esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Grid Impact
### Smart Charging/Charge Management and Location
On the other hand, there are greater benefits for DSOs, as more stored energy is available with capacity for instant use due to using EVs as energy vectors, allowing critical aspects of the network such as active and reactive power, current harmonics, load balancing, and peak load shaving to be regulated [134; 135]. Successful integration of V2G technology is related to communications systems, compatible infrastructure, and regulatory support, which differ depending on regions and market dynamics [68]. The use of a distributed charging strategy maintains user privacy and is favorable when there are low communications requirements; however, for the EV and RES high-penetration cases, it is advisable to use centralized charging, which optimizes the energy usage of the network thanks to the information provided by the communication systems. In cities and for slow or moderate charging speeds, EVs are typically parked for 22 h per day [136; 137]. Regarding the location of smart chargers, while the number of EVs is undeniably increasing, wider adoption will only be possible with charging infrastructure deployment that considers the location of EV owners and their behaviors, setting two possible objectives: (1) maximization of the service provided for a given cost, or (2) minimization of the cost of charging infrastructure for a defined service level [138]. In this sense, studies have considered whether it is better to install smart chargers in a specific area or along a highway, as well as the appropriate number of chargers. Other studies have searched for the best location of these elements through optimization strategies, with other approaches calculating the spatial distribution of load demand and deducing the best locations. Study of the installation location of smart chargers allows network managers to predict the points of greatest demand within the network. Researchers have extensively studied whether current charging infrastructure is appropriate and what additions or modifications would be necessary to achieve high EV penetration [139]. In relation to EV uptake barriers, researchers have indicated that the main problems are that users are not interested in purchasing an EV when there is not enough charging infrastructure, while operators do not want to invest too much in infrastructure if there are not enough EVs. According to various authors, DSOs should invest more in charging infrastructure than in subsidizing batteries [138]. Smart charging will have an impact not only in cities, but in rural areas as well. Studies carried out to evaluate their viability by 2030 have concluded that the use of EVs in these locations is possible without building new lines or other infrastructure; however, the electric charging process must be controlled so as not to affect the electrical network [140]. Finally, new problems related to the environment, such as the dissipation of amounts of large heat and the electromagnetic compatibility of the charging stations, still need to be addressed [141].
## 2 Materials and Methods
### Evaluation of the Efficiency of AutoCAD and AutoCAD Mechanical
#### 2.1.2 Analyzing Command Usage, Access Frequency, and Task Completion Time  
The methodology for collecting command usage and access frequency involved enabling the logging function at the start of each task by entering LOGFILEON in the command line. This function allowed for the recording of every command executed throughout the task. Upon task completion, the logging session was terminated using LOGFILEOFF, which saved all recorded command entries in a *.log file. This process was systematically applied to each task in both AutoCAD and AutoCAD Mechanical, ensuring a direct and consistent comparison of user behavior across both software environments. To eliminate potential biases, all log files were collected anonymously. These *.log files were analyzed to extract two key metrics: the number of unique commands used and the total frequency of command executions. This analysis was conducted through a combination of manual review and automated scripts that parsed the log data. For instance, if a log file contained commands such as LINE, CIRCLE, TRIM, and DIMLINEAR, with multiple instances of LINE and TRIM, the analysis would determine both the total count of unique commands and the frequency of their usage.  
Task completion times were measured using a stopwatch-based approach, supplemented by automation scripts that logged task start and end times. The recorded times were then processed to compute the average task duration for both AutoCAD and AutoCAD Mechanical. To evaluate the statistical significance of any observed differences, paired \(t\)-tests were conducted. The null hypothesis assumed no difference in efficiency between the two software environments, while the alternative hypothesis proposed that AutoCAD Mechanical significantly reduced task completion time and command repetition. The results were deemed statistically significant if the \(p\)-value was less than 0.05.  
