esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Analysis of the Contact Characteristics  
### Numerical Analysis of Contact Stress  
In order to analyze the stress distribution at different positions of the variable diameter raceway, combining the two factors of the variable diameter raceway structure and normal load, the variable diameter raceway structure in reference [37] was taken as an example. The variation range of its curvature radius \(R_{i}\) is 24.92 mm-25.7 mm. \(r_{i}\) selects four sizes of 0.5 mm, 1.5 mm, 15 mm, and 150 mm. Other dimensions and structural parameters of bearings with local variable diameter raceway are shown in Table 1.  
Combined with Table 1 and Equations (8)-(12), the influence of different variable diameter curvatures on contact stress was analyzed from a macro perspective, the contact stress at different positions of the raceway was obtained as shown in Figure 3.  
Figure 3: Relationship between contact stress and position angle for different curvature radii: (**a**) The complete process of rolling elements movement, (**b**) transition region of variable diameter raceway, (**c**) The junction of the variable diameter raceway and the transition region.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Numerical** & **Parameter** & **Numerical** \\ \hline Inner diameter & 30 mm & X load & 0 N \\ \hline Outer diameter & 62 mm & Y load & 500 N \\ \hline Inner raceway diameter & 36.48 mm & Rolling element density & 3.18 g/cm\({}^{3}\) \\ \hline Outer raceway diameter & 55.53 mm & Bearing ring density & 7.81 g/cm\({}^{3}\) \\ \hline Ball diameter & 9.525 mm & Rolling element Poisson’s ratio & 0.26 \\ \hline Inner raceway radius of curvature & 4.905 mm & Bearing ring Poisson’s ratio & 0.3 \\ \hline Outer raceway radius of curvature & 4.953 mm & Speed & 6 K–18 K RPM \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the cageless bearing and initial values in the numerical simulation.  
diameter raceway. In order to reduce the influence of centrifugal force on the contact stress during the movement of the rolling elements, combined with the radial load, it is necessary to control the rotational speed below 13,950 r/min, to ensure that there is no obvious stress mutation during the operation.  
According to the above analysis, it can be seen that in the process of rolling elements passing through the variable diameter raceway, the maximum stress on the variable diameter raceway is determined by the variable diameter raceway structure and the rotational speed. Therefore, the position of the maximum stress can be determined in different working conditions. Then the micro-contact characteristics generated at the maximum contact stress can be analyzed.
# Computer Tools to Analyze Lung CT Changes after Radiotherapy
## 2 Structure of the System  
Analyzing CT slices provides information about the tissue density. It is expressed in different shades of grey in relation to its X-ray absorption. The full scale is in the range of (\(-\)1000, +3095). The example values of the Hounsfield scale for different matter are presented in Table 1.  
Having information about the Hounsfield value of a given pixel, we can decide about the item located at this point according to the scale presented in Table 1, and the classification of pulmonary fibrosis can be quantified.  
The proposed system consist of several main blocks:  
* The import of DICOM (Digital Imaging and Communications in Medicine) files (including radiation doses),
* The election of CT slices containing lungs,
* The reduction of the number of slices in the moving set (after radiation series),  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Matter** & **Hounsfield Scale Range** \\ \hline Bone & \(+400\rightarrow+1000\) \\ Soft tissue & \(+40\rightarrow+80\) \\ Water & \(0\) \\ Fat & \(-60\rightarrow-100\) \\ Lung & \(-400\rightarrow-600\) \\ Air & \(-1000\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hounsfield scale.  
* Lung segmentation,
* Affine lung registration,
* Elastic lung registration,
* Calculations on preprocessed images,
* The export of results to a CSV file.  
The following sections present the most important modules, and finally, some implementation details will be described. The data flow of the proposed system is presented in Figure 1. The input is a set of CT examinations of a patient in the DICOM format. First, this set of CT images is fed into the preprocessing CNN module in order to select slices with lung images. Next, the selected images are transferred for further processing, i.e., lung segmentation and registration. Finally, such prepared lung images are used in the calculation module, and the results are presented in a chosen form.  
Figure 1: The general dataflow of the proposed system.
