esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images
## 3 Data and Methodology
### Deep Learning Models
#### 3.2.3 RegNet Model  
The RegNet model is recognized for its methodically structured architecture and exceptional performance in vision tasks. It efficiently extracts multi-scale features from  
Figure 4: The architecture of the Res2 Net model.  
Figure 3: The architecture of the ResNet model.  
high-resolution images, making it well-suited for remote sensing applications. In this research, spectral-spatial feature extraction commences with an initial convolution layer, succeeded by a sequence of meticulously optimized RegNet blocks. Each block is specifically engineered for proficient feature extraction and dimensionality reduction, streamlining the process [30]. RegNet's advanced proficiency in handling intricate imagery and its efficiency in delineating structural attributes of wetlands make it particularly advantageous for enhancing the accuracy of high-resolution wetland remote sensing image segmentation [31].  
In this research, the model incorporates fully connected layers, specifically comprising 256 and 128 units. These layers execute non-linear transformations on the input data, utilizing 'ReLu' as the activation function [32]. Concluding the model architecture is an output layer with three units, tailored to differentiate designs across three distinct classes. The'softmax' activation function is employed here to ensure non-negative outputs that sum to one. For model optimization and loss computation, the'sparse_categorical_crossentropy' is designated as the loss function, and 'RMSprop' acts as the optimizer with a learning rate set at 0.001. The performance evaluation of the model is primarily based on its accuracy. In the study, 200 epochs were selected as the optimal number, following close observation of the convergence in the validation error rate. The strategy of early stopping was implemented to mitigate the risk of overfitting. The distinct architecture of the implemented RegNet model is depicted in Figure 5.
# Particle and Particle Agglomerate Size Monitoring by Scanning Probe Microscope
## 2 Materials and Methods  
In the present study, there are examples of the SPM application for studying fullerene-containing powder (the particle sizes in the range of 0.7-100 nm) and nanoscale particles (the sizes in the range of 0.1-40 \(\upmu\)m). The fullerene-containing powder was obtained in Udmurt Federal Research Centre (Izhevsk, Russia) by electrochemical deposition and dispersion at high pressure. The nanoscale particles were obtained in the multi-stage centrifugal mill designed and assembled in Udmurt Federal Research Centre. Highly oriented pyrolytic graphite (HOPG) and polished ceramic plates (1 \(\times\) 1 cm) were used as substrates. The substrates were purchased from the NT-MDT company.  
The electrochemical deposition of fullerene-containing powders on HOPG was performed from a fullerene solution (the concentration \(\sim\)150 \(\upmu\)g/g) in parasylene with sodium metal at the voltage of 100-150 V and galvanic current of 10-30 \(\upmu\)A. HOPG was preliminarily subjected to mechanical cleaning using scotch tape.The cooper particles dissolved in polystyrene were deposited on the plate surface by the spin-coating method. The plate was preliminarily cleaned with ethanol.  
In order to automate the investigations, a microscope based on a commercial P47 SPM was used. The field of view of the microscope was 100 \(\times\) 100 \(\times\) 6 \(\upmu\)m (\(\pm\)10%), and the minimum scanning step was 0.006 nm. It was upgraded as follows. Firstly, the manual positioning screw drivers were motorized. Secondly, the software designed for motion control and image processing was upgraded. To minimize any actions upon a surface the modes of tunnelling microscopy and semi-contact atomic force microscopy were used.
## 4 Discussion
### Drivers, Positive Trends, and Best Practices
#### 4.2.3 TF3 Practices & Implementation  
Overall, filling the knowledge gap is among the most mentioned drivers of CE uptake in the building sector, acting on policies, practices, and tools. [PERSON] et al. (2018), for instance, argue that better knowledge of building stock as embodied environmental impact may help city councils better manage renovation programs. On the same wavelength, more effort should be put into improving material bank databases and cadasters, which is a key prerequisite for the implementation of a paradigm shift towards a circular construction industry ([PERSON] and [PERSON], 2018). At a higher level, it is crucial to strengthen secondary resource markets. In this context, novel concepts emerge to help waste (component or material) management across the entire life cycle; it is the case of the \"product-as-a-service\" business model, which gives the manufacturer the responsibility for the product from production to the end-of-life stage and beyond (EPR--Extended Product Responsibility), paving the way to the \"sharing economy\" ([PERSON], 2018; [PERSON], 2018).  
