esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Land Use Changes Comparison
However, this change is distributed over the entire analysis period, which in this case is equal to about 200 years. The 1960 and 2009 comparison, in these terms, has a more significant impact: the growth rate of urban areas in this case is 500% but is spread over a much smaller number of years, about 60. The significant increase of the urban LU type over the last two centuries has inevitably affected other land use classes, as reported in Table 10.  
Specifically, the comparison between 1817 and 2009 highlights that more than 50% of current urban areas occupy lands which two centuries ago were destined for agricultural uses. This rises to almost 80% when vineyards are also considered. Of the increase in urban areas, 10% were previously forest areas. In addition, in the case of the comparison between 1960 and 2009, the urban areas that increased were previously agricultural areas, both vineyards and other types. The urbanization process scarcely affected forests, while it occupied 7% of sparsely vegetated areas, probably due to their increase in surface, in the 1960 map, related to the volcanic eruption of 1944. The Kappa statistics are not noteworthy at a local-urban scale, because the comparisons were made with a 2009 map which present only one class; thus, the statistics are distorted and take values of either 0 or 1. Figure 10 spatially illustrates the areas in which the urban use type is the same (in green) and the areas in which land use changes occurred (in red). \begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Land Uses**}} & **Hectares** & **Hectares** \\  & **1817 Map** & **1960 Map** \\ \hline
1—Urban & 7067.44 & 11,053 \\
2—Agricultural uses & 36,290.08 & 27,890.08 \\
3—Vineyard & 14,938.6 & 19,551.44 \\
4—Forest & 6386.16 & 2706.92 \\
5—Sparse vegetated & 898.52 & 4427.76 \\ \hline Sum Map & 65,637.96 & 65,637.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Land uses in 1817 and 1960 maps, inside the perimeter of urban areas in the 2009 map (hectares). Figure 10: Land use changes in maps comparisons.
# Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment
## 5 K-Means Clustering Results and Discussion
### Finding the Best Set of Features Within the Connection-Based Features
#### 5.5.1 First Method: Clusters with at Least One Attack as Attack Clusters
* Lower Performing Feature Sets
* Set 2 showed the weakest performance, with precision dropping to 80.13% and recall to 66.91%. The significant drop in performance on set 2 and 5 indicates that excluding \"duration\" and \"proto\" respectively negatively impacted the model's ability to distinguish between attacks and normal traffic, hence these two features were actually kept. * For the First Method, set 11 (bolded) was determined to be the optimal feature set, as it provided the best balance of precision, recall, and overall accuracy and used the fewest features. Hence, the most significant connection-based features, using the First Method are: duration, proto, conn_state, src_port_zeeek, dest_port_zeeek, orig_pkts, orig_ip_bytes, resp_pkts, and resp_bytes. Also, set 11 had the lowest memory usage estimation.
# A Cloud Based Disaster Management System
## 1 Introduction
to the target disaster location while minimizing a set of objectives. A three-phase mechanism based on the analytical hierarchy process (AHP) was proposed. The benefits of AHP usage include effectively assigning weights to objectives based on their level of importance in the system and not just common sense. Figure 1 shows the flowchart of the proposed system. The challenge lies in how to collect and transfer sensor data in a dynamic disaster environment, and in how to construct a rich and detailed 3D virtual environment on the fly based on received sensor data and wireless network resources, in which bandwidth is highly dynamic and high computation time is required. For this, we can benefit from cloud computing to provide an attractive disaster recovery system, offering a more rapid and efficient rescue plan with dedicated infrastructure. To the best of our knowledge, this is the first proposed complete system that considers both WSNs and a 3D rendering engine using cloud computing. In summary, this paper improves the design of a cyber-physical objective function, tailored for disaster management applications, that addresses the above-mentioned gaps between application requirements and network requirements. Initial simulation and results analysis demonstrate the efficiency of Cyber-OF in handling dynamic changes in the importance of event data and in availing a reasonable performance balance between contradictory performance metrics such as energy and delay. Additionally, a modular game engine was developed to address and solve the following problems: simplifying the 3D scene modifications and facial avatars in a real time fashion, and removing the need for designers and professional programmers. The application of a virtual environment markup language (XVEML) as a dynamic event-based state machine language can ease development processes and fasten them during the runtime. A multi-objective solution was proposed for the formation of an efficient customized rescue plan. All components were integrated with cloud computing to gain increased efficiency and rapid services. The remainder of this paper is organized as follows. In Section 2, we discuss the main related works that have focused on cloud based systems, WSN communication protocols, game engines, and new real time 3D reconstruction. Section 3 describes the overall proposed system architecture and the modeling scenario used to implement the proposed approach. A description of the enhancement proposed to the RPL routing protocol is provided in Section 4, followed by a description in Section 5 of the 3D near real-time reconstruction approach we used to build and/or extend a virtual environment (VE) application during runtime. In Section 6, we present an MTSP approach to develop an optimized rescue plan. The simulation results and 3D outputs are given in Section 7. Finally, we conclude the paper and give recommendations for future directions of search and rescue applications.
