to \(k\), there is at least one scheme for each value of \(s\) such that it satisfies the constraints. Then \(k\) and \(s\) can be regarded as a fixed value in a specific solving process, and whether or not the objective function with a solution is transformed, the equations in the constraint conditions have a solution. **Conclusion 1:** The System of Linear equations (6) with integer coefficient has multiple integer solutions. Proof. : In a certain solving process, the number of variables is \(s\times m\), and the coefficient matrix and the augmented matrix of System of Linear equations (6) are, respectively, denoted as \(B\) and \(\bar{B}\). Let \(b_{i}^{{}^{\prime}}=\lceil\frac{k}{s}\rceil-\eta_{i}\) and \(b_{i}^{{}^{\prime\prime}}=\lceil\frac{k}{M}\rceil-\mu_{j}\), then \(\bar{B}\) is expressed as follows. \[\bar{B}=\left(\begin{array}{ccccccccccccc}x_{11}&x_{12}&\cdots&x_{1M}&x_{21 }&x_{22}&\cdots&x_{2M}&\cdots&x_{s1}&x_{s2}&\cdots&x_{sM}\\ \par\par\par\par\par\par\par\par\par\par\par\par\par\par\par\par\par\par\par \par\par\par\par\par\par\par\par\par\par\par\par\par\par\par\par\par\par\par \par\par\par\par\par\par\par\par\par\par\par\par\par\par\par\par\par\par\par \par\(B\) is composed of the first \(s\times M\) columns in \(\bar{B}\). We can work out the invariant factors of the matrix \(B\) and the augmented matrix \(\bar{B}\) through matrix elementary transformation. The invariant factors of \(B\) and \(\bar{B}\) are both \((1,1,\cdots,1)_{M+s-1}\). In addition, the ranks of \(B\) and \(\bar{B}\) are both \(M+s-1\). The number of effective equations in the constraint conditions is \(s+M\) and \(s+M-1\leq s+M\leq sM\). According to references [34; 35], System of Linear equations (6) has multiple integer solutions. Hence, there must be a scheme to make the objective function reach the local or global optimum. Furthermore, the presented scheme is feasible, and the objective function can reach the local or global optimum.
## 3 Materials and Methods
### Chemical Analysis of Sediment Samples  
All geochemical analyses were conducted at Wuhan Sample Solution Analytical Technology Co., Ltd. (Wuhan, China), an ISO/IEC 17025-accredited laboratory (ISO/IEC 17025:2017 is an international standard jointly developed by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), specifying the general requirements for the competence of testing and calibration laboratories). The concentrations of selected potentially toxic elements (PTEs)--including V, Cr, Co, Ni, Cu, Zn, Tl, Pb, Th, and U--were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) following acid digestion. Approximately 50 mg of each finely powdered sediment sample was digested with a mixture of HNO\({}_{3}\), HClO\({}_{4}\), HF, and HCl in a closed Teflon vessel using a CEM Mars 6 (CEM Corporation, Matthews, NC, USA), microwave digestion system, which enables high-pressure and high-temperature decomposition of silicate-rich matrices. The digestion protocol was based on USEPA Method 3052 [43], a widely accepted procedure for the preparation of geological and environmental samples. Following digestion, the solutions were diluted to the required volume and analysed using an Agilent 7700x ICP-MS instrument.
## 2 Shaft-Bearing System Model
### Preload and Angular Misalignment Factors  
In the current analysis, the ball bearing is preloaded axially on the outer ring including constant-displacement preload and constant-force preload. The angular misalignment taken into account here is caused by mounting error, and the misaligned inner and outer rings due to mounting error can both lead to angular misalignment. In order to simplify the analysis, the inner ring is assumed to be mounted accurately. The outer ring is misaligned, and its misalignment angle keeps constant when the shaft-bearing is loaded.  
