esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 VLC System Model for Underground Mining
### Optical Receiver  
VLC systems typically use PDs in the reception stage, which is composed of a non-imaging concentrator (lens) and a physical area \(A_{p}\). PDs collect the incident power produced by the light intensity of \(T_{x}\). The optical gain of a PD, \(g(\theta_{0})\), based on a non-imaging concentrator, is given as follows [11]  
\[g(\theta_{0})=\begin{cases}\frac{\eta^{2}}{sin^{2}(\theta_{0})},&\text{if }0\leq \theta_{0}\leq\Theta\\ 0,&\text{if }\theta_{0}\geq\Theta\end{cases}, \tag{12}\]  
where \(\eta\) is the refractive index of the concentrator. Along with the PD lens, an optical transmission gain band-pass filter, \(T_{s}(\theta_{0})\), is also added. The block diagram of the complete VLC system is shown in Figure 3.  
As mentioned, one of the goals of this research is to study the problem of ICI in underground mining VLC environments. As such, we will focus on the proposal of three types of \(R_{x}\) as possible solutions to ICI. The first proposal is a single-PD in the \(R_{x}\), which is pointing up. In the second solution, a pyramidal ADR is presented that consists of four PDs located on the side faces of the pyramid. Finally, the third solution analyzes a new ADR hemi-dodecahedron scheme that consists of one PD located at the upper base pointing up and five PDs located on the side faces of the polyhedron. Note that all \(R_{x}\) are located at the top of the mining workers helmets. A rigorous mathematical analysis of the ICI problem and the proposed solutions is presented in the following section.
## Materials and Methods
### Milk Volatile Organic Compound Analysis  
The extraction and analysis of VOCs was performed using dynamic headspace-solid phase micro-extraction (SPME) and GC/MS, according to Villeneuve et al. [18] and Lee et al. [52], with some modifications. First, 22.5 g of milk was transferred into a 50 mL bottle, then 30 \(\upmu\)L of 2-methyl-3-heptanone, (99% purity, Sigma-Aldrich, St. Louis, MO, USA) as internal standard (408 mg L\({}^{-1}\), in water solution), and 2.75 g of sodium phosphate (NaH\({}_{2}\)PO\({}_{4}\)) (Sigma-Aldrich), were added. The sample bottle was put in an apparatus that can regulate milk temperature and stirring of the sample. In order to homogenize it and accelerate the equilibrium of headspace-volatile compounds between the milk matrix and the headspace, the sample was magnetically stirred for 5 min at 55 \({}^{\circ}\)C. SPME fiber was inserted through the Teflon septum in the bottle and exposed to sample headspace for 60 min at 55 \({}^{\circ}\)C while stirring. The SPME device (Supelco Co., Bellefonte, state abbreviation, USA) was equipped with a 50/30 \(\upmu\)m thick divinyl-benzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber coated with 2 cm length stationary phase. The same type of fiber was reported to be highly efficient for cheese aroma analysis [53]. Volatile compounds were analyzed by GC coupled with mass spectrometry using a Hewlett-Packard 6890 N (Agilent Technologies, Palo Alto, CA, USA), equipped with a J&W HP-5 MS capillary column (30 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m film thickness; J&W Scientific, Folsom, CA, USA). The temperature was set at 40 \({}^{\circ}\)C for 2 min and increased to 160 \({}^{\circ}\)C at the rate of 6 \({}^{\circ}\)C min\({}^{-1}\) and to 210 \({}^{\circ}\)C at 10 \({}^{\circ}\)C min\({}^{-1}\). The injector was kept at 250 \({}^{\circ}\)C. Helium was used as a carrier gas at 0.9 mL min\({}^{-1}\). Volatile compound thermal desorption was carried out by exposing SPME fiber in the injector for 10 min. The identification of volatile compounds was carried out by comparing retention times and mass spectra obtained by analyzing pure reference compounds in the same conditions. The identification was confirmed by comparing mass spectra with those of the National Institute of Standards and Technology (NIST) database. All chemical standards were supplied by Sigma-Aldrich (St. Louis, MO, USA). In a few cases, the pure chemical standard was not available, and the compounds were labeled as tentative (t). Mass spectra were recorded at 70 eV. The source temperature was 230 \({}^{\circ}\)C and the interface temperature was 250 \({}^{\circ}\)C. Before use, the fiber was conditioned at 270 \({}^{\circ}\)C for 1 h for the analysis. In order to prevent the release of undesirable compounds, a blank test was performed before each analysis. The quantitative data of milk volatile compounds was carried out by normalizing the peak areas of each compound with respect to the area of the internal standard peak. Peak area data were processed by ChemStation software (Agilent Technologies). The analysis of volatile compounds was performed in triplicate.