# Affect Analysis in Arabic Text: Further Pre-Training Language Models for Sentiment and Emotion
###### Abstract  
One of the main tasks in the field of natural language processing (NLP) is the analysis of affective states (sentiment and emotional) based on written text, and attempts have improved dramatically in recent years. However, in studies on the Arabic language, machine learning or deep learning algorithms were utilised to analyse sentiment and emotion more often than current pre-trained language models. Additionally, further pre-training the language model on specific tasks (i.e., within-task and cross-task adaptation) has not yet been investigated for Arabic in general, and for the sentiment and emotion task in particular. In this paper, we adapt a BERT-based Arabic pretrained language model for the sentiment and emotion tasks by further pre-training it on a sentiment and emotion corpus. Hence, we developed five new Arabic models: QST, QSR, QSRT, QE3, and QE6. Five sentiment and two emotion datasets spanning both small- and large-resource settings were used to evaluate the developed models. The adaptation approaches significantly enhanced the performance of seven Arabic sentiment and emotion datasets. The developed models showed excellent improvements over the sentiment and emotion datasets, which ranged from 0.15-4.71%.  
sentiment analysis; emotion detection; pretrained language models; model adaptation; task-adaptation approach 2023  
3
## 4 Accuracy Analysis with Different Sampling Parameters  
To evaluate the accuracy of the proposed algorithm, a single slit with a size of 2.4 mm was placed at the center of the source plane, to calculate the diffraction fields with different parameters. The sampling interval on the source plane was 8 \(\upmu\)m, and the sampling number was 1024. The wavelength was 633 nm. The accuracy was evaluated with a signal-to-noise ratio (SNR) of the wavefield that was compared to the Rayleigh-Sommerfeld diffraction integral.  
Figure 7 illustrates the accuracies with different sampling rates in the observation plane as a function of the propagation distance. Due to the paraxial approximation, the accuracies of the shifted-Fresnel diffraction were low for the short propagation distances. The SNR obviously improved with an increase in the propagation distance. On the other hand, the sampling rate in the observation plane also affected the calculation accuracy. Larger sampling intervals would lead to a larger sampling range, according to Equation (6), which induces a calculation that satisfies the paraxial approximation at a longer propagation distance. Additionally, all of the SNRs are larger than 30 dB when the propagation distance is larger than 150 mm.  
Figure 6: Optical reconstructions of CGHs at different sampling rates. (a,c,e) focus on the head; (b,d,f) focus on the tail.  
Figure 7: Accuracies of the diffraction calculation with different sampling rates in the observation plane.  
Figure 8 illustrates the amplitude profiles of the diffraction fields with different scaling factors, calculated by the proposed method and the Rayleigh-Sommerfeld diffraction integral. In the simulation, the propagation distance was set to 300 mm. From the figure, we can see that the calculation results were in excellent agreement.
# A Critical Review of the Status of Current Greenhouse Technology in China and Development Prospects
## 3 Types of Greenhouses in China
### Influence of Climatic Conditions on Greenhouse Design and Operation  
All over China, different climatic conditions, topographies, and terrains affect the design of greenhouses and the equipment used, especially for energy supply management and saving. Greenhouse types may be divided into two categories: greenhouses in southern China and greenhouses in northern China. In northern China, due to lower temperatures and more rain and snow, greenhouses are mostly welded steel structures designed to resist the wind and snow loads in the north and also consider the need to take certain thermal insulation measures, such as the use of blankets to cover the greenhouse, the use of forced air heaters (fan coils or electric heaters), a thermostat-based control box, and other auxiliary heating equipment. The terrain in regions of northern China is relatively flat, so large greenhouses can be built. The temperature in southern China is relatively high, so greenhouses are only insulated in early spring. The southern region is rich in bamboo and wood materials, which are suitable local materials for greenhouses, while the southern regions have more mountainous terrain, and the land is not flat. Most of the greenhouses in this region are covered with plastic films. Most of the glass greenhouses in the northern regions use vacuum double glazing with toughened glass or multi-layer PC as covering materials, while most greenhouses in the southern regions use single-layer toughened glass or two-layer PC.