Axial preload and angular misalignment can lead to additional deformation in each ball element and cause ball element-raceway contact forces to be redistributed; therefore, they should be included in the calculation of ball element deformation to reflect their effects on the fatigue life. Both the axial preload and angular misalignment affect mainly the axial distance \(D_{1j}\) between inner and outer raceway groove curvature centers at the \(j\)th ball element position. The translational displacement \(\delta_{x}\) should be replaced by \(\delta_{x}+\delta_{a}\) in Equation (5) to calculate the axial distance \(D_{1j}\) when the axial preload is taken into account. The axial displacement \(\delta_{a}\) is caused by axial preload on the outer ring, which is known when constant-displacement preload is applied and unknown if constant-force preload is applied. When angular misalignment components of the outer ring of the ball bearing, \(\theta_{x0}\) and \(\theta_{y0}\), are considered, the angular displacements \(\theta_{x}\) and \(\theta_{y}\) should be replaced with \(\theta_{x}-\theta_{x0}\) and \(\theta_{y}-\theta_{y0}\) in Equation (5), respectively. Then, the axial distance \(D_{1j}\) considering axial preload should be rewritten as:  
\[D_{1j}=(f_{e}+f_{i}-1)D\sin\alpha_{0}+(\delta_{x}+\delta_{a})+R_{i}(\theta_{x }\sin\varphi_{j}-\theta_{y}\cos\varphi_{j}) \tag{26}\]  
The axial distance considering the angular misalignment is:  
\[D_{1j}=(f_{e}+f_{i}-1)D\sin\alpha_{0}+\delta_{x}+R_{i}\Big{[}(\theta_{x}- \theta_{x0})\sin\varphi_{j}-\big{(}\theta_{y}-\theta_{y0}\big{)}\cos\varphi_{ j}\Big{]} \tag{27}\]
###### Abstract  
This paper presents an energy analysis of the influence of the movement limit of a horizontal single-axis tracker on the incident energy on the photovoltaic field. The procedure used comprises the following steps: (i) the determination of the periods of operation of a horizontal single-axis tracking; (ii) the analytical determination of the annual, daily, and hourly incident solar irradiance on the photovoltaic field; (iii) the validation of the model; and (iv) the definition of the evaluation indicators. The study focused on three photovoltaic power plants in Spain (Miraflores \(PV\) power plant, Basir \(PV\) power plant, and Canredondo \(PV\) power plant). Four evaluation indicators (annual energy loss, daily energy loss, beam component, and diffuse component) and ten movement limits, ranging from \(\pm\)50 (\({}^{\circ}\)) to \(\pm\)60 (\({}^{\circ}\)), were analysed. In Spain, photovoltaic power plants usually have a movement limit of \(\pm\)60 (\({}^{\circ}\)), which is why it has been called the current scenario. According to this study, the following conclusions can be drawn: (i) It is necessary to calculate the optimal movement limit for each site under study at the design stage of the PV power plant. Although the energy loss per square metre for not using the optimal boundary movement is small, due to the large surface of the photovoltaic field, these energy losses cannot be neglected. For example, in the Canredondo photovoltaic power plant, the limit movement is not optimised and the annual energy loss is 18.49 (MWh). (ii) The higher the range of the limiting movement, the shorter the duration of the static operating period. Therefore, when the current scenario starts the normal tracking mode (where the beam component is maximised), the other scenarios remain in the static mode of operation in a horizontal position, which impairs the incidence of the beam component and favours the diffuse component. (iii) The type of day, in terms of cloudiness index, prevailing at a given location affects the choice of the movement limit. If the beam component is predominant, it favours the performance of the current scenario. In contrast, if the diffuse component is predominant, it favours scenarios other than the current scenario.  
single-axis tracker; limit of movement; annual energy loss; daily energy loss Article
# Review of Anomaly Detection Algorithms for Data Streams
## 6 Algorithm Complexity and Scalability Analysis
### Online-Learning-Based Data Stream Anomaly Detection Algorithms
* Relatively real-time adaptability: The algorithms can adapt to changes in the data stream to some extent through incremental updates or partial retraining, capturing changes in new patterns or concepts. 3. Online-learning-based data stream anomaly detection algorithms: * Highly scalable: Online learning-based algorithms generally have good scalability, enabling real-time processing of large-scale data streams and model updates. * Real-time adaptability: Since the algorithms learn and update the model gradually on the data stream, they can promptly adapt to changes in the data stream, providing good real-time performance. It is important to note that the scalability of each algorithm is also influenced by other factors, such as algorithm complexity, choice of underlying models, and availability of hardware resources. Therefore, in practical applications, the performance and scalability of the algorithm need to be considered in combination, and the choice of the algorithm should be based on specific requirements.
