esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks
## 4 Overview of Energy Consumption in WSNs  
This section explains the energy dissipation by SNs during the network data transmission phase. We explain how SNs consume or conserve energy using a theoretical WSN energy dissipation model. Moreover, we illustrated how these SNs consume energy during the network transmission phase using an example of a typical WSN scenario. With this example, we explain the concept, objectives, and challenges of designing an energy-efficient and energy-balanced routing protocol which forms the basis of our classification.
## 4 Discussion
### Land Cover Distribution and Change  
The dominance of Terra-Firme Forest in both study areas underscores the continued importance of these regions for biodiversity conservation and ecosystem services (Table 6). However, the significant loss of forest over the short study period is concerning, pointing to persistent deforestation pressures despite conservation efforts.  
The contrasting secondary land covers--Water Bodies in Manaus and Agriculture/Pastureland in Porto Velho--reflect the distinct developmental trajectories and environmental contexts of these areas. While Manaus has a longer history (dating back to the 19 th century \"rubber boom\"), Porto Velho experienced a significant transformation beginning in the 1970s. This change was primarily driven by the implementation of colonization projects and settlements by the Instituto Nacional de Colonizacao e Reforma Agraria (INCRA). The Brazilian government's initiative to expand the agricultural frontier in Rondonia state led to a substantial migratory flow of people to the region who, encouraged by federal policies, rapidly converted vast areas of forest into agricultural lands and cattle reaches [61; 62]. Consequently, Porto Velho and its surrounding areas underwent a more extensive and rapid deforestation process compared to Manaus.  
Porto Velho's higher proportion of agricultural land suggests a more intense anthropogenic modification, which is further supported by its larger gains in Agriculture/ Pastureland over the study period. This trend aligns with broader patterns of agricultural expansion in the Amazon, often at the expense of Primary Forests [63; 64; 65].  
The LULC dynamism observed in Manaus, and more greatly in Porto Velho, through the frequency of change (Table 7), suggests an intense land use pressure and potentially unsustainable practices in the area. One of the common LULC trajectories in the Amazon is the conversion of Primary Forests to pasture. This transition often follows a pattern where forests are cleared and then converted to pasture for cattle grazing. A cyclical pattern of regrowth and re-clearing of Secondary Forests is also commonly observed, where farmers may leave areas unused, on purpose or not, for a period of time to restore soil fertility before reusing them. This transition may happen many times over the years and the use of slash-and-burn techniques can also be widely applied in these processes, which can increase soil degradation and carbon emissions [59].
# Point Cloud Segmentation from iPhone-Based LiDAR Sensors Using the Tensor Feature
## 3 Methodology
### Point Cloud Segmentation Based on Tensor Clustering  
With the shape descriptor and refined normal vector computed as in Section 3.3, the point cloud data can be further processed in the following three aspects:  
1. point feature filtering based on the ball tensor detecting Since the 3D indoor/outdoor scene scanned by the LiDAR sensor is composed of objects with continuous surfaces, the point with a high geometric structure saliency of \(s_{N}\) (\(N=3\), in 3-dimensional space), which is presented as the ball tensor and turns out to be the scattered point, can be taken as noise point data. To deal with such cases, a threshold value \(\mathbf{\eta_{point}}\) to remove of the scattered noisy point cloud data, can be interactively set through a trivial operation, as shown in Equation (16). \[\mathbf{P_{point}}=\{\mathbf{p}|s_{N}\in\mathbf{p(s_{1},s_{2},\cdots,s_{N})\ \&\ s_{N}>\eta_{noise}\}\] (16)
2. line feature extraction based on the line tensor classifying Due to the noise and untrusted sampling of the point cloud, the saliency of the line geometric feature is affected to some extent. To extract the line feature, a threshold value \(\mathbf{\eta_{line}}\) is applied to each point, and points with saliency higher than \(\mathbf{\eta_{line}}\) are selected, as shown in Equation (17). The extracted points are actually in the buffer of the line feature, so the point in the extracted line feature is further simplified to become the line feature. \[\mathbf{P_{line}}=\{\mathbf{p}|s_{2}\in\mathbf{p(s_{1},s_{2},\cdots,s_{N})\ \&\ s_{2}>\eta_{line}\ \&\ Simplifying}\}\] (17)
3. surface feature segmentation based on the surface tensor clustering Based on the regional growth method, the surface feature is segmented using the surface saliency \(s_{1}\) and the refined normal vectors \(\mathbf{\kappa_{1}}\) of each point. The detailed operations are as follows: 1. sort the surface saliency \(s_{1}\) in descending order, and take the point with the largest saliency as the seed point \(p\); 2. initialize the cluster set \(C\) and seed set \(E\), push \(p\) into \(E\), and search the neighborhood \(\mathbf{\Omega}\) points of \(p\) using the searching radius \(r\); 3. for each of the point \(p_{i}\) in the \(\mathbf{\Omega}\), compute the angle difference \(\theta\big{(}\mathbf{\kappa_{1}^{p},\kappa_{1}^{p_{i}}}\big{)}\) between the normal vector of \(p\) and \(p_{i}\). Push \(p_{i}\) to \(C\) if \(\mathbf{\theta(p,p_{i})<\theta_{surface}}\), in addition, push \(p_{i}\) to \(Q\) if \(\mathbf{s_{1}^{p_{i}}<\eta_{surface}}\); 4. delete the current seed point \(p\) from \(Q\), and repeat step 3 until there is no seed point in \(Q\).  