# Security Hardening and Compliance Assessment of Kubernetes Control Plane and Workloads
## 5 Kubernetes Vulnerabilities and Kubernetes Penetration Testing
### Penetration Testing Tools, Demonstration, and Security Compliance Assessment
#### 5.4.3 Penetration Testing with Kube-Hunter  
Kube-hunter was executed in remote scanning mode to emulate an external attacker, focusing on the master node IP. The instrument initially indicated the following:  
* Disclosure of API Server version;
* Kubelet API accessible on port 10250;
* Etcd port 2379 is available without firewall restrictions.  
Following remedial measures, including the deactivation of debug handlers (-enable-debugging-handlers=false), implementation of host-based firewall regulations, and restriction of network CIDR ranges in the API server manifest, a subsequent scan revealed no publicly accessible sensitive endpoints, thereby validating the mitigation efforts.  
This use case illustrates the gradual enhancement of a vulnerable default Kubernetes configuration into a fortified, compliant, and monitored cluster. It emphasizes that incorporating Trivy, kube-bench, and kube-hunter into the operational lifecycle can enhance early detection, remediation, and ongoing assurance of security posture.  
Table 2 consolidates the practical implementations covered in the paper, summarizing the hardening of each Kubernetes architectural component and the associated security outcomes. This review improves the scientific foundation of the study by linking specific technical measures to their functional security advantages and emphasizing the breadth of the suggested framework.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Kubernetes Component** & **Applied Security Controls** & **Security Outcome** \\ \hline API Server & TLS encryption, client certs, RBAC, token auth, restricting anonymous access & Secure API access, authenticated requests only, reduced attack surface \\  & TLS encryption, certificate authentication, restricted file permissions, snapshot & Confidentiality and integrity of the cluster state and secrets \\  & Encryption & Disabled anonymous access, webhook & Controlled access to workloads and node-level operations \\  & & authorization, certificate-based TLS, restricted permissions & Prevention of workload \\  & & TLS encryption, profiling disabled, access & manipulation or unauthorized \\  & restriction via bind-address & scheduling \\  & & Reduced supply chain risk and \\  & & base images, secure build pipeline. & minimized the attack surface of workloads \\ Access Management (IAM) & Client certificates, bootstrap/static tokens, OpenID Connect, RBAC, node authorization & Enforced least privilege access and contextual identity control \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of security measures by architectural component.  
This structured mapping illustrates how integrating layered controls across components collectively enhances the cluster's security posture, resilience, and alignment with compliance requirements.
## 4 Results and Discussion
### Daily Energy Gain  
For this section, the daily energy gain (\(DEG\)) was analysed. Equation (27) was used for this purpose. The daily energy gain for the \(PV\) module mounting systems under study is shown in Figure 13.  
The following observations can be deduced from Figure 13:  
1. In Scenario 1, and considering the theoretical study, on days 1 to 69 and 278 to 365 the fixed tilt angle system performed better than the horizontal single-axis tracker. On the other days, the opposite happened. When Scenario 2 was considered, there was variation on these days: that is to say, on days 1 to 59 and 288 to 365 the fixed tilt angle system performed better than the horizontal single-axis tracker.
2. Considering the theoretical study, in Scenario 1 the highest and lowest \(DEG\) values were \(43\%\) and \(-30\%\), respectively, and in Scenario 2 these values were \(47\%\) and \(-23\%\), respectively.  
Figure 12: Monthly energy gain.  
3. Considering the study under real operating conditions, in Scenario 1 the highest and lowest \(DEG\) values were \(58\%\) and \(-47\%\), respectively, and in Scenario 2 these values were \(68\%\) and \(-30\%\), respectively.
