esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Residual Stress Evaluation Using the Cos \(\alpha\) (Alpha) Technique  
Residual stress plays a critical role in the mechanical performance and longevity of materials, particularly in applications like railway rails where durability and fatigue resistance are essential. To evaluate residual stress, particularly after surface treatments like deep rolling, accurate measurement techniques are required. One of the most widely used methods for this purpose is the cos \(\alpha\) (cosine alpha) technique, which is highly effective in determining the strain and stress in crystalline materials through X-ray diffraction. The cos \(\alpha\) technique is based on the relationship between the diffraction angle of X-rays and the strain present in the material. When X-rays are directed at a material's surface, they interact with the atomic planes within the crystal structure. If the material is under stress, this interaction results in a shift in the diffraction angles. By measuring these shifts, it is possible to calculate the strain on the material. From the strain data, the residual stress can be derived. The cos \(\alpha\) method is particularly effective in measuring biaxial residual stresses, which are common in components subjected to surface treatments like deep rolling. In this method, the X-rays are directed at a range of angles on the material's surface, and the shift in diffraction angles is recorded. The diffraction data are typically represented as Debye rings, which are circular patterns observed during X-ray diffraction. The cos \(\alpha\) technique then uses these Debye rings to determine the stress state by analyzing the deformation in the rings due to stress-induced strain.
# The Domain Mismatch Problem in the Broadcast Speaker Attribution Task
## 5 Results
### The Influence of Diarization  
In our first set of experiments, we evaluate our two proposed traditional alternatives: the direct and the indirect assignment systems. While the former system only works in terms of isolated segments, the latter performs clustering to obtain diarization labels used during the identity assignment. The quality of these diarization labels is shown in Table 1, where both development and test subsets from Albayzin 2020 are evaluated. The analysis is extended to both closed-set and open-set conditions.  
The results in Table 1 show interesting performances in all the experiments but far from a perfect estimation of the labels. The diarization system shows a similar behavior between development and test regardless of the scenario, showing robustness against domain mismatch. We also see how the restrictions in the audio under analysis, i.e., the closed set assumption, implies important improvements in performance (up to a relative 61%).  
Once the diarization system is characterized, we compare the two systems in the speaker attribution task. In order to exclusively analyze the impact of diarization, we get rid of the domain issues by applying adaptive oracle calibrations to the data. These calibrations are obtained by the previously described criteria, only substituting the subset playing the role \(\mathbf{\Phi}_{\text{calib}}\), which is now played by the same data under analysis. This oracle calibration has been estimated in three different degrees of generality: a calibration for all the audios in the development and test subsets (subset-level), an individual treatment per show (show-level) and a particular calibration per audio (audio-level). The obtained results for this experiment are included in Table 2.  
The results illustrated in Table 2 indicate significant benefits (from a relative 27% up to a 34% improvement) when diarization is applied rather than considering individual segments. Thus, diarization clusters, despite their impurities, provide robustness to those segments with a low amount of audio. Furthermore, we also see the importance of specificity in calibration. The more particular the calibration, the larger potential improvements can be achieved, although resources must be more specific for the particular domain.
# Reconfigurable Formation Control of Multi-Agents Using Virtual Linkage Approach
## 4 Simulation and Results
### Formation Reconfiguration Using Virtual Linkage  
An important property of the proposed virtual linkage approach is that it is easy to be reconfigured into some different formation patterns by coordinating the virtual joint angles. To show the formation reconfiguration ability of the proposed virtual linkage approach a simulation is conducted for the following scenario. Knowing the map of the environment, the agents are given the predefined trajectory and varying formation shapes (shown in Figure 14) to pass through the gallery, which is surrounded by obstacles.
