esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Energy Measurement Results
### Transferred Energy Results  
#### 4.2.1 Energy Measurement Results of the Absorbing Cup Type of Device  
The firing test of the pin puller was conducted with a total of 15 rounds. The compression length of the cup was obtained as shown in Table 2.  
From Table 2, it is shown that the maximum compression length of the energy absorbing cup is 21.97 mm, and the minimum is 9.58 mm. There is a big difference between these results, so there may be abnormal values. Using the \"Grubbs test method\" [16] to make judgments, the final result is that there are \"no outliers\" and the value should not be discarded.  
According to the equation fitted in the previous section, the sample statistical data of transferred energy measured by the absorbing cup device can be calculated as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Compression** & \multirow{2}{*}{**No.**} & **Compression** & \multirow{2}{*}{**No.**} & **Compression** \\  & **Length (mm)** & & **Length (mm)** & & **Length (mm)** \\ \hline
1 & 12.65 & 6 & 18.28 & 11 & 21.53 \\
2 & 12.82 & 7 & 16.68 & 12 & 19.99 \\
3 & 9.76 & 8 & 21.97 & 13 & 15.42 \\
4 & 19.47 & 9 & 9.58 & 14 & 13.57 \\
5 & 18.61 & 10 & 16.67 & 15 & 14.31 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Compression length of the energy absorbing cup in the firing test.  
Figure 15: The fitted energy–length curve.
## 4 Performance Test of SOADS
### Test Environment  
The benchmark test of the proposed SOADS was carried out by configuring Hadoop and Spark Cluster using four servers. Among them, three have the same specifications and play the role of data node, and one server plays the configuration role. For this test, Apache Hadoop, Apache Hbase, and Apache Spark were installed on every server. Table 1 lists the specifications of the configuration server and data node servers. It also allocated 20 cores per executor created when running Spark and configured the cluster to use 60 GB of memory.
## 4 Results
### Disaster Management  
This intelligent disaster prevention system can perform data collection, analysis, actuation, and display through the 3D BIM visual model during disasters. For example, a 3D model can be used to query the location of fire and disaster relief equipment in the building to ensure the consistency, continuity, and integrity of information transmission between various operating units in the building. Moreover, through centralized data management and functional modules in the operation platform, emergency management, inspection, and response within the organization can be coordinated among units, thereby achieving management effects such as response efficiency and resource utilization optimization. The content included the following:  
A Adoption of response-triggered standard operating procedures. The related operations include (a) dismiss the alarm, (b) file a case, (c) report, (d) evacuate, (e) initiate disaster relief, (f) expand area and (g) manage the aftermath (Figure 9). This standard handler avoids panic-stricken errors. B Use of graphics instead of text to explain relevant locations, telephone numbers, and disaster relief facilities. The images in Figure 10 include (a) the location of the exits, (b) the parking areas of the fire truck, (c) the location of fire hydrant, and (d) temporary injured placement. C Application of a one-click trigger to report relevant integrated information to the fire department and all co-construction units. The system used BIM to automatically display disaster relief information, including (a) fire locations, (b) smoke prevention, (c) fire protection, (d) control area, (e) buffer and (f) rescue waiting areas, as summarized in Figure 11. D The intelligent disaster prevention system developed in this research has the function of triggering all actions with one key, including: (a) One click action, (b) LINE report, (c) marquee information, (d) disaster location, (e) BIM escape, (f) Level-3 signal trigger, (g) CCTV, (h) nearest exit, and (i) nearest fire escape (Figure 12). E Reporting follow-up information for expanded response measures: Integration of replies from designated units with current situation information, such as (a) review of the extended areas, (b) buffer areas, (c) examine extended areas and (d) disaster relief  
Figure 8: Television screen display of intelligent disaster prevention system in Taipei Main Station. (Source: Research Data).
# The Sensitivity Analysis of Parameters in the 1D-2D Coupled Model for Urban Flooding
## 5 Conclusions
### Practical Implications for Urban Flood Management  
These findings have significant implications for urban flood management and model optimization. The identification of key sensitive parameters aids in efficient model calibration, leading to more accurate flood predictions while reducing computational costs. Additionally, the variation in sensitivity across rainfall patterns underscores the necessity of scenario-based calibration strategies to improve model adaptability under different storm conditions.  
