esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation of an IoT Based Radar Sensor Network for Wastewater Management
## 3 Implementation and Results
### LoRaWAN Radar Network Deployment  
A LoRaWAN base station (gateway) was installed within Shoalhaven City Council's communication tower located on top of Cambewarra Mountain (Figure 17); a significant infrastructure asset at 650 m above sea level, this communications tower overlooks most of the Shoalhaven region.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Well Diameter (m)** & 1.5 & 1.7 & 2.0 & 2.5 & 3.2 & 3.6 \\
**SPS Surface Area (m\({}^{2}\))** & 1.767 & 2.269 & 3.142 & 4.909 & 8.042 & 10.179 \\
**Flow Error (L/s)** & 3.534 & 4.539 & 6.283 & 9.817 & 16.080 & 20.358 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The effect of radar accuracy on flow measurements.  
Figure 16: Site C normalised radar vs manual testing results.  
With the gateway mounted within one of the site's communications racks, the 915 MHz fiberglass omnidirectional antenna was installed on the transmit edge of the tower (Figure 18) to maximise the 3 dBd vertical beam width over the region.  
An initial device was designed to first conduct the LoRaWAN signal strength measurements (radio channel ranging survey) within the Shoalhaven region. This consisted of a device constructed with a MCU and Multitech mDot LoRaWAN radio node [18].  
A program was designed to send packets at five-second intervals, which included both Global Positioning System (GPS) location and an incrementing counter from end-device to gateway. The gateway measures received packet signal characteristics Received Signal Strength Indication (RSSI) and Signal-to-noise Ratio (SNR), attached as metadata, before forwarding the packet to the backend server. This allowed a comprehensive heat map of signal characteristics to be produced for the Shoalhaven region; showing maximum transmit distances localised to the region's geographic area, as well as the corresponding signal statistics.
# Cleaning Phenolic Compounds Present in Water Using Salting-Out Effect with DCA-Based Ionic Liquids
## 3 Results and Discussion
### Effect of the Cation Structure of the IL in the Extraction Yield  
Ionic liquids are salts formed by anions and cations, and both have an important role in their properties as extraction agents. The influence of the structure of the anion is usually greater than the cation. However, there are studies that showed the influence of the extraction efficiency as a function of the length of the alkyl chain of the cation since this increase increased the hydrophobicity of the solvent [37]. In the present work, the influence of the alkyl chain length of the cation was not significant, and all the results were high. Nevertheless, it is possible to note a slightly better difference in the extraction when [EMim] [DCA] was used as an extraction agent (Figure 3). This is in line with the conclusions obtained by [PERSON] et al. [37], which indicate that the more hydrophilic the cation of the IL is, the greater its ability for extraction.  
Figure 2: Extraction efficiency (\(E\%\)) versus initial concentration (\(C_{i}\)) of phenol (), o-cresol () and 2-chlorophenol () for the extraction using [EMim] [DCA] and 20% of K\({}_{3}\)PO\({}_{4}\).
# Mapping Wild Leek through the Forest Canopy Using a UAV
## 5 Conclusions  
The unique phenology and spectral characteristics of wild leek means that its green leaves are clearly visible through the leafless canopy for a short period each year in early spring. We used UAV imagery collected during this period, processed through the SfM framework, to generate dense point clouds of two areas, removed non-ground points, and produced orthomosaics at a 5-cm resolution from the remaining points. A simple classification based on a greenness threshold allowed wild leek to be mapped in these orthomosaics with a high accuracy. The high spatial resolution obtainable with low-altitude UAV flights allowed the detection of individual wild leek patches, which are too small (typically ~1 m\({}^{2}\)) to detect in satellite imagery, and the ability to fly under clouds ensures that data collection is possible during the critical period in early spring when the wild leek leaves are visible through the tree canopy. The primary disadvantage of UAV imagery is the inability to cover large areas. However, complete mapping of an area of interest is rarely necessary to produce sufficient information for management decisions. Maps based on UAV imagery and covering small areas may be produced at a low cost to detect changes in wild leek distribution.  
