esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Intelligent Cluster-Based Routing Approach
### Routing Attack Scenario  
In the context of VANET, communication delays are an inevitable consequence of its open and peer-to-peer communication structure. Within our proposed methodology, these delays are primarily attributed to the robust and influential group-based management policy that underscores the system. A salient concern stems from the number of intermediary nodes that come into play when two communication vehicles interact within VANET. The openness of this environment brings to light two critical issues related to black hole node attacks. Firstly, there is the matter of path availability, and secondly, the identification of malicious nodes. Notably, not every vehicle can relay a path to other nodes. Against this backdrop, the proposed routing approach contemplates and addresses black hole attacks. These nefarious nodes initiate their assault by targeting nearby nodes with a carefully orchestrated scenario, aiming to disrupt the communication chain from the outset. At the inception of its operation, the attacker node transmits the shortest route to another node. Subsequently, upon reaching the midpoint along the shortest path, it subtly withholds packet transmission, thus diverting packets away from the intended path. This surreptitious packet withholding engenders the characteristic black hole effect, resulting in the drop of all packets traversing that route, rendering them incapable of reaching their intended destination. The attack scenario and its ramifications, specifically concerning the mitigation of distributed denial of service (DDoS) attacks, are elucidated in Figure 6.  
In addition to the concern, the intricacies of routing in VANET consistently pose a formidable challenge owing to network nodes' rapid and dynamic movement. These VANET nodes encompass both homogenous and heterogeneous elements, with the vehicle-to-vehicle (V2V) environment significantly influencing content behavior. Transmitting or receiving data for content sharing and placement is impeded by the transient nature of node mobility and the resultant delays in the hop count within the VANET infrastructure. To tackle this issue, we introduced an intelligent cluster-based secure routing protocol to mitigate content request delays.
## 4 Experimental Comparison and Analysis
### Parameters and Benchmark Functions  
This section evaluates the performance of the aHSDE on the CEC2014 benchmark suite [45] compared with the original HS, IHS, SGHS and IGHS. The CEC2014 benchmark suite consists of 30 test functions, which include three unimodal functions, 13 multimodal functions, six hybrid functions and eight composite functions. In particular, these hybrid functions (f17-f22) are very similar to real world problems, such as transportation networks [46], circuit theory [47], image processing [48], capacitated arc routing problems [49] and flexible job-shop scheduling problems [50]. The search range for each function is [\(-100\), \(100\)]\({}^{\text{DIM}}\), where DIM is the dimension of the problem. The experiments are conducted in 10, 50 and 100 dimensions and the maximum function evaluation number is DIM \(\times\) 10000. The number of multiple runs per function is 30, and the average results of these runs are recorded. When the value difference between the found best solution and the optimal solution is lower than \(1\times 10^{-8}\), the error is considered as 0.  
For the comparing HS variants, the parameter settings are the same as the respective literatures, which are also shown in Algorithm 5.
```
HS [1] \(\text{HMS}=5,\text{HMCR}=0.9,\text{PAR}=0.3,\text{bw}=0.01\) HIS [3] \(\text{HMS}=5,\text{HMCR}=0.9,\text{PAR}_{min}=0.01,\text{PAR}_{max}=0.99\), \(\text{bw}_{min}=0.0001,\text{bw}_{max}=\frac{\omega^{2}-\omega^{2}}{20}\) SGRS [4] \(\text{HMS}=5,\text{HMCR}_{m}=0.98,\text{PAR}_{m}=0.9,\text{LP}=100\), \(\text{bw}_{min}=0.0005,\text{bw}_{max}=\frac{\omega^{2}-\omega^{2}}{10}\) IGHS [6] \(\text{HMS}=5,\text{HMCR}=0.995,\text{PAR}=0.4\) aHSDE \(\text{HMS}_{min}=5,\text{HMS}_{max}=18\times\text{DIM},HMCR=0.99,LP=100,BW=0.01\)
```  
**Algorithm 5** Parameters.
# Spatial Distribution and Monitoring of Land Subsidence Using Sentinel-1 SAR Data in Java, Indonesia
## 3 Results
### Spatial Distribution and Land Subsidence Rate
#### 3.1.3 Cianjur  
Cianjur Residence (6.7961\({}^{\circ}\) S, 107.1411\({}^{\circ}\) E) is located in West Java Province, north of the Bandung area, with a population of 175.000 (according to 2020 concensus) spread across an area of over 3600 km\({}^{2}\). It is considered to have a relatively low population density [16]. Cianjur is morphologically varied. In the southern part, it is dominated by southern mountains, domes, and ridges [17], while in the northern part it is dominated by a Quartenary volcanic region, characterized by alluvial plains and undulating hills [18]. Most of these areas are dedicated to agriculture, while only a small portion is allocated for industrial use.  