Regarding means, digitalization is largely considered a powerful driver to shift towards the circular economy, mainly providing info about resources to be reused/recycled ([PERSON] and [PERSON], 2018) and optimization methods for upcoming waste and recycling masses ([PERSON] and [PERSON], 2018). Urban mining platforms can also benefit from this, as well as green demolition by law, deconstruction practices, and hubs, which are also deemed key means for the circularity shift. Novel approaches in this field make use of BIM and GIS (respectively, at the building and urban scales) to provide detailed and interconnected information about material/component life span, embodied carbon, and reuse/recycling potential ([PERSON] and [PERSON], 2018; [PERSON] and [PERSON], 2018). Similarly, material passports, as encouraged by the EC, can be extremely important to this end. It can be concluded that detailed LCA procedures are too technical and demanding to enable the application of CE on a large and systematic scale on the market, where there is a general lack of specialists and/or difficulties are faced in supporting the specialists' calculation of cost, which is not mandatory yet. Accurate LCA calculations are valuable to support research findings, but then, more agile procedures should be embedded in current tools to support sustainability assessment in ordinary interventions.
# Bridging Macro-Microscopic Parameters: Correspondence and Calibration Approaches
## 3 Fundamental Parameters in Particle Mechanics
### Macroscopic Parameters  
Three sets of repeated uniaxial compression tests were conducted using sand samples, with the setup as shown in Figure 4. The actual operating area of the apparatus is the dashed circular region in the figure, which is the sample loading area, with a diameter of 61.8 mm and a height of 52 mm. The vertical loading setup is consistent with the vertical loading apparatus used in direct shear tests in geotechnical experiments. The loading is applied using standard weights, and the sample is subjected to vertical pressure by a stainless steel cap.  
Initially, to eliminate gaps or ineffective contacts between particles, a normal force of 50 kPa was applied to the compacted sandy soil sample for preloading. We applied a preloading pressure of 50 kPa instead of a lighter preload, such as 1 kPa, because lighter pressure is less effective in bringing the particles into close contact. Our goal is to observe the deformation of particles under normal stress; however, particle displacement can complicate the curve. Therefore, we applied a moderate stress to minimize the impact of initial displacement on our observations. Of course, if necessary, 100 kPa could also be used, depending on whether a higher stress is required to induce more significant initial particle displacement. This stress was not designed to be applied over an extended period. This is because the particle size of our material is relatively large, and the particles themselves have a high hardness, so creep has a minimal effect on our material. Then, the sample was subjected to incremental normal loading up to 500 kPa, and the corresponding displacement values were recorded. The relationship curve between normal stress and  
Figure 4: Uniaxial compression test apparatus. The uniaxial compression test apparatus can be more specifically described as a uniaxial compression apparatus under constrained lateral deformation.  
normal strain during the application of normal load is shown in Figure 5. The relationship between normal stress and normal strain demonstrates a nearly linear proportionality; as the normal load applied to the sample increases, a corresponding increase in normal displacement occurs. The compression modulus of the sand sample was calculated using the formula \(E_{S}=\frac{\Delta\sigma}{\Delta\varepsilon}\).
# Performance Analysis of DF Relay-Assisted D2D Communication in a 5G mmWave Network
## 3 Problem Formulation and Analysis
### Zone 2  
As the distance between the D2D user and the BS exceeds the value derived in Equation (11), the mode is switched to Mode 2. In Zone 2, D2D communication may take place through two modes, Mode 2 and Mode 3. Therefore, Zone 2 can be further sub-divided into the primary and secondary phases.
