esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Composite Laminates Toughened by Nano-Particles
### Zero-Dimensional (0D)  
A rich selection of physical and chemical procedures have been developed to fabricate 0D NMSs with well-controlled dimensions, for instance by in situ sol-gel methods or by polymerization promoted directly from their surfaces [59; 60; 84], carbon black [85], fullerene [61], TiO\({}_{2}\) and alumina particles [62; 63].  
Positive effects of 0-D Nano particulates such as rubber [64], nanosilica [65; 66; 86; 87; 88; 89], carbon black [90], fullerene [91], and alumina [67] on fracture toughness of composite laminates have been reported. A localized inelastic matrix deformation in form of shear banding between particles, void nucleation and growth as well as crack deflection at agglomerates have frequently been cited as the key mechanisms leading to the increases in fracture toughness. For nano-particulate materials, such as nanosilica, debonding and subsequent plastic void growth were most likely to be responsible for the increase in fracture toughness [92]. The nanoparticles were also found to reduce the damage area and increase the absorbed energy resulting from low velocity impact [64; 68; 69; 90; 93; 94] and ballistic impact [70; 71; 95], with more tangible effects in the ballistic impact compared with quasi-static loading [96]. A higher residual shear strength after impact resulted by the Nano particles modification [70], however, the ultimate laminate compression strength after impact was not necessarily improved [64; 68], most probably due to agglomerates of nanoparticles found in the cured resin systems. Nanoparticles were also used for multi-functionality purposes, where they improved the impact performance, and the electrical resistivity tomography was introduced as an impact damage detection method in composites, due to conductive nature of the nanoparticles [90].  
For 0-D nanoparticles, the localized inelastic matrix deformation such as shear banding between particles, debonding in the particle/resin interface and subsequent void nucleation, plastic void growth, nanoparticle-induced dimples, as well as crack deflection at agglomerates (see Figure 12 as an example) were most likely to be responsible for the increase in fracture toughness [60; 84; 92]. For instance, as shown in Figure 13 the brittle fracture in a pure epoxy was overwhelmed by extensive plastic deformation in the nano-silica modified epoxy, when subjected to a compact tension test [84].  
Figure 11: Schematic of 0D (C60 fullerene), 1D (CNT), 2D (graphene), and 3D (nanocarbon aerogels) nanostructures of carbon-based materials [57].
## 4 Discussion
### The Development of Vibration and Acoustic Technologies  
Only two studies tried using acoustic and vibration technologies simultaneously. However, they did not attempt applications other than to detect falls. The observation persisted even when reviewing the excluded articles. Therefore, there have been few, if any, efforts at using both technologies in remote monitoring non-fall applications. Most of the reviewed articles utilised either vibration or acoustic technologies for remote monitoring, with the former primarily focused on detecting falls. In contrast, the latter covered a broader range of applications and tended towards training machine learning to recognise different activity patterns. These results suggest that research in the field diverged from each other instead of converging to explore any common ground between the two technologies. However, the two studies using both technologies showed that the combination could enhance the accuracy of sensors in detecting human falls. Exploring the convergence between the two technologies represents an opportunity for future research.
## 3 Methods
### Evaluation Methods
#### 3.1.2 Random Forest Model  
Random forest (RF) modeling is a bagging-type ensemble method that integrates multiple decision trees [14]. RF models randomly select samples with replacement, use specific features as inputs, and make predictions based on the average values of the decision trees. This approach is effective not only in dealing with nonlinear problems but also with numerous samples and features [13; 15]. Consequently, RF models exhibit strong generalizability and void issues related to overfitting. RF modeling is implemented as follows (Figure 3):  
(1) Multiple data subsets containing K samples are created by drawing samples with replacements from the original sample set;2. When each sample has N attributes, m (where m << N) attributes are randomly selected. An information-gain strategy is then applied to identify a split attribute for the given node from the m selected attributes;
3. Decision trees are constructed by splitting each node according to Step 2 until further splits are no longer possible;
4. The above steps are repeated until the desired number of decision trees has been generated;
5. Samples are divided into training and test sets, with factor FRs as inputs and actual states (landside or nonlandslide) as outputs. Each decision tree predicts an outcome for each sample, which is then averaged to obtain the final regression result (Equation (2)).  
\[f(x)=\frac{1}{M}\sum_{m=1}^{M}f_{m}(x) \tag{2}\]  
where \(f(x)\) denotes the model output, \(M\) represents the number of decision trees, \(f_{m}(x)\) denotes the output of each decision tree, and \(x\) represents the input evaluation factor of the RF model.
