esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 3 Mobile Ad Hoc Networks  
Mobile ad hoc networks (MANETs) are a type of wireless network that is both adaptive and self-organizing. Unlike traditional WiFi networks in offices or homes that rely on base stations or access points, MANETs operate in an infrastructure-less manner, and a comparison example between the infrastructures can be seen in Figure 1. Unlike the left image, which portrays an infrastructure-less MANET with all nodes functioning as routers, the right image depicts a traditional WiFi network featuring centralized base stations.  
Due to dynamic topologies where nodes freely move in and out, the network's multi-hop structure constantly changes. To adapt to this, MANETs operate as self-configuring infrastructures, and this means they can discover new devices and have all nodes function as both routers and hosts. In MANETs, nodes constantly move in and out of each other's broadcast range. To find the most efficient routes, they use a network discovery method. This method allows new nodes to announce their presence and all nodes to dynamically update the routing information. This continuous discovery process makes MANETs highly scalable, enabling the network to seamlessly grow as new nodes join [12].
## 3 Results and Discussion  
### Material Selection  
EF, based on water electrolysis, is governed by two dominant reactions: the dissolution of metal at the anode due to oxidation and the generation of hydrogen gas at the cathode from the electrolysis of water [47]. These reactions can be described using Equations (1) and (2), respectively:  
\[\mathrm{M}(s)\ \rightarrow\ \mathrm{M}^{n+}(aq)+ne^{-} \tag{1}\]  
\[n\mathrm{H}_{2}\mathrm{O}+ne^{-}\ \rightarrow\ \frac{1}{2}n\mathrm{H}_{2}(g)+n \mathrm{OH}^{-} \tag{2}\]  
\[\mathrm{M}^{n+}(aq)+n\mathrm{OH}^{-}\ \rightarrow\ \mathrm{M}(\mathrm{OH})_{n}(s) \tag{3}\]  
During EF, metals dissolve into the solution, forming divalent or trivalent cations depending on the solution pH and potential. Simultaneously, the generated hydroxide ions (OH\({}^{-}\)) react with dissolved metal cations to form metal hydroxides, which precipitate as described in Equation (3). These precipitates can aggregate with other impurities, positively contributing to water purification [48].  
If improper electrode materials are selected or inappropriate electrolyte concentrations and voltage/current conditions are applied, unwanted side reactions may occur. For instance, gas evolution from the electrolyte can happen at the anode, or metal precipitation can occur at the cathode. In some cases, the actual oxidation rate at the anode does not match predictions based on [PERSON]'s law. This discrepancy can be attributed to the local  
Figure 2: Photographs of the results of (**a**) spontaneous flotation and (**b**) EF of the oil-in-water mixture for 10 min.  
pH increase caused by continuous OH\({}^{-}\) production or, at sufficiently high anode potentials, the occurrence of the oxygen evolution reaction, as represented by Equation (4) [43; 49; 50]:  
\[2\mathrm{H_{2}O}\rightarrow\ \mathrm{O_{2}}+4\mathrm{H^{+}}+4e^{-} \tag{4}\]  
These reactions, including gas evolution and metal hydroxide precipitation, can be visually represented in Figure 1. Based on this theoretical understanding, an experiment was conducted to determine the most suitable electrode material for enhancing EF performance by maximizing bubble generation.  
Since a higher bubble generation rate increases the likelihood of collisions between bubbles and oil droplets, leading to improved separation efficiency, it was crucial to select a material with a high bubble generation rate. To this end, we observed and compared the bubble generation rates for aluminum, titanium, and copper electrodes, as shown in Figure 3. The results indicated that aluminum electrodes exhibited the highest bubble generation rate of 19.2 mm\({}^{3}\)/s, surpassing both titanium and copper electrodes (Figure 3d). This outcome aligns with the known ionization tendencies of the metals (Al \(>\) Ti \(>\) Cu) under constant voltage conditions [51]. Based on these observations, aluminum was selected as the most suitable electrode material for further studies aimed at optimizing EF performance.
