esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Optical Properties and Laser-Induced Damage Testing  
The refractive index n and the extinction coefficient k were measured by ellipsometry using a SENresearch 4.0 SER 850 DUV from SENTECH Instruments at 5 angles of incidence from 50\({}^{\circ}\) to 70\({}^{\circ}\) in 5\({}^{\circ}\) steps combined with transmission data measured with an AGILENT Cary 5000 UV-VIS-NIR Spectrophotometer measured at an angle of incidence of 0\({}^{\circ}\). Using SpectraRay 4 software from the ellipsometer, we applied a Tauc-Lorentz model with two oscillators and fitted it to the ellipsometric and transmission data.  
The measurement of the LIDT was performed at the laser damage test bench located at the RhySearch optical coating laboratory [17; 18]. Herewith the fused silica substrates coated with 480 nm HfO\({}_{2}\) at 100 \({}^{\circ}\)C and different applied second ion beam parameters were laser damage-tested. The samples were laser irradiated under an angle of incidence of 5\({}^{\circ}\) in filtered air and at room temperature. The laser irradiation at 1064 nm was performed using a Monfort Nano diode-pumped Nd:YAG laser system. The effective pulse duration was about 10 ns and the repetition rate was 80 Hz. An S-on-1 measurement strategy was applied with S = 5000. The centered test area had a diameter of 20 mm and was divided into a hexagonal test matrix with 223 testing sites. The distance between the adjacent test point was 1.2 mm. The detection of damage events was done by a scattered light detector placed in front of the test sample. In order to verify the detected damage events, a visual inspection by means of differential interference contrast microscopy (Leica DM4000 M Led) was performed.  
The fluence of the pulses went up to 400 Jcm\({}^{-2}\), while the effective beam diameter (1/e\({}^{2}\)) of the Gaussian beam profile was about 45 \(\upmu\)m. This small beam size is advantageous to study the properties of the pure, defect-free material, as the probability of irradiating a defect site is relatively low.
# Investigation of Impact Behavior of STS304L Steel Plate Under Cryogenic Temperature
## 1 Introduction
Unlike cyclic impact testing, which simulates long-term fatigue, drop-weight impact testing is crucial for directly evaluating single-impact energy absorption and fracture behavior relevant to the transient impacts caused by sloshing in LNG cargo containment systems. Additionally, austenitic steels, such as STS304L, undergo strain-induced martensitic transformation at low temperatures [14; 15]. This phenomenon involves a phase transition in the material's crystal structure, changing from austenite (a face-centered cubic (FCC) structure) to martensite (a body-centered cubic (BCC) structure). The formation of strain-induced martensite significantly alters the steel's mechanical properties, including its strength, ductility, and fracture resistance, thereby influencing its overall impact performance [16]. To address this research gap, this study investigated the impact resistance of STS304L at various temperatures (20 \({}^{\circ}\)C, \(-100\)\({}^{\circ}\)C, and \(-193\)\({}^{\circ}\)C) using drop-weight impact tests. The impact energy was varied from 50 J up to the crack initiation point to assess the correlation between energy absorption and deformation behavior. Additionally, electron backscatter diffraction (EBSD) analysis was conducted to examine plastic deformation and strain-induced martensitic transformation in the impact zone.
## 2 Laboratory Tests and Results
### CAI--Cerchar Abrasivity Index  
The Cerchar abrasivity test is one of the most common procedures for laboratory measurement of the abrasiveness of rocks [26; 27; 28; 29; 30; 31; 32]. An order of magnitude of the wear potential of the rock can be obtained by evaluating this index, which is a tested material's aptitude for wearing steel tools [33; 34]. As described in [35], a stylus (composed of HRC 55 steel) loaded with 70 N is scratched for 10 mm on the rock sample at a rate of 1 mm/s. The CAI is evaluated by measuring the wear surfaces experienced by the stylus [36; 37; 38]. In this work, Cerchar tests were performed according to [35]. The selected sample had a size of 50 \(\times\) 30 \(\times\) 15 mm, Figure 8a, and 10 different tests were performed. In accordance with the reference, five scratches were carried out horizontally and another five vertically (tests 1-5 and tests 6-10, respectively, Figure 8b).  