It is important to note that no formal optimization algorithms or dedicated optimization software were used in this study. The evaluation of efficiency was based exclusively on empirical metrics, including the number and frequency of command executions and task completion times. Statistical significance of observed differences between the AutoCAD and AutoCAD Mechanical groups was assessed using paired \(t\)-tests, with \(p\)-values reported to determine the likelihood that differences occurred by chance. This approach provides a robust comparative framework without relying on model-based optimization techniques.
# Dynamic Analysis Method for Fault Propagation Behaviour of Machining Centres
## 1 Introduction
### Calculation of Fault Propagation Probability of Machining Centre System Components  
#### 2.2.1 Calculation of Machining Centre System Components  
All \(n\) data of machining centres' fault data and right truncation data are sorted and recorded as \(j(1\leq j\leq n)\). Then, only \(m\) fault data of a certain component of a machining centre are sorted from small to large and are recorded as \(i(1\leq i\leq m)\). The order number \(n\) of the \(i\)-th fault data is as follows:  
\[r_{i}=r_{i-1}+\frac{n+1-r_{i-1}}{n+2-j} \tag{3}\]  
The corrected component fault time order number is substituted into the median rank equation to obtain the empirical distribution function of the component, as follows:  
\[r_{i}=\frac{r_{i}-0.3}{n+0.4} \tag{4}\]  
The Weibull distribution commonly used in engineering is taken as the hypothetical distribution of the fault interval time of machining centre components. In this paper,  
Figure 3: Flow chart of the hierarchy structure model construction based on DSM.  
Weibull distribution is used to construct the reliability model and the cumulative fault distribution function is as follows:  
\[F(t)=1-\exp\left[-\left(\frac{t}{\beta}\right)^{\gamma}\right],t\geq 0, \tag{5}\]  
where \(\beta\) represents scale parameter, \(\beta>0\), \(\gamma\) represents shape parameter, and \(\gamma>0\), \(t\) is a time variable, \(t\geq 0\).  
Equation (5) is transformed as follows:  
\[\ln\left[\ln\left(1/(1-F(t))\right)\right]\ =\ \gamma\ln t+\gamma\ln\beta \tag{6}\]  
The linear regression model is fitted between \(\ln\left[\ln(1/(1-F(t)))\right]\) on the left side of the equation and \(\ln t\) on the right side. The Weibull model parameter value \(\hat{\beta}\) and \(\hat{\gamma}\) can be obtained on the basis of the fault information and empirical distribution function value. The model test value (\(\rho\)) is calculated by the linear correlation coefficient test method. Next, the minimum value (\(\rho_{\alpha}\)) of the correlation coefficient is calculated on the basis of the number of fault data (\(n\)) and significance level (\(\alpha\)). When \(\rho>\rho_{\alpha}\), it is considered that \(\ln\left[\ln(1/(1-F(t)))\right]\) and \(\ln t\) are linearly related and the fault data is subject to the assumed distribution. Otherwise, the hypothesis is rejected.
## 5 Evaluation
### Performance Analysis
#### 5.2.2 RINA L2 VPN Performance  
In order to evaluate the performance of RINA L2 VPN, two different tests have been performed. In the initial test depicted in Figure 18, a traffic generator was employed to transmit L2 packets across a single overlay. This test scenario is illustrated in Figure 6, where the network service took on the role of the traffic generator, while the receiver and sender sides functioned as dedicated network functions (NFs). For the traffic generator, we opted for pkt-gen v11.3 [40], a traffic generator leveraging the netmap API. This choice was motivated by the netmap API's capacity to attain higher data rates. It is important to note that all experiments were conducted within a fully virtualized environment, thus avoiding hardware limitations. Consequently, these controlled experiments serve as a benchmark.  
