esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Sensor Networks for Big Data Systems
## 3 Technical Approaches to WSN-Based Big Data Systems
### Data System
#### 3.4.2 Data Collection
present an energy-efficient big data algorithm called \"big data efficient gathering\" (BDEG). Cluster header in BDEG is determined depending on the level of both the received signal strength indicator and the residual energy of the sensor nodes. Each cluster header (CH) within a cluster and relay node (RN) is connected to the cluster coordinator (CCO) nodes for inter-cluster communication. The proposed algorithm builds an energy-efficient route by balancing the load on the cluster headers, cluster coordinators, and relay nodes for gathering big data in an effective manner. Finally, adaptive distributed data-gathering (ADiDaG) technique to save energy in periodic WSN applications is proposed in [45]. Depending on operations in every round, ADiDaG takes data-gathering, sampling decision, and transmission phases. The main decision algorithm for each phase depends on the longest common subsequence similarity and grouping approach, as well as adaptive sensor sampling rate. Finally, a special case for data collection in indoor WSN is explained in [46]. [PERSON] et al. propose a real-time big data-gathering (RTBDG) algorithm for the risk analysis of the industrial operations. The proposed algorithm is performed according to the requirements of the risk analysis under the clustering architecture, which is built by using received signal strength indicator and residual energy information. Simulation results are given to demonstrate the suitability of RTBDG for industrial environments. In summary, Table 3 lists a comparison of data collection schemes in terms of advantages and disadvantages. Even though most of the schemes are well-designed to achieve their objectives, there are still research challenges in the aspects of deployment or optimization.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline
**Material** & Layer Height [mm] & Ra [mm] & Rq [mm] & Rq [mm] \\ \hline \multirow{4}{*}{PLA} & 50 & 5.661 & 6.913 & 32.66 \\  & 100 & 10.131 & 11.946 & 49.014 \\  & 200 & 16.696 & 20.056 & 78.051 \\ \hline \multirow{4}{*}{Iron} & 50 & 18.539 & 22.834 & 95.728 \\  & 100 & 13.782 & 16.504 & 73.623 \\ \cline{2-5}  & 200 & 20.832 & 25.058 & 20.832 \\ \hline \multirow{4}{*}{Steel} & 50 & 31.331 & 36.884 & 140.483 \\  & 100 & 12.211 & 14.95 & 68.322 \\  & 200 & 18.793 & 22.925 & 97.937 \\ \hline \multirow{4}{*}{Copper} & 50 & 11.382 & 14.309 & 70.356 \\  & 100 & 11.192 & 13.565 & 58.321 \\ \cline{1-1}  & 200 & 18.488 & 22.337 & 95.888 \\ \hline \end{tabular}
\end{table} 
Table 4: Results of surface roughness measurements on the surface directly after 3D printing. Figure 19: Sidewall of test specimens with 0.2 layer height, before and after grinding (**A**) Pure PLA, (**B**) Copper, (**C**) Iron, (**D**) Steel.
# Deep Learning-Based Classification of High-Resolution Satellite Images for Mangrove Mapping
## 1 Introduction
[16] achieved an overall accuracy of 87% when using a deep-learning model called Capsules-Unet for monitoring mangroves in Hainan Island from Landsat-5 TM (Thematic Mapper), Landsat-7 ETM+ (Enhanced Thematic Mapper Plus), and Landsat-8 OLI (Operational Land Imager and TIRS) images with a resolution of 30 m. Most of these studies focused on mangrove extraction based on relatively low- to medium-resolution satellite data, with resolution values up to 10 m. However, the number of small mangrove patches with an area of less than 1 hectare (ha) is increasing in China [17]. Therefore, it is necessary to obtain more detailed information about mangroves from higher-resolution satellite images. In this study, we aim to provide high-resolution maps of mangroves in coastal areas of Guangdong Province, China. A deep learning network, U\({}^{2}\)-Net, combined with an attention gate was proposed to extract multi-scale features of mangroves from Chinese GF and ZY satellite images with a resolution of up to 0.8 m. The remainder of the paper is organized as follows: Section 2 describes the study area, satellite data sources, and the methodology. The performance of the attention U\({}^{2}\)-Net model in explained in Section 3. A case study on the spatiotemporal evolution of the mangrove forest in Maoming Shuidong Bay based on satellite observations is introduced in Section 4. The conclusion is given in Section 5.