## 3 Experimental Results
### Evolution of the Crack Network under an SSR of 0.3  
Figure 7 shows the initiation and propagation process of the crack network during crater blasting under an SSR of 0.3. Under the coupling of dynamic and static loads, an initial radial crack that was parallel to the static stress was observed first at the center of the blast hole at 80 \(\upmu\)s (see Figure 7b). Similar to the experimental results that were obtained under the SSR of 0.2, radial cracks that were perpendicular to the static stress were not observed. At 120 \(\upmu\)s, the initial circumferential cracks started to appear on both sides of the blast hole, perpendicular to the static stress (see Figure 7c). At 130 \(\upmu\)s, the initial circumferential crack was completely formed. Meanwhile, the second radial cracks were randomly distributed outside the initial circumferential crack (see Figure 7d). At 170 \(\upmu\)s, a second circumferential crack started to appear on the left of the blast hole, accompanied by a small amount of blasting dust that was starting to escape as well as light that was generated by the electrical wire explosion (see Figure 7e). At 300 \(\upmu\)s, the second circumferential crack was closed (see Figure 7f). At 350 \(\upmu\)s, the main radial and circumferential cracks were formed (see Figure 7g). After 350 \(\upmu\)s, many small radial cracks outside the first circumferential crack gradually increased, and the blasting fragments flew outward.  
Figure 7: The crack network at different times during crater blasting under an SSR of 0.3. (**a–i**) The crack network after 0, 90, 120, 130, 170, 300, 350, 500, and 890 \(\upmu\)s of explosion, respectively.
# CommC: A Multi-Purpose COMModity Hardware Cluster
## 2 Preliminaries
### Containers and Their Impact on Virtualization  
Containers are lightweight and portable units of software that encapsulate an application along with all its dependencies into a single entity. This packaging ensures consistent performance across diverse environments, making containers highly reliable for modern computing needs. Unlike virtual machines (VMs), which emulate an entire hardware stack and require individual operating systems, containers share the host operating system's kernel. This architectural difference makes containers faster to start and more resource-efficient, as they isolate processes at the OS level.  
The primary distinction between containers and VMs lies in their design. Containers are optimized for lightweight, agile operations, making them ideal for microservices [24] and cloud-native applications. In contrast, VMs provide stronger isolation by running full operating systems, making them better suited for legacy applications, untrusted workloads, or scenarios requiring diverse OSs. Containers excel in portability and scalability, whereas VMs offer comprehensive flexibility and robust isolation.  
Container technologies such as Docker and Kubernetes have transformed the management and orchestration of containerized workloads. Docker simplifies container creation and deployment, and Kubernetes automates the scaling, management, and operation of containers across clusters. Containers are integral to modern development workflows,particularly in Continuous Integration/Continuous Deployment (CI/CD) pipelines and microservices architecture. Conversely, VMs remain the preferred solution for running legacy systems, multi-tenant environments, and workloads requiring diverse operating systems.  
Linux Containers (LXC) [25] is one of the earliest containerization technologies, enabling multiple isolated Linux environments to run on a shared Linux kernel. Introduced in 2008, LXC leverages kernel features like namespaces for process isolation and control groups (cgroups) for resource management. Each container operates as a process on the host system, making LXC lightweight and efficient compared to traditional virtualization. LXC has influenced the development of modern container tools and remains a foundational technology for Linux-centric environments
## 2 Innovative System Configuration, Dynamics Equations, and the Simulator
### Development of the IHTMS Simulator  
The IHTMS simulator was coded on the Matlab/Simulink platform, as shown in Figure 4. For block a, it contains the driving cycles and the derived waste heat governed by the following equations:  
\[P_{d}=\bigg{[}M_{vch}V_{vch}+\frac{1}{2}C_{d}A_{vch}\rho V_{vch}{}^{2}+M_{vch} g\cos(\theta)+\mu M_{vch}g\sin(\theta)\bigg{]}V_{vch} \tag{13}\]  
\[P_{fc}=\frac{P_{d}}{2};\;P_{bat}=\frac{P_{d}}{2} \tag{14}\]  
\[\dot{Q}_{fc}=\Big{(}1-\eta_{fc}\Big{)}P_{fc}/\eta_{fc} \tag{15}\]  
\[\dot{Q}_{bat}=(1-\eta_{bat})P_{bat}/\eta_{bat} \tag{16}\]  
where \(M\), \(C_{d}\), \(A\), \(\rho\), \(V\), \(g\), \(\theta\), \(\mu\), \(\eta\) represent mass, zero drag coefficient, frontal area, air density, velocity, gravity acceleration constant, climbing degree, rolling resistance coefficient and efficiency, respectively. Subscripts \(d\) and \(vch\) are demand and vehicle, respectively. Equation (13) was the required vehicle power which was affected by the vehicle acceleration, wind resistance, climbing resistance, and rolling resistance [5]. Equation (14) indicated that the fuel cell and the battery set shared the demanded power equally. Equation (15) determined the waste heat in Equation (9) where the fuel cell efficiency was a function of \(P_{fc}\). Equation (16) determined the waste heat in Equation (11) where the battery efficiency was a function of \(P_{bat}\), battery SOC, and battery inner resistance [4]. For block b, the thermal dynamics of five coolant pipes (pipe 1-5), PEMFC and battery bodies and coolant flow channels according to Equations (3)-(11) was constructed. For block c, it was for the control strategies according to Section 2.3 using the Stateflow toolbox. For block d, it was the inner-loop model; for block e, it was the heat exchanger model derived in Equation (12); for block f, it modeled the proportional valve to separate the coolant flow; for block g, it was the coolant pump model where the coolant flow rate was proportional to the controlled voltage. From the above, Section 2 derives the thermal dynamics of each of the components at first, and the rule-based control was designed next. The integrated system/controlMatlab/Simulink model was coded to evaluate the system performance.