## 8 Conclusions  
This paper presents the proposed SES line code scheme to deal with PSD and network bandwidth for IoT-based communication. The SES is compared with other well-known line code techniques. The comparison is performed concerning statistical analysis and PSD calculation of waveforms with x and y coordinates of PSD values. The \"x\" coordinates show the fixed interval of a waveform, which is almost 0.5. The next column, \"y\", shows the waveforms of PSD variations.  
The SES is a better digitizing scheme in many respects. The SES does not require the double bandwidth to transmit digital bits over a transmission medium in signal form. The SES does not have a synchronization issue due to the toggle nature of the signal. The SES looks better for fixing the DC component issue due to the use of three voltage levels for signal presentation. The SES provides a better solution for the BER and SNR, concerning CSS modulation for smart cities. SES uses the ideal power to transmit a signal over the channel. The results for PSD are 50% better than common digitizing schemes. SES is also applicable to IoT-based communication in smart cities' ecosystems. To improve the computation cost and speed of signal modulation for short and long-distance communication, as well as the implementation of code on real hardware such as the ESP32 or STM32 (B-L072Z-LRWAN1) platforms, can be the future direction of this research work.  
The research conceptualization and methodology were done by [PERSON] and [PERSON]. The technical and theoretical framework was prepared by [PERSON] and [PERSON]. The technical review and improvement were performed by M.I. The overall technical support, guidance, and project administration was done by [PERSON], and M.A. Finally, responses to the queries of the reviewers were done by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research work was funded by the Ministry of Education, Research and Development Office, Saudi Arabia. The APC was funded by the Ministry of Education Saudi Arabia in cooperation with Najran University Saudi Arabia in the form of a post-doctoral fellowship.  
The authors acknowledge support from the Research and Development Office, Ministry of Education, Saudi Arabia in cooperation with Najran University Saudi Arabia in the form of a post-doctoral fellowship.  
The authors declare no conflict of interest.  
Figure 13: Signal to noise ratio of SES vs. CSS.
# Highly Imbalanced Classification of Gout Using Data Resampling and Ensemble Method
## 3 Methods
### Machine Learning Classifiers
#### 3.3.3 K-Nearest Neighbors  
The k-nearest neighbors (KNN) algorithm is a simple yet powerful supervised learning technique used for classification and regression tasks [41]. It operates by identifying the k-nearest neighbors of a given data point based on a distance metric, such as the Euclidean distance. The algorithm then determines the class or predicts the value of the target variable based on the majority vote or average of the values of these k neighbors. The selection of the value k, which represents the number of nearest neighbors to consider, is a crucial factor in KNN. It is important to choose an appropriate value of k to achieve optimal performance. A smaller value of k may result in a more flexible and sensitive model that captures local patterns, but it may also be affected by noise or outliers. Conversely, a larger value of k can provide smoother decision boundaries, but it may lead to oversimplification or loss of finer details in the data. Overall, the KNN algorithm is versatile and can be applied to various classification and regression tasks. It offers simplicity, interpretability, and adaptability to different datasets.
# Software Project Risk Identification Based on Scrum Artifacts
## 3 Risk Identification Methodology for Scrum Projects
### Analyzing Risks  
Once the adaptation is complete, the next stage is the risk analysis (Figure 8), which is conducted during the execution of the project. Project execution data are periodically imported from data sources and mapped to the corresponding attributes as attribute values. In most cases, data import occurs bi-weekly, aligning with the two-week sprint cycle typical in Scrum methodology. However, the team may adjust the frequency of data tracking as needed. It should be noted that reducing the frequency of data gathering is not advisable, as it can lead to losing visibility and control over the project. Attribute values can also be entered manually by team members. In such cases, ensuring that both the imported and manually entered values are updated simultaneously is important. Failure to do so may result in inconsistencies, particularly when derived attributes depend on other attribute values.  
