esa-sceva/satcom-chunk-collection huggingface dataset:
# Temperature State Awareness-Based Energy-Saving Routing Protocol for Wireless Body Area Network
## 2 Related Work
This type of routing protocol has the characteristics of multi-hop transmission, sensing node temperature, and setting temperature thresholds. [PERSON] et al [25] proposed a Mobility Based Thermal Aware Energy Efficient Multi-Hop Protocol (M-ATTEMPT), this protocol supports WBANs to transmit sensitive data, on-demand data, and normal data using single hops. For multi-hop transmission, the selected best forwarding node has the characteristics of fewer hops and high residual energy. Moreover, M-ATTEMPT uses temperature thresholds to control the hot node problem in WBANs [26; 27]. However, when the temperature of a node reaches the threshold, the node disconnects all connections with its neighboring nodes to prevent the temperature from rising, but the disconnection leads to data retransmission and reduced network reliability. [PERSON] et al. [28] proposed a weighted temperature and energy-aware QoS routing protocol (WETRP). In the routing decision process, the protocol uses the node's temperature, remaining energy, and link delay estimation to construct a cost function and selects the node with the minimum cost function to transmit data. This protocol not only reduces energy consumption but also reduces the generation of hot nodes. However, this protocol cannot provide corresponding QoS requirements for multiple data types. A temperature and energy optimization routing protocol (TAEO) was proposed by [PERSON] [26; 27; 29; 30; 31]. The protocol selects nodes with low temperature, a short path to sink, and high residual energy as the forwarding nodes to transmit data, and the detection of hot nodes was performed by estimating the temperature rise value of nodes in each round. When the temperature of the selected forwarding node is higher than the threshold, it will not forward data until the node returns to normal. TAEO can effectively reduce the generation of hot nodes and extend the life of the network.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 4 Discussion
### Socieconomics and Regional Implications  
Coastal and Andean regions face severe GWS declines, threatening economic and social stability. In Ica, a 40% GWS drop has reduced asparagus yields by 15%, costing USD \(\sim\)50 M annually and jeopardizing a USD 1.2 billion agricultural GDP [55]. Lima's 44% GWS decline, serving 10 million residents, has raised pumping costs by 20% (USD 10 M/year) [56]. Northern aquifers (e.g., Chira-Piura) decline 1.2-2.5 m/year due to mango and lime irrigation, with 40% saltwater intrusion [57]. In the Andes, collapsing springs in Huancavelica threaten food security for 50,000+ households [58]. The Amazon's hydrological stability is at risk from 20% deforestation (2003-2023), potentially cutting recharge by 5-10% [59]. These effects highlight the need for region-specific interventions to balance water use with sustainability.  
Peru's GWS depletion aligns with global aquifer overexploitation. Central Chile's agricultural pumping mirrors Ica and Tacna's declines [60,61], while Northern China and Ecuador's coastal aquifers face similar irrigation-driven depletion [62,63]. India's groundwater crisis, driven by intensive agriculture, parallels Peru's coastal zones [5]. These trends reflect a global challenge of climate change and unsustainable extraction [64, 65, 66]. Peru's diverse hydrogeological regions (coastal deserts, Andes, Amazon) demand tailored solutions, distinct from one-size-fits-all approaches in less varied regions.
## 4 Results and Discussion  
The experiments were conducted using three sets of problem instances. The first set comprises ten test instances proposed by [PERSON] and [PERSON] [4]. The second set comprises 57 instances proposed by [PERSON], [PERSON] and [PERSON] [11] that were adapted from [PERSON] and [PERSON] [10]. The last is from [PERSON], [PERSON] and [PERSON] [29] and adapted by [PERSON] and [PERSON] [18]. All test instances involve two transporters.  
Like [PERSON] and [PERSON] [18], each instance is characterized by the number of machines, M, the number of jobs, J, the number of operations, O, and the average number of alternate machines assignable to an operation. Percentage makespan standard deviation, \(\sigma\), is also used as a measure of methodology robustness for all test instances. Each test instance is run over 20 runs. Each run of a particular test instance comprises 30 iterations, unless otherwise stated. The percentage makespan deviation is used to measure the spread of the solution. It is calculated as follows:  
\[\frac{\mid\mid Best\;Makespan-Mean\;Makespan\mid}{Makespan\;deviation}\times 100\% \tag{68}\]  
The time of each run required to find the best solution is averaged to obtain its average time, \(\overline{T}\), in seconds.  