LS in Cianjur has not been mentioned in any published study. However, this study has found that the LS in this region is relatively high, with an LS rate up to 80 mm/year (Figure 5a), which occurs in an industrial area (Figure 5(b.1)). Interestingly, the LS area covers a relatively small area with an almost perfectly elongated oval shape (Figure 5b), indicating that the source may originate from a few adjacent sources. The dimension of the LS area is approximately 1.5 \(\times\) 0.85 km, with an industrial facility located adjacent to it.  
Figure 4: LS in Greater Jakarta: (**a**) LS rate in mm/year in Greater Jakarta; (**b**) LS time series retrieved from InSAR from 2017 to 2023 in: Tangerang (**b.1**), Jakarta (**b.2**), and Bekasi (**b.3**); and (**c**) land use or situation of the region highlighted in black boxes areas, (**a.1**) which correspond to industrial complexes in Tangerang (**c.1**), (**a.2**) shoreline in North Jakarta with buildings submerged in seawater (**c.2**), and (**a.3**) infrastructure in Bekasi where water levels exceed the surrounding ground (**c.3**), respectively.
# An AUV-Aided Cross-Layer Mobile Data Gathering Protocol for Underwater Sensor Networks +
## 1 Introduction  
A wide range of aquatic applications, such as pollution monitoring, military, intrusion detection, disaster prevention, and resource exploration, are now supported by the Underwater Sensor Networks (UWSNs) [1]. UWSNs face different challenges such as transmission reliability, low bit rate, sensor movement, limited bandwidth, and high latency [2; 3; 4].  
A UWSN is an ad hoc network consisting of a group of acoustic sensors distributed within the marine environment for data collection. Some networks only use stationary sinks for data collection while some also take advantage of the mobile sinks (e.g., autonomous underwater vehicle (AUV)) [5]. In a UWSN with a stationary sink, sensors nodes close to the sink will die out sooner as they relay a high number of data packets [6]. This can affect network connectivity if the operation needs to continue for a longer time. Using a mobile sink can reduce energy consumption as well as limit the number of transmissions [7].  
In an AUV tour, data packets are constantly collected from cluster heads (CHs) and uploaded into the static sink at the end of each tour. The network lifetime is extended as the packet transmission is limited to fewer hops (e.g., intracluster communications).  
The channel communications are managed by using a Medium Access Control (MAC) protocol to share the medium among several sensors. Collisions and retransmissions are avoided by proper scheduling. Reliable transmissions are guaranteed by conflict management for sensors which provide fairness, low channel access delays, high throughput, and energy efficiency [8].  
A distributed cross-layer solution is proposed to combine the functionality of routing and MAC layer while most of the works have only focused on a specific layer without any consideration for another one [9]. Cross-layer communication solutions have proven to be more efficient for resource management (e.g., bandwidth ) and energy-saving in UWSNs [10; 11].  
To the best of our knowledge, this work is the first cross-layer mobile data gathering protocol which has integrated the network and data link layers for three-dimensional (3D) UWSNs [12]. In this work, sensors are clustered within one-hop distance to their cluster head (CH) and an AUV can freely move through CHs for data collection. Proposed simulation results show significant improvement in the network performance.  
The remainder of this paper is organized as follows. Section 2 introduces the related works. Section 3 provides a detailed description of the system model. Section 4 describes Cross-Layer Mobile Data gathering (CLMD) protocol in detail. Section 5 presents and discusses the results of our simulation study. Finally, Section 6 concludes the paper.
