esa-sceva/satcom-chunk-collection huggingface dataset:
# An Electric Fence-Based Intelligent Scheduling Method for Rebalancing Dockless Bike Sharing Systems
## 2 Methodology
### System Description
#### 2.1.2 Electric Fence  
An electric fence is a predetermined \"virtual fence\" without a physical installation [6], and a typical electric fence is shown in Figure 1. As a virtual site based on satellite positioning, radio frequency identification (RFID), and Bluetooth technology, an electric fence can obtain the current location of a bike and match it with the border of the electric fence to determine whether the bike is parked in the correct position. Based on electric fences, operators can use different strategies to guide users during the parking process. With reasonable settings, electric fences can help bike systems find a balance between flexibility and standardization.  
Considering the dynamic changes in users' needs, the capacities of the electric fences in our method are adjustable and they are the decision variable of this problem. By changing the capacities of electric fences, we can change the distribution of bikes to adapt to the riding needs of users. According to the actions electric fences take, electric fences are divided into target electric fences and dispatch electric fences. Target electric fences are electric fences that need to be expanded to attract more bikes. In contrast, dispatch electric fences are electric fences that need to reduce their capacities. According to the current vehicle distribution and user needs, we increase the areas of target electric fences and reduce the areas of dispatch electric fences to guide users in parking their bikes within fences with greater demand, which can help the system achieve a balance between supply and demand.
# Study on Denoising Method of Weld Defect Signal Based on SSA-VMD-WPD
## 3 Results and Discussion
### Evaluation of Denoising Effect
Secondly, a method of coefficient of variation weighting was utilized to assign weights to the indicators. The specific process is as follows:  
\[CV_{PRMSE}=\frac{\sigma_{PRMSE}}{\mu_{PRMSE}} \tag{23}\]  
\[CV_{\text{Pr}}=\frac{\sigma_{\text{Pr}}}{\mu_{\text{Pr}}} \tag{24}\]  
\[W_{PRMSE}=\frac{CV_{PRMSE}}{CV_{PRMSE}+CV_{\text{Pr}}} \tag{25}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum\limits_{t=1}^{n}\left(f(t)-f^{\prime}(t)\right)^{2}}\]0  
where \(CV\) represents the coefficient of variation, \(W\) represents the weighted values after weighting, \(\sigma\) represents the standard deviation, and \(\mu\) represents the mean. Next, utilizingthe method of linear combination, a composite evaluation metric \(T\) was constructed, as shown in Equation (27). \[RMSE=\sqrt{\frac{1}{n}\sum\limits_{t=1}^{n}\left(f(t)-f^{\prime}(t)\right)^{2}}\]1  
Based on the relationship between each individual metric and the denoising effect, it was observed that the smaller the value of the metric \(T\), the better the denoising effect of the signal.
## 3 Results
### Spectral Reflectance Measurements  
Figure 5 shows the mean reflectance spectra of the three treatments over time as recorded by the SVC field spectrometer. In accordance with results of the chemical analysis, alterations of the spectral reflectance of the glyphosate treatment became evident on day 2, i.e., the third day of the experiment. In the course of time, spectral reflectance of the glyphosate-treatments changed considerably. It increased in the visible (VIS) and in the shortwave-infrared (SWIR) part of the spectrum while reflection in the near-infrared (NIR) decreased. The grass sod under drought stress exhibited distinct changes at the last two sampling dates just as it was observed with the photosynthetic pigments and the phenols. The spectral signal of these samples was very similar to the spectral signal of the  
Figure 4: Development of the total chlorophyll (Ch_lab_) on a fresh (**A**) and dry matter basis (**C**) and of the polyphenol content on a fresh (**B**) and dry matter basis (**D**) during the course of the experiment.  
glyphosate-treated samples at these days (day 16, day 23) (Figure 6). The control showed only minor variations during the experiment. A slight increase in reflectance was detected in the NIR on day 16 and 23 when the chlorophyll and carotenoid content was the highest (Table 2, Figure 4A,C).  