# A Context-Aware Neural Embedding for Function-Level Vulnerability Detection
## 3 Experiments and Evaluation
### Result Analysis
#### 3.4.3 Performance Evaluation and Comparison on Real-World Open Source Projects  
Table 4 lists the results of performance comparison among our framework, Cross-VD [14], Multi-VD [27], DeepBalance [27] and Flawfinder, on open-source projects, FFmpeg, LibTIFF and OpenSSL. In general, the embeddings generated by our framework produce better results with RF and/or SVM than the other four systems on three projects. When using the embeddings generated by our framework, the results achieved by RF are better than those achieved by SVM in the majority of cases.  
As Table 2 shows, the test set of project FFmpeg contains 143 vulnerable and 3820 non-vulnerable samples. Retrieving 10 of the most probable vulnerable functions could identify 9 actual vulnerable ones, using our framework. In comparison, using the method proposed by Cross-VD finds only 7 vulnerable functions with the same number of functions retrieved. The other two baseline systems could also identify 9 vulnerable functions. When returning 200 of the most likely vulnerable functions, our framework finds 72 vulnerable ones. Namely, using our method to examine 200 functions ranked by the probabilities of being vulnerable, one could identify 72 out of 143 total vulnerable functions from more than 3800 functions. This result outperforms the other four baseline systems. In contrast, Multi-VD [27] and DeepBalance[38] could find 70 and 71 vulnerable functions, respectively, which is very close to our method. Flawfinder returns 519 potentially vulnerable functions on the same test set. There are 44 actual vulnerable functions included. However, none of the 44 vulnerable functions are in the top 200 list based on the ranked severity level returned by the Flawfinder.  
Despite project LibTIFF having the smallest number of function samples for training, using our method with SVM classifier achieves the best performance, compared with the performance achieved on the other two projects, which is 59 out of 64 vulnerable functions found when retrieving the top 200 ranked vulnerable functions. Using the other four systems, Cross-VD [14] could only find 49 vulnerable functions and the figures for Multi-VD [27], DeepBalance [38], and Flawfinder are 53, 56, and 12, respectively. On project OpenSSL, however, the performance deviation among four baseline detection systems is not as obvious as on the other two projects. Using our proposed framework, one could find 68 out of 96 vulnerable functions when retrieving 200 potentially vulnerable functions. In contrast, the performance of Multi-VD [27] and DeepBalance [38] is similar, with 65 and 67 vulnerable functions found, respectively.  
Figure 5: The top-200 recall of our framework with different number of Bi-LSTM layers on projects: FFmpeg, LibTIFF and OpenSSL.
# The Big Picture: An Improved Method for Mapping Shipping Activities
## 5 Experimental Results and Evaluation
### AIS Trajectory Reconstruction  
#### 5.1.1 Dataset  
For the purpose of an experimental evaluation of the proposed approach, a dataset of synthetic AIS gaps was created. More precisely, focusing on the eastern part of the Mediterranean Sea, i.e., the Aegean and Levantine Sea, a sample of 3000 trajectories was provided by MarineTraffic. A cleaned dataset from the month of October (5-31 October 2021) was used as a starting point. The extracted trajectories were continuous vessel paths, with no temporal gaps larger than 15 min between consecutive messages. Additionally, in order to avoid including vessel stops at port areas, segments shorter than 30 min where the vessel remained stopped or idle were removed from the trajectories. These segments were substituted by a single transition, from the start of the stop until the end, with the corresponding time interval subtracted from the total trip duration. For each extracted trajectory, a synthetic \"gap\" was formed using their starting and ending points. Overall, in our evaluation we consider large temporal gaps, meaning that the synthetic gaps range from a duration of 1 h to a full day (24 h).  