As it is possible for the best solution to appear at different iterations for different runs, the average iteration by which the best solution appears, \(Iter_{best}\), is used as a measure of efficiency and is obtained by averaging the iteration count across all runs. For each test instance, the time per iteration per run averaged across all runs is given by \(T_{iter/run}\).  
For all test instances, LAHC proposed by [PERSON] and [PERSON] [18] is run under two settings of its history list length parameter, namely 1000 and 100, obtaining two results for each test instance. This is indicated by the number in the parenthesis attached to the LAHC columns in the tables used for performance comparison of various solution methodologies.  
The MILP models are coded in Pyomo [30; 31], which is used to invoke Gurobi [32] on a 3.70 GHz Intel(r) Xeon(r) E5-1630 CPU (Intel, Santa Clara, CA, USA) with 16 GB RAM.
## 3 Results  
### Physicochemical Properties of EHN/SVO Double Blends and D/EHN/SVO Triple Blends  
Kinematic viscosity plays a fundamental role in combustion efficiency for fuels used in diesel engines. It affects key processes such as fuel atomization, the size and distribution of fuel droplets, and the uniformity of the air-fuel mixture within the combustion chamber. To ensure proper engine operation, the European standard EN 590 ISO 3104 specifies that the kinematic viscosity of diesel fuels should fall within the range of 2.0 to 4.5 cSt. Given its importance, viscosity was selected as a fundamental parameter for assessing the binary blends of EHN with sunflower oil and castor oil. Table 2 highlights the viscosity measurements for the EHN/SVO double blends, showing a notable decrease in viscosity with increasing EHN content. This reduction aligns the blends' viscosity closer to that of fossil diesel, meeting the specifications outlined by the EN 590 ISO 3104 standard. This demonstrates the potential of EHN to enhance the suitability of straight vegetable oils (SVOs) for diesel engine applications. As shown in Tables 3 and 4, the addition of EHN/SVO biofuel mixtures to fossil diesel (ranging from B20 to B80) results in a progressive increase in the kinematic viscosity of the triple blends as the proportion of EHN/SVO increases. Both sunflower oil (SO) and castor oil (CO) mixtures exhibit higher viscosity with lower diesel content. Similarly, density values increase with the proportion of EHN/SVO increased in the blends, regardless of the SVO used. Regarding cold flow properties, the blends showed notable improvements compared to pure fossil diesel, particularly in their cloud points, while maintaining similar pour point values. For instance, B20 SO and B80 CO exhibited cloud points of \(-12\ ^{\circ}\)C, representing a \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Name** **(\% Biodiesel)**} & \multirow{2}{*}{**D/EHN/SO**} & **Density** **(kg/m3)** & **Kinematic Viscosity (cSt)** & **Cloud Point** & **Pour Point** & **Calorific Value (MJ/kg) 1** \\ \hline Diesel & 100/0/0 & 820 \(\pm\) 6 & 3.20 \(\pm\) 0.04 & \(-6.0\pm 1.0\) & \(-16.0\pm 1.0\) & 35.1 \\ Bio20 SO & 80/15/5 & 820 \(\pm\) 6 & 3.60 \(\pm\) 0.04 & \(-9.1\pm 2.8\) & \(-17.7\pm 2.0\) & 34.8 \\ Bio40 SO & 60/30/10 & 860 \(\pm\) 8 & 3.81 \(\pm\) 0.04 & \(-7.3\pm 0.4\) & \(-17.0\pm 2.0\) & 34.5 \\ Bio60 SO & 40/45/15 & 880 \(\pm\) 4 & 3.92 \(\pm\) 0.09 & \(-8.9\pm 0.8\) & \(-15.7\pm 1.5\) & 34.2 \\ Bio80 SO & 20/60/20 & 900 \(\pm\) 8 & 4.70 \(\pm\) 0.02 & \(-13.5\pm 0.7\) & \(-15.8\pm 0.7\) & 34.0 \\ Bio100 SO & 0/75/25 & 930 \(\pm\) 6 & 4.71 \(\pm\) 0.04 & \(-6.5\pm 1.5\) & \(-17.2\pm 0.5\) & 34.6 \\ \hline \hline \end{tabular}  
* The flash point values were calculated by using Equation (1).  