The second test shown in Figure 19 validates the viability of deploying multiple network overlays within a single RINA network, as well as characterizing how performance degrades as more network overlays are added in parallel. An idea of that experiment is illustrated in Figure 7. This characterization was accomplished through the stacking of L2 overlays, coupled with the deployment of the traffic generator tasked with transmitting traffic at its maximum attainable capacity. This test served as a pivotal assessment of the system's scalability and adaptability under conditions of heightened demand.
# See-Through Near-Eye Display with Built-in Prescription and Two-Dimensional Exit Pupil Expansion
## 2 Design Rules
### Plano-Concave Lens  
The design of the plano-concave lens was related to the visual acuity, and it plays an important role in imaging the real object, as shown in Figure 2, where \(R\) is the radius of curvature of the front surface of lens, \(s_{r}\) the real object distance, and \(s^{\prime}\) the image distance. For the real image, rays emitted from the real object were diverged by the plano-concave lens so as to offset the over-focusing of the eye. A \"real\" image--by which we mean that it is the image of a real object, albeit this image is technically virtual--was formed at a closer distance. The object distance \(s\), image distance \(s^{\prime}\) and diopter or optical power of the lens \(P\) shall be correlated via the lens-maker's equation [18]  
\[s^{\prime}=\frac{s_{r}}{P_{S_{P}}-1} \tag{1}\]  
Figure 1: Schematic of the proposed see-through near-eye display (NED). \(W_{1}\), \(W_{2}\), \(H\), \(T\), and \(\gamma\) are the front width, back width, height, thickness and bevel angle of plano-concave lens, respectively.  
where  
\[P=\frac{n-1}{R} \tag{2}\]  
where \(n\) is the refractive index of the lens. Suppose a user has only 3 diopters of myopia, disregarding other types of refractive errors. Then, \(P=-3\) m\({}^{-1}\) when \(s_{r}=\infty\) m and \(s^{\prime}=-0.333\) m. It should be mentioned that the \"real\" image was not observed by the eye. Rather, the eye saw the real object, as the rays derailed by the lens got back on track through the accommodation of the eye [19].  
The front width \(W_{1}\) of the lens rested partly upon the interpupillary distance \(d_{ip}\), which was on average 64 mm [20]. If the front surface of the lens is center-aligned with the eye, then the front width \(W_{1}\) of lens is  
\[W_{1}=d_{ip}-d_{b} \tag{3}\]  
where \(d_{b}\) is the width of the bridge of the smart glasses. Say \(d_{b}=18\) mm, \(W_{1}=46\) mm. Further, the front width \(W_{1}\), back width \(W_{2}\), thickness \(T\), and level angle \(\gamma\) shall be correlated via  
\[\frac{T}{W_{2}-W_{1}}=tan\gamma \tag{4}\]  
Say \(\gamma=75.52^{\circ}\) (as will be discussed later), \(W_{2}=47.93\) mm, and \(T=7.47\) mm. The height \(H\) was a freelance parameter as long as it met the ergonomics. Pursuant to the above design rules, a plano-concave lens can be tentatively designed with the parameters itemized in Table 1.
# A Sun-Tracking Algorithm for Satellite-Borne Spectrometers Based on the Orbital Motion Model
## 1 Introduction
This is the adaptive estimation of the mean value of acceleration in the Singer model [15]. CSM and other improved algorithms based on CSM are widely applied in engineering [16; 17]. However, these systems will become unstable when faced with a weak maneuverable target such as the spectrometer with respect to the sun. [18]. In this study, in order to compensate for the impact of angular offset lag on the tracking performance and improve the accuracy of spectrum data, the target motion model of the spectrometer when tracking the sun, and the actual working condition were first obtained. Then a Kalman predictive filtering algorithm was proposed based on the motion model mentioned above and the actual control model was acquired. Finally, simulations and tests were conducted to verify the accuracy and effectiveness of the model and algorithm presented in this paper. The remainder of this paper is organized as follows. The system composition and working principle of the spectrometer are described in Section 2. The angular motion model based on satellite orbital motion is established, the prediction filtering algorithm is deduced, and the control system model of spectrometer on-axis tracking is established in Section 3. Simulations and tests conducted to verify the models are presented in the paper in Section 4, and conclusions are drawn in Section 5.