The changes in the spectral signal of the treatments can be clearly attributed to alterations of the structural and chemical characteristics of plants (Figure 6). In the VIS, the reflectance of plants is mainly controlled by leaf pigments, which strongly absorb light. The decrease of the chlorophyll and carotenoid content due to the glyphosate treatment led to a continuous decrease of absorption in this part of the spectrum. Reflectance in the NIR is determined by the cell structure. The scattering process at the air/water interfaces at the surface of cells results in a high reflection of the incident radiation. It declines with a degradation of cell structure caused by glyphosate application. In the SWIR, reflectance of green vegetation is generally low due to the high plant water content. The signal was dominated by two strong water absorption bands near 1400 nm and 1940 nm. With the dieback of vegetation, the reflectance increased as plant water decreased. Absorption bands of biochemical compounds such as lignin and cellulose, which were masked by water before, became visible.  
Figure 5: Reflectance spectra (mean of replications) of the three treatments (control, glyphosate, drought stress) as recorded by the SVC HR1024i field spectrometer (SVC) from Spectra Vista Cooperation over time starting at 9 July (day 0) until 1 August (day 23).  
Figure 6: Selected reflectance spectra of the control (day 0, day 23), glyphosate (day 0, day 3 to day 23) and drought stress (day 0, day 23) as recorded by the SVC field spectrometer. Major absorption and reflectance features of the vegetation are indicated.
## 2 Results and Discussion
### The Structural Elucidation of New Compounds  
Compound **1** is a white solid, and it exists in 75% ethanol, methanol, acetone, and dichloromethane extracts of _A. altissima_ leaves (Figures 1 and S29). Based on HR-ESI-MS (\(m/z_{\text{obsd}}\) 483.3816 [M + Na]\({}^{+}\); \(m/z_{\text{calcd}}\) 483.3814), its molecular formula was determined to be C\({}_{30}\)H\({}_{52}\)O\({}_{3}\) with 5 degrees of unsaturation. The low field of the \({}^{1}\)H NMR spectrum (Table 1) showed three singlet methyl groups at \(\delta_{\text{H}}\) 0.85 (3H, s), \(\delta_{\text{H}}\) 0.99 (3H, s), and \(\delta_{\text{H}}\) 1.24 (3H, s); two doublet methyl groups at \(\delta_{\text{H}}\) 0.86 (3H, d, \(J\) = 6.5 Hz) and \(\delta_{\text{H}}\) 0.87 (3H, d, \(J\) = 6.5 Hz); and one triplet methyl group at \(\delta_{\text{H}}\) 0.89 (3H, t, \(J\) = 7.3 Hz). In the midfield, there were two oxygenated methylene protons at \(\delta_{\text{H}}\) 3.34 (br s) and \(\delta_{\text{H}}\) 3.45 (m), and one methoxy proton at \(\delta_{\text{H}}\) 3.35 (3H, s). In the high field, there was one double singlet proton at \(\delta_{\text{H}}\) 5.77 (d, \(J\) = 4.1 Hz), with the remaining proton signals ranging from \(\delta_{\text{H}}\) 0.94 to \(\delta_{\text{H}}\) 2.29 (Table 1). \({}^{13}\)C NMR and DEPT spectra indicated that the compound contained 30 carbon atoms, including 7 methyl groups, 10 methylene groups, 9 methyne groups (comprising 2 oxidized methine carbons \(\delta_{\text{C}}\) 72.2 and 75.2 and 1 methylene carbon \(\delta_{\text{C}}\) 121.5), and 4 quaternary carbons. Based on 1D and 2D NMR spectra, we inferred that compound **1** was a stigmasterol steroid. The skeletal carbon signals of **1** were similar to those of compound **4** (stigmast-5-ene-3\(\beta\),7a,205-triol) [16; 32]. The difference between these two compounds is that one of the oxygenated methine carbons (\(\delta_{\text{C}}\) 75.2) in compound **1** appears at a lower field position in the \({}^{13}\)C NMR spectrum compared to the corresponding carbon (\(\delta_{\text{C}}\) 65.2) in **4**. From the HMBC spectrum of compound **1** (Figure 2), we found that H-6 (\(\delta_{\text{H}}\) 5.77) and the methoxy proton signal \(\delta_{\text{H}}\) 3.35 correlated with the oxygenated methine carbon \(\delta_{\text{C}}\) 75.2, indicating that C-7 of compound **1** was methoxylated. Analysis of ROESY spectra revealed that H-7 correlated with H-9 and H-14, indicating that the methoxy group at the C-7 position had \(\beta\)-configuration. And the calculated ECD curve was consistent with the experimental CD curve of 7\(R\), 17\(S\), indicating that the absolute configuration of compound **1** was 7\(R\), 17\(S\) (Figure 3). In this way, the structure of **1** was eventually determined and was named ailanstigol A.  