## 7 Conclusions  
The vehicle-to-grid concept was introduced as a tool for better integration of green technologies into transportation and power systems. But for this system to be successful, it should be efficient and beneficial for both system operators and electric vehicle drivers. Therefore, this paper proposes a control mechanism trying to minimize the discomfort of all participants of the system consisting of the charging station with its renewable energy source and connected EVs with their drivers.  
The defined control mechanism minimizes the cost for all EV drivers. It is based on the results from the SP survey [27] and the cost function of the charging station operator aiming to decrease their monetary expenses and thus increase gains in such a way that the output from the charging station's photovoltaic power plant is used for charging EVs. To prevent one of the drivers from trying to disrupt the social welfare concept in their favor by declaring false preferences, the incentives mechanism is also proposed. The defined incentives mechanism is incentive compatible, meaning that the truthful report of preferences is the optimal strategy for each EV driver. The proposed mechanisms were tested in simulation scenarios that confirmed the expected behavior of the system.  
The proposed mechanism, which maximizes the utilization of the photovoltaic power plant for EV charging and thereby reduces the impact of these systems on the main grid, is the main contribution. Furthermore, the proposed mechanisms can support EV drivers to participate in such systems and truthfully report their preferences since it minimizes their costs. This approach may also be advantageous to charging station operators because it takes into account their preferences, and so meets all of the V2G concept's requirements.  
To ensure the balance between the consumed and produced power at the charging station throughout the whole operation of the system, the storage system can be applied to the charging station in future work. Future research should also focus on issues of creating coalitions between the participants or on more complex prediction methods for photovoltaic power plants.  
Conceptualization, M.K. and P.B.; methodology, M.K.; software, M.K.; validation, M.K. and P.B.; formal analysis, M.K.; investigation, M.K.; resources, M.K. and P.B.; data curation, M.K.; writing--original draft preparation, M.K.; writing--review and editing, P.B.; visualization, M.K.; supervision, P.B.; project administration, M.K.; funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This publication was realized with the support of Operational Program Integrated Infrastructure 2014-2020 of the project: Innovative Solutions for Propulsion, Power and Safety Components of Transport Vehicles, code ITMS 313011V334, co-financed by the European Regional Development Fund.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies
## 5 Technologies for Autonomous Water Basin Mapping and Monitoring
### Issues Related to Robotic Systems  
#### 5.2.1 Localization Issues  
As far as surface (ASV) robot localization, GPS is generally a feasible choice, although physical obstructions and reflective surfaces from the same FPV structures can be a limitation. GPS accuracy can also be affected by atmospheric conditions, which may cause signal degradation or multipath errors. For underwater drones, the total absence of GPS signal below the water surface brings back the issue of underwater localization. So, as discussed in [70], both ROVs and AUVs need localization devices to get them geo-referenced underwater. This issue is faced using two major methods: (1) setting a short time submerged trajectory, based on the Doppler velocity technique (with Doppler Velocity Logs devices), combining INS (Inertial Navigation System), more or less expensive, and first GPS data acquired at the initial diving point and (2) long time continuous control from the surface, by an operator or a computer, through an acoustic bidirectional link. Communication links can typically be made with a mono-/bi-directional transponder anchored to the vehicle that can return the position underwater in real time to a USBL (Ultra-Short Base Line) acoustic positioning system, locating ROVs/AUVs just below the surface. These acoustic devices can be less accurate in shallow or cluttered environments ([PERSON], [PERSON], and [PERSON], 2006) [128]. Acoustic positioning systems, while effective in deep and open waters, struggle with reflections and multipath effects in confined spaces, leading to reduced accuracy. Acoustic systems are also known to be susceptible to noise and multipath effects [129]. As a consequence of the previous considerations, the main limits of these acoustic systems are the bandwidth reachable and both the range and the latency over long distances, ranging from a few hundred meters in cheaper systems to a few kilometers.