## 4 Data Analysis and Methodology
#### Data Collection and Integration  
For this work, a database with 266,298 records, containing technical and economic variables and different tariffs, including residential, commercial, and industrial ones, was used. The data matrix has 80 variables. In order to reduce and optimize the computing processes in subsequent steps, variables that are duplicated and have different names but contain the same information are removed. After this process, the matrix is reduced to 53 variables. In Table 3, the variables are presented with a brief description of each one. Among the 53 variables (see Table 3) is the content of monthly consumption of customers of the distributor, for the period between June 2018 and May 2021. To categorize each consumer, other variables extracted from the consumption curve during the mentioned period are integrated [17; 30; 64; 94]. The variables that are integrated are shown in Table 4. \begin{table}
\begin{tabular}{l l l} \hline \hline
**No.** & **Variable/Attribute** & **Description** \\ \hline A1 & CLICOD & Customer identification code \\ A2 & CLINOMABR & Customer name \\ A3 & CLICADIR & Address of the customer’s meter location \\ A4 & PROV & Province where the customer’s meter is located \\ A5 & CANTON & Canton where the customer’s meter is located \\ A6 & AGENCY & Agency to which the customer belongs \\ A7 & CAT\_TARIFF & The category of tariff to which the customer belongs; these can be 1. residential, 2. \\  & & commercial, 3. industrial, 4. special, and 5. illumination \\ A8 & TARIFF & The tariff assigned to the customer’s meter, which determines the use of electricity \\  & & by the customer \\ A9 & BRAND & Indicates the brand of the installed meter \\ A10 & SERIE & Indicates the series of the installed meter \\ A11 & SEQUEN & Sequence of meters according to geographical location \\ A12 & YEAR & Year of the installed meter \\ A13 & DATE\_INSTALLED & Installation date of the meter \\ A14 & AMOUT / DEBT & Value of the debt that the customer has with the distribution company \\ A15 & MONTHS\_DEBT & Number of months of outstanding debt \\ A16 & DATE\_RE-INV & Date on which re-invoicing is carried out \\ A17 & P\_FACTOR & Power factor \\ A18\_A53 & TJUN\_18\_TMAY\_21 & Electricity consumption of 36 months in kWh \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Attribute dictionary. Figure 6: Steps carried out for data analytics. Exploratory Data Analysis  
To obtain information about the numerical variables, descriptive statistics are applied. Non-numerical variables are disregarded, as these contribute with location references of the users such as CLINOMABR, PROV, CANTON, and AGENCY, among others. The results obtained can be visualized in Table 5, in which the following is shown:  
* count: Number of non-null data in the variable. * mean: Average of the data in the variable. * std: Standard deviation of the variable. * min: Minimum value in the variable. * max: Maximum value in the variable. An analysis of null values, which are presented in Table 6, is also carried out.
# A Review of Oil Spill Remote Sensing
## 2 Detection and Mapping of Oil on a Water Surface
### Image Processing  
All visual imaging has been improved by the use of advanced image processing systems, especially satellite images have benefitted from this processing [31; 32]. The algorithms can now automatically process images to correct them for aberrations, color, light levels, and most importantly, correspondence to GIS requirements. This has also reduced the delivery time of images from satellites, making these images useful to oil spill responders. Further, processing of optical images has enabled the fusion of different types of sensor outputs to produce new images [32].
## 5 Multi-Level Visual Design and Analysis of RGN Predicted Results
### Interactive Exploration  
The RGN is introduced through different visualization charts in terms of prediction accuracy, structural difference, and structural stability. This section introduces interactive operations in detail at each structural level, which simplifies the complexity of protein structure.  
Figure 11 shows the interactive operations between the visualization charts. The Pie chart **a** shows the distribution of RGN prediction data in the _TM-Score_ value. Selecting _TM-Score_\(>\) 0.6 and above will display the Mixed Line and Bar chart **b**, which shows the comparison of _GDT_TS_, _TM-Score_, and _dRMSD_. In this paper, we mainly use _GDT_TS_ as the similarity evaluation criteria, because it eliminates the excessive _dRMSD_ in the structure due to the distance between the structure regions and the influence of _TM-Score_ on the large deviation in the structure. Taking T0315 as an example, it can be observed that its _GDT_TS_ and _TM-Score_ have reached similar standards, but its _dRMSD_ have deviated from a similar level, and we will analyze it in detail. It can be seen from the tertiary structure visualization **d** (green frame part) and distance matrix visualization **c** (green frame part) that the start and end parts of the actual structure are too far apart, which causes the _dRMSD_ to be large and judged to be dissimilar. Figure 11e shows the alignment structure between the predicted structure and the real structure is processed by the structure alignment algorithm. Red is the predicted structure, gray is the real structure. Then we perform local visualization of T0315. The line chart **f** shows the deviation and torsion angle fluctuation of this chain where the green shading is the part of the drastic change in the torsion angle, which corresponds to the tertiary structure visualization **g** (red is the predicted structure, blue is the actual structure). The Ramachandran Plot **h** analyzes whether the conformation of this chain is reasonable and judges its stability.
