esa-sceva/satcom-chunk-collection huggingface dataset:
# Abrasive Surface Finishing on SLM 316L Parts Fabricated with Recycled Powder
## 2 Experimental Procedure
### 2D and 3D Surface Roughness Measurement  
After fabrication, the as-built specimens were first sandblasted utilizing Cabinet Sandblaster 350 L XH-SBC 350 with either S170 steel medium (grain size from 355 to 425 \(\upmu\)m) or F-24 aluminum oxide medium (grain size from 710 to 850 \(\upmu\)m) for a recorded duration of 50 s. Subsequently, the specimens were feedforward to the tumbler OTEC CFI \(\times\) 32 EL for surface finishing within 120, 180, and 240 min, with one of three media being ceramic (DZS 10/10, Otec company, Pforzheimer, Germany), plastic (XS 12K, Wather Trowal company, Germany), porcelain (P 2/5 Otec company), and a chemical compound (KFL, Walther Trowal company). The total surface treatment durations of different specimens were recorded.  
Eventually, Mitutoyo SJ-210 was employed to measure the 2D surface roughness of nine surfaces (eight sides and one top). Special stands, which were printed from polyethylene terephthalate glycol (PETG) with fused deposition modeling (FDM) technology, were deployed to position the inspected surfaces perpendicular to the measuring needle tip. The needle tip was chosen according to the standard DIN EN ISO 3274 dimensioning of  
Figure 5: Scan track patterns on the exposed surfaces produced with meander and stripes scanning strategies before and after Upskin treatment.  
Figure 6: Layers of exposed surfaces treated with Upskin.  
2 \(\upmu\)m/60\({}^{\circ}\). The instrument was calibrated so that it can deliver up to 0.001 \(\upmu\)m accuracy. Each surface was measured five times, and the mean value of the arithmetical mean high of a line (Ra) and the mean maximum peak to valley of five consecutive sampling lengths of a line (Rz) were then calculated to minimize the measurement error. The projections of the planes of symmetry on the surfaces of the specimen, as can be seen in Figure 1, were used as measuring lines. Because the specimens were printed layer by layer from the truncated bottom to the top, these measuring lines can capture the best the layer nature of the surfaces.  
Subsequently, one post-treated specimen with the best 2D roughness from each scanning strategy pack was selected together with their as-built counterparts for 3D surface roughness assessment using the optical microscope Alicona InfiniteFocus 5G (IF MeasureSuite, Alicona ImagingGmbH, Raaba/Graz, Austria). The inspected surfaces were oriented perpendicularly to the microscope axis utilizing as well the designed stands. For 3D roughness measurement, CSN EN ISO 25,178 -2/-3 standards were followed. The focus variation method was described in the CSN EN ISO 25,178 -606 standard. Due to the nature of the optical microscope, the Sa and Sz, which are 3D extensions of Ra and Rz to surfaces, were measured once.  
The 2D and 3D surface roughness were calculated according to the CSN EN ISO 4287/4288 standards. Additionally, the normal and macrostructure images of the specimens were presented for a better understanding of the characteristics of the SLM 316L surfaces.
## 1 Introduction
In this study, ECG, ABP, and PPG signals were employed to assess existing methods and develop novel solutions for potential BP-related challenges, with the ultimate goal of facilitating the clinical application of this technology. Moreover, PPG can be utilized to remotely measure HR using just a smartphone's camera, eliminating the need for costly equipment typically found in wearable devices [16]. Wearable sensors are commonly used to monitor physiological signals and daily activities. However, in studies [17; 18], [PERSON] et al. endeavored to track them without direct contact, creating Internet of Things (IoT)-based systems. This work underscores the burgeoning significance of utilizing non-invasive methods to gauge physiological signals in the new era. This work contributes to the prevention of cardiovascular diseases (CVDs) by developing a cost-effective continuous blood pressure (BP) measurement system with improved accuracy. The paper also focuses on reducing data dimensionality while keeping important information. Furthermore, the proposed algorithm is suitable for clinical applications as well as everyday health monitoring devices. The remainder of this article is organized as follows:  
First, background and existing work are discussed in Section 2. In Section 3, the materials used in this work and the method are presented. Signal feature extraction is discussed in Section 4. Next, in Section 5, the deep learning methodology used in the experiment is discussed. In the results, dimensionality reduction and the multivariateregression are presented in Section 6. Discussion of the estimation models and limitations and future recommendations are presented in Section 7. Finally, this work is concluded in Section 8.