The comparative assessment of the [PERSON] and Sobol methods reinforces the advantages of hybrid sensitivity analysis, striking a balance between computational efficiency and analytical comprehensiveness, making it a practical choice for large-scale urban flood assessments. Integrating these insights into flood risk assessments and drainage system planning allows for urban water management agencies to prioritize critical parameters, improve early warning systems, and enhance resilience against extreme rainfall events.  
Conceptualization, J.C. and Z.Z.; methodology, J.C. and Z.T.; software, Z.T.; validation, J.C., Z.T. and T.Z.; investigation, J.C. and Z.T.; resources, J.C. and T.Z.; data curation, Z.T. and K.Y.; writing--original draft preparation, Z.T.; writing--review and editing, J.C. and T.Z.; visualization, Z.T.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Joint Major Project of \"Science and Technology + Water-Conservancy\" in Jiangxi Province (2022 KSCG1007), the Major Science and Technology Project of the Ministry of Water Resources (SKS-2022070), and the project of Department of Water Resources of Jiangxi Province(202426 ZDKT04).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
This work was supported by the State Key Laboratory of Simulation and Regulation of River Basin Water Cycle, China Institute of Water Resources and Hydropower Research, Beijing.  
The authors declare no conflicts of interest regarding the publication of this paper.
# Application of Compound Terrain Factor LSW in Vegetation Cover Evaluation
## 3 Materials and Methods
### Overview of the Study Area
#### 3.1.3 Vegetation Cover
Sample areas were randomly selected from GF-1 remote sensing imagery to collect the real initial classification of feature types (image download webpage: [[http://www.sasclouds.com/chinese/normal/](http://www.sasclouds.com/chinese/normal/)]([http://www.sasclouds.com/chinese/normal/](http://www.sasclouds.com/chinese/normal/)), accessed on 6 May 2023), with the number of samples of each type being more than 50. The overall accuracy of 93.85% and the Kappa coefficient of 0.9231 were obtained after performing the classification accuracy evaluation, which provided a reliable guarantee for the follow-up experiments. Based on the resolution of Landsat-8 data is 30 m \(\times\) 30 m, fine vegetation type identification is difficult to achieve [50]. Therefore, only vegetation and non-vegetation areas were distinguished in this study, and the final vegetation cover data are obtained, as shown in Figure 1c.
## 3 Hydrogen Production Technologies
### Water Electrolysis Technologies
#### 3.2.1 Alkaline Water Electrolysis  
Hydrogen production by alkaline water electrolysis was first introduced by [PERSON] and [PERSON] in 1789 [50], so it is by now a well-established technology. Figure 10 shows a generic depiction of an alkaline water electrolyzer.  
This electrolyzer is characterized by having two electrodes operating in a liquid alkaline electrolyte solution, generally potassium or sodium hydroxide (KOH or NaOH, respectively); the electrodes are kept apart by a diaphragm, where the transport of the hydroxide ions (OH\({}^{-}\)) occurs, from one electrode to the other [51].  
So, the process initiates at the cathode, the site of reduction, where two molecules of an alkaline water solution are reduced to one molecule of hydrogen (H\({}_{2}\)), and two hydroxyl ions are produced (OH\({}^{-}\)). The produced hydrogen emanates from the cathode surface to recombine in a gaseous form, while the hydroxyl ions transfer through the porous diaphragm to the anode, under the influence of the imposed electrical potential. Here a similar reaction occurs: two water molecules are oxidized, forming one diatomic oxygen (O\({}_{2}\)) molecule and four hydrogen atoms. These half-reactions are shown below:  
\[\text{Cathode:}\quad 2\,\text{H}_{2}\text{O}+2\,\text{e}^{-} \longrightarrow\text{H}_{2}+2\,\text{OH}^{-} \tag{6}\] \[\text{Anode:}\quad\quad\quad 2\,\text{OH}^{-} \longrightarrow\text{H}_{2}\text{O}+\frac{1}{2}\,\text{O}_{2}+2\, \text{e}^{-} \tag{7}\]  
To obtain the overall cell equation, one just has to add both half-equations:  
\[\text{H}_{2}\text{O}\longrightarrow\text{H}_{2}+\frac{1}{2}\,\text{O}_{2} \tag{8}\]  
which is the same as Equation (5).  