## 3 Performance of the Optical Wireless Link  
From the link's irradiance \(I_{j}\), and Equation (1), the instantaneous signal-to-noise ratio (SNR) can be estimated as \(\gamma_{j}=\left(\eta I_{j}\right)^{2}/N_{0}\)[20; 40], while the average SNR is given as \(\overline{\gamma}_{j}=\left(\eta E\big{[}I_{j}\big{]}\right)^{2}/N_{0}\). Thus, by using the appropriate RV transformation in Equation (7), the PDF for the instantaneous SNR is expressed as follows [20; 40]:  
\[f_{\gamma_{j}}(\gamma_{j})=\frac{T_{pw}E\big{[}I_{j}\big{]}}{2\sigma_{T}Z\gamma _{j}\sqrt{2\pi\ln\left(B\sqrt{\overline{\gamma}_{j}/\gamma_{j}}/E\big{[}I_{j} \big{]}\right)}}\exp{\left(-\frac{T_{pw}^{2}\ln\left(B\sqrt{\overline{\gamma} _{j}/\gamma_{j}}/E\big{[}I_{j}\big{]}\right)}{2\sigma_{T}^{2}}\right)}. \tag{11}\]
# Systematic Encoding and Shortening of PAC Codes
###### Abstract  
Polarization adjusted convolutional (PAC) codes are a class of codes that combine channel polarization with convolutional coding. PAC codes are of interest for their high performance. This paper presents a systematic encoding and shortening method for PAC codes. Systematic encoding is important for lowering the bit-error rate (BER) of PAC codes. Shortening is important for adjusting the block length of PAC codes. It is shown that systematic encoding and shortening of PAC codes can be carried out in a unified framework.  
PAC codes; polar codes; systematic encoding; code shortening +
Footnote †: journal:
###### Abstract  
The Article presents the results of the experimental research and numerical analysis of a compression ignition (CI) engine adapted for running on dual fuels of different composition (diesel and natural gas, diesel and biogas, biodiesel and natural gas, and biodiesel and biogas). The main goal was to find out the impact of different dual fuels on energy performance and emissions depending on the start of injection (SOI) of diesel and the crank angle degree (CAD). Pure conventional diesel fuel and second generation hydrotreated vegetable oil (HVO) (Neste) was used in the research. Natural gas contained 97 vol. % of methane. Biogas (biomethane) was simulated using a methane and carbon dioxide blend consisting of 60 vol. % of methane and 40 vol. % of carbon dioxide. Dual (liquid and gaseous) fuels were used in the tests, with the energy share of liquid fuels accounting for 40% and gas for 60%. The research results have shown that having replaced conventional diesel fuel with dual fuel, engine's BTE declined by 11.9-16.5%. The use of methane in the dual fuel blend reduced CO2 volumetric fraction in the exhaust gases by 17-20%, while biomethane increased CO2 volumetric fraction by 10-14%. Dual fuel significantly increased CO and HC emissions, but NOx volumetric fraction decreased by 67-82% and smoke by 23-39%. The numerical analysis of the combustion process revealed changes in the ROHR (Rate of Heat Release) that affected engine efficiency and exhaust emissions was done by AVL (_Anstalt fur Verbrennungskraftmaschinent List_) BOOST program.  
biogas; biodiesel; pollutant emissions; engine efficiency; rate of heat release +
Footnote †: journal: Applied Sciences
## 3 Numerical Methods
### Stationary System  
We construct the solution on a finite domain \(D=[0,r_{\max}]\), where the boundary conditions are redefined approximately as \(\psi(r_{\max})\approx\psi^{\prime}(r_{\max})\approx 0\), that is, we use a finite value \(r_{max}\) in which we seek to satisfy the boundary conditions approximately at the external boundary \(r_{max}\).  
We define the discrete domain as \(D^{d}=\{r_{i}\in D|i=0,\ldots,N_{r}\}\), where \(N_{r}\) is the number of points. The simplest way to construct \(D^{d}\) is by employing a uniform partition, where the points are chosen as \(r_{i}=i\Delta r\), with \(\Delta r=r_{\max}/N_{r}\) the resolution of the discrete domain.  