## 4 Main Causes and Response Surface Analysis
### Analysis of the Main Causes  
Analysis of the main causes was used to compare the extent to which each variable affects the outcome, generally ignoring the effect of other variables when examining the effect of one of them. Linear effects were calculated using a linear regression model, where the range of design variables was set to [0, 1] in proportion to the change and expressed as a linear effect. The slope of the linear regression model represents the degree of influence of the parameter on the results; the larger the slope, the greater the influence of the parameter on the results. In this paper, we analyzed the degree of influence of variables such as the shrink tube cone angle \(\alpha\), shrink tube thickness \(T\), friction coefficient \(u\), and axial length of the friction cone surface \(S\) on the three responses, peak shrink tube force \(F_{\text{PC}}\), mean shrink tube force \(F_{\text{MC}}\) and the specific energy absorption \(E_{\text{SA}}\). The results of the main cause analysis are shown in Figure 6. The peak force \(F_{\text{PC}}\) was positively correlated with the cone angle \(\alpha\), the shrinkage tube thickness \(T\) and the friction coefficient \(u\). The degree of influence, in descending order, was the cone angle \(\alpha\), the shrinkage tube thickness T and the friction coefficient \(u\), and it was negatively correlated with the axial length of the friction cone surface \(S\). The influence was the least for the axial length of the friction cone surface \(S\). The effects of the four variables on \(F_{\text{MC}}\) and \(F_{\text{PC}}\) were basically the same, with only the amplitude being slightly reduced. The cone angle \(\alpha\) had the greatest effect on \(E_{\text{SA}}\), and the gap with the other influencing factors was large. The friction coefficient \(u\) had a greater effect on \(E_{\text{SA}}\) than that of the thickness \(T\), and the axial length of the friction cone surface \(S\) showed the same negative correlation with \(E_{\text{SA}}\). Through the main cause analysis, it could be seen that the main factors affecting the energy absorption index were the cone angle \(\alpha\) and the thickness of the shrinkage tube \(T\). The friction coefficient was related to the lubrication method used in the actual working conditions, and the axial length of the friction cone had a small influence on the collision resistance index. In this paper, the outer surface of the shrink tube was evenly coated with lithium grease for lubrication. In order to improve efficiency, the cone angle \(\alpha\) and the thickness of the shrinkage tube \(T\) were used as the input variables in the subsequent optimal design.