Figure 4: Shape representation based on the tensor features.  
The point cloud segmentation result is the line feature and segmented surface features, while the point feature is filtered from the original point cloud data.
## 2 Materials and Methods
### Data Acquisition and Preprocessing
#### 2.6.2 AE Data Acquisition  
Figure 6a shows the field installation scene of soil MC nodes, while Figures 6b and 7 display the AE nodes' on-site diagram and data acquisition exhibiting interface. As can be seen, AE feature parameters such as amplitude, rise time, duration, ring count, and signal energy can all be precisely measured and calculated.  
Figure 6: Actual sensor node installation diagram: (**a**) deployment of the soil MC node; (**b**) testing of the wireless AE node.  
Afterward, the original AE dataset should be normalized to values between [0, 1]:  
\[{x_{i}}^{*}=\frac{x_{i}-x_{\min}}{x_{\max}-x_{\min}};\;i=1,2,\cdots \tag{16}\]  
where \(x_{min}\), \(x_{max}\), are the minimum and maximum values of input feature data, and \(x_{i}\)* and \(x_{i}\) are the normalized data and original data.
## 3 Materials and Methods
### Data Transmission
#### 3.5.5 AES Encryption  
The use of the advanced encryption standard (AES) in this study aims to fulfill the additional requirements stated. AES is a block cipher encryption method, where all the data are encrypted in blocks, and the length of each block is 128 bits. In addition, it is a symmetric algorithm requiring the same key for encryption and decryption of data. The key size would determine how many rounds of encryption are required [39].  
From a performance point of view, symmetric algorithms are less resource-intensive and faster than asymmetric algorithms. The radio simulation dataset is the selected payload to encrypt in this study.
###### Abstract  
Filtered orthogonal frequency division multiplexing (F-OFDM), employed in visible light communication (VLC) systems, has been considered a promising technique for overcoming OFDM's large out-of-band emissions and thus reducing bandwidth efficiency. However, due to Hermitian symmetry (HS) imposition, a challenge in VLC involves increasing power consumption and doubling inverse fast Fourier transform IFFT/FFT length. This paper introduces the non-Hermitian symmetry (NHS) Flip-F-OFDM technique to enhance bandwidth efficiency, reduce the peak-average-power ratio (PAPR), and lower system complexity. Compared to the traditional HS-based Flip-F-OFDM method, the proposed method achieves around 50% reduced system complexity and prevents the PAPR from increasing. Therefore, the proposed method offers more resource-saving and power efficiency than traditional Flip-F-OFDM. Then, the proposed scheme is assessed with HS-free Flip-OFDM, asymmetrically clipped optical (ACO)-OFDM, and direct-current bias optical (DCO)-OFDM. Concerning bandwidth efficiency, the proposed method shows better spectral efficiency than HS-free Flip-OFDM, ACO-OFDM, and DCO-OFDM.  
Keywords:complexity reduction; Flip-F-OFDM; PAPR; spectral efficiency; visible light communication
# Tamper and Clone-Resistant Authentication Scheme for Medical Image Systems
## 5 Watermarking Analysis and Security Evaluation
### Experimental Results
#### 5.2.2 Robustness Analysis
[41] in terms of NC, we can see that the results obtained after applying sharpening, median filtering \(2\times 2\), rotation (1\({}^{\circ}\)), Gaussian low-pass filter, and image scaling \(\times 1.1\) attacks to the watermarked image are better with our method while in the case of attacks such as cropping, salt and pepper, histogram equalization, the method in [41] is more robust than ours. The experimental results of our method show that after all attacks the extracted watermarks are visually recognizable and all extracted watermarks are similar to the original watermark. The average NC value is equal to 0.9055 which is a good ratio, the BER value on average is equal to 0.0374, the SSIM on average is equal to 0.9823, and the PSNR on average is equal to 53.45 dB. Therefore, our method is robust against different attacks.