Figure 1: Chemical structures of new compounds from the leaves of _Ailanthus altissima_. Figure 2: The main HMBC (H\(\rightarrow\)C) correlation of compounds **1–3**.
## 2 System Models
\begin{table}
\begin{tabular}{l l} \hline \hline
**System bandwidth** (MHz) & 2.5 \\ \hline Bandwidth of subcarrier (Hz) & 1500 \\ \hline Number of RBs & 13 \\ \hline Radius of the coverage area of relay station (meter) & 200 \\ \hline Distance between base station and relay station (meter) & 125 \\ \hline Number of CeUE & 9, 12, 15 \\ \hline Number of D2D pairs & 3, 6, 9 \\ \hline Minimum distance between base station and UEs (meter) & 10 \\ \hline Power of relay station (\(P_{\mu}\), dBm) & 30 \\ \hline Power of UEs (\(P_{\mu}\), dBm) & 23 \\ \hline Minimum throughput requirements of CeUEs (\(R_{h,C}\), K\(\mathrm{p}\)s) & 128 \\ \hline Minimum throughput requirements of D2D pairs (\(R_{h,D}\), K\(\mathrm{p}\)s) & 256 \\ \hline Standard deviation of Shadowing fading between relay and BS (dB) & 6 \\ \hline Standard deviation of Shadowing fading between UEs and relay station (dB) & 10 \\ \hline Power Spectral density of AWGN (\(\mathrm{dB}\mathrm{/}\mathrm{Hz}\)) & –174 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: System simulation parameters. Figure 2: D2D pairs deployed on the circle around the relay station.
## 3 Proposed CG-LIB Algorithm and User Vehicle Localization
### Proposed CG-LIB Algorithm
Let the beamforming design problem can be written by  
\[(\mathbf{F}_{A}^{opt},\mathbf{F}_{D}^{opt})=\operatorname*{arg\,min}_{ \mathbf{F}_{A},\mathbf{F}_{D}}||\mathbf{F}^{opt}-\mathbf{F}_{A}\mathbf{F}_{D}|| _{F},\ \ \text{s.t.}\ \mathbf{F}_{A}\in\mathcal{F}_{A}, \tag{28}\]  
where \(\mathcal{F}_{A}\) is the set of the analog beamformer, \(\mathbf{F}^{opt}=\mathbf{V}_{1}\), which is measured by using a singular value decomposition method of \(\mathbf{H}_{e}=\mathbf{U}\mathbf{V}^{H}\) in [41]. Consider \(\mathbf{F}_{A}\) into \(\mathbf{F}_{A,m}\) as  
\[\mathbf{F}_{A,m}=\frac{1}{\sqrt{N_{t}}}e^{i\Omega_{m}}, \tag{29}\]  
where \(\mathbf{\Omega}_{m}\) denotes the \(N_{t}\times N_{s}\) phase shift matrix, which phases need to be quantized into the nearest point in \((2\pi m/2^{b},m=0,1,\dots,2^{b}-1)\), if the phase shifters are of infinite or finite \(b\)-bit resolution. Hence, the phase optimization problem can be expressed as  
\[\mathbf{\Omega}_{m}^{opt}=\operatorname*{arg\,min}_{\mathbf{\Omega}_{m}}f( \mathbf{\Omega}_{m}). \tag{30}\]  