# Energy Harvesting Hybrid Acoustic-Optical Underwater Wireless Sensor Networks Localization
## 2 System Model and Proposed Technique
### Proposed Localization Technique
#### 2.4.3 Impact of Energy Harvesting on Localization Performance  
The energy consumed by all the nodes in the network is expressed as  
\[E_{t}=\sum_{i=1}^{m+n}E_{f_{i}}+(m+n)\sum_{i=1}^{m+n}E_{r_{i}}, \tag{33}\]  
where \(E_{f_{i}}\) is the energy required for basic operation of the electronic circuitry and \(E_{r_{i}}\) is the energy consumed for transmission. \(E_{r_{i}}\) can be expressed as  
\[E_{r_{i}}=E_{b}\left(\frac{4\pi r_{i}}{\lambda^{\prime}}\right)^{2}, \tag{34}\]  
where \(E_{b}\) is the energy required for single bit transmission, \(r_{i}\) is the transmission range of node \(i\) and \(\lambda^{\prime}\) is the wavelength. The energy consumption is directly proportional to the square of the transmission range \(r_{i}\). Since the accuracy of the localization technique and the transmission range \(r_{i}\) depend on the energy arrival into the network, the efficiency \(\eta\) of the localization technique can be defined as  
\[\eta=\frac{E_{t}}{\delta^{2}}, \tag{35}\]  
where \(\delta^{2}=E\left((\tilde{\mathbf{l}}-\mathbf{l})(\tilde{\mathbf{l}}-\mathbf{l})^{T}\right)\) is the mean square error of a single node localization. As a result, the efficiency of the proposed localization technique increases with the energy arriving from the ambient underwater energy sources.
# Application of Molecularly Imprinted Polymers in the Analysis of Waters and Wastewaters
## 3 Synthesis of Molecularly Imprinted Polymers
### Electropolymerization  
In situ electro-polymerization is a special class of surface imprinting on the surface of a working electrode (glassy carbon, gold, platinum, etc.) rather than a solid non-conductive support. Electropolymerization of MIPs was utilized in the late 1990s as an alternative method to the traditionally introduced free radical polymerization. This method depends on the generation of free radical species via monomers oxidation on the sensor surface due to the applied potential. After that, these radical species are adsorbed strongly on the sensor surface and subsequently form a polymeric network. By adjusting the potential and current, thin polymeric film of controlled thickness and density can be deposited in a simple way. Additionally, this method is recommended for the direct patterning and deposition of thin films on a transducer surface with high accessibility and mass transfer.  
The development of MIPs synthesis based on the electrochemical method can be considered as a parallel progress in the field of MIPs, since traditionally utilized vinyl and acrylic monomers are not electropolymerizable, and accordingly, they must be replaced by other electroactive monomers. The resulting polymeric films can be classified as conducting films and non-conducting films. The main advantage of such an approach is controlling the thickness of the polymeric layer through controlling various deposition parameters, such as current density, applied voltage, and number of deposition cycles, which gives more uniform coating of the imprinted polymer onto the electrode surface [22; 23]. Alternatively, the imprinted polymers can be immobilized on the surface of the electrode by either spin coating or electrospraying of the monomer/template mixture onto the surface. This approach is very applicable especially for highly expensive templates, where a very small amount is used in the imprinting stage; additionally, it is extremely fast when compared to other chemically assisted polymerization processes [18].
## 2 Experimental Program  
### Specim for Bond Testing  
To more thoroughly understand the effects of the recycled aggregate quality on the bond behavior of the interaction between the recycled aggregate concrete and the steel rebar, different water-to-cement ratios (0.30, 0.45, 0.60, and 0.75) and different diameters of the deformed steel rebar (13 and 19 mm) were considered. Due to the large variation within the specimens, the specimens were cast with 13-mm or 19-mm-diameter deformed steel rebar for the pull-out tests, to understand the failure behavior of the recycled aggregate concrete bond strength at the ultimate load and in the post-peak region. The specimens for the bond tests were developed, such that bond stresses for different steel diameters could be compared by maintaining the embedment-to-steel diameter ratio, following the procedure presented by [PERSON] et al. [14] and [PERSON] et al. [15]. They reported that the bond stress of specimens with short embedment depths exhibits a higher strength than those with long embedment depths, for a given steel diameter. The concrete specimens, made with natural and recycled aggregate concrete, for the pull-out tests, were cubic in shape, with a side length of 150 mm. The concrete was poured into the mold, as shown in Figure 1a, to keep the rebar stable and at the correct position, in the RC beam.  