Alkaline electrolysis normally operates at low temperatures, around 30-80 \({}^{\circ}\)C, and, as seen, uses an aqueous solution as the electrolyte [52; 53]. The diaphragm is usually made of asbestos [54] and electrodes made of nickel; this diaphragm in the middle of the cell is what separates the cathode side from the anode side, avoiding the mixing of the produced gases [55]. This method does have some negative aspects, such as limited current densities, low operating pressure, and low energy efficiency, making this type of electrolyzer just suited to operate at almost constant power while connected to the grid; one of the highest-powered electrolyzers of this type, however, has shown to have a dynamic response fast enough to track the production of a renewable power plant [56].  
An investment cost analysis from the past 30 years [57] revealed that capital expenditure (CAPEX) values for large alkaline electrolyzers can vary significantly over plant capacity, ranging from EUR 1250 /kW\({}_{d}\) to around EUR 700/kW\({}_{d}\)--depending on whether the plant is small (<1 MW) or large (>40 MW), respectively. The annual operational expenditures (OPEX) are generally around 7% for medium-sized plants, but, due to superior technical approaches and a higher lifespan, it is expected to decrease to 2% in 2030 [57].  
Figure 10: Schematic illustration of an alkaline water electrolyzer.
# Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System
## 2 Problem Formulation
### Optimal Allocation of ESSs
#### 2.2.2 Constraints  
To a hybrid wind/diesel/ESS power system, the following operational constraints should be satisfied.  
1. Equality Constraints: the power balance that is related to the nonlinear power flow equations is considered in this paper, which is shown in Equation (11). \[\left\{\begin{array}{l}P_{i}-V_{i}\sum_{j=1}^{N}V_{i}(G_{ij}\cos\delta_{ij}+ B_{ij}\sin\delta_{ij})=0\\ Q_{i}-V_{i}\sum_{j=1}^{N}V_{i}(G_{ij}\sin\delta_{ij}-B_{ij}\cos\delta_{ij})=0 \end{array}\right.\] (11)
2. Inequality Constraints: the inequality constraints are those associated with the bus voltage \(V_{k}\), the reactive power of generation \(Q_{Gi}\), the tap of the transformer \(T_{i}\), and the maximum charge/discharge power of the ESS. \[\left\{\begin{array}{l}V_{\min}\leq V_{k}\leq V_{\max}\\ T_{\min}\leq T_{i}\leq T_{\max}\\ Q_{G\min}\leq Q_{Gi}\leq Q_{G\max}\\ -n_{c}\cdot C_{E}\leq P_{e}\leq n_{d}\cdot C_{E}\end{array}\right.\] (12) where \(n_{c}\) and \(n_{d}\) are taken to be 3 C in this paper.
## 4 Experiment and Result  
The PPP/INS positioning experiments of smartphones were conducted using GNSS observations including L1/L5 of GPS and B1 of BDS from smartphones and IMU data from smartphones and triaxial inertial sensor ADIS16405. The products, such as precise satellite orbit and clock, final global ionospheric map (GIM), and differential code bias (DCB), were provided by the IGS data center. The hydrostatic troposphere error was corrected with the Saastamoinen model [43], and the zenith delay of the wet troposphere was estimated as a parameter. Moreover, the other effects, such as Earth tide, relativity, and Sagnac, were modeled and corrected sufficiently. The cutoff elevation of satellites was 15 degrees, and the position was initialized from standard single point positioning (SPP) using pseudorange on the L1 frequency with a variance of \(60^{2}\) (m\({}^{2}\)). The receiver clock was initialized with a variance of \(60^{2}\) (m\({}^{2}\)) per epoch, and other parameters, such as the integer ambiguities of carrier phase, were usually initialized at the beginning and estimated as a float solution.
## 2 Problem Formulation
### Volt-Var Curve  
In the proposed strategy, mitigating the voltage unbalance at the PCC is achieved by passively injecting or absorbing reactive power into the points connected in the PV system by following the defined volt-var curve, as illustrated in Figure 2, without altering the active power injection, where the \(x\)-axis represents the phase to a neutral voltage at the bus, which is connected to the PV system. The IEEE 1547 standard provides a minimum requirement guideline for the volt-var curve, where \(V_{1}=0.92\times V_{\text{ref}}\), \(V_{2}=0.98\times V_{\text{ref}}\), \(V_{3}=1.02\times V_{\text{ref}}\), and \(V_{4}=1.08\times V_{\text{ref}}\) for DER in category B [21]. The proposed scheme adjusts the scales of \(V_{1},~{}V_{2},~{}V_{3},~{}V_{4}\). When agents observe the state, the output of the NN is a combination of \(V_{1},~{}V_{2},~{}V_{3},~{}V_{4}\). After training, each NN creates an optimal volt-var curve in a given situation. \(Q_{i,t}^{lim}\) is the reactive power capability at time t for \(i\)-th PV system.