## 3 The Description of the Model in Numerical Simulation
### The Parameters of Numerical Simulation  
The finite volume method was used to solve the turbulence equations numerically, which was realized by the FLUENT codes. The outside of the model was the calculation domain of the flow field, with a rectangular shape. The distances between the model and inlet flow, the model and outlet flow, were 1 \(L\) and 2 \(L\), respectively. The width of the flow field was 6c and the height was 3c, where c is the chord length. The boundary of computational domain, including the inlet, the outlet, the plane of the object, and the regions of the outside boundary, were set as the velocity inlet, the pressure outlet, the symmetrical boundary conditions, and the solid wall boundaries. The velocity of the flow was 8.68 m/s.  
The method of wall function was combined with the RNG model to calculate the steady-state flow field, where \(y^{*}\approx 35\), \(Re=1.6\times 10^{7}\). The mesh thickness of the first boundary layer, namely, \(\Delta y_{p}\) was 0.0001 m, which was obtained from Equation (21):  
\[\Delta y_{p}=\frac{Ly^{+}}{0.172\mathrm{Re}^{0.9}}. \tag{21}\]  
Considering time cost and computational power, we performed a steady-state grid-independent verification. We calculated the resistance value of the models with the grid number of \(1\times 10^{6}\), \(1.5\times 10^{6}\), \(2\times 10^{6}\), \(2.5\times 10^{6}\), \(3\times 10^{6}\), \(3.5\times 10^{6}\), and \(4\times 10^{6}\). Through the comparison, we found that the grid number of \(3\times 10^{6}\) could obtain the appropriate resistance. Therefore, the grid number of the flow field calculation was \(3\times 10^{6}\). The transient simulation of the flow field was performed by the dynamic sub-lattice model in the LES.  
\[f_{\mathrm{max}}\frac{1}{2\Delta t}\frac{1}{n\Delta t}, \tag{22}\]  
We adopted a second order upwind scheme to discretize the convection term. We adopted the central difference scheme to discretize the diffusion term. Additionally, we adopted the second order implicit scheme to discretize the temporal term. We used the PISO method to solve the pressure-velocity coupling equation [37].  
Since the maximum frequency of the analysis was 2 kHz, the time step was determined to be \(2.5\times 10^{-4}\) s according to Equation (22). The number of samples was selected as 800 time steps to calculate the sound field.  
To analyze the flow field and the sound field in detail, some planes were set up as shown in Figure 3. Plane A is longitudinal and aligned with the center of the height of the sail hull. Plane B is longitudinal and aligned with the center of the thickness of the sail hull. Plane C is transversal and aligned with the center of the length of the sail hull.  
Figure 3: The diagram of the planes. Plane A is in the longitudinal direction, Plane B is in the longitudinal direction, and Plane C is in the transversal direction.