## 2 Research Model and Hypothesis  
### Theoretical Model  
Figure 1 portrays this study's research model, which consists of four reflective constructs: DA, BDAC, IC and SCA. The model is theorized based on the dynamic capabilities view, in which firms must adapt and innovate their strategic capabilities to cope with the rapid advances and changing trends in the marketplace [47]. This model represents the dynamic capabilities provided by BDAC and IC and is based on the proposition that DA is capable of enhancing firms' sustainable performance, empowered by BDA and innovative processes. Finally, the aforementioned capabilities are proposed to enhance firms' SCA. Table 1 clarifies the constructs' items and their referents.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Construct** & **Item no.** & **Indicator** & **Referents** \\ \hline \multirow{2}{*}{**DA**} & \multirow{2}{*}{DA1} & IT systems, i.e., Enterprise resource planning (ERP) software, are & [29; 31] \\  & & integrated with big data analytics. & \\  & & All devices and sensing machines, e.g., sensors, mobiles, RFID, & [48; 49] \\  & & etc., are connected to the IoT to generate big data. & [48; 30] \\  & & DA3 & Backup system for big data is effective. & [28; 30] \\ \hline \multirow{2}{*}{**BDAC**} & \multirow{2}{*}{BDAC4} & Continuously examine innovative opportunities for the strategic & [18; 24; 50] \\  & & use of business analytics. & \\  & & BDAC5 & Enforce adequate plans for the utilization of business analytics. & [18; 24; 50] \\  & & BDAC6 & Perform business analytics planning process in systematic ways. & [18; 24; 50] \\  & & BDAC7 & Frequently adjust business analytics plans to better adapt to & [18; 24; 50] \\  & & changing conditions. & \\ \multirow{2}{*}{**IC**} & IC8 & Apply new technologies to the firm’s processes. & [51; 52] \\  & IC9 & Continuously introduce new products. & [51; 52] \\  & IC10 & Use the latest technologies in products. & [51; 52] \\  & IC11 & Adopt innovativeness in marketing and promotion. & [51; 52] \\ \hline \multirow{2}{*}{**SCA**} & \multirow{2}{*}{SCA12} & Average ROS (return on sales) is higher than other competitors. & [14; 24; 53; 54] \\  & & CC13 & Procedures, that help ensure the health and safety of employees, & \\ \multirow{2}{*}{SCA14} & \multirow{2}{*}{Reward system for employees is better than other competitors.} & [44; 55] \\  & & are better than other competitors. & \\ \multirow{2}{*}{SCA15} & \multirow{2}{*}{Superiority in reducing and recycling waste compared to other competitors.} & [53; 55] \\  & & cover the possibility of the proposed research model. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model operationalization.  
Figure 1: The proposed research model.
## 4 Result
### Results of Underground Deformation Characterization  
Selected monitoring results are illustrated in Figure 14. Inclinometer boreholes ZK3-5, ZK3-7, and ZK3-9 experienced shear failure at depths of 27 m, 22 m, and 53 m, respectively, on 20 November 2019, 27 December 2019, and 6 March 2020. During the supplementary investigation phase, inclinometer boreholes BZK1-1, BZK1-5, and BZK1-6 completed a total of 21 monitoring cycles. Among these, shear failure occurred at depths of 53.50 m and 26.5 m in BZK1-1 and BZK1-6, respectively, on 9 July 2020, and 19 July 2020.  
The five inclinometer boreholes installed in the third phase (CYP-ZK01\(\sim\)CYP-ZK05) completed a total of 8 monitoring cycles. Currently, these inclinometer boreholes are in good operating condition, with no shear failure observed.  
Upon a comprehensive analysis of the deformation data from various deep inclinometer boreholes, the deformation characteristics of the landslide are primarily manifested as follows:  
(1) Between the elevations of 1805 m and 1619 m along the lower section of the riverside road, monitoring holes show deformation in both shallow and deep parts. Shallow surface deformation is primarily observed between 20 m and 25 m, while deep deformation occurs between 45 m and 50 m. An analysis reveals that the shallow surface deformation is significantly influenced by changes in the reservoir water level, manifesting as arcuate  
Figure 13: Surface displacement monitoring map: (**a**) horizontal displacement and (**b**) vertical displacement.  
traction-type deformation. This deformation, characterized by a considerable magnitude, reflects the internal stress adjustment within the landslide to gradually adapt to the changes in the reservoir water level. In contrast, deep deformation represents the landslide's intrinsic deep-seated creep deformation, with a smaller magnitude.  