where \(\mathbf{\Omega}_{m}\) is converged using a conjugate gradient method, which optimizes along the direction \(\Delta\mathbf{\Omega}_{m}\)  
\[\mathbf{\Omega}_{m+1}=\mathbf{\Omega}_{m}+\Delta\mathbf{\Omega}_{m}, \tag{31}\]  
where \(\Delta\mathbf{\Omega}_{m}=\eta_{m}\Xi_{m}\) and \(\Delta\mathbf{\Omega}_{m}\) satisfy \(\Delta\mathbf{\Omega}_{m}\leq\tau\), \(\eta_{m}\) is determined by minimizing \(f(\mathbf{\Omega}_{m+1})\), \(\Xi_{m}\) is given by  
\[\Xi_{m}=-\
abla f(\mathbf{\Omega}_{m})+\xi\Xi_{m-1}, \tag{32}\]  
and the \(\xi\) is given by  
\[f(\mathbf{f}_{D,i},\kappa)=\mathbf{f}_{D,i}^{H}\mathbf{A}\mathbf{f}_{D,i}+ \kappa^{T}(\mathbf{H}_{e}\mathbf{F}_{A}\mathbf{f}_{D,i}-\varsigma_{i}). \tag{24}\]0Using (29) to (33), we summarize the proposed CG-LHB algorithm in Algorithm 1. ```
1:Input parameters: H, \(b\), \(\tau\), \(J\)
2:Output: \(\mathbf{F}_{A}^{opt},\mathbf{F}_{D}^{opt}\). 3:Begin: Set \(m=0\), and \(\Omega_{0}\in\mathbb{R}^{N_{k}\times N_{k}}\)
4: If \(m=0,1,\ldots,\infty\), \(\Xi_{m}=-\
abla f(\mathbf{\Omega}_{m})\)  else compute (31)-(33). 5: If \(y_{m}\) is determined by minimizing \(f(\mathbf{\Omega}_{m+1})\) in (31), then \(\Omega_{s}(m+1)\gets f(\mathbf{\Omega}_{m+1})\); \(f_{s}(m+1)=f(\mathbf{\Omega}_{m+1})\). 6:\(\Delta f=f_{s}(m+1)-f_{s}(m)\); \(\Delta\mathbf{\Omega}=\mathbf{\Omega}_{s}(m+1)-\mathbf{\Omega}_{s}(m)\),  If \(\Delta f\leq\tau_{1}\); stop  If \(\Delta\mathbf{\Omega}^{T}\Delta\mathbf{\Omega}\leq\tau_{2}\); stop  If \(m+1=J\); stop  If \(\
abla f(\mathbf{\Omega}_{m+1})^{T}\
abla f(\mathbf{\Omega}_{m+1})\leq\tau_{3}\); converged \(m\gets m+1\)  Go To Step 4. 7: if \(b<\infty\) then
8:\(\mathbf{\Omega}^{opt}=f(\mathbf{\Omega}^{opt})\)
9: Compute \(\mathbf{F}_{A}^{opt}\)
10: Compute \(\mathbf{F}_{D}^{opt}\). ```  
**Algorithm 1** Proposed CG-LHB Algorithm
# Use of Social Media Data in Disaster Management: A Survey
## 3 Materials and Methods
### Scope  
Social media data provide a rich footprint of real-world events that can be used to facilitate the management of disasters. Several research works have proposed methods for exploiting social media data for the efficient management of disasters. The scope of our survey is determined by the common issues discussed in existing research works. Based on these issues, we have developed a taxonomy of social media data management and analysis for disaster management. Figure 1 depicts the taxonomy that shows six different aspects discussed in existing research, including the data source, language, social media user, information dimension, methodology, and application.  