## 4 Simulator Environments
Figure 2 shows which network simulators were used in all explored studies and in the recent ones since 2013. A tendency can be observed that recent studies use more standard simulators, especially the ONE, and fewer custom built ones. This is surely a positive trend, as it helps towards re-usage of models and reproducibility. There are various reasons one might argue against and for custom-built simulators. One possible reason is speed and optimization. Standard simulators are typically of general use and tend to be slower. This potentially limits the scale of the OppNet simulation. Figure 3 shows the correlation between number of nodes (simulation scale) and whether a standard or a custom simulator has been used. This figure shows that in fact the largest simulations with 12,000 and 64,000 nodes have been conducted on custom simulators. However, these are rather outliers and most of the custom-built simulators have been used for much smaller network sizes. In addition, standard simulators have been used for studies with 1000 and 2000 nodes. Another argument for implementing custom-built simulators has been the learning experience and the deep understanding of the built system. However, self-built solutions tend to be simpler and ignore many important properties. Thus, an argument we find in various references [80] and  
Figure 2: Used evaluation environments: for all surveyed publications (**top**); and for all recent studies since 2013 (**bottom**). from speaking directly to the authors is that, while a deep understanding and mastering of the used environment is surely of great importance, learning from well-designed and carefully tested already existing simulators is probably the better choice. An important question is which simulator to choose. We compared the scalability and availability of simulation models in an earlier work in [8], where we concluded that the ONE has the largest variety of simulation models, while C++ based simulators like OMNeT++ or Adyton are much faster. Our current work is partially focused on further improving the scalability of our own OMNeT++ based OppNets framework OPS [81] and increasing the number of available models there. Finally, it does not matter which simulator is selected, as long as the original code is made available to the research community and the evaluation methodology is carefully designed. Unfortunately, only one of the studies point to where their source code can be found: CGR [74]. Surely, some of them have found the way and became part of the ONE or other simulators (e.g., MaxProp, Epidemic, BubbleRap, SimBet, and ProPhet), but this is rather the exception. Reproducibility is highly important for fostering research in general and providing the used source code would be an important first step. _Take home message: Standard simulation environments are to be preferred against self-built ones and source code should be made available to the research community._
# Understanding and Classifying Permanent Denial-of-Service Attacks
## 1 Introduction  
In today's dynamic cyber-threat environment, permanent denial-of-service (PDoS) attacks are recognized as particularly devastating cyber threats. Unlike temporary denial-of-service (DoS) attacks, which cause transient disruptions, PDoS attacks lead to irreversible hardware damage and significant economic repercussions and, in contexts like healthcare and critical infrastructure, can even pose threats to human life. As adversarial methods continue to advance, it becomes essential for the detection and defense mechanisms to evolve.  
Despite the severe implications of such attacks, exemplified by the incidents like the 2017 NotPetya malware outbreak that resulted in billions of USD in global damages through the irreparable compromise of thousands of computers [1], there is significantly less dedicated research on PDoS attacks compared to its temporary counterparts [2]. This lack of systematic study leaves a deep gap in the scientific knowledge, while industries, governments, and individuals remain vulnerable to this specific threat.  
Recognizing this knowledge gap and the risks associated with PDoS attacks, this paper endeavors to present a comprehensive study of the anatomy of the PDoS threats. Our multidimensional framework, designed specifically for PDoS attacks, is aimed to equip both academic researchers and industry practitioners with the expertise required to predict, identify, and defend against these high-impact threats.  
Due to the limited number of malware cases that cause direct physical damage to devices, the scope of the literature review was expanded to include the cyber attacks that prevent devices from booting the operating systems and similar cases [3, 4]. While the most widely used example of PDoS malware is BrickerBot [5], this research surveyed other known malware and malware strains, for example, TDL4 [3], StoneDrill [1], Mamba [6], Remaiten [7], Bad Rabbit [4], Silex, PaperW8 [8], and more.  
The primary contributions presented in this paper are as follows:A detailed exploration of PDoS attack vectors, evaluated with real-world case studies, including tactics like Internet of Things (IoT) exploitation [7; 9], boot process disruption [4], and strategic data destruction [10].
2. The introduction of a novel classification framework for PDoS attacks, categorizing threats across dimensions such as damage mechanism, impact effect speed, target device and software, and recovery effort needed.
3. A severity score that allows the proposed classification to be applied in an accessible way.  
Providing a systematic study of the PDoS attacks, this work also suggests future research directions to enable the development of specialized detection methodologies, informed mitigation plans, applications for incident response, and impactful metrics for quantifying PDoS repercussions. With increasing instances of PDoS attacks [6], this in-depth analysis is timely and of essential need for the global cybersecurity community.