# Methods of Analyzing Microsized Plastics in the Environment
## 3 Spectroscopy Analyses
### Raman Spectroscopy  
In Raman spectroscopy, when an object is irradiated with a laser beam, a unique spectrum for each polymer is generated because the frequency of the backscattered light varies depending on the molecular structure and atoms present in the target. Moreover, Raman spectroscopy identifies plastics and provides a polymer composition profile for each sample, similar to FTIR [33]. It can be used to interpret the obtained results through data libraries and algorithms [61]. Raman spectroscopy is similar to the FTIR method in terms of the combination of non-destructive chemical analysis and microscopy [27]. Microplastics as small as a few micrometers in size can be identified because of the smaller diameter of the laser beam in Raman spectroscopy than FTIR. Non-contact analysis with Raman spectroscopy offers the advantage of preserving the microplastic sample before and after analysis for possible further analysis. However, the differences in the responses and spectra of microplastics between FTIR and Raman spectroscopy may make it more difficult to interpret the results of each method in identifying complex microplastics. It is difficult to identify the target polymer type using Raman spectroscopy because the Raman signal is sensitive to additives and pigment chemicals in microplastics [36]. In addition, errors may occur due to the surface curvature of worn microplastics. It also has similar disadvantages to FTIR: it has a high price and requires expertise for analysis.  
In micro-Raman spectroscopy, the identification of material differences is possible only for materials for which an appropriate Raman reference spectrum is provided in the database, and the possibility of confusion between materials has been input in advance. PE is a substance that can be confused with sodium stearate or SDS; it may be difficult to identify the target microplastic, and so the possibility of false positives should be considered [33].
## 2 Background and Literature Review
### Literature Review
In the proposed model, offers are received by a salesperson from different suppliers and clients. The broker works out the client's and supplier's bid densities and distributes administrations to the most proficient clients from the most productive suppliers, as per their expected solicitation. After the fruitful running of undertakings, the barker requests criticism of the pre-owned administrations from clients and computes the last positioning of suppliers for future sales. The reproduction results show that the proposed model ensured SLA infringement up to almost 100% because of two times the discipline punishment and positioning of suppliers. The surefire QoS clients/representatives will give additional installment to that, and the additional installment will be from 1% to 10% of the absolute value, as per the last cost. Moreover, we tackled the bidder drop issue by up to 10% by expanding administration costs by adding QoS costs [30]. These days, cloud computing is pulled into wider consideration as it can convey IT administrations and assets on an interesting premise over the internet. Load balancing is a vital test in cloud computing. Because of the perplexing structure of cloud computing, it is troublesome and expensive to assess the way of behaving of load-balancing procedures on various cloud assets in light of the QoS boundaries in a genuine cloud climate. Subsequently, to beat what was going on, cloud computing devices are utilized for reproduction to test the way of behaving of load balancing strategies in the cloud framework under various circumstances in a rehashed way by changing different boundaries. Today, a variety of tools are available, including CloudSim, WorkflowSim, CloudSim4 DWf, GreenCloud, and CloudAnalyst. Each tool has a different trademark, engineering, boundary, and outcome evaluation. Therefore, it is crucial to choose a capable, load-balancing device that complies with the QoS requirements. The researchers focus on important cloud load-balancing tools and provide a comparative analysis of the important, recently suggested, and existing load-balancing tools. In addition, they will look at load-balancing plans divided into three categories [31]. Giving wise task execution and responsible resource usage, however, is essential. Due to the nature of the administration, the executives' assets, task planning, burden adjustment, and board duty, a few solutions have been considered in the writing to improve the execution and asset utilization. The ability of server farms to avoid overloading or underloading virtual machines, thanks to the load adjustment in the cloud, is in and of itself a test for cloud computing [32]. Engineers and scientists must, therefore, build and implement an appropriate load balancer for equal and distributed cloud situations. This study presents a cutting-edge survey of the issues and difficulties related to the existing burden-adjusting methods for specialists to foster more Efficient methods [33].