\end{table} 
Table 4: Viscosity at 40 \({}^{\circ}\)C (cSt), cloud point, pour point, and calorific value of D/EHN/CO blends. Errors are calculated as the standard deviation from the average of three measurements.
## 2 Materials and Methods
### Modelling
#### 2.6.2 Modelling Overview  
For an overview of the modelling process, see flowchart in Figure 2. ML solutions were built on the training dataset using FeatureDS1 and FeatureDS2. Code was written in Python 2.7 and packages used were sourced from Scikit-learn 1.0.2 version library (SKlearn). Model formations for regression varied in terms of the feature dataset used (FeatureDS1 or Feature DB2), the number of features in the model (up to five for raw and 20 PCA features), and the degree of the regression Equation (linear, quadratic or cubic fit). Cluster models could vary in the number of clusters defined and the feature dataset used to define them. Models for every different formation combination were built for comparison and final models chosen from among them using a leave-one-out cross validation (LOOCV) approach.  
Figure 2: Flowchart illustrating the ML process.  
In all cases, 'left-in' patient features were scaled to a mean of zero and standard deviation of 1 using the SKlearn StandardScaler package. This ensured consistency and uniformity during modelling. The left-out patient was scaled according to the left-in data before prediction. Two approaches were explored for each model type: (1) modelling with raw features (not reduced by Principal Components) and (2) modelling with Principal Components. The SKlearn Decomposition package was used for PCA transformation of FeatureDS1. Principal Component generation was performed on left-in patients and the same transformation applied to the left-out patient prior to prediction.  
It is not known a priori which models will be most appropriate for clinical use. Therefore an evaluation of all candidate models was implemented using the mean squared error (MSE) between predicted values and \(\text{MCO}_{\text{gs}}\) during LOOCV as a quality score. The model minimising MSE was selected. Once the model was identified, it was retrained using the entire training dataset (i.e., no patients left out) to create the final ML solution. This solution could then be used to generate patient-specific AutoPlan protocols for novel patients with their features transformed with respect to the training datasets scaling and PCA parameters (where applicable) before prediction, as was the case with the left-out patients during model selection. The patient-specific protocol was then used as the input for the PGAP solution for patient-specific AP (PGAP-MLeg or PGAP-MLclus).
# Detecting Phishing Domains Using Machine Learning
## 2 Background
### Ensemble Classification Techniques  
A neural network (NN) is a mathematical model that mimics the behavior of biological neurons and the nervous system. ANNs utilize technological solutions to imitate the architecture and functions of the neural system of human brains [29]. They use neural network topologies to represent physical systems in this way. [PERSON] and [PERSON] introduced the ANN theory for the first time in [30]. ANNs are appropriate for addressing the mapping issue from one dataset to another when they have strong nonlinear mapping capabilities [31]. ANNs can be categorized into two types of signal transmission modes: feedforward and feedback neural networks, each of which has a distinct framework. Feedback neural networks play a significant role in AI; however, they have only been used in a few applications  
Figure 4: Ensemble learning (bagging) [25].  
Figure 5: Ensemble learning, bagging, and boosting machine learning techniques [28].  
due to solid waste concerns. In the application of biosorption capacity, several researchers compared the models of feedforward neural networks such as multilayer perception ANNs and feedback neural networks, which found that feedforward neural networks had lower prediction errors than feedback neural networks [32].  
In a multilayer feedforward neural network, neurons in one layer communicate with those in the next layer through various weighted linkages. There are three kinds of neuron layers: input, hidden, and output. The neurons in the input layer receive external data, such as from sensory receivers; the neurons in the hidden layer imitate a biological neural network to transmit that data, and the neurons in the output layer offer a judgment output. Although several hidden layers are feasible, typically, only one hidden layer is employed, especially with small sample sizes. Neurons only link between layers, not inside them. In a feedforward neural network, signals can only go one direction, from input to output [33]. ANNs have been extensively employed in numerous activities, including environmental difficulties and even solid waste-related issues, due to these simplifications.  
Complex systems and correlations in labeled data are recognized using these models. Deep neural networks (DNNs) are more complicated neural networks with hidden layers that conduct much more complex functions than basic sigmoid or ReLU activations [34]. The architecture of a deep learning model is shown in Figure 6.