Note that in order to integrate the system (16)-(19), we must set the parameters \(\psi_{c}\), \(V_{c}\), \(\delta\), \(\omega,r_{max}\), from which we do not know the eigenvalue \(\omega\), and for reasons of numerical precision, the upper radius \(r_{\max}\). Therefore, it is necessary to find these values in such a way that they approximately satisfy the boundary conditions at the outer boundary \(r_{\max}\).  
Instead of using the shooting method to search for the eigenvalue \(\omega\) of the problem (16)-(19) as traditional (e.g., [6; 25]), here, we propose an alternative method based on optimization.
## 4 Discussion
### Effect of the Ultrasound Operation Frequency  
The effect of ultrasound operation frequency on the temperature distribution can be obtained by comparing Figure 4g,h. The temperature distribution pattern changes significantly when the ultrasound operation frequency changes from 1 MHz to 3 MHz. The temperature of the composite system with a circular stainless-steel inclusion for the 3 MHz ultrasound is lower than that for the 1 MHz ultrasound. With a circular stainless-steel inclusion, the peak temperature decreases dramatically from 75.54 \({}^{\circ}\)C to 47.38 \({}^{\circ}\)C when the ultrasound operation frequency increases from 1 MHz to 3 MHz. However, it is worth noting that these results were obtained from the ultrasound transducer operation under the specific powers. Different ultrasonic powers may lead to different results.
# Comparative Accuracy Analysis of Truck Weight Measurement Techniques
## 2 Material and Methods  
To verify the accuracy of vehicle weight measurements, the same vehicles needed to be measured by all tested weighing systems. Therefore, it was necessary to find a location that has in close proximity a truck weigh station and additional space to evaluate the weight by portable scales and the WIM system. The selected weigh station in Alabama is located close to the WIM system and allows collecting data for three measuring techniques. Next, the bridge had to be selected to install the B-WIM system. Figure 1 presents (a) an overview of weigh station and B-WIM locations, and more closely, (b) the weigh station, designated space for portable scales measurements, and the Alabama Department of Transportation (ALDOT) WIM system.  
First, vehicles were captured by the WIM station; then heavy trucks stopped at the weigh station. The most typical 5-axle vehicles were considered and were recorded by truck weigh station and then portable scales. Alabama State Troopers conducted the measurements. Lastly, the vehicles were recorded by the B-WIM system. The vehicles measured by weigh station and portable scales were selected from WIM and B-WIM databases. The matching procedure was based on time en
# Reconfiguration Decision-Making of IoT based Reconfigurable Manufacturing Systems
## 4 Internet of Things (IoT) Based Data Collection System for RMS
### Data Acquisition and Flow in RMS  
Data acquisition tools are installed in at various points of the RMS, as shown in Figure 2. The data are acquired through IoT sensors in various levels of the RMS and are transferred to a factory management system. The sensors on machine tools collect the wear and crack data of tools, and the sensors on machines collect information on the inside of machines and the relationship with other modules. The collected data are gathered in a software system, transferred to the factory management system, and applied to reconfiguration decision-making.  
In this study, the status data of RMS were acquired by a small module with wireless communication with the structure shown in Figure 3. In the data acquisition module, various IoT sensors are installed to detect the status of the attached module, the processing module to convert the analog signal to digits, and wireless and wired communication modules. As a low power technology was applied to the sensors and the data acquisition module, they work in a low power or small battery environment.  
Table 1 describes a list of sensors in the data acquisition module. With these sensors, tool abrasion, expansion, and crack and machine temperature, vibration, tilting, and connection between modules can be detected. Collected data are then applied to reconfiguration situation detection and decision-making, reconfiguration plan building, and reconfiguration completion check.  
Figure 2: Data acquisition and flow in the RMS.
## 3 Experimental Design
### Performance Evaluation Metric  
This study uses three performance metrics to measure the accuracy of forecasts to see how well the augmented data perform in forecasting compared to the original data.  