## 2 Materials and Methods
### Empirical RBE Models  
The majority of proton RBE models in PBT are empirical. Most of them are based on the well-established linear-quadratic (LQ) model, which is widely used in clinical practice for the comparison of different fractionation protocols [49]. Using the LQ formalism, the following general expression for the RBE is [50]:  
\[\rm RBE_{LQ}=\frac{1}{2D_{P}}\left[\sqrt{\left(\frac{\alpha}{\beta}\right)_{R }^{2}+4}\left(\frac{\alpha}{\beta}\right)_{R}D_{P}\rm RBE_{LD}+4 RBE_{HD}^{2}D_ {P}^{2}-\left(\frac{\alpha}{\beta}\right)_{R}\right], \tag{31}\]  
where \(D\) is the dose per fraction, \(\alpha\) and \(\beta\) are the linear and quadratic radiosensitivity coefficients of the LQ-model, respectively, \(\rm RBE_{LD}=\alpha_{P}/\alpha_{R}\) is the low-dose RBE (when the dose tends to zero) and \(\rm RBE_{HD}=\sqrt{\beta_{P}/\beta_{R}}\) is the high-dose RBE [51]. The subscripts \(P\) and \(R\) refer to protons and the reference radiation, respectively (in practice, \(R\) is usually \(\gamma\)-rays from Co-60 or Cs-137 sources).  
Empirical models only differ in terms of how they evaluate \(\rm RBE_{LD}\) and \(\rm RBE_{HD}\). [PERSON] et al. [52] and [PERSON] et al. [20] have provided comprehensive reviews of available empirical RBE proton models. Table 1 presents a summary of these models. The model of [PERSON] et al. [53] is not included in Table 1, since it uses the dose-weighted energy spectrum of the beam \(d(\rm E)\) as a radiation quality index, instead of \(\rm LET_{D}\), making the evaluation of RBE more complex and outside of the scope of the present work. Instead, [PERSON] et al.'s [54; 55] model was added, although it uses \(Z^{2}/E\) as a beam quality index (instead of LET), where \(Z\) is the ion's charge and \(E\) is the ion's kinetic energy per nucleon. [PERSON]'s model [56] is also not examined as it assumes a cubic dependence of \(\text{RBE}_{\text{LD}}\) and \(\text{RBE}_{\text{HD}}\) on \(\text{LET}_{\text{D}}\), which is not consistent with the literature. All the remaining models assume an (almost) linear dependence of \(\text{RBE}_{\text{LD}}\) on \(\text{LET}_{\text{D}}\) (see Table 1). Regarding \(\text{RBE}_{\text{HD}}\), some models assume the same \(\text{LET}_{\text{D}}\) dependence as for \(\text{RBE}_{\text{LD}}\), while others take it as a constant equal to 1.
# Influence of Noble Metals on the Microstructure and Properties of Biodegradable Mg-Nd-Zr Alloy
## 3 Results and Discussion  
### Microstructure Examination  
The microstructure of the alloy under study without modification was the \(\delta\)-solid solution, \(\delta+\gamma\) eutectoid in the form of spherical formations and individual intermetallic inclusions of the \(\gamma\)-phase. The addition of silver and gold into the alloy refined its grains. At the same time, the addition of silver contributed to a stronger grain refinement than the addition of gold. Joint modification of the alloy (Ag + Au) contributed to the formation of finer grains (Figure 1).  