# Improving Healthcare Facilities in Remote Areas Using Cutting-Edge Technologies
## 4 Mathematical Modeling
#### Case Study  
[PERSON] is a cardiac patient who uses wearable sensors to monitor his vital signs. He lives in a rural region with few medical services. In his neighborhood, 5G internet service is available, and [PERSON] is using his smartphone to take advantage of it. [PERSON]'s vital signs are recorded by wearable sensors and assessed using threshold vital sign values. If the vital signs are aberrant, they are transmitted immediately through 5G to the cloud, where AI algorithms operate to predict abnormal vital signs. AI algorithms evaluate vital signs and classify them into normal and abnormal vital signs. The abnormal vital signs are then accessed by the doctor from the cloud. The doctor determines the severity of [PERSON]'s vitals by evaluating his vitals following clinical standards and his prior health. He sends the patient's vitals to the telehealth system if he determines they are of high severity. The telehealth system monitors [PERSON]'s vitals and then selects from the three available alternatives. (1) If it considers that vitals severity is high and an expert panel needs to be contacted, then it transmits vitals to the expert panel. The expert panel evaluates vitals and sends prescriptions back to the telehealth system using 5G and the cloud. The telehealthsystem sends this prescription to the remote physician via the cloud. The remote physician accesses the prescription and sends it to [PERSON] through a smartphone app installed on [PERSON]'s smartphone. (2) If the telehealth system considers that vitals need lab examinations, then it transmits the vitals to the lab experts. The lab experts evaluate the vitals and send the required diagnostics using 5G and the cloud to the telehealth system. The telehealth system sends these diagnostic details to the remote physician via the cloud. The remote physician accesses the diagnostic information and sends it to [PERSON] through a smartphone app installed on [PERSON]'s smartphone. (3) If the telehealth system considers that vitals need medicines based on the prescriptions received by the doctor, then it transmits vitals to the pharmacy. The pharmacist evaluates vitals and dispatches the required medicines to the telehealth system using 5G and the cloud. The telehealth system sends medicine details to the remote physician via the cloud. Remote physicians access the medicine details and send them to [PERSON] through a smartphone app installed on [PERSON]'s smartphone.  
The detailed working of the case study is shown in the sequence diagram in Figure 4. The aforementioned case study scenario is a success if all the requirements (IoT sensors are operational, the patient follows clinical recommendations, 5G is operational, healthcare data are stored and retrieved through the cloud, the telehealth system is operational, etc.) listed in the objective function are met. The timely accessibility of a healthcare facility to a remote patient will be impacted, nevertheless, if any of the aforementioned constraints are not present.  
Figure 4: Detailed working of the case study.
# The Relationship between Radon and Geology: Sources, Transport and Indoor Accumulation
## 4 Risk Assessment and Mitigation
### Geographical and Seasonal Variations in Radon Concentration
#### 4.2.2 Seasonal Variations  
The concentration of radon can also vary seasonally due to changes in atmospheric conditions such as temperature, humidity, and barometric pressure [185, 186]. In general,radon concentrations tend to be higher during the winter months when the temperature difference between the inside and outside of buildings is greater. This temperature difference can create a suction effect, drawing radon into buildings. Additionally, ventilation is typically reduced in the winter, which can contribute to the accumulation of radon in indoor environments. On the other hand, during the summer months, increased ventilation and more uniform temperatures between the inside and outside of buildings can result in lower concentrations of radon. The study by [PERSON] et al. (2020) investigated the variations in indoor radon concentrations as influenced by environmental factors, such as outdoor temperature, relative humidity, and air pressure, across different university building premises [185]. Every working day for eight months, measurements were taken in four different locations for the research. The study's findings indicated a significant relationship between indoor radon levels and outside temperatures as well as relative humidity. On the other hand, the connections between indoor radon levels and the air pressures inside and outside were the weakest. It is interesting to note that the relationship between radon levels and the surrounding environment changed depending on the location. A significant association between radon concentration and outside temperature was found, with values of 0.94 and 0.92, in two buildings where ventilation impact from inadvertent air leakage was prevalent throughout the winter. The association between radon levels and outdoor temperature, however, was shown to be negative in buildings with better airtightness (R = \(-\)0.96 and R = \(-\)0.62). The results point to high-quality air isolation in buildings as a potential contributor to higher indoor radon levels in the summer than in the winter. It emphasises how important seasonal fluctuations and hygrometric factors are to the behaviour of indoor radon concentrations.