The taxonomy elements are described below.  
* Data source --refers to the sources of social media data and ancillary data provided by other sources (e.g., physical sensors, wireless sensor networks (WSNs), and web services) that are used to facilitate social media data analysis in disaster management. We frame the dimension of data sources into four sub-classes, Sensor, Social Media User, Social Media Platform, and Third Party, based on the common attributes of data sources mentioned in the selected papers. The Sensor class is divided into Physical Sensor and Human Sensor, and the Social Media User is divided into four types of social media users, including Government Authorities, Research/Academic Institutions, Non-Government Organisations (NGOs), and Public.
* Language--refers to the language used for making a post on social media. Language is classified into the Global Language, Local Language, Mixed Language, and Mixed Script types.
* Information dimension--there are two major dimensions of information available in social media content: Spatial and Temporal. The methodologies for analysing social media content to extract spatial and temporal information are also included  
Figure 1: Taxonomy of social media data management.  
in this category. The spatial dimension refers to the representation of geographical information in social media and the methodologies for geo-location identification and analysis. The geographical information is presented in several ways, including Geo-tagging, User-defined, and Spatial Coverage. The Temporal dimension refers to the utilisation of temporal information describing disaster-related events in the existing system for event detection. The temporal dimension is further classified as Pre-event, Real-time, and Post-event based on the temporal categories of real events.
* Methodology--refers to the methodologies and algorithms used to analyse social media data, especially the spatial and temporal information. We categorise the methodologies based on data analysis stages, which include the Methodology for Data Management and Methodology for Data Analysis. The evaluations for each methodology are also summarised.
* Application--refers to the current uses of social media data for disaster management, which are classified into the two aspects of Disaster Management Phases and Disaster Management Types.
## 5 Evaluation Scenario
### CapEx and Power  
The CapEx cost model based on [38] is used in the network design phase. CapEx of an OXC is assumed to be \(\kappa^{\text{occ}}=2.75\) units. CapEx of a lightpath with two LCs takes \(\kappa^{\text{lp}}=38.84\) units. Cost of a router \(\kappa^{\text{ipr}}_{n}\) is in the range of 16.67-830.86 units for all routers \(n\in N\). Cost of a fiber \(\kappa^{\text{f}}_{e}\) depends on the length of the physical link \(e\in E\) (0.0091 units/km and 2\(\cdot\)10.42 units contributed by OXCs at both ends of the fiber, and required number of Optical Line Amplifiers (OLAs) and Dynamic Gain Equalizers (DGEs) [37]).  
The EWA is run during network operation. In this phase power of active devices matters. EWA targets LCs for energy savings as the most convenient energy-hungry devices to dynamically change power states between SM and AM. We assume that a single (colored) LC consumes \(\pi^{\text{lp}}=500\) W [39]. Power consumption of a LC in SM is negligible. Energy consumption of other network devices is not attempted to be reduced due to their relatively long activation times. Furthermore, it is unlikely that network devices such as core routers are idle due to the aggregated nature of traffic. An analysis of partial deactivation of routers can be found in [33; 40].
## 4 Conclusions  
In this paper, we have benchmarked the performance of the 4G-based and 5G-based C-V2X in the V2I communication and urban scenarios context from the communication perimeter, traffic intensity and frequency of message generation perspective and in terms of the PDR and E2E latency. In both cases, the infrastructural mode and device-to-device mode 3 were considered. The obtained results were put into a context of the communication requirements of the popular V2I C-ITS services from the E2E latency and PDR perspective. The results clearly indicate that both C-V2X technologies are capable to support all the considered V2I services with no limitations in terms of the communication perimeter as well as traffic intensity. Moreover, the impact of the infrastructure density was also investigated in the above-mentioned context. The results show that the more dense infrastructure deployment is very beneficial for the both investigated technologies and their modes. To be more precise, the upgrade from 2 BSs to 4 BSs offers the remarkable improvement for all the considered V2I services as well as C-V2X technologies and their modes. The benefit is higher for the 5G-based C-V2X as the transition from no coverage to the full support without the limitations is obtained for this technology and both its modes. On the other hand, there is no further benefit of the upgrade from four BSs to eight BSs for any of the investigated cases. Considering also its economic feasibility, we can simply conclude that it is not beneficial at all.  