## 3 FLARoute: Fair Locality Aware Routing
### FLARoute Design
#### 3.2.4 Phase 4: Fair Message Forwarding
Hence, based on the following burden balancing rules, a high burdened node may remove from its buffer a message that is taking the less-burden path, after a copy has been forwarded to a less burdened node. Definition 3 (Burden balancing). : _After a node a forwards \(m_{l}\) to a node \(b\), the former removes the message from its buffer if all the following conditions hold true: (i) \(a(B_{i})>B_{th}\), where \(B_{th}\) is the overload threshold; (ii) \(a(B_{i})\geq[a(G_{i})\times\gamma]\); and (iii) \(b(B_{i})<[a(G_{i})\times\gamma]\)._  
In the first condition, messages are only removed if the instantaneous relative burden exceeds an \"overload threshold\" that denotes when the burden actually matters and starts to reflect noticeably on energy consumption. In real-world implementation, the overload threshold can be taken as \(B_{th}=E_{no}/E_{tr}\), where \(E_{no}\) is the noticeable energy consumption and \(E_{tr}\) is the average energy expended per transmission (i.e., for either sending or receiving)--for \(E_{no}\), insights on device energy consumption and user concerns can be obtained from surveys. The second condition prevents low burdened nodes from participating in burden balancing (i.e., deleting messages from their buffer). Hence, low burdened nodes contribute more in forwarding messages that are taking the less-burden path than high burdened nodes. The third condition verifies that the message has been forwarded to a low burdened node, and can continue travelling towards the destination through the less-burden path. The forwarding decisions made by FLAFord are summarised in Algorithm 1. ```
Input messages in \(a\)'s buffer \(\{m\}\), \(U_{a}(d_{m})\), \(U_{b}(d_{m})\), \(a(B_{i})\), \(b(B_{i})\), \(a(G_{i})\), \(B_{th}\) Output suitable relay nodes and respective messages \(\{B,M\}\)
1 foreach\(\{m\}\)do
2 if\(m\) is \(m_{th}\)then
3 if\(U_{b}(d_{m})>U_{a}(d_{m})\)then
4 Insert \(\{b,m\}\) into \(\{B,M\}\);
5 Remove \(m\) from buffer;
6
7 end if
8
9 else
10 if\([U_{b}(d_{m})>U_{a}(d_{m})]\bigwedge[b(B_{i})<a(B_{i})]\) or \(b(B_{i})<[a(G_{i})\times\gamma]\)then
11 Insert \(\{b,m\}\) into \(\{B,M\}\);
12 if\(a(B_{i})>B_{th}\)and \(a(B_{i})\geq[a(G_{i})\times\gamma]\)and \(b(B_{i})<[a(G_{i})\times\gamma]\)then
13 Remove \(m\) from buffer;
14
15 end if
16
17 end if
18
19 end if
20
21 return\(\{B,M\}\);
```  
**Algorithm 1** Making fair forwarding decisions with FLAFord
# Toward SDM-Based Submarine Optical Networks: A Review of Their Evolution and Upcoming Trends
## 2 Submarine Optical Networks Basics and Demonstrations Technologies on Submarine Cable Systems
### Cable-Installing Ships  
An important part of submarine networks is the cable ship whose work is to install, bury, repair, and service cables. Figure 10 illustrates a typical cable ship profile and describes in detail all the basic elements and the use of each element [11]. Specially designed ships are used for sinking the wet plant cables and devices.  
As cable feeding from one ship to another is not an easy job to perform in the middle of an ocean, most ships are able to carry enough cable to cross the Atlantic, thus approximately 6500 km. The whole long cable is stored in cable tanks and sinked into the ocean by using specialized equipment. As cable damage most often occurs near shores due to the presence of other ships, there are existing options to bury the cable if needed. Moreover, modern GPSs can ensure the exact position of sinking, considering possible underwater current and winds in an effort to keep up with the pre-selected, shortest possible path route.