# Wear Behavior of Uncoated and Coated Tools in Milling Operations of AMPCO (Cu-Be) Alloy
## 2 Materials and Methods
### Materials
#### 2.1.2 Tool Substrate and Coating  
The cutting tools used in machining tests were made of a sintered substrate of tungsten carbide (WC-Co), grade 6110, with about 0.3 \(\upmu\)m grain size and a binder of 6% Co (wt.), supplied by InovaTools, SA (Leirai, Portugal). Regarding its dimensions, it is a toric tool of 4 mm in diameter with a 0.5 mm tip radius, and a total length of 57 mm. Its shank was 6 mm to make the tool more robust and reduce vibrations from the cutting process as much as possible. The tools had three cutting knives. The criteria for its selection fell on the fact that it is the ideal number of knives for machining soft materials, and, at the same time, they provide better surface finishes on the machined part.  
In the study, only this type of cutting tool was used. The main difference between these tools was the inclusion of a coating. Therefore, the main objective was to carry out a comparison of wear mechanisms between coated and uncoated tools, to verify which tool is more suitable for machining copper-beryllium alloy.  
Regarding the coating, it was selected based on the material to be machined. Therefore, and since the material tends to adhere to the cutting tool, a coating that works as a self-lubricating agent was chosen. The selected coating presented a multilayer structure, with chromium nitride (CrN) sublayers and a diamond-like carbon (DLC) top layer. For its application to the tools, a CemeCom CC800/9 ML PVD Unbalanced Magnetron Sputtering tool (CemeCon AG, Aachen, Germany) was used.  
To carry out the coating deposition, it was necessary to clean the sample with acetone in an ultrasonic bath for 15 min. After completion, the acetone was changed, and a new ultrasonic bath was performed again for 5 min to complete the cleaning process.  
After the tool-cleaning process, they were mounted on a proper support, where only the shank was not exposed to the coating deposition process. In order to make the surface coating of the tools as homogeneous as possible, they were mounted on a support with a rotating base in the deposition chamber, which rotated at 1 rpm during the deposition time. The parameters used in the tools' coating deposition can be seen in Table 3.
# Balancing the Leakage Currents in Nanometer CMOS Logic--A Challenging Goal
## 3 Bspl
### General Design Issues in BSPL  
Although the goal of balancing drain-source currents is indeed achieved in BSPL cells, we point out that these gates suffer from several shortcomings with respect to their usability as universal CMOS standard cells. First, the simulation results reported in [27] have been acquired while sizing both NMOS and PMOS transistors identically. In reality, however, it is required to adapt the size of the transistors in a standard cell to ensure that the rise and fall times of its output signal are balanced. Typically, the width of a PMOS transistor needs to be significantly larger than that of an NMOS transistor in the same cell (typically by a factor of 2.5 or 3). The reason for this disparity is the difference between electron mobility and hole mobility (factor of about \(\times 2.7\)). Although this deficit is fairly easy to correct in the design of the BSPL cells and should not significantly influence their balancing capability, it certainly impacts the area, latency and energy overhead. Thus, to provide a fair comparison, we have adapted the transistor sizes to achieve balanced rise and fall times in all our simulation results presented in Section 5. A second issue of the BSPL design strategy becomes apparent when noting the bulk connections in Figures 2-4 and 6. Traditional bulk CMOS processes are built on a p-type wafer. Thus, n-type field effect transistors can be built directly in the substrate, so that all NMOS devices share a common bulk voltage. Yet, in the BSPL design it is required to realize NMOS (and PMOS) devices with different bulk voltages in the same gate, which severely complicates the layout process and increases the design area. The third issue originates from the old technology, which BSPL is designed based on it. In larger technologies ( old technologies), gate leakage is meaningfully smaller than subthreshold leakage. Hence, the spice simulation using the 180 nm technology file does not show any reasonable leakage through gates and bulks. Meantime, for sub-100 nm technologies, gate leakage increases exponentially with decreasing the oxide thickness [29]. For oxide thickness less than 3 nm due to gate tunneling, the gate leakage current reaches the order of subthreshold leakage [30]. Consequently, by moving to smaller technologies, these leakages are obvious and degrade the balance of the design. Finally, the type of XOR gate that is chosen in BSPL is based on pass-transistor logic which is typically not used in standard cell design due to its lack of drive strength and the large transistor sizes.