The 13- and 19-mm deformed bars were embedded in the concrete, with a length that was four times the steel diameter, and the remaining portion of the bars was covered by plastic piping to prevent the steel surface from contacting the concrete. This short embedment length allowed a uniform bond-stress distribution to be assumed. Sixty-four specimens were prepared for the pull-out tests of the steel rebar embedded in the natural and recycled aggregate concrete. For each natural (N) aggregate concrete and concrete with high-quality (H), medium-quality (M), and low-quality (L) recycled coarse aggregate replacement, three specimens were cast with a 13-mm-diameter deformed steel rebar and one specimen was cast with a 19-mm-diameter deformed steel rebar to consider the effects of the steel bar diameter on the bond behavior. The tensile load provided by the hydraulic universal testing machine was recorded automatically. The loading rate for the deformed rebar with diameters of 13 and 19 mm was varied from 2 to 6.6 MPa/min. Following the experimental procedure specified by JSCE-G 503-1998 \"Bond strength testing of reinforcement and concrete by pull-put\" [16], the vertical slip of the steel rebar (i.e., the relative displacement of the steel rebar and the concrete specimen) was measured using a dial gauge, installed at the top of the concrete specimen, as shown in Figure 1b.  
Figure 1: Specim installation for the pull-out test using the Japan Society of Civil Engineers (JSCE). (**a**) Dimension of specimen and (**b**) specimen installation.
## 3 Dataset Description  
In this research, the dataset BdSL 49 [35] is used. The dataset contains a total of 14,745 images. It has 49 distinct classes, such as 36 Bengali alphabet characters, 10 numeric characters, and 3 special characters (Space, Compound Character, and End of Sentence). The Label and Class name of each character is presented in Figure 1.  
Each character has around 300 sample images that are distributed between two phases, which is illustrated in Figure 2. For training purposes, 80% of the samples are used. The remaining 20% of the images are used for testing purposes. The dataset contains images in the RGB format along with a pixel size of 128 \(\times\) 128.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Research** & **Year** & **Dataset** & **Method** & **Performance** \\ \hline [PERSON] et al. [19] & 2021 & OkkhorNama & Yolo V5 & mAP: 98.02\% \\ \hline [PERSON] et al. [20] & 2021 & Shongket & KNN, SVM, Random Forest & \begin{tabular}{c} Digit Accuracy: 95\% Letter \\ Accuracy: 91.3\% \\ \end{tabular} \\ \hline [PERSON] et al. [9] & 2021 & Bangla 10 Words Dataset & Custom CNN Model & Test Accuracy: 97\% \\ \hline [PERSON] et al. [13] & 2020 & BdSL Dataset & Yolo V4 & Overall Accuracy: 97.95\% \\ \hline [PERSON] et al. [26] & 2020 & BdSL36 & MobileNet & Overall Accuracy: 95.71\% \\ \hline [PERSON] et al. [30] & 2023 & IsharaBochon and Ishara-Lipi & Hybrid Model & Overall Accuracy: 91.67\% \\ \hline [PERSON] et al. [31] & 2022 & \begin{tabular}{c} BdSL Alphabet, KU-BdSL \\ and Ishara-Lipi \\ \end{tabular} & BenSignNet & \begin{tabular}{c} Overall Accuracy: 94\%, \\ 99.60\% and 99.60\% \\ \end{tabular} \\ \hline [PERSON] et al. [32] & 2022 & \begin{tabular}{c} Author-Developed \\ Private Dataset \\ \end{tabular} & Custom CNN Model & Overall Accuracy: 92\% \\ \hline [PERSON] et al. [33] & 2023 & \
\begin{tabular}{c} Author-Developed \\ Private Dataset \\ \end{tabular} & Blazepose and LSTM & Overall Accuracy: 87.14\% \\ \hline [PERSON] et al. [34] & 2022 &
\begin{tabular}{c} Author-Developed \\ Private Dataset \\ \end{tabular} & Yolo V5 & Overall Accuracy: 51.44\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Literature overview summary.  
Figure 1: Sample dataset.  
There are three special sign, and their purposes are defined below:  
1. Compound Character (Label 46): It is known as hasantha and is utilized to create a compound character. The structure of a Compound Character is shown in Figure 3.
2. Space (Label 47): It is used to create a gap between two words.
3. End of Sentence (Label 48): It is used to indicate a sentence's end.
# Numerical Analysis of Cyclic Impact Damage Evolution of Rock Materials under Confining Pressure
## 3 Results and Analysis  
### Dynamic Stress-Strain Curve Analysis  
When the amplitude of half-sine incident stress waves is 180 MPa, the dynamic stress-strain curves of rock specimens after cyclic impacts under different confining pressures are presented in Figure 5. In Figure 5, \(n\) represents the number of cyclic impacts.  