The highlighted CAI values ranged from 2.08 to 3.30, with a mean value of 2.77 (standard deviation = 0.50). The mean CAI value corresponds to the average wear potential.
literature [40; 41; 42; 43]. An aluminum disk rotates around its axis at a constant speed inside a cylindrical tank (Figure 9), embedded with granular material [44]. The rotational speed is kept constant, equal to 160 rpm, for a time span of 10 min. The difference in disk weight before and after the test is the weight loss. As for the aluminum disk, it is characterized by Vickers hardness values of 116 MPa [45]. A normal confining pressure of about 2 kPa is applied to ensure continuous contact between the disk and the soil.  
In this work, wear tests were performed according to [45; 46]. Marble was previously crushed, obtaining powdered rock and chips with a maximum size of 20 mm. The particle size distribution is shown in Figure 10.  
The first step of wear testing focused on studying the wear bell, while the second phase focused on testing the conditioning additives. The wear bell is a chart with a typical bell shape that shows in the x and y axes the moisture content and weight loss of the tested material [41; 42]. This type of curve is very important, since each material exhibits a different curve (depending on the particle size distribution, mineralogy, and grain shape) and by taking a typical wear bell of a highly wearable material as a reference for comparison, the wear potential of the material studied can be deduced. The results for the tests performed with conditioning agents are also shown on the same chart. Once the conditioning amounts were chosen and the weight loss was obtained thanks to the  
Figure 8: Sample before the test (**a**) and after the test (**b**).  
Figure 9: Soil abrasion testing apparatus. Image modified from [45].  
wear test, the moisture content was calculated by considering the water used for producing the foam or activating polymers; then, the representative test point could be drawn on the chart.
## 3 The Problem of Selecting the Next Network
### Network Selection Process according to the Proactive Cross-Layer Protocol
#### 4.1.1 Model of Naive Bayes  
Naive bayes is a model for classification based on bayes theorem. This model estimates the per-class probability by assuming that the attributes are conditionally independent. Let \(C\) denote defined class variable, in this case \(C_{i}\) is WLAN network, \(E_{i}\) denote conditional attribute I used for classification, and \(P(C|E_{1},\ldots,E_{n})\) denote conditional probability of class \(C\), given that features of \(GPS^{t}\) and \(AV^{t}\) have happened. The probability model for naive bayes classifier can be defined as shown in Equation (3).  
\[P(C|E_{1},\ldots,E_{n})=\frac{P(C)P(E_{1},\ldots,E_{n}|C)}{P(E_{1},\ldots,E_{ n})} \tag{3}\]  
In case of independence, assumption was assumed instead of computing the conditional probability for every combination of attributes \(E_{i}\) given class \(C\). We can derive the Equation (3) as follows:  
\[P(C|E_{1},\ldots,E_{n})=\frac{P(C)P(E_{1}|C),\ldots,P(E_{n}|C)}{P(E_{1}, \ldots,E_{n})} \tag{4}\]  
Figure 3: Proactive cross-layer framework block diagram.  
where:  
\(C_{i}\) are the available WLAN networks to change.  
\(E_{1}\),..., \(E_{n}\) are the features of \(GPS^{t}\) and \(AV^{t}\).  
From a classification point of view, Equations (3) and (4) are also called the likelihood functions when they are expressed as a function of \(C\) given \(E\). For class variables \(C_{1}\),..., \(C_{m}\) we can classify the evidence into m value of likelihood. Then, we assign the evidence to the class with maximum likelihood. When we compare likelihood function, we can filter out \(P(E_{1},\ldots,E_{n})\) from Equation (4) because it is constant. Therefore, let us rewrite the likelihood function as follows:  
\[Likelihood(C_{i})=P(C_{i})P(E_{1}|C_{i}),\ldots,P(E_{n}|C_{i}) \tag{5}\]  
Equation (5) is more practical because it does not require a very large training set. The reason is evidence was partitioned into multiple attributes by independence assumption. In other words, it allows the class conditional densities to be calculated separately for each attribute and reduces a multidimensional task to a number of one-dimensional tasks. Therefore, the next network to switch is \(NR_{i}\).  
\[NR_{i}=argMax(Likelihood(C_{i})) \tag{6}\]  
where:  
\(C_{i}\) are the available WLAN networks to change.  