The compound word Transformer [8] was used to represent music and to consider the following factors in learning musical features part: beat, density, strength, pitch, instrument type and duration. The first three factors are related to rhythm and the last three are related to notes and using types to distinguish the grouping of factors. Compared to RNNs, the Transformer [9], a multihead model based on a self-attention network is more effective and explainable in time-based long sequence tasks such as BERT [10] and so on. The proposed model used two linear Transformers [7] with a time position to train the rhythm and note-related features separately. Taking into account the correlation between the various musical elements, the proposed model adjusted the complementary features of the two groups and trained the network in a joint way. The Lakh Pianoroll Dataset [3] was used in the training and inference stages. Furthermore, the background music was specified by using the specific features of the video. To sum up, our work has the following contributions:  
1. In this paper, we propose a video background music generation model. Most background music generation works use only one Transformer to learn and extract all music elements, which leads to a weak melody. To establish the correlation and independence between rhythm-related and note-related music features, we use two linear Transformers training jointly. In particular, the two kinds of music features are put into each Transformer network separately;
2. Compared to an RNN network such as LSTM and GRU, the proposed model uses a linear Transformer, considering its lightweight and linear complexity. We use the timing information of the given video to guide the generation of music by adding a time beat encoding to the Transformer network;
3. After the model has learned the music features, we replace the density and strength features of the music with the optical flow information and rhythm of the video in the inference step, inspired by the state-of-the-art music video inference method. The proposed model combines the music feature learning step and the inference step to form a complete video background music generation model.
FMM accuracy may be compromised by several problems affecting data, like overdispersion, heterogeneity, loss of information. Thus a new methodology called _Bayesian Hierarchical Finite Mixture_, is implemented, where the FMM parameters are estimated by maximum likelihood technique. _Bootstrap resampling techniques_, approximating statistical distributions, are presented in a general Bayesian framework in [7]. Several bootstrap techniques are used and compared in predictive classification and regression models based on ensemble approaches, i.e. bagging models involving DTs. In particular, _Proper Bayesian bootstrap_ is used to sample the posterior distribution over trees, introducing prior distributions on the covariates and the target variable. The results obtained on simulated and real data, are compared with respect to other competitive procedures employing different bootstrap techniques. In summary, this Special Issue covers aspects like learning BN structure and parameters from data, and the utilization of BN modeling and analysis for monitoring, fault diagnosis and future state prediction of safety-critical systems like power plants and autonomous vehicles. Besides BNs, the Special Issue collects works about Bayesian approaches for data analysis, such as machine learning and FMM (both applied to traffic barrier crashes), or bootstrap resampling techniques. We hope that these articles help us advance in our understanding of the capabilities of BNs and Bayesian methods, in particular when exploited for risk and reliability assessment of infrastructures characterized by dependability requirements. This work is original and has a financial support of the Universita del Piemonte Orientale. The author declares no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} BN & Bayesian Network \\ CES & Complex Engineering System \\ DBN & Dynamic Bayesian Network \\ DN & Decision Network \\ DT & Decision Tree \\ EM & Expectation-Maximization \\ FDIR & Fault Detection Isolation and Recovery \\ FMEA & Failure Mode and Effects Analysis \\ FMM & Finite Mixture Model \\ UAV & Unmanned Aerial Vehicle \\ \end{tabular}
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 5 The Telco Cloud Enabled 5G Applications
### Telco Cloud Industrial Applications
#### 5.5.2 Big Data Analytic  
In [205], the Telco Cloud, can be used to analyze big data at the network's edge, potentially saving bandwidth and energy consumption rather than wasting a large amount of bandwidth and energy when that large amount of data is sent to the core network to be analyzed.