## 3 Results and Discussion
### Unsupervised PCA Data Analysis of C. sativus and L. Siceraria Primary Metabolites
Siceraria_ with results expressed as relative percentile of the total peak area (n = 3). In _C. sativus_, sugars represented the most abundant class at ca. 63% of detected primary metabolites, mostly represented by monosaccharides at 61% and trace disaccharides at ca. 2%. Fructose, glucose, and galactose were the main identified monosaccharides, amounting  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Peak** & **Rt (min)** & **KI** & **Metabolite** & **M. formula** & _C. sativus_ & _L. siceraria_ \\ \hline \multirow{3}{*}{10} & \multirow{3}{*}{9.80} & \multirow{3}{*}{1245} & \multicolumn{3}{c}{**Nitrogenous compounds**} \\  & & & Urea, 2 TMS derivative & C\({}_{7}\)H\({}_{22}\)N\({}_{2}\)OSi\({}_{2}\) & 1.31 \(\pm\) 0.48 & 4.10 \(\pm\) 1.98 \\ \cline{3-6}  & & & **Total nitrogenous compounds** & & **1.31 \(\pm\) 0.48** & **4.10 \(\pm\) 1.98** \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{6.44} & \multirow{3}{*}{1051} & \multicolumn{3}{c}{**Organic acids**} \\  & & & Methylmalonic acid (2 TMS) & C\({}_{10}\)H\({}_{22}\)O\({}_{4}\)Si\({}_{2}\) & 0.17 \(\pm\) 0.06 & 0.28 \(\pm\) 0.14 \\  & & & Lactic acid, 2 TMS derivative & C\({}_{9}\)H\({}_{22}\)O\({}_{3}\)Si\({}_{2}\) & 1.43 \(\pm\) 0.45 & 0.99 \(\pm\) 0.38 \\  & & & Glycolic acid, 2 TMS derivative & C\({}_{8}\)H\({}_{20}\)O\({}_{5}\)Si\({}_{2}\) & 0.22 \(\pm\) 0.07 & 0.26 \(\pm\) 0.09 \\  & & & 4-Hydroxybutanoic acid, 2 TMS deriv. & C\({}_{10}\)H\({}_{24}\)O\({}_{3}\)Si\({}_{2}\) & 1.77 \(\pm\) 0.44 & 3.83 \(\pm\) 1.81 \\  & & & Benzoic acid, TBDMS derivative & C\({}_{13}\)H\({}_{20}\)O\({}_{2}\)Si\({}_{2}\) & 0.17 \(\pm\) 0.05 & 0.21 \(\pm\) 0.12 \\  & & & Phosphoric acid, tris(trimethylsilyl) ester & C\({}_{9}\)H\({}_{22}\)O\({}_{4}\)Pi\({}_{3}\) & 2.35 \(\pm\) 0.85 & 2.22 \(\pm\) 1.41 \\ \hline
**Peak** & **Rt (min)** & **KI** & **Metabolite** & **M. formula** & _C. sativus_ & _L. siceraria_ \\ \hline \multirow{3}{*}{15} & \multirow{3}{*}{10.88} & \multirow{3}{*}{1315} & \multicolumn{3}{c}{L-Glutamic acid, N-Al-di(3-methylbutyl)-,} \\  & & & dimethyl ester & & & \\  & & & Succinic acid (2 TMS) & C\({}_{17}\)H\({}_{33}\)NO\({}_{4}\) & 3.35 \(\pm\) 0.93 & 5.92 \(\pm\) 2.46 \\  & & & & Sucenic acid (2 TMS) & C\({}_{10}\)H\({}_{22}\)O\({}_{4}\)Si\({}_{2}\) & 0.75 \(\pm\) 0.21 & 1.21 \(\pm\) 0.53 \\  & & & Malic acid, 3 TMS derivative & C\({}_{13}\)H\({}_{20}\)O\({}_{5}\)Si\({}_{3}\) & 2.45 \(\pm\) 0.35 & 1.00 \(\pm\) 0.41 \\  & & & 3-Methylvaleric acid, TMS & C\({}_{9}\)H\({}_{20}\)O\({}_{5}\)Si\({}_{3}\) & 0.27 \(\pm\) 0.09 & 0.47 \(\pm\) 0.21 \\  & & & Erythronic acid, tetrakis(trimethylsilyl) & & & \\  & & & deriv.