When data are gathered from the data source or entered manually, the next step in the process is reviewing risks associated with attributes that have reached their thresholds. These risks are presented to the team for review (Step 2.3 in Figure 8), where a decision is made on which attribute artifact risks should be registered for further analysis.  
The states of the attribute artifact risk are illustrated in Figure 9, which demonstrates the progression of each risk through three states: Monitored (during the adaptation stage, when the team selects the risk for ongoing observation), Detected (when the gathered attribute value exceeds its threshold), and finally Registered or Rejected (based on the team's evaluation of the risk's significance). Detected risks are not automatically registered; instead, team members assess whether the artifact with the detected risk truly warrants concern.  
Figure 8: The stage of analyzing risks.  
For example, a significant drop in velocity might raise a flag. Still, if the decrease occurs during a holiday season, it may be an expected fluctuation rather than an actual problem.  
The risk analysis stage should be conducted continuously throughout the execution of the project. The framework should be adjusted accordingly if the team identifies any shortcomings in the adapted artifacts, attributes, risks, or their associations. Furthermore, new data sources may be added, thresholds may require fine-tuning, and new attributes may be identified as the project progresses. However, it is important to consider the impact of these modifications on the calculated attribute values, as these calculations may rely on previously imported data. Adjustments must be made carefully to maintain the integrity of the calculations. In general, the framework is designed to evolve during project execution. As the team's knowledge of the project and its environment deepens, the framework is refined to enable more accurate risk identification.
## 3 Results
### Correlation Between Oceanpal GNSS and ELBARA-II Radiometer Measurements  
The measurements lasted from August 2014 to May 2016, and there were some gaps due to observation interruptions of different kinds. During the measurement period, there were 238 days which had all GNSS LHCP reflectivity (Rrl), RHCP reflectivity (Rrr), ELBARA-II, and in-situ measurements.  
By contrasting GNSS-R and radiometer data, a significant correlation was found between the LHCP reflectivity measured by GNSS-R and the horizontal/vertical liner polarization reflectivity measured by the radiometer. Figure 8 displays Oceanpal GNSS reflectivity and ELBARA-II reflectivity measurements for different incidence angles (35\({}^{\circ}\)-55\({}^{\circ}\)). The Pearson correlation coefficient between GNSS LHCP reflectivity (Rrl) and ELBARA-II horizontal reflectivity (Rhh) is higher than the correlation coefficient between GNSS LHCP reflectivity (Rrl) and ELBARA-II vertical reflectivity (Rvv).  
Figure 8: _Cont._  
Figure 7: Correlation power of the direct and reflected signals in contrast with the elevation and incidence angles with respect to the antenna for different PRNs. (**a**) Direct, D, and LHCP reflected signals, Rl, of PRN 02, (**b**) direct, D, and RHCP reflected signals, Rr, of PRN 02, (**c**) direct, D, and LHCP reflected signals, Rl of PRN 05, (**d**) direct, D, and RHCP reflected signals, Rr of PRN 05, (**e**) direct, D, and LHCP reflected signals, Rl of PRN 06, (**f**) direct, D, and RHCP reflected signals, Rr of PRN 06, (**g**) direct, D, and LHCP reflected signals, Rl of PRN 10, (**h**) direct, D, and RHCP reflected signals, Rr of PRN 10. The 0908 and 0909 respectively represent 8 th and 9 th September 2014 (As an exception, in this figure, the incidence angle represents the angle between antenna bore sight and the reflected signal).