## 4 Suggested Modifications of RQD
### Rock Quality Designation from Weighted Joint Density (RQD\({}_{\text{WJD}}\))  
To obtain more accurate information from observations of boreholes, [PERSON] [18] created the weighted joint density (WJD). Its main method relies on calculating the angle (\(\delta\)) formed between each joint and the scan line at the drill hole's surface or axis. This is shown in Figure 8:  
\[\text{WJD}=\frac{1}{\text{L}}\sum\frac{1}{\text{sin}\delta} \tag{3}\]  
In accordance with [PERSON] [12], the \(\delta\) angles can be divided into four intervals, with each group having an average value of fi (see Table 4). Thus, the following is a rewriting of the equation above:  
\[\text{WJD}=\frac{1}{\text{L}}\sum f_{i}, \tag{4}\]  
[PERSON] [12] examined the traditional RQD, WJD, and RQD\({}_{\text{WJD}}\) measures for two boreholes drilled in the same jointing area but in different directions (refer to Figure 9). The RQD numbers (90 and 9%) were obtained differently, whereas the WJD values (16 and 19) were roughly comparable, as shown in this Figure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Angle Interval** & **1/sin\(\delta\)** &
\begin{tabular}{c} **Chosen Rating of Factor** \\ **\(\mathbf{f_{i}}\)** \\ \end{tabular} \\ \hline \(>\)60\({}^{\circ}\) & \(<\)1.16 & 1 \\
30–60\({}^{\circ}\) & 1.16–1.99 & 1.5 \\
15–30\({}^{\circ}\) & 2–3.86 & 3.5 \\ \(<\)15\({}^{\circ}\) & \(>\)3.86 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Rating factor f\({}_{i}\) in each interval to calculate WJD value [12].  
Figure 8: Definition of WJD measurement in boreholes [12].  
Using Equation (5) to calculate the \(\rm{RQD_{WJD}}\) from the weight joint density, values of 70 and 62% were found. Comparing the RQD and the \(\rm{RQD_{WJD}}\) values one can realize that the \(\rm{RQD_{WJD}}\) values are reasonably close and situated in the fair rock mass RQD classification. As a result, the rock mass fracturing degree is better described by the weighted joint density technique, which accounts for inaccuracies resulting from the threshold value and coring direction.
# Compressive Behavior of Inconel 625 and Ti-6 Al-4V Strut Lattices Fabricated by LPBF
## 4 Discussion
Significant differences were also recorded in the present study among the different cell types, demonstrating that multiple studies should be performed to select an optimum cell type and its dimensions for a specific application. [PERSON] et al. [44] studied the compressive properties of open-cell lattices with many cell types (cube, truncated cube, diamond, truncated cuboctahedron, rhombic dodecahedron, and rhombic dodecahedron) manufactured by SLM from Ti-6 Al-4V. They obtained the first maximum compressive strength in the range of 40-70 MPa, values lower than some results obtained during the present study (for octet, truncated octahedron, and Kelvin cell types). [PERSON] et al. [45] studied the mechanical properties of diamond lattices manufactured by LPBF from Ti-6 Al-4V with different orientations, in as-built and hot isostatic-pressed (HIPed) states. They obtained values in the range of 83-145 MPa, with the best results obtained for HIPed specimens. The results obtained during the present study are consistent with the values reported in the literature concerning the compressive properties of Ti-6 Al-4V lattices. Multiple studies were found regarding the compressive properties of Ti-6 Al-4V lattices; because it is a material of interest for the medical field, many studies have been conducted for such applications, as lattices can replicate trabecular bone configuration. However, in the case of Inconel 625 or other Inconel alloy lattices, less information was found. [PERSON] et al. [46] investigated the mechanical properties and microstructure of Inconel 718 lattices manufactured by SLM (two types of lattice structures with spherical or hyperbolical stiffening elements). Based on their graphical representation, it was determined that the compressive strength was in the range of 42-90 MPa and the compressive modulus was between 1 and 1.8 GPa depending on the post-processing conditions. In the present study, average compressive strength in the range of 16.22-258.71 MPa and compressive modulus between 1.13 and 4.46 GPa were recorded depending on the cell type. It is well known that deviations are encountered in AM, even if this AM method is among the most precise. Differences in mechanical properties are caused by cell geometry. [PERSON] et al. [47] conducted a comprehensive study on Inconel 625 lattice structures with cell sizes of 2, 3, and 4 mm manufactured by SLM. They obtained the highest compressive yield strength of approximately 18 MPa for the 2 mm lattices with body cubic cells and vertical Z struts. In this study, the lowest average compressive yield strength was 14.72 MPa, and the highest was 124.13 MPa for Inconel 625 manufactured lattices. These values are lower than the highest values recorded during the present study.