## 3 Results and Discussion
### Spatio-Temporal Variation Characteristics of Vegetation Coverage
The area of vegetation increase is mainly concentrated in the north, south, and part of the middle of the study area, with a significant increase of 74.1 km\({}^{2}\) and a slight increase of 120.47 km\({}^{2}\). The reduction part is distributed in all regions. In general, the reduction in vegetation is mainly concentrated in the southwest, a small part of the central and northeast regions, and the reduction is more serious. Among them, the area of slight reduction is 118.4 km\({}^{2}\), and the area of significant reduction is 38.99 km\({}^{2}\). Most of these reduction areas are located in the core area of desertification and areas with frequent human activities. In addition to being affected by climatic factors, overgrazing in the early stage has caused serious degradation of vegetation cover. Figure 7: The trend of the spatial distribution of vegetation coverage in the study area from 2019 to 2024. Annually, 2024 precipitation (327.2 mm) rose 4.3% vs. 2019 (313.7 mm), boosting water availability. Mean temperature dropped (4.6 \({}^{\circ}\)C vs. 5.3 \({}^{\circ}\)C), with more extreme minima (\(-\)24.1 \({}^{\circ}\)C vs. \(-\)23.3 \({}^{\circ}\)C), potentially stressing herbaceous vegetation. Wind speed fell (5.4 m/s vs. 6.3 m/s), reducing erosion and stabilizing microenvironments. In August (a critical growth month), 2024 precipitation (128.3 mm) was 16.9% higher than 2019 (109.8 mm), supplementing soil moisture. Cooler mean temps (19.6 \({}^{\circ}\)C vs. 20.7 \({}^{\circ}\)C) avoided heat stress, and lower wind speed (1.6 m/s vs. 2.4 m/s) cut physical damage and moisture loss, aiding physiology. These changes--more growing, more season rain, moderated temps, less wind--likely improved conditions for vegetation growth.
## 5 Efficiency Analysis
### RSU Performance  
Again we implement a FIFO waiting queue. For the RSUs we have implemented a single server scenario, were messages are processed at a constant time of 1.6 ms per message (0.615 ms for decrypting and 0.897 ms for signature verification). Again we use the same notation as in the SA analysis. As shown in Table 6 an RSU server can handle up to 400 messages per second with almost 1 s delay time. Figure 5a,b demonstrate the average wait time and the average queue length for the RSU.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**AM** & **PDM** & **AQL** & **AW** & **MP** \\ \hline
100 & 0.01 & 14 & 0.15 & 347,002 \\ \hline
200 & 0.005 & 65 & 0.33 & 709,494 \\ \hline
300 & 0.00333 & 190 & 0.61 & 1,120,710 \\ \hline
400 & 0.0025 & 406 & 0.99 & 1,462,935 \\ \hline
500 & 0.002 & 1014 & 2 & 1,816,057 \\ \hline \end{tabular}
\end{table}
Table 6: RSU performance (100–500 messages/s).  
Figure 4: SA performance. (**a**) SA: Average Wait time (in sec). (**b**) SA: Average Queue Length.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**AM** & **PDM** & **AQL** & **AW** & **MP** \\ \hline
50 & 0.02 & 7 & 0.14 & 178,182 \\ \hline
100 & 0.01 & 28 & 0.29 & 347,005 \\ \hline
151 & 0.0066 & 84 & 0.57 & 532,075 \\ \hline
200 & 0.005 & 192 & 0.97 & 709,188 \\ \hline
250 & 0.004 & 537 & 2.1 & 908,106 \\ \hline \end{tabular}
\end{table}
Table 5: SA performance for 50 to 250 messages/s.
## 3 Proposed Scheduling Scheme
### Recurrent Learning
The equation below integrates joint scheduling and power allocation to improve power utilization and decrease latency:  
\[\delta(z_{i})=\int_{r_{n}}^{v_{n}}(S+a_{0})\times k_{0}+o_{0}+\ldots+o_{n} \times h_{a}+\left(V(r_{0})\times\frac{X+s_{u}}{s_{u}}\right)+\gamma-e_{t} \tag{10}\]  
The analysis for utilization is examined in Equation (10); here, scheduling-related queuing is performed by deploying power management and resource allocation. In this computation step, the prediction is performed by mapping with the existing resources and providing relevant information sharing. The service distribution is performed for the trained and valid resources posted to the hidden layer processing. The analysis defines the current and next states of the neuron and estimates the reliable process that integrates scheduling and allocation and addresses the deficiency. From this processing, latency and outage are addressed and decreased by improving power utilization, whereas deficiency is addressed. Based on the allocation, training is performed on time for successive intervals. This objective is addressed by introducing the CS3, which includes joint scheduling and power allocation. Thus, the SDN-based IoT application resolves the cumulative congestion of networks and devices and manages power. Access is provided on time in this processing concurrency in service sharing and resources. In Table 1, the failed requests in different scheduled intervals are presented. In the SDN, Algorithm 3 shows the recurrent learning function that improves routing choices and flow conditions. Routing decisions and flow demands are initialized. It performs data transmission simulation, assesses network performance, modifies routing choices depending on metrics for performance, and modifies flow matrices iteratively. This recurrent process iterates until the maximum number of provided iterations is reached. In an SDN setting, the function produces optimal flow and route matrices that facilitate effective data transfer and resource allocation.