Additionally, we demonstrate the impact of this gap-filling technique by comparing the density maps for the original cleaned AIS and the corresponding improved dataset. For this task, we employ a dataset from the month of October originating from SAT-AIS, focusing again on the Aegean and Levantine Sea (Figure 11). During the cleaning process, a 3 min downsampling is applied to the data.  
The AIS datasets were provided by MarineTraffic ([[https://www.marinetraffic.com](https://www.marinetraffic.com)]([https://www.marinetraffic.com](https://www.marinetraffic.com)), (accessed on 6 September 2023)). Due to the nature of the research and commercial restrictions, supporting data are not available.  
Figure 11: Improved trajectory of a single vessel; in green—the segments from the original AIS messages, in orange—the reconstructed parts of the trajectory using the proposed method.
# Mapping [PERSON] Using Sentinel-1 SAR Time Series in Camargue, France
## 3 Methodology
### Temporal Behavior of \(\sigma^{\circ}\) SAR Backscattering over Agricultural Plots
#### 3.1.3 VH Linear Fitting  
Another unique observation for rice cultivation is the increasing linear profile of the VH backscattering during the growing cycle (between DOY 120 and 270). Thus, the VH temporal series of each plot was fitted with a linear regression model according to Equation 4:  
\[y=Sx+n \tag{4}\]  
Therefore, for each plot we can obtain the resultant slope (S in dB/DOY) and intercept (\(n\)). Rice crops can be thus discriminated using high positive slope values of the fitted linear model compared to other crop types.
## 3 Research Results and Discussion  
### SEM and XRD Analyses  
Figure 3 shows the cross-sectional morphology and topography of Cr, CrN, TiCrN, and TiCrCN multilayer nitride coatings obtained from Si glass. Figure 3 shows the synthesized multilayer coating has a columnar and dense microstructure [5,13]. The thickness of the coating acquired from the cross-sectional SEM image is 1.085 \(\upmu\)m. The coating's average surface roughness was measured to be Ra = 1.327 \(\upmu\)m. The maximum roughness value was measured to be Rz = 6.43 \(\upmu\)m.  
Upon examining Figure 4, the tables presenting the EDAX values of AA6063 Al and AZ91 Mg alloys in the Cr/CrN/TiCrN/TiCrCN-coated and uncoated conditions are observed. Considering the Al and Mg alloys used in Figure 4, AA6063 alloy with deposited coating contains 5.87% C, 2.99% N, 1.64% O, 7.61% Al, 6.21% Ti, 73.9% Cr, and 1.77% Mn by weight. Coated AZ91 contains 16.21% C, 15.09% N, 13.01% O, 22.28% Cr, 25.9% Mg, 0.98% Al, and 6.53% Ti by weight.  
When AZ91 Mg alloy's XRD diffraction peaks are examined in the 2\(\uptheta\) = 20-90\({}^{\circ}\) scattering range, it is seen that the diffraction peaks from interdendritic eutectic \(\upbeta\) phase (Mg17 Al12) and \(\upalpha\)-Mg dendrites (matrix) are the prominent compositions [37, 38, 39]. XRD patterns confirm that \(\upalpha\)-Al (111), AlFeSi (222), Mg\({}_{2}\)Si (011) and (004), and CuAl\({}_{2}\) (002) diffraction peaks were prominent compositions for AA6063 Al alloy [40, 41, 42, 43, 44]. For AA6063 and AZ91, in Cr, CrN, TiCrN, and TiCrCN multilayer nitride coatings, high-intensity diffraction peaks (211) were detected for Cr, (111) and (200) diffraction peaks were detected for CrN coating, (111), (200) and (222) diffraction peaks were detected for TiN, and (200) and (311) diffraction peaks were detected for TiCN [15, 45, 46, 47, 48] (Figure 5).  