\(NR_{i}\) is the next network to switch to.  
It is important to mention that the present data will become historical but the forecast data will not necessarily be part of the historical data set.
## 3 Research Methods
### Tracking and Speed Measurement Central-ByteTrack Module  
ByteTrack is an efficient multi-object tracking (MOT) algorithm. Its core concept is to generate candidate targets through an accurate detector and then use a Kalman filter to predict each target's state, such as position and velocity. By combining appearance features and motion information, the algorithm evaluates the matching degree between detection boxes and existing trajectories. The greedy algorithm is then used to achieve optimal frame-to-frame association. This method significantly enhances both tracking stability and target association accuracy, making it highly effective for multi-target tracking in complex dynamic environments.  
In this research, to accurately measure the speed of fast-moving targets such as splashing drilling debris, we propose a speed measurement module based on the center point displacement of the detection box, built upon the ByteTrack framework. This module extracts the center point coordinates of the detection box for each target across consecutive frames, calculates its displacement in pixel space, and uses camera parameters along with the calibration distance between the target's movement plane and the camera. The pixel displacement is then converted into a real-world speed value, enabling precise estimation of the target's instantaneous velocity. The design principles, calculation methods, and related equations of this module are elaborated below.
# Weighted Cluster-Range Loss and Criticality-Enhancement Loss for Speaker Recognition
## 1 Introduction
To achieve satisfactory speaker recognition, a well-designed loss function is also crucial. Traditional softmax loss lacks the ability of discrimination [32], and numerous loss functions have been proposed to address this problem [33; 34; 35; 36; 37]. [PERSON] et al. [35] proposed a Large Margin Cosine Loss (LMCL), which minimizes intra-class variance and maximizes inter-class variance by virtue of normalization and cosine decision margin maximization. The famous triplet loss [37] and its variants have achieved satisfactory performance. [PERSON] et al. [38] proposed a Weighted Triplet Loss (WTL), which imposes weights on the terms of the triplet loss. The weights in this loss function are obtained by computation and vary with the time steps. Both weighted triplet loss and weighted triplet loss with only a positive constraint have achieved better performance than weighted triplet loss with only a negative constraint. Besides, negative samples are randomly selected in weighted triplet loss, while such random selection has been reported to be inefficient in recent literature [9; 39]. Due to the low efficiency brought by random sample selection, anchor and hard sample selection are taken into consideration in many emerging approaches with triplet loss. In Coupled Cluster Loss (CCL) [39] and end-to-end loss [40], cluster centers take the place of stochastic anchor selection. The Cluster-Range Loss (CRL) [9] emphasizes the hardest positive and the hardest negative of each speaker and focuses on the cluster distribution instead of separate samples. Drawing on the recent success of self-attention and CBAM, we propose to combine these two attention mechanisms in our work and form a Dual-path Attention (DA) block. Besides, based on the work in [9] and inspired by the weighted-triplet loss [38], a novel loss is proposed here, i.e., Weighted Cluster-Range Loss (WCRL). Additionally, in order to further improve the convergence efficiency of CRL, we also design another loss function, Criticality-Enhancement Loss (CEL), which focuses on the most easily optimized samples per step. With the proposed methods, the training efficiency and classification performance tested on the VoxCeleb1 dataset are significantly improved. The rest of this paper is organized as follows. In Section 2, the mechanisms of self-attention, the convolutional block attention module, and cluster-range loss are briefly introduced. Section 3 depicts our proposed dual-path attention architecture, weighted cluster-range loss,and criticality-enhancement loss. The experimental setup and results are given in Section 4. Finally, the conclusions are presented in Section 5.
## 4 Implementation Scenario and Conceptual Framework
### Computing Resource Utilization of NTCA Using Different ML Models
#### 5.6.2 CPU Usage  
Figure 18 shows the statistical differences of each traffic classifier performance, including the minimum, mean, and maximum CPU usage. The SVM traffic classifier has the least maximum CPU utilization of 31.2%, while the DT-based NTCA has the largest maximum CPU utilization of 79.2%.