# Detecting and Analyzing Botnet Nodes via Advanced Graph Representation Learning Tools +
###### Abstract  
Private consumers, small businesses, and even large enterprises are all at risk from botnets. These botnets are known for spearheading _Distributed Denial-Of-Service_ (DDoS) attacks, spamming large populations of users, and causing critical harm to major organizations. The development of _Internet of Things_ (IoT) devices led to the use of these devices for cryptocurrency mining, in-transit data interception, and sending logs containing private data to the master botnet. Different techniques were developed to identify these botnet activities, but only a few use _Graph Neural Networks_ (GNNs) to analyze host activity by representing their communications with a directed graph. Although GNNs are intended to extract structural graph properties, they risk causing overfitting, which leads to failure when attempting to do so from an unidentified network. In this study, we test the notion that structural graph patterns might be used for efficient botnet detection. In this study, we also present SIR-GN, a structural iterative representation learning methodology for graph nodes. Our approach is built to work well with untested data, and our model is able to provide a vector representation for every node that captures its structural information. Finally, we demonstrate that, when the collection of node representation vectors is incorporated into a neural network classifier, our model outperforms the state-of-the-art GNN-based algorithms in the detection of bot nodes within unknown networks.  
machine learning; deep learning; botnet detection; graph neural networks; structural graph representation learning; Internet of Things +
Footnote †: This research has been made in the context of the Excellence Chair in Big Data Management and Analytics at University of Paris City, 75006 Paris, France.
# Cross-Layer-Aided Opportunistic Routing for Sparse Underwater Wireless Sensor Networks
## 2 Relate Works
Furthermore, CARP supports transmission for a train of data packets whenever possible, significantly improving transmission efficiency. However, CARP still suffers from the congestion problem at high traffic rates for lack of a congestion avoidance mechanism in the negotiation phase. To this end, this paper designs a novel cross-layer OR routing protocol for improving the transmission reliability at high traffic rates in sparse UWSNs. This paper is different from our previous paper [23], which is developed for resolving the routing problem in mobile UWSNs scenarios. CORS is an OR protocol-based on receiver-side-based CSS, whereas CLOR uses a hybrid approach of CSS. In addition, a cross-layer design based on the fuzzy logic method is used to select an optimal forwarder. CLOR combines system network coding with a burst transmission strategy to improve the transmission performance. However, the CORS is based on an opportunistic coding strategy, and each time, a forwarder only can send one coded packet. Moreover, link metric, advance to sink, and congestion metric are introduced in CLOR, whereas these metrics are not used in CORS. Thus, this paper is novel protocol compared with CORS.
## 4 Conclusions  
In the present work, foamed concrete blocks were developed with the addition of construction and demolition waste (CDW) as a substitute aggregate of natural sand. Based on the results and discussion, the following conclusions are made:  
* CDW can be used as an aggregate in the manufacture of foamed concrete blocks, reducing the liabilities of construction civil companies and reducing environmental impacts due to the irregular disposal of this waste. Also, with the use of this residue, it is possible to reduce the consumption of natural sand, which is a finite aggregate and its extraction causes environmental damages, mainly in riverbeds and lakes.
* The results of the CDW characterization show that the granulometry and the chemical composition were suitable for the development of foamed concrete. It is relevant to note that CDW was used only with the sifting process. According to the Brazilian standard NBR 7211, the CDW with medium distribution granulometry is the most appropriate for the development of cellular concrete blocks. The chemical elements, densities, fineness modulus, and swelling found in the CDW from Passo Fundo, RS region resemble the CDW of other Brazil regions.
* For the concrete blocks developed, foam amount and CDW granulometry had significant influences on compressive strength, dry and wet density, water absorption, and air voids. Foam amount was the independent variable with the highest influence on dependent variables. However, in order to improve the properties of foamed concrete blocks, other proportions of foams may be studied, and also the addition of additives in the concrete may be made with greater compressive strength. In this way, the concrete blocks can be competitive with concrete blocks already marketed.  