**Acknowledgments:** We thank the Canadian Foundation for Innovation, the National Capital Commission, and the Friends of Gatineau Park for funding this research. We also thank [PERSON] for providing us with logistical support. We are very grateful for the advice provided by [PERSON], [PERSON], and [PERSON] and their assistance in field data collection.  
**Author Contributions:** [PERSON]-[PERSON] analyzed the data, contributed to image processing, and managed the field data. [PERSON] oversaw image collection, and contributed to image processing and data analysis. Both authors designed the project and wrote the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 The Method to Download Clear-Sky Data Volume during Rainfall
### Power Margin and Bandwidth Expansion  
The concept of mean efficiency is at the foundation of method [6]. If it could be perfectly implemented, the mean symbol rate in the interval \(T_{A}\), and hence also in the observation period \(T_{obs}\), would be equal to the symbol rate obtainable when \(A=0\), therefore restoring the full data volume downloaded/uploaded. As we have shown in [6], the cost is only a small increase in the transmitted power and a relatively small expansion of the bandwidth, the latter a less intuitive fact.  
Let us introduce in (1) a fixed power margin \(1/\eta\geq 1\), so that the received power is  
\[P_{r}^{*}=P_{r}/\eta \tag{8}\]  
Accordingly, the transmitted power would be \(P_{T}^{*}=P_{T}/\eta\), with \(P_{T}\) the power in clear-sky conditions. This power can deliver a constant \(\rho_{o}\) only if the instantaneous symbol rate is  
\[R_{S}^{*}(t)=(R_{o}/\eta)\times 10^{-A(t)/10} \tag{9}\]  
The ratio, \(\gamma(t)\), between the symbol rate \(R_{s}^{*}(t)\) and \(R_{o}\) is given by:  
\[\gamma(t)=\frac{R_{s}^{*}(t)}{R_{o}}=\frac{1}{\eta}\times 10^{-A(t)/10} \tag{10}\]  
According to Equation (9), the bandwidth is multiplied by \(1/\eta\) when \(A(t)\to 0\), i.e., at the beginning and at the end of rainfall. This is the maximum bandwidth required. For example, assuming a QPSK modulation scheme [6], the radiofrequency bandwidth is \(B_{RF}^{*}=(1+\delta)R_{s}^{*}(t)/2\); therefore, \(B_{RF}^{*}\leq(1+\delta)R_{o}/(2\eta)\), the equality sign that holds when \(A(t)\to 0\). Moreover, compared to the bandwidth of QPSK in clear--sky conditions \(B_{RF,CS}=(1+\delta)R_{o}/2,\leq(1+\delta)R_{o}/(2\eta)\), with the equality sign holding, of course, only if \(\eta=1\), i.e., no rainfall.  
In other words, as soon as \(A(t)=0^{+}\), the power and symbol rate are both simultaneously increased according to Equations (8) and (9), and the latter increase is counterintuitive. For example, if \(\eta=0.5\), then at \(t=0^{+}\)\(P_{r}\), \(\eta=2P_{r}\) and the symbol rate starts with the maximum value required \(R_{S}^{*}(0)=2R_{o}\) (and maximum bandwidth), which decreases according to Equation (9).  
In conclusion, by using the fixed power margin \(M\) (dB):  
\[M=-10\times\log_{10}\eta \tag{11}\]and a maximum bandwidth expansion factor:  
\[W=1/\eta \tag{12}\]  
the total data volume would be equal to that up-- or downloaded in clear--sky conditions during the same interval \(T_{A}\).  
The flow-chart shown in Figure 1 summarizes the theoretical processing necessary to apply the method. Notice, however, that its actual implementation depends on the technology available at the time.  
In the next few sections, we apply these concepts to rain attenuation at 80 GHz, circular polarization, simulated with the SST in zenith paths at the sites listed in Table 1.