As shown in Figure 5, dynamic stress-strain curves exhibit a relatively similar trend, which can be roughly divided into two stages, namely the initial nearly straight loading stage and the nonlinear unloading stage with a gradually decreasing slope. When rock specimens remain integrity, the partial rebound at the end of the unloading stage indicates the release of stored elastic energy.  
When the confining pressure is constant, with the number of cyclic impacts increasing, both the slope in the straight loading stage, namely elastic modulus, and peak stress of the dynamic stress-strain curve gradually decreases. Under cyclic impacts, internal crack initiation and propagation after each impact load lead to the decrease of mechanical properties of rock specimens. Consequently, the cumulative crack development of cyclic impacts intensifies the damage to the internal structure of the rock. The larger the number of cyclic impacts, the greater the damage, leading to a significant reduction in mechanical properties. With the increase of confining pressure, the number of cyclic impacts increases, which implies that the confining pressure can slow down the crack propagation and reduce damage accumulation.
# LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis
## 5 Experiments
### Experiments with Offline Keypoint-Map Tracking  
This section shows the results of the Offline Keypoint-Map Tracking, explained in Section 4. As already indicated, the idea consists of rendering images along the expected path that the camera will follow and obtaining the corresponding keypoint map. In this experiment, we have tested two different approaches to building the keypoint map. In the first one, we have used the point-based rendering approach employed in the Online Rendering and Tracking method. In the second approach, we have employed the novel view synthesis method M2 Mapping [2], which takes as input the laser scans and obtains a three-dimensional representation of the environment, which can be used later to render views from novel camera viewpoints. As noted, this method requires a lengthy offline training phase followed by a rendering phase. Although not measured with high precision, on our hardware, the training phase took approximately two hours per sequence, and the subsequent rendering of all necessary views took about one hour. This significant computational overhead confirms its unsuitability for any real-time application but makes it ideal for precomputing high-quality keypoint maps where time is not a constraint. However, runtime performance is dictated solely by the lightweight tracking thread, achieving high frame rates of approximately 30 FPS (33.3 ms latency) with AKAZE and 80 FPS (12.5 ms latency) with ORB.  
Figure 3 shows the differences between the point-based rendering approach and M2 Mapping. Figure 3a shows the three-dimensional reconstruction obtained with FastLivo2, and Figure 3b is a real image from the dataset. Figure 3c shows the image rendered by the point-based projection approach, and Figure 3d the one obtained by M2 Mapping. As can be seen, the last one is less noisy. It is true, though, that some fine-grained details are lost, and the image is somewhat blurry.  
Below, we show the results obtained using both approaches.
# The Big Picture: An Improved Method for Mapping Shipping Activities
## 4 Improving AIS Data
### Vessel Detection Based on Satellite Images
At the end of this process, the image vessels are annotated according to their matched AIS trajectory, hence being an additional source of vessel tracking to the original AIS datasets. Vessels detected in satellite images that do not have matching AIS messages, given a distance threshold, are considered as \"dark vessels\", meaning that they are either located in a low coverage area or they have intentionally switched off their transponders. In general, a vessel can only be visible via AIS and not in a satellite image due to one of the following reasons:  
* The resolution of Sentinel-1 and Sentinel-2 imagery does not allow for the detection of small vessels with high confidence. As previously mentioned, with the highest resolution provided by the satellite images, each pixel corresponds to an area of 100 m\({}^{2}\) (since each pixel is of 10 m length). This means that vessels of small sizes can in some cases be ignored by the CNN. * The AIS position of the vessel might be wrong. Since AIS is a collaborative maritime reporting system, a vessel's crew might alter the GPS position of the vessel when transmitting the AIS messages. The phenomenon is called spoofing and has been the subject of several studies over the years [24; 55; 69; 70]. * Interpolation error: when the AIS messages that we have around the acquisition time of an image transmitted by a vessel are not enough, and the vessel has changed its navigational status in the meantime (e.g., a vessel suddenly stops or it accelerates and changes heading), the estimated position of the vessel in the interpolation step might differ considerably from the actual position of the vessel.