# Additively Manufactured Polymers for Electronic Components
## 3 Polymers Used for AM in Electronic Applications
### Extrinsically Conductive Polymers (ECPs)
#### 3.2.3 MXenes  
In recent years, MXenes, which are a family of two-dimensional transition metal carbides, nitrides, and carbonitrides, have emerged as a highly promising class of conductive fillers for additively manufactured polymer composites. Unlike CNTs and graphene, which are well established in enhancing electrical and mechanical properties, MXenes offer several unique advantages, such as surface chemistry and enhanced dispersibility, high electrical conductivity, and good performance in 3D-printed architectures. MXenes feature surface terminations (-OH and -O) that confer hydrophilicity and enable facile dispersion in aqueous or polar solvents [114]. This is particularly advantageous for formulating inks compatible with PEDOT:PSS [115], which are DIW-printed, showing an electrical conductivity of up to 2 S m\({}^{-1}\) and excellent mechanical resilience. Similarly, 3D-printed MXene/RGO hybrid aerogels achieved conductivities above 1000 S m\({}^{-1}\) and impressive electromagnetic interference (EMI) shielding (max. 86.9 dB) [116].  
Moreover, with metallic-level conductivity and tunable surface chemistries, MXenes create robust interfacial coupling with polymer matrices, facilitating efficient electron transport that is similar to graphene but often superior to unfunctionalised CNTs. As a result, ink formulations with MXenes can simultaneously deliver high conductivity and superior mechanical properties [117].  
Furthermore, MXenes exhibit excellent biocompatibility along with high electrical conductivity. Hence, they are regarded as more promising materials in the field of biomedical implantable devices, compared to CNTs and graphene, whose cytotoxicity is well known [118]. Nevertheless, MXenes may undergo oxidation over time, which necessitates thoughtful stabilisation strategies [119].
# Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA
## 5 Experiments
### Algorithm Performance Comparison  
Convergence and diversity are important factors influencing the performance of multi-objective evolutionary algorithms. Convergence refers to the algorithm gradually approaching the optimal solution of the problem, while diversity refers to the algorithm maintaining a wide distribution of samples in the search space during the search process to avoid falling into local optima. In this section, we compare the CPS-MOEA with some classical multi-objective evolutionary algorithms to demonstrate its superiority in terms of convergence and diversity. Additionally, we compare the computational efficiency of CPS-MOEA with classical algorithms to highlight its superior computational performance.
## 3 Experimental Data Acquisition
Training and testing are operated after LDA-based dimensional reduction is accomplished, and then 5-BSSID values are appended to them. In the classification scheme, we compared MLP with Support Vector Machine (SVM) and KNN algorithms to evaluate our proposed system's performance. The comparisons are done in three scenarios: using original datasets (Scenario 1), reduced features only (Scenario 2), and reduced data having appended BSSIDs (Scenario 3). To carry out the proposed system, we used the Python 3.6 programming language with the Tensorflow framework because of the ease of use of the language for the researchers. For coordinate-based localizations, errors are computed as root mean square errors (RMSE), and error distributions in each testing point are also shown graphically. The RMSE is mathematically calculated by Equation (7):  
Figure 1:1  
where \(X_{i}^{rl}\) and \(X_{i}^{est}\) are the real and estimated distance at the \(i\)th testing point, respectively, and \(k\) is the number of tested points. The error at each distinct testing point is evaluated in terms of position estimation errors by Equation (8):  
Figure 1:2  
where \(PEE_{i}\) is proposed system's estimated error, \(X_{ai}\) and \(X_{ei}\) are the \(i\)th actual and estimated X-coordinates, and \(Y_{aj}\) and \(Y_{ej}\) are the corresponding \(j\)th actual and estimated Y-coordinates, respectively. The test results and extensive explanations are given in Section 5. Figure 1:3 Proposed system structure.
# Secure and Efficient Authentication Scheme in IoT Environments
## 3 Proposed ECC-Based Lightweight Authentication Scheme
### Password-Change Phase  
The proposed scheme allows a legitimated embedded device \(ED_{i}\) to change the password periodically, thereby ensuring security. The embedded devices first perform authentication and authorization to change the password and prove their genuineness. Once device \(ED_{i}\) successfully performs mutual authentication with server \(TS\), device \(ED_{i}\) must obey the following steps for password change, as depicted in Figure 3:  
* Step 1. \(ED_{i}\to TS\): \(<I_{i}^{*}>\). The authorized legitimated embedded device \(ED_{i}\) selects a new password \(Psw_{i}^{*}\) and then recomputes the hashed identity \(\left(I_{i}^{*}=H(ID_{i}\ ||Psw_{i}^{*}\ )\right)\) and public key \(\left(P_{kc}^{\prime}=Psw_{i}^{*}.G\right)\). Subsequently, it sends the updated \(<I_{i}^{*},\ P_{kc}^{\prime}>\) to \(TS\) through a trusted public channel by using the session key \(S_{K}\).