# A Dynamic Multi-Layer Steganography Approach Based on Arabic Letters' Diacritics and Image Layers
## 3 The Proposed Approach
### The Metrics of Evaluating Steganography  
Steganography is a technique that fits into a broader category of hiding confidential information inside various forms of media. This method offers several benefits, including the ability to store large amounts of data, a high level of capacity, security, imperceptibility, and robustness [50].
# Image Shadow Removal Using End-To-End Deep Convolutional Neural Networks
###### Abstract  
Image degradation caused by shadows is likely to cause technological issues in image segmentation and target recognition. In view of the existing shadow removal methods, there are problems such as small and trivial shadow processing, the scarcity of end-to-end automatic methods, the neglecting of light, and high-level semantic information such as materials. An end-to-end deep convolutional neural network is proposed to further improve the image shadow removal effect. The network mainly consists of two network models, an encoder-decoder network and a small refinement network. The former predicts the alpha shadow scale factor, and the latter refines to obtain sharper edge information. In addition, a new image database (remove shadow database, RSDB) is constructed; and qualitative and quantitative evaluations are made on databases such as UIUC, UCF and newly-created databases (RSDB) with various real images. Using the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) for quantitative analysis, the algorithm has a big improvement on the PSNR and the SSIM as opposed to other methods. In terms of qualitative comparisons, the network shadow has a clearer and shadow-free image that is consistent with the original image color and texture, and the detail processing effect is much better. The experimental results show that the proposed algorithm is superior to other algorithms, and it is more robust in subjective vision and objective quantization.  
Keywords:end-to-end; encoder-decoder; convolutional neural network; shadow removal +
Footnote †: journal: Applied Physics Letters
# Limit Equilibrium Analysis of Landfill Instability Based on Actual Failure Surface
## 1 Introduction
However, it is difficult to model such a large geological landfill, and the influencing factors are complex; so, the centrifugal model test becomes a possible method to study landslide instability [26; 27]. The centrifuge applies N times the gravity acceleration to the scaled model, achieving the same self-weight stress in the model as the prototype, with the prototype's scale model macroscopic geometry size reduced by N times [28]. Therefore, the application of the centrifugal model to the study of slope stability in landfills has many advantages, such as the same stress conditions as large-scale geological slopes, easy-to-realize variation in water level, and simple model preparation. [PERSON] et al. [29] studied the influence of water level rise on the stability of centrifugal landfill slopes with different slope ratios. [PERSON] et al. [30] explored different failure patterns of centrifugal landfill slopes and predicted the reasonable failure surface close to the actual slip surface according to the limit equilibrium method. Their centrifuge model tests results showed that the failure surface is at an angle to the horizontal. An image analysis method can help us to identify the slip plane [31]. Therefore, considering the actual surface angle between the active wedge and the passive wedge when analyzing slope instability is practical and has engineering significance. Based on this assumption, it can help us predict the slope stability more accurately. Based on prior research on landfill failure's critical water level and common failure modes at high water levels, we enhanced the conventional two-wedge and three-wedge  
Figure 1: Slope failure in the Payatas landfill in Quezon City, Philippines (source: International Journal of Geoengineering Case Histories). limit equilibrium analysis methods. We established the interface between the active and passive wedges based on the actual failure surface during the slip process and calculated the stability factor _FS_. The sensitivity analysis evaluated the impact of failure surface angle, water level, and soil parameters on the factor of safety.
## 1 Introduction
Moreover, it has broad application prospects in military, commercial, and legal domains, safeguarding information security and ensuring the safety and concealment of critical information during transmission. The main work of this article is as follows:  
(1) Proposed VRIS image steganography model: This study proposes a novel image steganography model named VRIS, which aims to deceive both the human visual system and the machine learning model to ensure the security and concealment of secret information during transmission. (2) Feature processing of secret images: The VRIS model performs feature extraction and processing of secret images, and performs feature-level fusion with cover images to generate visually indistinguishable encrypted images that successfully deceive the human eye. Meanwhile, adding random Gaussian noise and using discriminators for adversarial training ensures that the encrypted image can successfully deceive the machine learning model. (3) Extraction of Secret Information, Quality Assessment and Testing of Secretly Reconstructed Image: The VRIS model ensures that legitimate users are able to extract and reconstruct the secret image without any loss, and at the same time prevents unauthorized users from accessing the secret information. In addition, the quality of the encrypted image is evaluated by PSNR and SSIM, and its effectiveness in spoofing machine learning models is verified by classification tests.