* The Mean Absolute Error (MAE): It demonstrates the median of the absolute errors among the actual solar radiation values and the anticipated values, as shown in Equation (7): \[\text{MAE}=\frac{1}{\text{N}}\sum_{i=1}^{\text{n}}\left|\text{Ga}_{i}-\text{ GF}_{i}\right|\] (7)
* The Root Mean Square Error (RMSE): It is calculated by finding the quadratic root of the median of the quadratic variances that exist between the values measured and those forecasted for the solar irradiance. This calculation is shown in Equation (8): \[\text{RMSE}=\sqrt{\frac{1}{\text{N}}\sum_{i}^{\text{n}}\left(\text{Ga}_{i}- \text{GF}_{i}\right)^{2}}\] (8)
* The Correlation Coefficient (R): It indicates the strength of the linear relation between the actual and forecast solar radiation and is computed as in Equation (9): \[\text{R}=\frac{\sum_{i}^{\text{n}}\left(\text{GF}_{i}-\overline{\text{GF}_{i} }\right)\left(\text{Ga}_{i}-\overline{\text{Ga}_{i}}\right)}{\sqrt{\sum_{i}^ {\text{n}}\left(\text{GF}_{i}-\overline{\text{GF}_{i}}\right)\sum_{i}^{\text{ n}}\left(\text{Ga}_{i}-\overline{\text{Ga}_{i}}\right)^{2}}}\] (9) where n represents the entire numeral of data points, \(\text{GF}_{i}\) and \(\text{Ga}_{i}\) are the forecasted and actual values, respectively. \(\overline{\text{Ga}_{i}}\) and \(\overline{\text{GF}_{i}}\) represent the mean of the actual and forecast values, respectively.  
Evaluation metrics provide feedback on forecasting accuracy against the benchmark models, allowing models to be fine-tuned to achieve a target degree of precision. However, without metrics for comparison it is impossible to describe the performance of deep learning models. A few performance evaluation metrics, such as Mean Absolute Error (MAE), Correlation Coefficient R, and Root Mean Square Error (RMSE), are frequently used to assess the accuracy of forecasting models. These metrics can be effectively utilized in the majority of models, including those for rainfall, solar irradiance, wind power, etc. [51].  
Given the debate regarding evaluation metrics' efficacy, R, MAE, and RMSE are adequate indicators for determining the optimum forecasting model [52; 53]. It is also worth noting that R, MAE, and RMSE are different evaluation metrics reflecting various aspectsof model performance. R is a measure of the correlation, whereas MAE and RMSE are measures of forecasting error. The results have been evaluated using statistical metrics that are the most popular ones: MAE, R, and RMSE. This last one is still the most commonly used metric in forecasting models [54; 55; 56; 57]. In addition, RMSE is considered in the literature as a good measure to evaluate and compare forecasting models; therefore, the RMSE metric has also been applied to provide a more significant representation of the results, which would help to make more reliable future studies.
# Distance Estimation with a Stereo Camera and Accuracy Determination
## 4 Potential Application of the System
Integrating stereoscopic technology with endoscopic systems opens up new possibilities for precision surgery and diagnostics, supporting the surgeon not only in visual orientation, but also in accurate positioning of tools, which can significantly improve the safety and efficiency of medical procedures.
## 4 Discussion
### Dependence of DER on Distance between the Gold Nanoparticle and DNA  
In the simulation, the distance between the gold nanoparticle and DNA varied in the range of 30 to 130 nm as shown in Figure 2. It is seen that the DER value decreases with an increase of distance between the nanoparticle and DNA. This is reasonable because the closer the gold nanoparticle to the DNA, the higher probability that the secondary electrons from the nanoparticle can reach the DNA and contribute energy deposition. This results in a larger DER value. Comparing Figure 2a-c, it is found that the variation of DER value on the distance between the nanoparticle and DNA is at maximum for the 0.5 MeV proton beam. The DER range is equal to 1.33-1.4, 1.62-1.83, and 1.21-1.29, when the distance increases from 30-130 nm for the nanoparticle size (radius) equal to 15, 25 and 50 nm, respectively. This change of DER value is not significant when compared to higher proton beam energy (>0.5 MeV) with different nanoparticle sizes. It is seen that the effect of distance between the gold nanoparticle and DNA on the DER value is larger when lower proton beam energy is used to irradiate the nanoparticle. This is because the secondary electron range for the lower proton beam energy is shorter than the higher energy. This may cause some secondary electrons to be unable to reach the DNA when the nanoparticle is far away from the DNA molecule.