* Step 2. \(TS\to\ ED_{i}\): \(<CID_{i}^{*},CK^{\prime*}>\). \(TS\) receives updated parameters \(<I_{i}^{*},\ P_{kc}^{\prime}>\), and then \(TS\) selects a random number \(R^{*}\) and then recomputes all parameters \(\left(CID_{i}^{*}=h(R^{1}\ ||\ s)\oplus\ s,CID_{i}^{\prime*}=CID_{i}^{*}.G,\ CK^{*}=h(R^{*}|| \ s||_{E}||CID_{i}^{*}),\ CK^{*}=CK^{*}.G,\ T=R^{*}\oplus h(R^{*}\ ||\ I_{i}^{*}\ ||\ s),A=T\oplus I_{i}^{*}\oplus CK^{*},\ t^{ \prime}=T\oplus CID_{i}^{*}\oplus s\oplus ID_{TS},\ a^{\prime}=A\oplus CID_{i} ^{*}\oplus s\oplus ID_{TS},\ a^{\prime}=T\oplus ID_{TS},\ a^{\prime}=T \oplus ID_{TS},\ a^{\prime}=T\oplus ID_{TS}),\) and \(e^{\prime}_{i}=E_{t}\oplus CID_{i}^{*}\oplus s\oplus ID_{TS})\), which are mentioned in Section 3.2.  
Figure 3: Password-change phase.  
Subsequently, \(TS\) stores the parameters \(<t^{\prime},a^{\prime},\ e^{\prime}_{t}>\) in the server database and sends the updated \(<CID_{i}{}^{*},\ CK^{*}{}^{\prime}>\) to the server \(TS\) through an open trusted channel by using the session key \((S_{K})\).
# Harnessing Biotechnology for the Remediation of Organic Pollutants in Coastal Marine Ecosystems
## 3 Organic Pollution in Coastal Marine Ecosystems
### Microplastics and Synthetic Polymers  
While not traditionally categorized as organic pollutants, microplastics and their associated chemical additives contribute significantly to marine pollution. These pollutants originate from primary microplastics that are manufactured as small particles (e.g., microbeads in personal care products, synthetic fibers from textiles), and secondary microplastics result from the breakdown of larger plastic debris due to UV radiation, wave action, and mechanical degradation [44]. Microplastics act as carriers for hydrophobic organic pollutants, such as PAHs and PCBs [45]; this facilitates their ingestion by marine organisms [46]. Examples are chemical additives such as bisphenol A (BPA), a known endocrine disruptor that leaches from polycarbonate plastics and affects reproductive health in marine species [47]. Additionally, environmental monitoring studies on BPA residues have shown that its presence in various natural aquatic systems can lead to harmful environmental impacts and contribute to metabolic syndrome [48]. However, BPA has been incorrectly evaluated in past risk assessments, often due to outdated toxicity thresholds or incomplete data on environmental persistence and bioaccumulation potential. Phthalates are used as plasticizers and these compounds disrupt hormone regulation and impair larval development in fish and crustaceans [49]. Microplas further disrupt microbial community structure by serving as artificial substrates for biofilm formation [50], altering Community Assembly Theory in marine ecosystems, where pollutant-resistant bacteria thrive while sensitive species decline. Table 1 summarizes major organic pollutants in coastal marine ecosystems and their impacts. Figure 1 presents categorized data from the study by [PERSON] et al. [51], indicating that most studies focus on pharmaceuticals (28%), followed by pesticides and agrochemicals (24%), PAHs (21%), microplastics and plastic-derived chemicals (17%), and PCBs (10%).
## 4 Risk Analysis for Current Protocols
### Automated Identification System (AIS)
#### 4.4.3 Packet Sniffing  
An additional point that we would like to highlight is that most AIS messages are delivered over port 5321 via UDP without any form of encryption. The protocol is extremely vulnerable against packet sniffing allowing for adversaries to track communications between multiple entities that utilize AIS [37].