# Data Compression Based on Stacked RBM-AE Model for Wireless Sensor Networks
###### Abstract  
Data compression is very important in wireless sensor networks (WSNs) with the limited energy of sensor nodes. Data communication results in energy consumption most of the time; the lifetime of sensor nodes is usually prolonged by reducing data transmission and reception. In this paper, we propose a new Stacked RBM Auto-Encoder (Stacked RBM-AE) model to compress sensing data, which is composed of a encode layer and a decode layer. In the encode layer, the sensing data is compressed; and in the decode layer, the sensing data is reconstructed. The encode layer and the decode layer are composed of four standard Restricted Boltzmann Machines (RBMs). We also provide an energy optimization method that can further reduce the energy consumption of the model storage and calculation by pruning the parameters of the model. We test the performance of the model by using the environment data collected by Intel Lab. When the compression ratio of the model is 10, the average Percentage RMS Difference value is 10.04%, and the average temperature reconstruction error value is 0.2815 \({}^{\circ}\)C. The node communication energy consumption in WSNs can be reduced by 90%. Compared with the traditional method, the proposed model has better compression efficiency and reconstruction accuracy under the same compression ratio. Our experiment results show that the new neural network model can not only apply to data compression for WSNs, but also have high compression efficiency and good transfer learning ability.  
Keywords:wireless sensor networks; data compression; stacked RBM; transfer learning; energy consumption optimization +
Footnote †: journal: Sensors
## 3 Analytical Expressions for Vortex Beams Formed with Binary Axicons
### Perfect Vortex Beams: Graphical Representation  
As already mentioned in Section 3.1, Bessel beams formed by diffractive axicons have features that are not manifested in the intensity distribution, but are present in the phase distribution. When the beams are transformed by a lens, which is a Fourier transformer, these features are manifested in the spatial distribution too. Since our next task will be excitation of surface plasmon polaritons using perfect beams, the efficiency of plasmon excitation will depend on the features of the amplitude-phase distribution in the beams. In Figure 4, we present graphs of the distributions of fields and intensities in the lateral and longitudinal sections of perfect beams with the topological charges \(\ell=0,1,2\) constructed based on Equations (21) and (22). Together with the plots for the Bessel beams presented in [26], they can serve as a useful reference for planning experiments with binary phase axicons. It is seen from the figure that in the case of beams created by binary spiral axicons, the intensity distribution at the focus is much more complicated than that for the ideal \"perfect beams\" (12). Instead of a single ring, we see (Figure 4a,b) a structure of nested rings (at \(\ell=0\)) or nested segments of spirals (at \(|\ell|>0\)). However, most energy is concentrated in the annular region corresponding to the radius \(R_{f}\). In Figure 4a, the direction of the twisting of the spirals corresponds to beams with a negative topological charge when the observer looks along the axis \(z\).  
Figure 4c shows the effect of the diffraction of beams because of the limited beam diameter. It is seen that the beam cross-section is not symmetric with respect to the focal plane. This feature can be used for adjustment of the diameter of the \"perfect beam\" to the diameter of the conductor on which surface plasmon polaritons are to be excited. The phase distribution in the ring is also quite complex and can affect the efficiency of SPP generation.
# Maritime Cyber Security: A Global Challenge Tackled through Distinct Regional Approaches
## 2 Literature Review
### Classification Societies  
Classification societies are private, not-for-profit organizations that provide quality assurance to the maritime transport sector. The International Association of Classification Societies (IACS) is the prominent organization where most classification societies are members. While these societies were historically based in and focused on certain countries and markets, they have gradually increased their global representation. Furthermore, those societies organically grow in countries by establishing offices there and have also followed the merger and acquisition approach in which two classification societies merge and grow. The most prominent example is the merge of Norwegian DNV (Det Norske Veritas) with the German classification society (Germanischer Lloyd) to form a multinational classification society [18]. There is a large number of additional classification societies that function similarly.  