(2) Data from inclinometer boreholes above 1805 m along the riverside road indicate that the deformation of this section of the landslide mainly concentrates in the depth range of 50 m to 60 m. The analysis suggests that the outer landslide of the riverside road is significantly affected by reservoir storage and water level fluctuations, leading to noticeable displacement. The rear landslide has a high self-weight stress, and the forward deformation of the front landslide causes deformation and failure in the rear landslide, manifesting as intermittent creep deformation of the landslide under specific conditions or triggers.  
Figure 14: Deep underground displacement monitoring results.
# Nearest Neighbours Graph Variational AutoEncoder
## Appendix C ReNN Graph VAE vs. CNN VAE  
We performed a quantitative comparison between our ReNN Graph VAE and a standard 2D CNN VAE on the Sprite benchmark dataset. The CNN comprise 2D convolutional layers and mean pooling in the encoder and 2D transpose convolutions and upsampling in the decoder. A full description of the model is given in Table 10. The model was trained for 50 epochs with a batch size of 50, setting the latent space to have 5 dimensions. For the weight update, we used the Adam optimiser with an initial learning rate of 0.005 and an exponential scheduler with \(\lambda\) = 0.95. In order to obtain a disentangled representation for the hair style in the latent space, we had to set the \(\beta\) parameter to 4.  
To quantitatively evaluate the performance of our model on this dataset, we considered the Structure Similarity Index Measure (SSIM). It is a perception-based measure that considers image degradation as the perceived change in structural information. While pixel-per-pixel reconstruction metrics, such as as MSE or the previously used \(\delta\)-index, estimate absolute errors, the structural information considers the strong inter-dependencies between spatially close pixels that carry important information about the image as a whole. Both CNN VAE and ReNN Graph VAE reached an average SSIM on the test set of 0.90.  
In Figure 11, we also report a comparison between some ground truth, ReNN Graph VAE reconstructed sprites and CNN VAE reconstructed sprites from the test set. Both VAEs work well, but it is possible to spot some differences. CNN VAE reconstructed images are slightly blurrier that the originals, while the ReNN Graph VAE has slightly less bright colours.
# Underwater Wireless Communications for Cooperative Robotics with UWSim-NET
###### Abstract  
The increasing number of autonomous underwater vehicles (AUVs) cooperating in underwater operations has motivated the use of wireless communications. Their modeling can minimize the impact of their limited performance in real-time robotic interventions. However, robotic frameworks hardly ever consider the communications, and network simulators are not suitable for HIL experiments. In this work, the UWSim-NET is presented, an open source tool to simulate the impact of communications in underwater robotics. It gathers the benefits of NS3 in modeling communication networks with those of the underwater robot simulator (UWSim) and the robot operating system (ROS) in modeling robotic systems. This article also shows the results of three experiments that demonstrate the capabilities of UWSim-NET in modeling radio frequency (RF) and acoustic links in underwater scenarios. It also permits evaluating several MAC protocols such as additive links online Hawaii area (ALOHA), slotted floor acquisition multiple access (S-FAMA) and user defined protocols. A third experiment demonstrated the excellent capabilities of UWSim-NET in conducting hardware in the loop (HIL) experiments.  
underwater robotics; underwater communications; simulation; Hardware In The Loop _Applied_  
D
## 4 Results
### Hemipelagic Deposits  
The core of hemipagic deposits is commonly gray-black to black with faint alternation of light color and dark color (Figure 3b). Under polar microscope, the shale exhibits grading-type planar lamination [25; 26]. The boundaries of laminasets are commonly indistinct or absent (Figure 6) and are parallel or wavy with each other. Individual laminaset is commonly 1.5-15 mm thick (average 4.5 mm). The shale becomes lighter and then darker gradually along with a coarsening and then fining trend within the single laminaset. The changes of color are closely related to their mineral compositions. For the intervals enriched in carbonate minerals and depleted in organic matter, the shale is relatively lighter. In comparison, for the intervals depleted in carbonate minerals and enriched in organic  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Sample** & **Depth** & **Quartz** & **Clay** & **Calcite** & **Plagioclase** & **Dolomite** & **Pyrite (\%)** & **TOC** & **Porosity** \\
**ID** & **(m)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline Y101H3-8 & 3786.5 & 45.5 & 31.2 & 10.6 & 6.3 & 4.6 & trace & 0.7 & 1.58 \\ \hline Y101H3-8 & 3785.7 & 54.5 & 29.0 & 8.4 & 4.3 & 3.2 &matters, the shale is relatively darker. Generally, the shale has a massive appearance without any bioturbation and disruption.  