## 4 Results
### Best Experimental Condition  
Figure 4 shows the percentage of times that an experimental condition reported the best (lowest) error over the 134 generated forecasts. The plot thus reveals which experimental condition had the best forecasting results in predicting supplier delivery delay performance for most of the considered components.  
Figure 3: Distribution of the monitored response metrics values over the investigated experimental conditions.  
In line with the results reported in Figure 3, Figure 4 highlights the fact that the experimental condition representing the proposed approach (LSTM, MULTIVARIATE) was best for most of the considered components. However, the proposed approach was not able to generate the best performance for all the considered components. Indeed, the proposed approach was best for only 62% of the considered components in terms of _SMAPE_ and for only 50% and 41% of the examined components in terms of _RMSE_ and _MAE_, respectively.
## 4 Digital Watermarking System for JPEGs
### Feature Extraction Mechanism
#### \(8\times 8\) DCT Frequency Layout  
DCT matrix coefficients range from low-frequency components (top left) to high-frequency components (bottom right). The watermarking scheme targets mid-frequency coefficients to balance robustness and imperceptibility [66]. Zigzag scanning optimises coefficient access in JPEG-like structures to ensure consistent watermark placement across implementations [67; 68]. This approach maintains compatibility with JPEG encoding structures. Figure 2 shows the zigzag scanning method for the watermark distribution across the DCT matrix. This traversal covers both low- and high-frequency coefficients to optimise watermark placement [68]. The watermark is embedded in the mid-frequency band to enhance robustness and imperceptibility.
# Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold
## 3 Results and Discussion
### Comparison with Other Local Adaptive Thresholds  
[PERSON]'s method [46], [PERSON]'s method [47], and [PERSON] method [38] were compared with our method in local threshold selection in Figure 18. Less oil films were identified in [PERSON]'s method, and false excessive oil film were detected in [PERSON]'s method. The Otsu algorithm is not suitable for our experimental process, yielding a poor result. [PERSON]'s [36] adaptive threshold improved upon [PERSON]'s method, and was adopted for an excellent performance in our experiment. Compared with the above three methods, our approach is more congruent with the results of visual interpretation.
## 3 Materials and Methods
### Scanning Electron Microscope  
The polished sections were studied with a JEOL JSM-840A scanning electron microscope (SEM) (JEOL Ltd., Tokyo, Japan) equipped with an OXFORD INCA 300 energy-dispersive system (EDS) (Oxford Instruments Ltd., Abingdon, UK) at the Faculty of Sciences, AUTh. The operating conditions were a 20 kV accelerating voltage and 0.4 mA probe current, 80s analysis time, and a beam diameter of \(\sim\)1 \(\upmu\)m, in the back-scattering electron (BSE) mode. Internal empirical corrections were performed to compensate for peak overlap. SEM-EDS micro-analysis was targeted on the mineral chemistry of subnite and on the minerals found as micro-scale inclusions in it. Additionally, the antimony content was used as an internal standard in LA-ICP-MS analyses for stibnite. The average values of the conducted SEM-EDS analyses are given in Table S2.