In line with the IMO resolution regarding cyber risk management, classification societies have started to introduce detailed guidelines, assessing the cyber security posture of ships and shipping companies. For example, DNV, ClassNK, and ABS have developed relevant cyber security guidelines [19; 20; 21]. At the same time, classification societies are responsible for auditing ISM code implementation, which now includes cyber security.
## 3 Application to Experimental Data
### Fusion and Final Detection  
To compute the kernel density per sensor, we used [PERSON]'s KDE Toolbox for MATLAB [21]. The fused density, according to Equation (3), is a continuous function that must be evaluated at discrete locations. In fact, to circumvent the discrete sampling, a multivariate mode-seeking algorithm could be used for detection. However, for simplicity, we set up a discrete evaluation grid that is designed fine enough to not miss any mode of the density. Modes are then traced similarly to per-sensor detection by finding local density maxima along parallel lines on the specimen surface. Figure 8 displays the principle of the detection. Finding local maxima along one-dimensional lines is straightforward due to the density's smoothness, and also makes the detection results more stable across different kernel sizes.  
The final hits after fusion are presented in Figure 9, where fusion is performed according to Equation (3) and the _product_ fusion rule. Most of the single-sensor false alarms from Figure 7 were discarded by our fusion method by recognizing the sensor conflicts. Yet, there are a considerable number of remaining false hits. These spurious hits originate from single-sensor hits that overlap purely by chance. Nevertheless, all grooves but the shallowest, nr. 15, clearly stand out against the false alarms considering the fused density measure, which is represented by the marker colors in Figure 9.  
Figure 8: Final detection by evaluating the fused continuous density function at gridded points (circles) and finding local maxima (filled circles) among them. These hits approximate the true density modes (dashed lines).  
Because higher values of the fused measure correspond to increased defect likelihood, a threshold can be applied to produce a binary decision. In the following, the detection performance will be quantitatively assessed under various conditions.
# Algorithm for Generating S-Boxes with Prescribed Differential Properties
## 2 Methods for Generating Cryptographic S-Boxes
### Stochastic Search  
Random S-box generation is a complex computational problem, as it takes a very long time to enumerate and examine all possible candidates until a suitable candidate with the required properties is found. With increasing size of the S-boxes, the space of potential candidates grows super-exponentially. For a fixed S-box size, strengthening the criteria increases the search time exponentially. One of the methods for decreasing the search time and increasing the quality of S-box candidates is to apply various stochastic search heuristics from the areas of artificial intelligence and evolutionary computation.  
Artificial intelligence and evolutionary computation play an important in both cryptanalysis [13] and cipher design [14]. The seminal works in this area include the use of Simulated Annealing [15], Hill-Climbing [16; 17], and their combinations and extensions [17] for generating cryptographic S-boxes.  
Modern stochastic search methods provide very strong experimental results, especially when considering multi-objective optimization [18]. In [19], the authors presented an optimization of the simulated annealing (SA) algorithm. In [20], a parallel application of tabu search and simulated annealing was employed. A method based on a memorable simulated annealing algorithm was successfully applied in [21] to generate chaotic S-boxes. A reversed genetic algorithm for generating S-boxes was proposed in [22]. The search starts from a mathematically constructed set of candidates (see the next section) and improves the diversity and quality of candidates by stochastic search based on genetic algorithms. Genetic algorithms can be used to find cellular automata-based S-boxes [23; 24]. Stochastic search methods can be improved in terms of both the quality of results and the complexity of algorithms by investigating new implementation techniques and cost functions [25].  
Our proposed algorithm is based on an exhaustive search with early rejection. We believe that it might be possible to improve the proposed method by adopting a combination of the proposed search method and evolutionary search methods. Inspiration can be found in [26], for instance, where a combination of a special genetic algorithm and total tree searching produced S-boxes with high nonlinearity.