# Renewable Energy-Based Energy-Efficient Off-Grid Base Stations for Heterogeneous Network
## 3 System Configurations and Mathematical Model
### System Configurations  
#### 3.1.1 Solar PV  
The average daily solar radiation profile of the selected area throughout a year is illustrated in Figure 7.  
The total amount of energy generation using solar PV panels can be calculated by HOMER using the following equation [56; 62]:  
\[E_{PV}=R_{PV}\times PSH\times\eta_{PV}\times 365\quad days/year, \tag{23}\]
# A Survey on Parameters Affecting MANET Performance
## 2 Routing in MANETs
### DSR Routing Protocol
The default value for the maximum request period time is 10 s.
* Re-transmit buffer size: Re-transmit buffer holds the maximum number of packets waiting for the next-hop reachability confirmation. If the buffer is not sufficient to keep the new packet, this packet is discarded without notification. The re-transmit buffer size defines the buffer size with a default value of 50 packets. * Max maintenance re-transmission: The maximum number of re-transmissions for a packet waiting for a confirmation from the next hop should be limited by the configuration value of the max maintenance re-transmission parameter. The default value is only two transmissions. Table 3 summarizes the DSR configuration parameters with their default values.
## 1 Introduction
In addition, EBM is capable of designing customized titanium implants with different pore geometries, which helps in better osseointegration, and also lessens the bulk [14; 17; 19]. These findings have indeed been reported based on an in vivo study by a group of the present authors, which evaluated EBM titanium reconstruction plates for reconstructing mandibular continuity defects [14]. The same computer aided designing (CAD) protocols for EBM can also be used for custom fabrication of biomedical implants utilizing subtractive processes such as computer aided milling (CAM) [16]. Two biomaterials which are routinely used in dentistry and are capable of custom fabrication through CAM are zirconia and acrylic (poly (methyl methacrylate), PMMA). Both of these materials have been used for craniofacial biomedical implants, and zirconia has in fact been used to design the stock condyle and fossa elements in the Groningen total TMJ replacement system [21; 22]. It would be alluring to hypothesize that the above-mentioned biomaterials can be used as effective alternatives for custom fabrication of prosthetic TMJ. However, the most important considerations are the biocompatibility of the biomaterial to promote osseointegration or unhindered bone formation around the implant and the biomechanical ability to withstand rotational and translational TMJ movements over a long period of time [8]. While the favorable biological properties of EBM titanium, zirconia, and acrylic have been reported clinically [21; 22; 23], their mechanical performance when used in differing combinations of opposing joint parts are not clearly delineated. This is imperative considering the fact that materials with very weak mechanical properties might wear out easily, and a very strong material might hasten bone resorption through stress shielding [14]. Therefore, the aim of the present in vitro experiment was to evaluate the wear response of custom fabricated EBM titanium, zirconia, and acrylic TM) parts when subjected to biomechanical simulation testing.
# Fabrication of an Anti-Icing Aluminum Alloy Surface by Combining Wet Etching and Laser Machining
## 3 Results and Discussion
### Ice Adhesion Experiments Analysis  
These experiments presented the ice adhesion strength on four different fabricated structures. Figure 6a shows the real-time curve record of the force monitored by the dynamometer, and its peak value was recorded several times and averaged.  
It can be seen from Figure 6b that, in the SS sample, the adhesive force of the ice layer was approximately 800 kPa, while 200 kPa in the NS sample, and 170 kPa in the HS sample, which was 75% lower than that in the untreated sample. The triple point of water at the ice/gas and ice/solid surface contact interface migrated to the lower part and increased the temperature. This resulted in a water layer with negative temperature between the ice and the sample surface contact interface [21,22]. The water layer at the contact interface between ice and sample acted as a lubricating layer, significantly reducing the adhesion strength  
Figure 5: Ice conditions of four samples, **(a)** Ice conditions of SS; **(b)** ice conditions of MS; **(c)** ice conditions of NS; **(d)** ice conditions of HS; **(e)** histogram of freezing time of four samples.  