## 3 Methodology  
The study was carried out by adapting the methodological approach proposed in ref. [20]. In such a work, the method was intended to be a prevision process in order to decide, from a theoretical point of view, whether an installation of an integrated machine can be effective. In the current case, the method is applied on results coming from a real test in a worker village in Dubai, and it is used to define a revamping package suitable for a portion of such a worker village. To the initial procedure formulation, presented in ref. [20], some more steps are added in order to refine the approach from a sustainability point of view, such as the weather data frequency analysis, the economic indicators calculation and the possible plastic saving. The complete analysis flow is described below.  
* Data collection: the case study section reports information about the test site and the characteristics of the possible buildings to be served by the integrated machine. Moreover, needs about drinking water, primary air and thermal heating for domestic water were described too. Data concerning the existing plants were also collected, as well as costs about drinking water and energy sources.
* Weather environmental conditions: statistical hourly data about temperature and relative humidity from a weather station placed in Dubai, at only 5 km from the installation spot, were collected in order to have the climatic conditions of an entire statistical year. A brief analysis of the weather data frequency was also added.
* Integrated system behaviour analyses: the behaviour of the machine, installed nearby the kitchens building, was monitored by the data collection in some time periods. On the basis of such data, the simulator, used in ref. [20], was fine-tuned and run using the Dubai climate conditions, collected by means of the said weather station in order to determine how the machine was expected to behave all over the year in terms of produced water, energy consumption, heating power/energy and cooling power/energy. In order to evaluate the energy efficiency of the machine, in terms of produced water and consumed energy, the Water Energy Transformation (WET) indicator was employed [37].
* Covered needs: matching the integrated machine behaviour in the Dubai weather and the needs (drinking water, primary air and domestic water heating), it was calculated how many buildings it was possible to serve.
* Economic evaluations: all the voices, positive and negative, related to the machine installation and its use, were collected and the Pay Back Time (PBT) calculated. Moreover, the Net Present Value and the actualised Pay Back Time were calculated, because the installation can be seen as a sort of investment.
* plastic savings: on the basis of the literature data, savings in terms of avoided plastic bottles were calculated.
# Cross-Sectional Shape Optimization of Cylindrical Elastomer Spring for Sensitive Cargo Container
## 2 NFEA and Shape Optimization
### Concept Designs of NFEA  
As a preliminary design step for optimization from the existing natural rubber spring, NFEA carried out a conceptual design for six types of cross-section shapes. Figure 4 shows the maximum principal strain (MPS) distribution for six types of natural rubber cross-sectional shapes under the condition of an applied load of 3.4 kN based on the required design specification [2]. Figure 4f shape 6 shows the smallest MPS among them. This model is the model finally selected among the proposed cylindrical cross-sectional shapes in a limited height and diameter range and is called the original model from hereafter. Using this model, material constant prediction and optimal shape cross-section design were performed to reinforce the weak point of the spring and to reduce MPS. The advantage of reducing MPS is that it could increase the fatigue life for the spring [27; 28].  
Figure 4: Concept design of the natural rubber spring under 3.4 kN compressive load: (**a**) shape 1; (**b**) shape 2; (**c**) shape 3; (**d**) shape 4; (**e**) shape 5; (**f**) shape 6.
# A Framework for Constructing a Secure Domain of Sensor Nodes
## 5 Results and Discussion
### Analysis of Wireless Bandwidth Consumption
#### 5.2.2 Refreshing the Status of Domain Nodes after Powering on the M Node  
The procedure for updating the domain node status was initiated after powering on the M node. Table 2 presents the transmission requirements for the refresh status procedure for one registered node in the domain.  
This procedure must be performed for each registered node in the domain. The number of sent bytes required to refresh the status of all registered nodes in the domain will be the product of the values given in the last column of Table 2 and the number of registered nodes in the domain.