## 2 Applications for VANETs
### VANETs in the Cloud  
The connected vehicles can connect to the cloud and upload their data, including sensor readings, traffic information, _etc._, for downloading by other vehicles that are following behind. This allows the users to share data anonymously and preserve their security and privacy. The application servers in the cloud can aggregate the information coming in from vehicles and then compile the data to make announcements for traffic, road conditions, accidents, and weather conditions based on vehicle sensor data [9]. This improves the reliability of the information by filtering out biased or false information based on the number of users reporting such information.
# Dust Dispersion Characteristics of Open Stockpiles and the Scale of Dust Suppression Shed
## 2 Numerical Methods
### Governing Equations of Airflow and Particles
#### 2.3.2 Particle Transport Equations  
Dust from sand storage piles in the presence of wind is not a dense particle flow (fluid with a large volume fraction of solid particles). Therefore, the DPM was chosen to simulate the transport of particles in the study. By performing a force balance analysis on the particles, a DPM can be constructed. The equation [17] can be obtained as shown below:  
\[\frac{\mathrm{d}u_{\mathrm{P}}}{\mathrm{d}t}=\overrightarrow{F_{D}}( \overrightarrow{u_{\mathrm{g}}}-\overrightarrow{u_{\mathrm{P}}})+ \frac{\vec{g}(\rho_{\mathrm{g}}-\rho_{\mathrm{P}})}{\rho_{\mathrm{P}}+ \overrightarrow{F_{D}}} \tag{8}\]  
where \(\overrightarrow{F_{D}}(\overrightarrow{u_{\mathrm{g}}}-\overrightarrow{u_{ \mathrm{p}}})\) is the drag force per unit mass, \(\overrightarrow{F_{D}}=\frac{18\mu}{\rho_{\mathrm{p}}\rho_{\mathrm{g}}^{2} \frac{C_{\mu}D_{\mathrm{P}}}{2\pi}}\). \(\overrightarrow{u_{\mathrm{g}}}\) is the continuous phase velocity; \(\overrightarrow{u_{\mathrm{p}}}\) is the particle velocity; \(\mu\) is the molecular viscosity coefficient of the fluid;\(\rho_{\rm g}\) and \(\rho_{\rm p}\) are the densities of the fluid and the particles, respectively; \(d_{\rm p}\) is the particle diameter [24; 25]; \(Re\) is the relative Reynolds number, determined by:  
\[Re=\frac{\rho_{\rm g}d_{\rm p}|\overrightarrow{u_{\rm g}}-\overrightarrow{u_{ \rm p}}|}{\mu} \tag{9}\]  
The resistant coefficient, \(C_{D}=\alpha_{1}+\frac{\alpha_{2}}{Re}+\frac{\alpha_{3}}{Re^{2}}\)[26], where \(\alpha_{1}\), \(\alpha_{2}\), and \(\alpha_{3}\) are constants and are obtained based on the experimental results of smooth spherical particles.