# Recent Advances in Additive Manufacturing of Fibre-Reinforced Materials: A Comprehensive Review
## 3 Evaluation
### Quasi-Static Mechanical Testing
#### 3.1.3 Compression and Energy Absorption
The drawback is that the compressive strength and modulus are lower than those of pure thermoplastics. A few reasons for the worst behaviour in strength and modulus are possible micro buckling of fibres, de-bonding between fibre-matrix, and delamination. Another aspect derived from [PERSON] et al. 's [79] curves is the better energy absorption properties that the fibres give. The energy that is required to deform an object to a strain \(\varepsilon\) is the definition of energy absorption (EA) [88; 89]. The area beneath the stress-strain curve in compression represents an object's energy absorbed per unit volume (MJ/m\({}^{3}\)) and is usually measured up to 60% of strain. In addition, the specific energy absorption (SEA), by definition, is the energy that an object can absorb per unit mass, which is a critical indicator in designing an energy absorption device. \[\text{EA}=\int_{0}^{\varepsilon_{0}}\sigma(\varepsilon)\text{d}\varepsilon \tag{1}\]  
\[\text{SEA}=\frac{\text{EA}}{\text{m}} \tag{2}\]  
where \(\varepsilon\) is the strain, \(\sigma(\varepsilon)\) is the stress for a given strain (\(\varepsilon\)), and m is the mass. According to the aforementioned study by [PERSON] et al. [79], fibres increased the plasticity of the specimens under compressive loads and extended stress and strain recovery, thus enhancing the energy absorption behaviour of the specimens. Figure 7 provides insight from [PERSON] et al. 's [79] findings and the differences that building orientation causes as a result. In [PERSON], [PERSON] et al. [89], where columns of PA/CF were tested, the compression curves showed an improvement in energy absorption. In addition, different printing directions gave better results in energy absorption for different reinforcements. Short carbon fibres demonstrated a more significant improvement than glass fibres in columns printed in the circular direction, while in contrast, glass fibres provided a better enhancement in energy absorption for columns printed in the horizontal and vertical directions. Carbon fibre reinforcement achieved the highest enhancement in energy absorption, with an increase of 132.3% compared to the PA column. Meanwhile, glass fibres showed the greatest improvement at 128.8% for columns printed in the horizontal direction. Moreover, studies [90; 91] proved that with an increase in infill, there was a simultaneous increase in energy absorption by 30%. However, there are not many studies which investigate different materials and composites for energy absorption for a better understanding of the behaviour mechanism.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 2 Related Work and Problem Statement
### Problem Statement  
To efficiently utilize the node battery and to reduce the end-to-end delay, the research community has been devoted to bringing improvement in routing algorithms designed for UWSNs. For instance, WDFAD-DBR [4] considers only two metrics: the depth of current and next expected forwarder node. Although, the probability of void hole occurrence is reduced and inefficient energy consumption during nodes communication is minimized, the probability of void hole occurrence still exists, as illustrated in Figure 1.  
When the source node \(S\) initiates communication and finds \(S2\) in its communication range, before transmitting the data packet to \(S2\), it acquires information about its neighbor node. It locates \(S3\) in its transmission range and delivers the data to \(S2\). Thus, it acknowledges the \(S\) with non-void node status and receives the data packet. However, when \(S2\) looks for its neighbors, it finds \(S3\), which has no further nodes in its transmission range, resulting in loss of the data packet. Thus, this process will continue until the death of \(S3\). Additionally, this scheme is receiver based, where avoidance of duplicate packets is very difficult. The reason is that neighbors in the hidden terminal region are unable to receive the acknowledgment, leading to redundant transmissions at the destination. Moreover, it also leads to channel interference in the case of simultaneous transmissions over the acoustic wireless channel. Furthermore, it causes collision, leading to a high packet drop ratio and more end-to-end delay. To overcome the aforementioned problems, we propose four schemes: A-DBR, C-DBR, B-DBR and CA-DBR, to improve the network performance. The details are given in the following sections.