## 1 Introduction  
Nowadays, wire electrical discharge machining (WEDM) has become a dominating non-conventional machining process in the field of manufacturing industries [1]. WEDM is one of the most popular non-traditional machining processes capable of machining difficult-to-machine materials of complex shapes [2]. It has been successfully applied to thin-walled components in the manufacture of aerospace manufacturing and medical devices. Its advantages include non-contact machining, non-macro cutting force, and the ability to machine high-hardness materials and geometries with high precision [3]. WEDM is considered a unique adaptation of the conventional EDM process, which uses an electrode to initialize the sparking process. However, WEDM uses a wire which is consumed continuously. This electrode can be thin copper, brass, or tungsten, with diameter sizes from 0.05 to 0.3 mm. The use of a wire allows this machining process to achieve minimal corner radii. The wire is kept in tension using a mechanical tensioning device, reducing the possibility of producing faulty parts [4]. During the WEDM process, the material is eroded ahead of the wire with no direct contact between the workpiece and the wire, eliminating mechanical tensions and vibrations during machining. Moreover, the WEDM process can handle machining materials of high strength and hardness [5].  
Electrical Discharge Machining (EDM) processes are considered to require at least 1000 times more energy than conventional machining processes. This is due to the low material removal rates [6]. The rate of energy consumption in WEDM may vary depending on the construction of the machine and the machining parameters used during cutting. Supply power ranges from 5 to 8 kW for some machines when machining steel AISI P20 [7]. Very few works like [8; 9; 10] investigate the influence of process parameters in the energy efficiency of EDM.  
Despite being an energy-intensive process, almost no research has been carried out regarding the total energy consumption of WEDM and how the different machining parameters affect it. The authors in [11; 12] provide an extensive account of research conducted on WEDM. However, they do not report any work related to the use of the total energy consumption of the process as a response to being analyzed and optimized. This is surprising since electric discharge machining processes have been criticized for their high energy consumption [8].  
In this work, we analyze the total energy consumption in WEDM and the surface roughness for two plate thicknesses, 19 mm and 25 mm, of AISI D2 steel. The experiments were performed on a Sodick VZ300L machine. A Taguchi \(L_{27}\) orthogonal array was used to evaluate the influence that cutting parameters, namely, pulse on-time, pulse off-time, servo voltage, wire tension, wire speed, and voltage, have on the process. In addition to the energy consumption, the average roughness of the machined surface was used as a response to account for the quality of the machined part. A Taguchi \"smaller-the-better\" analysis [13] was used to find the optimal cutting parameters to reduce energy consumption and improve surface roughness.
## 4 Methodology
### Hj-Biplot  
To find the relationship between the centrality measures, the HJ-Biplot technique has been used. The HJ-Biplot is an exploratory data analysis method thus, no parametric assumptions are considered.  
A Biplot is a joint representation in a low dimensional Euclidean space (usually \(\mathbb{R}^{2}\)) of the rows and columns of a matrix \(X=\left(x_{ij}\right)_{1\leq i\leq n,1\leq j\leq p}\in\mathcal{M}_{n\times p} \left(\mathbb{R}\right)\), using vectors as points called markers, \(a_{1},\ldots,a_{n}\) for its rows, and vectors called markers, \(b_{1},\ldots,b_{p}\) for its columns, so that the scalar product \(a_{i}\bullet b_{j}\) defines the element \(x_{ij}\) of the matrix \(X\)[38; 39]. These markers are obtained from the singular value decomposition (SVD) of the data matrix \(X=UDV^{T}\), where the column vectors of \(U\) stand for the eigenvectors of \(X\cdot X^{T}\), the column vectors of \(V\) are the eigenvectors of \(X^{T}\cdot X\), and \(D\) is a diagonal matrix defined by the singular values.  
The HJ-Biplot is an extension of the classical Biplots introduced by [PERSON] [38]: the JK-Biplot and the GH-Biplot. The JK-Biplot is appropriate for visualizing the similarity/dissimilarity among row factors because it preserves row metric, while the GH-Biplot is appropriate for analyzing the relationships among column factors because it is column-metric preserving. In the HJ-Biplot the coordinates for columns coincide with the column markers in the GH-Biplot and the coordinates for the rows coincide with the row markers in the JK-Biplot, so that, both markers can be represented in the same reference system, achieving a good quality of representation for both rows and columns.  