Macrofractographic study of fractures of as-cast samples of the original alloy of the Mg-Zr-Nd system without modification showed the presence of a coarse-crystalline structure. The separate introduction of increasing additives of silver and gold into the alloy under study refined the cast metal structure. At the same time, their combined influence enhanced the refining effect and the nature of the fracture became matte and finely crystalline.  
Increasing Au and Ag additives contributed to a decrease in the grain size and the distance between the second-order axes (Table 1).  
Figure 1: Microstructure of Mg-based alloy with different contents of modifiers: (**a**) non-modified; (**b**) 0.05 wt.% Au; (**c**) 0.1 wt.% Au; (**d**) 0.2 wt.% Au; (**e**) 0.05 wt.% Ag; (**f**) 0.1 wt.% Ag; (**g**) 0.2 wt.% Ag; (**h**) (0.1 wt.% Ag + 0.1 wt.% Au).  
The dependence of the grain size on the content of Au and Ag can be expressed by the following regressions, respectively:  
\[\begin{array}{l}l=-599.4\cdot x+221.2;\ R^{2}=0.74,\\ l=-37.9\cdot\ln(x)-15.7;\ R^{2}=0.98,\end{array}\]  
where  
\(l\)--average grain size, \(\mu\)m;  
\(x\)--modifier content, wt.%.  
When constructing the dependence for silver, zero Ag content was supposed as 0.001% Ag for the sake of correct taking the logarithm.  
As follows from the EDX analysis of samples after heat treatment, the intermetallic phases contained both Ag and Au (Figures 2 and 3).  
According to compositions obtained for IMCs (Tables in Figures 2 and 3), only two elements (Mg and Nb) are present in relatively stable quantities. Therefore, it can be assumed  
Figure 3: EDX results for intermetallic phase in a Mg-based alloy modified with 0.1% Ag: (**a**) analyzed spot; (**b**) chemical composition (wt.%).  
Figure 2: EDX results for intermetallic phase in a Mg-based alloy modified with 0.1 wt.% Au: (**a**) analyzed spot; (**b**) chemical composition (wt.%).  
that the compositions under study are solid solutions on the Mg-Nb intermetallic base. In the Mg-Nd alloy system, the following five intermetallic compounds, namely Mg\({}_{12}\)Nd, Mg\({}_{41}\)Nd\({}_{5}\), Mg\({}_{5}\)Nd, Mg\({}_{2}\)Nd and MgNd can be formed [30]. According to the mass ratio, the closest composition is Mg\({}_{12}\)Nd.
## 4 Results
### Analysis of Ionospheric Data from Ionosonde
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**EQ** & **EQ Location** & **Magnitude** & **Ionosonde** & **Ionosonde** & **R [km]** \\
**Date Time** & **(UT)** & **(Lat Lon)** & **[Mw]** & **Ionosonde** & **Location (Lat Lon)** & **R [km]** \\ \hline
6 February 2023 & 37.20\({}^{\circ}\)N & & & & & \\
01:17 & 37.06\({}^{\circ}\)E & & & & & \\
6 February 2023 & 38.09\({}^{\circ}\)N & & & & & \\
10:24 & 37.27\({}^{\circ}\)E & & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Information about the Turkey EQs (time, location, and magnitude) and the ionospheric station of Nicosia (ionosonde type, location, and distance with respect to the EQ epicentres). Figure 20: The anomaly observed on 20 November 2022 by using \(\Delta\)hTEs, \(\delta\)fbEs, and \(\delta\)foF2 variations. This anomaly detected by the ionosonde is so unique: it appears just once for all the analysed 90-day period. Moreover, it appears in a moment that that ionosphere is very quiet, so it cannot be ascribed to an external solar origin. The work [23] showed that this method provides, although not perfect, a good prediction of an impending EQ (Accuracy of 69%). It is also noteworthy that, as shown in Figure 21, the anomaly is close to the relationship between \(\Delta T\cdot R\) and M (with \(\Delta T\) anticipation time in days, while \(R\) is in km) previously found by the analysis of 33 crustal EQs of magnitude M6 in the Pacific region near Japan in 1985-2000 [78]. As pointed out in previous works (e.g., [23]), such a relationship indicates the process of spreading the disturbance from the epicentre towards the periphery during the EQ preparation process. Besides, similar relationships were obtained in different parts of the world, suggesting the uniformity of the coupling processes between the lithosphere and the ionosphere during the preparation period.