Figure 6: Adhesion test results **(a)** Monitoring curve of dynamometer; **(b)** ice adhesion strength.  
of ice under a shear load. The reason may be that relative movement occurs between the inner layers of the lubrication layer under shear action, and static friction force does not need to be overcome. This would require less energy to remove surface ice, therefore less adhesive. Thus, in some cases, spontaneous deicing may occur due to a combination of gravity, wind, vibration, or airflow pressure. On the other hand, due to a lubricating water layer at the contact interface, the surface modified with low surface energy cannot form covalent chemical bonds with the water-based media components, which further reduces the bonding strength between the ice and the treated sample.
## 2 Problem Description and Model Formulation
### Map Modeling
#### 2.3.3 Directional Nodes  
In MAPF problems, map nodes are typically represented by their X, Y coordinates. However, since the AGV movement depends on the orientation, a traditional X-Y coordinate system is insufficient.  
To address this, this paper introduces directional nodes, where each grid cell is subdivided into four distinct nodes, each corresponding to a different AGV orientation--0\({}^{\circ}\) (North), 90\({}^{\circ}\) (East), 180\({}^{\circ}\) (South), and 270\({}^{\circ}\) (West)--but sharing the same X-Y coordinates. The concept of directional nodes was introduced to represent the AGV's orientation and to support the AGV-Referenced Action Space.  
The edges between the nodes are defined by the AGV-referenced actions and constrained by the grid map obstacles, as shown in Figure 3. Rotation actions create bidirectional links between the four direction nodes within the same grid cell; an AGV can rotate within its current cell. Forward actions create a unidirectional link from a node in one cell to the corresponding direction node in the adjacent cell, let an AGV can move forward to an adjacent cell.  
Before executing a forward action, collision detection is performed; if any of the directional nodes in the target cell is occupied by an obstacle, the path is considered blocked. Therefore, the conflict detection actually only uses the X-Y coordinates.  
This modeling approach enhances accuracy of AGV motion simulation and enables realistic turning constraints.  
Figure 3: Comparison between four-neighborhood nodes and directional nodes. With directional nodes, a rotation action will transfer the AGV between different nodes within the same grid cell, while a forward action will move the AGV to the node in the grid cell ahead with the same direction.  
Figure 2: The grid map.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 3 Proposed Methods
### Region Filling
#### 3.4.4. Filling the Coverage Circles Along the \(X\) and \(Y\) Axes
```
1:Input:\(d\)--a drone object representing the starting circle of the outer layer Output: updated \(D\)--collection of drones, updated \(y_{ref}\)--reference \(y\)-coordinate for \(x\) direction filling. 2:\(indication\gets EquiDistant(d,\{(0,0),(m,0)\},0)\)
3:while\(indication=-1\)do
4:\(r\gets d.r\)
5:\(x_{1}\gets d.x+\sqrt{d.r^{2}-d.y^{2}},y_{1}\gets 0\)
6:\(h_{ref}\gets H_{x_{1},y_{1}}\)
7:\(d.h_{ref}\gets Maximum(d.h_{ref},h_{ref})\)
8:\((d.b,d.h,d.r,d.e)\gets FindDroneParameters(d.x,d.y,d.h_{ref})\)
9:\(x_{2}\gets d.x+\sqrt{(d.r+r)^{2}-(r-d.y)^{2}},y_{2}\gets r\)