Hemipelagic deposits are commonly characterized by a mixture of terrigenous and biogenic materials. Under SEM, terrigenous materials include detrital quartz (12.3% on average), calcite (14.1% on average), and clay minerals (39.3% on average). Biogenic materials include autogenic quartz (24.2% on average) and dolomite (10.2% on average). The shale is commonly matrix-supported (Figure 7) with randomly oriented poorly sorted grain size. Detrital quartz commonly has irregular shape with grain size ranging from 8 mm to 12 mm (9.4 mm on average). Grains of calcite are commonly light gray and sub-rounded with harbor-like edges, diagnostic dissolution pores and grain size ranging from 2.1 mm to 11.5 mm (5.4 mm on average). Clay minerals commonly have curved stripe shape with grain size ranging from 3.1 mm to 8.1 mm (4.7 mm on average). Autogenic quartz is commonly self-shaped or semi-self-shaped with grain size <4 mm. Dolomite is commonly dark gray with angular shape and dissolution pores and grain size between 5.5 mm and 32.9 mm (14.5 mm on average). In black shale, a minor amount of framboidal pyrite is commonly present with grain size ranging from 4.1 mm to 6.1 mm (5.3 mm on average).  
Figure 5: Back-scattered electron SEM photos showing the characteristics of the fine-grained turbidite of the Wufeng Formation. (**a**) Y101H3-8, 3791.2 m; (**b**) Y101H3-8, 3792.3 m; (**c**) Y101H3-8, 3790.4 m; (**d**) DA2, 3598.4 m.
## 5 Discussion
### Vulnerability  
Vulnerability is considered a major issue which arises in spatio-temporal data management as well, and simply measures how vulnerable various mobile objects are to revealing their identity to potential adversaries, when moving from one position to another. Moreover, reducing vulnerability and enhancing resilience in the face of adversity are considered to be essential steps. To date, less attention has been given to vulnerability resulting from \(k\)-NN querying on non-linear trajectory data.  
This study investigates the vulnerability of mobile objects with respect to \(k\)-NN queries based on their geolocation data attributes. The proposed anonymity technique, as well as previous findings, are confirmed in the GeoSpark environment and, as a result, vulnerability is estimated for different risk conditions (i.e., for different number of nearest neighbors, namely \(k\) and pair of features, as presented in Tables 4 and 11).  
Furthermore, it is observed that for low values of \(k\), the vulnerability remained at the medium level, and the more the nearest neighbors are utilized to form the anonymity set, the lower the vulnerability becomes. In other words, the results of the present study indicate a low level of vulnerability for high number of nearest neighbors and uncorrelated features data.  
At this point, it should be noted that the results in Section 4 highlight the superiority, that is lower vulnerability, of Euclidean space in terms of privacy preserving, which is the main issue in \(k\)-anonymity and, thus, in applying robust \(k\)-NN queries. However, let us recall that, as it has been already investigated in [9], dual representative features, e.g., Hough-\(X\) of \(x\) and/or \(y\), are considered an appropriate choice to form the \(k\)-anonymity set from both privacy preserving and storage efficiency perspective. So, priority should be given to the use of low dimension independent features data; in that case, the utilization of dual space against the Euclidean one.
## Appendix A.3.3 Physical-Layer Cell Identifiers
### Summary of Identifiers  
The identification of cells, together with the other components, is the most relevant goal in a positioning system based on cellular networks, and a very relevant point in our work. As shown in Table A1, depending on the technologies, different kinds of logical attributes can be collected from an administrative point of view, leading to different cell global identifier constructions (concatenation of italic attributes in Table A1).  
As already mentioned, the retrieval of cell global identifiers can take up to some milliseconds, and to avoid delays, physical-layer cell identifiers can be used for quick and local identification of the available cells. As shown in Table A2, these identifiers are composed of two parts that depend on the considered technology: the frequency channel number (ARFCN for GSM, UARFCN for UMTS, and EARFCN for LTE) and an additional component, that is, the BSIC (for GSM), PSC (for UMTS), and PCI (for LTE).