## Nomenclature  
\begin{tabular}{l l} Ah & Ah-throughput [Ah] \\ \(B\) & Battery degradation coefficient [-] \\ \(C\) & Equivalent hydrogen consumption [kg] \\ \(C_{d}\) & Drag coefficient [-] \\ \(C_{R}\) & Current flux [C] \\ \(E_{a}\) & Activation energy [J/mol] \\ \(F\) & Frontal area of the vehicle [m\({}^{2}\)] \\ \(g\) & Gravitational acceleration [m/s\({}^{2}\)] \\ \(I\) & Current [A] \\ J & Total cost [kg] \\ \(k_{i}\) & Fuel cell degradation coefficients [\%/h] \\ \(L_{T}\) & Cost at time T [-] \\ \end{tabular}  
\begin{tabular}{l l} \(LHV\) & Lower heating value \\ \(m\) & Mass [kg] \\ M & Price [5] \\ \(P\) & Power [kW] \\ \(Q\) & Capacity (Ah) \\ \(R_{g}\) & Gas constant []/(kg\(\cdot\)K) \\ \(R\) & Resistance [\(\Omega\)] \\ \(t\) & Time [s] \\ \(T\) & Temperature [K] \\ \(U\) & Voltage [V] \\ \(v\) & Velocity [m/s] \\ Z & Power law factor [-] \\ Subscripts \\ \(v\) & Vehicle \\ \(b\) & Basic \\ \(d\) & Drag \\ \(fc\) & Fuel cell \\ \(bat\) & Battery \\ \(req\) & Require \\ \(GB\) & Gearbox \\ \(m\) & Motor \\ \(dcac\) & DC\(-\)AC converter \\ \(dcdc\) & DC\(-\)DC converter \\ \(opt\) & Optimal \\ \(ocv\) & Open-circuit voltage \\ \(avg\) & Average \\ \(dis\) & Discharge \\ \(chg\) & Charge \\ Greek symbols \\ \(\alpha\) & Penalty coefficient [-] \\ \(\beta\) & Specific energy of the battery [Wh/kg] \\ \(\gamma\) & Power-to-weight ratio of the fuel cell [kW/kg] \\ \(\epsilon\) & Discretization step [s] \\ \(\theta\) & Road grade [-] \\ \(f\) & Rolling resistance coefficient [-] \\ \(\eta\) & Efficiency [-] \\ \(\emptyset\) & Degradation [\(\%\)] \\ \(\rho\) & Air density [N*s\({}^{2}\)/m\({}^{4}\)] \\ Abbreviations \\ ADVISOR & Advanced Vehicle Simulator \\ CDCS & Charge–Depletion–Charge–Sustain \\ DP & Dynamic Programming \\ ECMS & Equivalent Consumption Minimization Strategy \\ EMS & Energy Management Strategy \\ FCHV & Fuel Cell Hybrid Vehicle \\ FCREx & Range-Extender Fuel Cell Vehicle \\ FCS & Fuel Cell Stack \\ H\({}_{2}\) & Hydrogen \\ NEDC & New European Driving Cycle \\ PEMFC & Proton Exchange Membrane Fuel Cells \\ PMP & Pontryagin’s Minimum Principle \\ SQP & Sequential Quadratic Programming \\ SOC & State of Charge \\ \end{tabular}
# Underwater Acoustic Target Tracking: A Review
###### Abstract  
Advances in acoustic technology and instrumentation now make it possible to explore marine resources. As a significant component of ocean exploration, underwater acoustic target tracking has aroused wide attention both in military and civil fields. Due to the complexity of the marine environment, numerous techniques have been proposed to obtain better tracking performance. In this paper, we survey over 100 papers ranging from innovative papers to the state-of-the-art in this field to present underwater tracking technologies. Not only the related knowledge of acoustic tracking instrument and tracking progress is clarified in detail, but also a novel taxonomy method is proposed. In this paper, algorithms for underwater acoustic target tracking are classified based on the methods used as: (1) instrument-assisted methods; (2) mode-based methods; (3) tracking optimization methods. These algorithms are compared and analyzed in the aspect of dimensions, numbers, and maneuvering of the tracking target, which is different from other survey papers. Meanwhile, challenges, countermeasures, and lessons learned are illustrated in this paper.  
underwater target tracking; instrument-assisted method; mode-based method; tracking optimization method; survey +
Footnote †: journal: Sensors