As this study presumes that the channels purely dedicated for C-ITS services in 4G-based and 5G-based C-V2X will be deployed in the real implementations as is foreseen in the literature [31; 32], background traffic seems not to play any role here. Regardless, when this will not be the case, i.e., the deployment of the channels purely dedicated for C-ITS services in 4G-based and 5G-based C-V2X, an investigation of the background traffic impact might be relevant. Moreover, 5G network slicing, as one of the very promising features of 5G networks to cope with an increasing traffic load, could be also considered in a further extension of this work.  
Conceptualization, T.P. and P.P.; methodology, T.P., P.P. and T.K.; software, T.P.; validation, T.P. and P.P.; formal analysis, T.P. and P.P.; investigation, T.P. and P.P.; resources, T.P. and P.P.; data curation, T.P.; writing--original draft preparation, T.P., P.P. and T.K.; writing--review and editing, T.P., P.P. and T.K.; visualization, T.P.; supervision, P.P.; project administration, T.K.; funding acquisition, T.K. All authors have read and agreed to the published version of the manuscript.  
This research received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 101007019. This work was supported by the Slovak Research and Development Agency under the contract no. SK-IL-RD-18-005 ICT and the Smart Cars for Efficient Emergency Response and Traffic Management (SENECA) program.  
Not applicable.  
The models and simulation data are available upon request.  
The authors declare no conflict of interest.
# Empirical Study of Test Case and Test Framework Presence in Public Projects on GitHub
## 3 Results
### Number of Projects Potentially Containing Tests  
Discussion in this section is an extension of RQ 4. Despite we found no correlation between the number of occurrences of the word \"test\" and the number of test cases, we decided to analyze the relation of the presence of this word and the presence of test cases. We know that most Java testing frameworks require the word \"test\" to implement a test case [15]. When considering this rule, the presence of this word in the project will in most cases also indicate the presence of tests. Figure 7 shows a percentage comparison of projects with non-zero \"test\" occurrences in files content and test case presence. Remember that data were collected in 2019 and the year 2019 does not include all projects created/committed, but only until 30 April 2019.  
Figure 6: Correlation between the number of the “test” word occurrences in the all java files’ content of a project and the number of test cases in the particular project in all GitHub projects with Java as a primary language.  
It can be seen in Figure 7,b, that annually approximately 60-80% projects include \"test\" in the files' content with a stabilized ratio of approximately 75% for the last four years. When comparing the proportion of projects with the occurrence of the word \"test\" and projects involving test cases, the annual difference median is 21.15% by year of creation and 23.54% by year of the last commit. These numbers mean that annually approximately 22% of projects contain the word \"test\" in the absence of an executable test case. We can consider it as a _false positive_ indicator of test case identification using \"test\" occurrence in the project. Compared to results presented in Section 3.2, there is no increase of the searched term occurrences due to the last commit.
## 7 Discussion and Open Questions
### Infrastructure Requirements for Effective DT Feedback Integration  
One of the key promises of DTs is the establishment of a constant feedback loop between the physical system and its digital representation. In practice, this feedback is often fragmented due to  
* A lack of standardized protocols for real-time data exchange;
* Limited capabilities for collecting and synchronizing field data;
* Insufficient edge or cloud-based computing resources to process telemetry efficiently.  
Without a robust and scalable digital infrastructure, the DT remains largely a static simulation model rather than a dynamic, real-time diagnostic and optimization platform. This limits its role in predictive maintenance, auto-calibration, and lifecycle performance management.