The HJ biplot is conceptually similar to the correspondence analysis, but it applies to continuous data rather than to categorical data.  
The rules for the interpretation of the HJ-Biplot are a combination of those used in other multidimensional scaling techniques such as classical Biplots, correspondence analysis and factor analysis. Specifically:  
1. The distances among row markers are interpreted as an inverse function of similarities, in a such a way that closer markers (stations) are more similar; this property is used to identify the clusters of stations with similar profiles.
2. The lengths of the column markers (vectors) approximate the SD of the centrality measures.
3. The cosines of the angles between the column vectors approximate the correlations among the centrality measures. So that, acute angles indicate high positive correlation, obtuse angles indicate negative correlation and finally right angles indicate that the variables are not correlated.  
Figure 3: Flowchart of the approach.  
4. The order of the orthogonal projections of the row markers (points) onto a column marker (vector) approximates the order of the row elements (values) in that column so, the projection of a point (station) away from the center of gravity (average coordinate point), the value that this station takes on the variable is far from its mean (see Figure 4). Data were analyzed using the software MultBiplot [40].
# Design and Implementation of Composed Position/Force Controllers for Object Manipulation
## 5 Grasping Stage
### Design and Implementation of the Controllers  
To manipulate an object, the forces applied by the fingers generally are sufficient to counteract gravitational and inertial components of the load force acting on the finger-tips [37]. Due to the architecture of the two-independent-finger gripper used on the robotic manipulator, the controllers should perform two main tasks: (i) to place and keep the object at the desired position (i.e., position control), and (ii) to regulate the force applied to the object (i.e., a force control). To accomplish these tasks, three controllers are proposed: a hybrid PID controller (position-force), a type-I fuzzy controller (Mamdani), and a type-II fuzzy controller.  
These controllers might use the following inputs provided by the robotic manipulator: two readings from angular positions (right \(Pos_{R\omega}(t)\) and left \(Pos_{L\omega}(t)\)) and two readings, one each from the force sensor of each finger (right \(F_{R}(t)\) and left \(F_{L}(t)\)).  
On the other hand, the controllers produce a speed command as an output, i.e., two speeds, \(M_{R}(t)\) and \(M_{L}(t)\), which will be applied to the right and left servo motors, respectively.  
Figure 4: Initial conditions for the object’s position being: (**a**) at the center with respect the fingers, (**b**) near the left finger, and (**c**) near to the right finger.
# Application of Graph Theory for Blockchain Technologies
## 5 Graph Theory Application in Blockchain Systems
### Analyzing Network Decentralization
#### 5.1.1 Mining Pool Power
The visualization of Figure 1 offers a nuanced view of the network, underlining the relationship between a node's degree (and thereby its mining power) and its centrality within the network. Such a representation aids in understanding the centralization of mining power within blockchain networks, providing insights into how certain nodes (or mining pools) can dominate based on their connectivity and influence. Figure 2 visualizations illustrate the theoretical evolution of a blockchain network towards centralization, based on the assumptions and theorem statement provided. Degree Distribution Figure 2a: The degree distribution follows a power law, characteristic of networks that evolve according to preferential attachment. This indicates that a few nodes (mining pools) become highly connected over time, representing the centralization of mining power. The log-log plot shows that a few nodes have a high degree while the majority have a low degree, which aligns with the concept of centralization as fewer entities control a larger portion of the network's power. Betweenness Centrality Distribution Figure 2b: The betweenness centrality values across nodes show variation, indicating that some nodes play a more critical role in the network as intermediaries in transactions or information flow. Nodes with higher betweenness centrality are pivotal in connecting various parts of the network, further highlighting the potential central points of control or influence within the network. Figure 1: Mining Pool Power. Figure 2: Distribution and Betweenness. This proof outlines the theoretical evolution of a blockchain network's mining power distribution towards centralization under specific graph-theoretic and network growth assumptions. It demonstrates how the dynamics of network growth and connectivity can influence the distribution of mining power in blockchain networks.