Figure 9: Cumulative frequency (%) as a function of average pore diameter for different amounts of foam. Granulometry: (**a**) fine; (**b**) medium and (**c**) coarse CDW.  
**Acknowledgments:** Two Authors ([PERSON] and [PERSON]) would like to thank Fundacao Meridional for financial support grant.  
**Author Contributions:** [PERSON] performed the tests, analyzed the results and wrote the paper. [PERSON] and [PERSON] assisted in the characterization of CDW and analysis of the results. [PERSON] and [PERSON] planned the study, assisted in the analysis of the results and with writing the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Advancements in the Intelligent Detection of Driver Fatigue and Distraction: A Comprehensive Review
## 4 Safety Warning and Response Strategies Based on Fatigue and Distraction Detection
### Safety Response Based on Advanced Driver Assistance Systems  
When drivers exhibit signs of fatigue or distraction, beyond issuing early warnings, safety responses can also be implemented through Advanced Driver Assistance Systems (ADAS). If it is detected that a driver is unable to control the vehicle promptly due to fatigue or distraction, an ADAS can automatically engage emergency braking or perform other evasive maneuvers to prevent potential collisions or accidents, thereby affording the driver more reaction time [71]. For instance, an Automatic Emergency Braking (AEB) system can autonomously apply the brakes to significantly reduce the risk of an accident if a collision is predicted and the driver has not responded in time. The Lane Keeping Assist System (LKAS) plays a vital role in preventing potential deviation accidents caused by driver fatigue or distraction by automatically adjusting the steering to help the vehicle stay in its current lane. Adaptive Cruise Control (ACC) reduces the risk of collisions caused by excessive speed or following too closely by automatically adjusting the vehicle's speed to maintain a safe distance. The integration of these ADAS features not only enhances driving safety, but also effectively reduces the accident risk associated with driver fatigue or distraction, creating a safer driving environment for both drivers and passengers.
# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
## 5 Evaluation
### Simulation Results
#### 5.5.5 Effect of Node Density  
Node density represents the total density of nodes including both seed and normal nodes in the 500 \(\times\) 500-unit area. A high node density represents a high number of sensor nodes in the environment that include both normal and seed nodes. The default node density parameter used is 10. This evaluation is performed to evaluate how well IACS with node density less and greater than 10. A density of 1 is equal to a total of 32 nodes. The seed nodes density tested for this evaluation is 1 in an environment of total nodes density ranging from 2 to 20. As the total number of nodes increases in the network, there will more nodes involved in the localization process that contribute to a better localization accuracy. However, this will increase the costs of the localization process. In that prospect, IACS provides the least costs of computation, communication and energy consumption compared to MSL* and LCC as the node density increases. IACS demonstrates a similar performance with MSL* and LCC in terms of average error estimation and closeness value wherein the average error estimation and closeness value increases with the increment of normal density (Figure 10a,b). For computation, communication and energy costs, the proposed IACS outperformed others by utilizing the least cost for different node density (Figure 10c,d).  
The costs increase as the nodes density increases because more nodes are involved in the localization process as the number of normal nodes increase (Figure 10c-e).  
Based on the simulation results, the proposed IACS reduced the amount of communication, computation and energy costs while maintaining its localization accuracy even at static mobility and mobile mobility with different DOI, normal density, seed density, maximum sample number and sensor nodes maximum velocity.  
Figure 9: **(a)** Total Average Estimation Error, **(b)** Total Average Closeness, **(c)** Total Average Computation Cost, **(d)** Total Average Communication Cost, and **(e)** Total Average Energy Consumption for Different DQI.  
Figure 10: **(a)** Total Average Estimation Error, (**b**) Total Average Closeness, (**c**) Total Average Computation Cost, (**d**) Total Average Communication Cost, and (**e**) Total Average Energy Consumption for Different Node Density.