# Deep-Learning-Based Approach to Detect ICMPv6 Flooding DDoS Attacks on IPv6 Networks
## 6 Conclusions  
The proposed approach employs two feature selection algorithms (IGR and chi-square) to select a set of features that helps to detect ICMPv6 DDoS flooding attacks. The feature selection is further improved by proposing a feature intersection based on a predefined threshold. Having the proposed approach deployed at the network gateway will enable it to monitor the inbound and outbound network traffic and extract the final feature set (\(f^{\prime}\)) that highly contributes to detecting ICMPv6 DDoS flooding attacks against vital servers, such as web and email servers, in the victim networks. \(f^{\prime}\) is then utilized for training three DL models (RNN, GRU, and LSTM). The three-DL detection model was evaluated using a benchmark dataset, and the results reveal that the LSTM algorithm outperformed RNN and GRU in terms of TNR, FNR, FPR, accuracy, F-measure, recall, and precision. The future work of this study includes improving the detection accuracy using bio-inspired algorithms as feature selection techniques, which are very efficient in feature selection and classification. Additionally, bio-inspired methods could potentially improve the LSTM algorithms' hyperparameters. In addition, the applicability of applying the proposed approach over other datasets for detecting different attacks, such as those based on NDP, could be explored. Another promising future work includes exploring the possibility of applying the proposed approach in other research domains or areas than network security.  
Conceptualization, M.A. and O.E.E.; methodology, M.A. and O.E.E.; software, M.A.; writing--original draft, M.A., O.E.E. and A.A.B.; writing--review and editing, M.A., I.H.H., A.A.B., S.H., S.F. and O.E.E.; supervision, M.A., S.H. and S.F.; project administration, S.H., S.F. and M.A.; funding acquisition, S.H., S.F., and M.A. All authors have read and agreed to the published version of the manuscript.  
This research was supported by research grant funded by Liwa College Of Technology (LCT), Abu Dhabi, UAE. Project No: IRG-BIT-005-2021 (USM External Grant Number: 304/PNAV/6501263/L136).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Run \#** & **RNN (Second)** & **LSTM (Second)** & **GRU (Second)** \\ \hline Run 1 & 5.566 & 8.311 & 8.440 \\ Run 2 & 5.421 & 8.270 & 7.860 \\ Run 3 & 5.603 & 8.431 & 8.029 \\ \hline Average Detection Time & 5.530 & 8.337 & 8.110 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Detection Time of RNN, LSTM, and GRU.
# On the Use of Bright Scatterers for Monitoring Doppler, Dual-Polarization Weather Radars
## 2 Materials and Methods
### The Dual-Polarization, Doppler Radar Located on Monte Lema at an Altitude of 1625 m
#### 2.2.2 Mean Radial Velocity (1 st Doppler Moment)  
Since the radar wavelength is 55 mm and the PRF of the lowest elevation is 600 Hz, the unambiguous Doppler velocity (\(\pm\)Nyquist) is \(\pm\)8.25 m/s. As previously stated, only 255 quantitative values are used in the quantization process: this corresponds to a quantization of 0.061 m/s. The conversion from DN to the mean radial velocity, VEL, is  
\[\mathrm{VEL}=(\mathrm{DN}-128)/127\cdot\mathrm{Nyquist}; \tag{4}\]  
Hence, DN = 1 (255) corresponds to a mean radial velocity of \(-\)8.25 (+8.25) m/s (DN = 0 is not used). It is worth noting that the minus sign in the MeteoSwiss radar network corresponds to an approaching target (although an approaching target is generally associated with a positive velocity).