Figure 3: Cross-sectional SEM images of multilayer Cr/CrN/TiCrN/TiCrCN films.
## 3 Results and Discussion
### Phenolic Compounds of C. pepo Seed Extract
Quantitatively, in addition to catechin, the compounds identified in higher amounts were caffeine and gallic acid in the UAE condition 1 and UAE condition 2 extracts, and caffeine, phloridzin, gallic acid, protocatechuic acid, and chlorogenic acid in the MAE condition 1 and MAE condition 2 extracts. Moreover, caffeine, catechin, naringin, gallic acid, protocatechuic acid, caftaric acid, chlorogenic acid, 4-O-caffeydquinic acid, vanillic acid, caffeic acid, syringic acid, \(p\)-coumaric acid, ferulic acid and 4,5-di-O-caffeoylquinic acid are present in all samples. Phloridzin and rutin were only detected in the UAE extracts. The phenolic compounds 3,5-di-caffeoylquinic acid, quercetin-3-O-galactoside, resveratrol, quercetin-3-O-glucopyranoside, ellagic acid, cinnamic acid, quercetin, kaempfer-3-O-glucose, isorhametin-3-O-glucose, kaempfer-3-O-rutinoside, isorhametin-3-O-rutinoside, naringenin, trans-epsilon viniferin, quercetin, phloretin, tiliroside, kaempfer, apigenin and chrysin were not detected in any of the _C. pepo_ extracts. To the best of our knowledge, this is the first work that identified and quantified the phenolic compounds present in _C. pepo_ seed extracts. Nevertheless, it is possible to compare the results with other Cucurbita seed extracts. The results obtained by [PERSON] et al. shown that _C. moschata_ seed extract contains less phenolic compounds than _C. pepo_, as only six phenolic acids (quinic acid, protocatechuic acid, caffeic acid, syringe acid, trans-ferulic acid and 4,5-Di-O-caffeoylquinic acid) were identified in _C. moschata_ seed extracts using LC-ESI-MS, employing hexane, chloroform, ethyl acetate and methanol as the extraction solvent [95]. Employing ultrasounds as the extraction method and methanol/H\({}_{2}\)O 80:20 (_v/v_) as the solvent, [PERSON] et al. identified 10 phenolic acids (\(p\)-coumaric acid, ferulic acid, caftaric acid, caffeic acid, 3-O-caffeoylquinic acid, caffeic acid, 2-O-caffeoylmale acid, dicaffeoyltaric acid, dicaffeic acid and sinapic acid) and 16 flavonoids (including luteolin O-glucose, quercetin 3-O-rahmosyl-rhammosyl-glucose, quercetin rutinoside, glucoside, isorhametin 3-rutinoside-7-rhammoside and different kaempfer glycosides) in _C. pepo_ whole fruit extracts, using LC-DAD-Q-TOF MS [96].
# Data Exchange in Cluster Structure for Longevity of IoT
###### Abstract  
In the Internet of Things (IoT), the scope of wireless sensor nodes is extended to things deployed in a pervasive world. For various IoT service applications, things can gather and share their information with each other through self-decision-making. Therefore, we cannot apply the existing information aggregation methods of wireless sensor networks to the IoT environment, which aim to transmit the collected data to only a sink node or a central server. Moreover, since the existing methods involve all the sensor nodes in the process of data exchange, they can cause an increase in the network traffic, delay of data transmission, and amount of energy consumed by things. In this paper, we propose a clustering-property-based data exchange method for efficient energy consumption in IoT networks. First, the proposed method assigns properties to each thing according to the characteristics of the obtained data. Second, it constructs a cluster network considering the location of things and their energy consumption. Finally, the things in a cluster communicate with other things in a different cluster based on their properties. In the experiment, the proposed method exhibits a better performance than the existing method. Owing to the energy-saving effect, we demonstrate that the proposed method results in a more reliable network and improves the longevity of IoT networks.  
things property; clustering; data exchange; energy efficiency; IoT +
Footnote †: journal:  
_Future Internet_**2020**, _12_, 32; doi:10.3390/f12020032