10:\(\theta\gets tan^{-1}\left(\frac{y_{2}-y_{1}}{x_{2}-x_{1}}\right)\)
11:\(x\gets x_{1}+r\times cos\theta,y\gets y_{1}+r\times sin\theta\)
12:\((b,h,r,e)\gets FindDroneParameters(x,y,h_{ref})\)
13:\(Perform\ 8\ t0\)
14:\(coordinates\leftarrow\{(x,y)\}\)
15:while\(true\)do
16:\((b,h,r,e)\gets FindDroneParameters(x,y,h_{ref})\)
17:\(Perform\ steps\ 8\ t0\)
18:\(Perform\ steps\ 8\ t0\)
19:if\((x,y)\ e\ coordinates\)then
20:\((b,h,r,e)\gets(b_{1},h_{1},r_{1},e_{1})\)
21:\(Perform\ steps\ 8\ t0\)
22:\((b_{1},h_{1},r_{1},e_{1})\gets FindDroneParameters(x,y,h_{ref})\)
23:endwhile
24:break
25:endif
26:\(coordinates\gets coordinates\cup\{(x,y)\}\)
27:endwhile
28:\(d_{1}\gets Drone(x,y,r,h,b,e,h_{ref})\)
29:\(d.east\gets d_{1},d_{1}.west\gets d\)
30:\(D\gets D\cup\{d_{1}\}\)
31:\(d\gets d_{1}\)
32:\((x,y)\gets ArbitraryDistant(x_{1},y_{1},m,0,0,0,r)\)
33:if\((x,y)\)\(are\ defined\)then
34:\(indication\gets 0\)
35:else
36:\(indication\leftarrow-1\)
37:endif
38:endwhile
39:\(indication\gets EquiDistant(d,\{(x_{1},y_{1}),(m,0)\},0)\)
40:\(y_{ref}\gets d.y+\sqrt{d.r^{2}-(m-d.x)^{2}}\)
```  
**Algorithm 2** FillAlongXAxis—Filling drone coverage circles along \(x\)-axis
# A Time-Series Model for Varying Worker Ability in Heterogeneous Distributed Computing Systems
## Notations  
\begin{tabular}{l l} Symbol & Description \\ \(N\) & The number of workers participating in the matrix multiplication task \\ \(N_{i}\) & The number of workers in group \(i\) \\ \(G\) & The number of groups \\ \(\mathbf{A}_{t}\) (\(\tilde{\mathbf{A}}_{t}\)) & (coded) Data matrix in \(\mathbb{R}^{k\times d}\) at time \(t\) \\ \(A_{i,j,t}\) (\(\tilde{\mathbf{A}}_{i,j,t}\)) & (Coded) data matrix in \(\mathbb{R}^{l_{i}\times d}\) at time \(t\) allocated to worker \(j\) in group \(i\) \\ \(A_{i,t}\) (\(\tilde{\mathbf{A}}_{i,t}\)) & (Coded) data matrix in \(\mathbb{R}^{N_{i}l_{i}\times d}\) at time \(t\) allocated to the workers in group \(i\), \\  & i.e., \(\mathbf{A}_{i,t}=[\mathbf{A}_{i,1,t};\mathbf{A}_{i,2,t};\ldots,\mathbf{A}_{i,N_{i},t}]\) (\(\tilde{\mathbf{A}}_{i,t}=[\tilde{\mathbf{A}}_{i,1,t};\tilde{\mathbf{A}}_{i,2,t};\ldots, \tilde{\mathbf{A}}_{i,N_{i},t}]\)) \\ \(\mathbf{b}_{t}\) & Input vector in \(\mathbb{R}^{d\times 1}\) at time \(t\) \\ \(l_{i,t}\) & The number of rows of the matrix allocated to workers in group \(i\) at time \(t\) \\ \(X_{i,t}\) & The ability of workers in group \(i\) for a subtask assigned at time \(t\) \\ \(\mu_{i,t}\) & Working rate of workers in group \(i\) at time \(t\) \\ \(\eta_{i,j,t}\) & Observed variable that indicates the runtime of worker \(j\) in group \(i\) at time \(t\) \\ \(N(m,\sigma^{2})\) & Normal distribution with the mean \(m\) and variance \(\sigma^{2}\) \\ \(\mathbf{\theta}\) & Model parameters, i.e., \(\mathbf{\theta}=(m,\sigma)\) \\ \(f_{0}(x_{i,0}|\mathbf{\theta}=(m,\sigma))\) & Probability density function (pdf) of the initial worker's ability \(X_{i,0}\) in group \(i\), \\  & where \(X_{i,0}\) follows a normal distribution \(\mathcal{N}(m,\sigma^{2})\) \\ \(f(x_{i,t}|x_{i,t-1})\) & pdf of worker's ability \(X_{i,t}\) in group \(i\) at time \(t\) provided the worker's ability \(X_{i,t-1}\) at the previous time \\ \(g(\eta_{i,j,t}|x_{i,t},l_{i,t})\) & pdf of the runtime \(\eta_{i,j,t}\) of worker \(j\) in group \(i\) at time \(t\), given the workers' ability \(X_{i,t}\) and load allocation \(l_{i,t}\) \\ \end{tabular}