# _CoVSCode_: A Novel Real-Time Collaborative Programming Environment for Lightweight IDE
## 6 Prototype Implementation and Evaluations
### Preliminary User Evaluations  
The _CoVSCode_ prototype implementation validated the feasibility of the proposed approach and techniques. A group of programmers participated in preliminary user evaluations on the prototype system (Figure 10 illustrates a picture taken in the experiment), and provided positive feedback on several key issues related to real-time collaboration features and performance. Firstly, the programmers reported that all existing functionalities and UIs of the _VS Code_ IDE were fully preserved without change, and the local responsiveness of editing operations was good (i.e., the same as that of the original IDE without real-time collaboration functionalities). Secondly, remote editing operations, remote cursors and remote highlights could be performed and displayed in real-time (without noticeable delay) in a LAN environment. Thirdly, when a programmer created a new project, the uploading of the initial source code copy took some time depending on the source code size, but even for large-size projects, it commonly took less than five seconds. Fourthly, when a new member joined an existing session, the transition would be completed in a timely manner (less than one second), and only the new member would encounter a slight delay for source code downloading during the initialization process (depending on the project size, and commonly less than five seconds).  
Figure 9: UI snapshot of the _CoVSCode_ client in a real-time collaboration session with three programmers.  
During the user evaluations, we also discussed several usability issues with the programmers and received positive feedback. Firstly, the programmers reported that the prototype was easy to use, because the real-time collaboration features had been smoothly embedded in the original IDE without modification to existing features and user interfaces. Secondly, the programmers reported that the prototype was effective and useful in supporting real-time collaborative programming, and that they could use the prototype for diverse collaboration needs. Thirdly, the programmers reported that they were satisfied with the real-time collaboration features. Fourthly, the programmers found the user interfaces intuitive and helpful for supporting real-time collaborative programming, especially the colored-highlighting-based collaboration awareness features (see Section 6.1.3), which had effectively assisted them in being aware of collaborators' editing locations, understanding relationships among collaborative work, and preventing potential conflicts accordingly.
## 2 Materials and Methods
### Methodology
#### 2.2.4 Model Evaluation  
To evaluate the ability of above models' forecast skill, correlation coefficient (r) and expected error, S [50] were used. Correlation coefficient describes the degree of collinearity between the predicted value and measured data. The coefficient S is computed as Equation (8):  
Figure 3: The designed network diagram for artificial neural network (ANN).  
\[\text{S}=1-\frac{\sqrt{\text{var}(\text{Q}^{\prime}-\text{Q}_{\text{s}})}}{\sqrt{ \text{var}(\text{Q}_{\text{s}})}} \tag{8}\]  
where \(\text{Q}^{\prime}\) and \(\text{Q}_{\text{s}}\) are forecasted and observed values of reservoir inflow, respectively. The coefficient S can range from \(-\infty\) to 1. The coefficient of 1 means a perfect match of predicted value to the observed data. The coefficient above 0 indicates that the model has certain skill in prediction. Consequently, the closer the model's efficiency coefficient is to 1, the more accurate the model's prediction is.  
The overall methodology adopted for monthly reservoir inflow prediction can be visualized in Figure 4.
# OPMSS: Optimal Producer Mobility Support Solution for Named Data Networking
## 5 Performance Evaluations
### Simulation Scenarios  
In this experiment, there are two simulation scenarios for the Kite solution and OPMSS. The two scenarios are based on the 4 \(\times\) 4 grid and Abilene topology. In scenario I, the infrastructural topology is a 4 \(\times\) 4 grid that consists of 19-23 nodes, with a distance of 100 m between them. Among the nodes, there are three consumers and one mobile producer by default, and the simulation runs for 100 s. The content mobile producer moves randomly within the 400 \(\times\) 400 square area covered by a different access point (AP) at a varying constant speed of 10 m/s -100 m/s. The grid nodes serve as routers and APs. Scenario II differs from Scenario I, as it consists of 12-13 nodes in an Abilene topology. Among the nodes, the mobile producer's number varies from 1 to 5 and the simulation runs for 100 s, while producers move randomly at constant speed varying from 1 m/s -100 m/s by taking the interval of 10 m/s into consideration. The scenarios are run for the Kite solution and OPMSS.  
The handoff performance of OPMSS is evaluated and benchmarked with the Kite solution using numerical investigation and a simulation experiment to ensure our proposed solution reduces unnecessary Interest packet loss and provides optimal packet delivery, good throughput, and a good packet delivery ratio.