# Formal Modeling of IoT and Drone-Based Forest Fire Detection and Counteraction System
## 2 Related Work
### Failure Detection Review  
The development and formal modeling of different systems has addressed a variety of research concerns that have attracted the interest of the research community. In the event of an inter-actor connectivity malfunction, the network topology will be disrupted, which may be inadequate to restore. Consequently, some of the researchers proposed failure recovery strategies. Below is a summary of that research. WSAN is partitioned into subnets in [36], that locate the fault recovery protocol at the subnet level to achieve the goal of performance. This paper [37] proposes a Partitioning Detection and Communication Restoration (PCR) algorithm to enable critical actor damage. Pre-designated replacement identifies the fault of its prime actor and establishes a post-failure restoration phase that could require organized multi-actor resettlement. The essential topic of failure restoration in WSANs is addressed in this paper [38], it introduced an innovative localized, distributed, hybrid subnet-based, and energy-efficient failure recovery algorithm.  
A study on topology control strategies of wireless sensor networks to accommodate failure of nodes is described in [39]. A further constructive algorithm, PADRA, is presented [40], identifying CDS (connected dominating sets) through the entire network. That approach is ineffective toward crucial node recognition, as depth-first search (DFS) is conducted across each CDS component to determine if a node is a cut vertex or not. The least destructive topology repair (LeDiR) algorithm is proposed [41]. The algorithm is reactive and focuses on maintaining connectivity based on inter blocks. Likewise, this paper [42] presents an Application-centric Recovery (ACR) hybrid algorithm that distinguishes primary crucial entities through localized information and selects an appropriate replacement. A variant algorithm of backup assigning based on subnet in WSANs is introduced in this paper [43], for security and mission-critical complex networks. Table 1 presents the most relevant existing studies with their limitations.  
According to the findings of the literature research, only a few systems have included formal methods; as a result, the existing system has not been thoroughly formalized, and the system's correctness cannot be guaranteed. We formalized the system using VDMSL to address these issues, and the system's correctness is ensured utilizing the VDM-SL toolbox's capabilities.
# Applications of Water Jet Cutting Technology in Agricultural Engineering: A Review
## 3 Applications of Water Jet Cutting Technology on Different Agricultural Materials
### Meats
#### 3.3.1 Cutting and Trimming  
Choosing the right operation parameters is important to cut and trim various meats, including the jet pressure, the nozzle diameter, and the transverse speed.  
[PERSON] et al. [54] investigated the method of removing undesirable substances from beef clip bone slices by high-pressure water jet. The results showed that the best cutting effect could be obtained when the diameter of the water jet nozzle was 0.15 mm, the water pressure was 380 MPa, the slice thickness was 19 mm, and the traverse speed was 0.18 m/s. [PERSON] et al. [55] conducted a replicated experiment to determine the optimum settings for cutting boneless chicken breast meat with high-pressure water jets. They found that the best results of sharp and clean cuts could be gotten with a 0.127 mm nozzle, a water pressure in the range of 179 to 224 MPa, and a 0.1 m/s or slower speed. Meanwhile, good cutting effects were obtained for chicken fillets to approximately 0 \({}^{\circ}\)C. That was because firm frozen meat was difficult to cut, and meat that was soft at room temperature had not enough firmness for even wide slices. [PERSON] et al. [56] conducted jet cutting experiments with fresh trout, and the results showed that water jet cutting had the characteristic of selective cutting fish tissues. When appropriate nozzles and specific pressure values are used, soft tissue (i.e., muscles, fat, and peritoneal membranes) can be cut without affecting harder tissues such as the intermuscular fibers or bones, providing a new idea for fish processing.  
Comparing the efficiency of trimming lamb fat by manual, machine, and water jet methods, [PERSON] et al. [57] found the productivity of water jets was approximately ten times more than both manual and machine trimming. However, the cost of water jet cutting equipment was at least ten times greater than blade trimming equipment. As a result, they proposed that water jet cutting equipment suitable for processing varieties of meats needed to be developed, which would increase its potential market size. Beef cattle are even-toed ungulates, which means that regular trimming must be carried out to avoid disease with hooves' continuing growth and unbalanced wear [58]. As the blade cutting method would produce heat, [PERSON] et al. [58] proposed using the AWJ cutting method to trim cattle hooves, increasing livestock welfare. They found the temperature at the cut was lower than the body temperature, and when the jet pressure was 150 MPa and the traverse speed was 70 mm/min, the cutting effect was the best.