## 2 PV Modeling and Problem Formulation
### Problem Formulation  
The goal of the proposed optimization problem is to determine the unknown parameters of PV cells and modules accurately. An optimization algorithm is commonly employed to minimize the differences between the estimated and experimental I-V data obtained from the PV systems. Hence, as a rule, it is common to consider that minimization of the root mean square error (RMSE) is an objective function that should be considered when determining an estimate of the current:  
\[\text{Minimize RMSE}=\sqrt{\frac{1}{S}\sum_{s=1}^{S}\left(\hat{I}_{o,s}-I_{o, s}\right)^{2}} \tag{11}\]  
subject to  
\[x_{i,\text{min}}\leq x_{i}\leq x_{i,\text{max}};\ \forall i=1,\ 2,\ \ldots 5\ (\text{SDM and PVMM});\forall i=1,\ 2,\ \ldots,\ 7\ (\text{DDM}) \tag{12}\]  
\[I_{o,s}=x_{1}-\frac{\hat{V}_{o,s}+x_{4}\hat{I}_{o,s}}{x_{3}}-x_{2}\left[\exp \left(\frac{\hat{V}_{o,s}+x_{4}\hat{I}_{o,s}}{x_{5}v}\right)-1\right] \tag{13}\]  
\[I_{o,s}=x_{1}-\frac{\hat{V}_{o,s}+x_{5}\hat{I}_{o,s}}{x_{4}}-x_{3}\left[\exp \left(\frac{\hat{V}_{o,s}+x_{5}\hat{I}_{o,s}}{x_{7}v}\right)-1\right]-x_{2} \left[\exp\left(\frac{\hat{V}_{o,s}+x_{5}\hat{I}_{o,s}}  
Here, \(S\) is the amount of experimental paired sample data, while \(\hat{l}_{o,s}\) and \(l_{o,s}\) are the \(s\)th measured sample and the determined value of the PV output current, respectively. The constraints in Equation (12) indicate the upper (\(x_{i,\text{max}}\)) and lower (\(x_{i,\text{min}}\)) bounds on the PV parameters (decision variables). For the SDM and PVMM, the five unknown parameters are \(\mathbf{x}=\big{[}I_{p},I,R_{sh},R_{s},\ u\big{]}\), and the seven decision variables (i.e., \(\mathbf{x}=\big{[}I_{p},I_{1},I_{2},R_{sh},R_{s},\ u_{1},u_{2}\big{]}\)) should be defined for the DDM using an optimization technique. Finally, the calculated PV output current in each sample \(s\), \(l_{o,s}\), can be expressed using Equations (13)-(15) for the SDM, DDM, and PVMM, respectively.
## 3 Results  
### Basic Assessment  
Technically successful 3D-fused angiography-guided TIPS (\(n=27\)) or DIPS (\(n=8\)) procedures were performed in \(35/35\) (100%) cases (Figures 1-4). No immediate procedure-related complications were noted during or immediately after the TIPS or DIPS procedures in any of the 22 swine (\(0/35\); 0%).  
Figure 1: **CBCT-guided TIPS:** (**A**) procedure for the creation of a fusion image of CBCT, with the green dot (which was derived from CBCT reconstruction images) showing where the target (portal vein) was; (**B**) a fluoroscopic image of the pre-portal vein puncture for the 3 DFIG TIPS procedure, demonstrating the virtual path (green) of the portal vein puncture, the location of the needle tip in the hepatic vein (black arrow) with the expected puncture distance and the expected location of the portal vein (white arrow).  
Figure 2: **3D Fusion Processing:** (**A**) screenshots of the image software that was used to produce the three-dimensional fusion images of the pre-procedural MRs (yellow images) and peri-procedural MRs (yellow images).  
cone beam CTs and angiograms (grey images), including the virtual path (green) of the portal vein. TIPS puncture and the direction and distance of the puncture; (**B**) visualized needle in the hepatic vein, which could be overlayed onto angiograms in real time for portal vein puncture guidance, with the green dot denoting the target (portal vein).  
Figure 3: **iGUIDE DIPS:** a fluoroscopic image of the pre-portal vein puncture for the 3 DFIG TIPS procedure using pre-procedural CT and peri-procedural cone beam CT images, demonstrating the virtual path (green) of the portal vein puncture, the location of the needle tip in the IVC (white arrowhead) with the expected distance of puncture and the expected location of the portal vein (white arrow). As expected, the expected distance from the IVC to the portal vein was very short for the DIPS procedure.  
D Angiography-Guided TIPS Procedure Using Pre-Procedural Cross-Sectional Imaging with CBCT/Angiography-Fused Images  
The 3 DFIG TIPS procedures that utilized CT and MR were successfully performed using a single PV puncture attempt in 90% (9/10) and 90% (9/10) of cases, respectively, and using two attempts in the remaining cases. The mean total procedure time (TPT: time from access to the right internal jugular vein (RIJV) to access to the PV) was 22.4 \(\pm\) 5.1 and 18.7 \(\pm\) 3.1 min for the CT and MR 3 DFIG procedures, respectively. The median fluoroscopy time (MFT: fluoroscopy time between access to the RIJV to access to the PV) for both CT and MR 3 DFIG procedures was 4.0 min (IQR, 3.0-5.3). The total time required to create the 3D images (TTC3D: time from importing images to 3D fusion to the creation of the iGuide(r) map) was 8.5 \(\pm\) 3.4 and 7.5 \(\pm\) 2.7 min for the CT and MR 3 DFIG procedures, respectively (Table 1).  
Figure 4: **Portal Venogram: a 3 DFIG TIPS procedural angiographic image that demonstrates the path of the needle from the hepatic vein to the portal vein (green) and the successful portal venogram after gaining access to the portal vein with TIPS needle.**
## 2 Experimental
### Pellet's Diameter Measurement Using Image Processing  
The pellets' diameters were measured using image processing to acquire the appropriate size distribution of the pellets accurately. Pellets from each group were scattered on a white paper and placed in a single-layer white fabric tent to ensure uniform light intensity over the sample. They were then photographed with a 24-megapixel Nikon D3300 digital camera (Nikon Corp., Tokyo, Japan) under a fluorescent lighting system. All settings of the camera were on manual, with the following parameters: exposure mode manual, shutter speed 1/40 s, aperture f/4.0, and ISO sensitivity 350.  
Within the tent, the digital camera was positioned vertically 46 cm from the sample. The images were recorded in Nikon raw format (NEF) and transferred to a computer for further processing. Each pellet was segmented from the background based on histogram thresholding. The threshold, \(T=60\), was found to be the most suitable one for the segmentation of pellets. The binary image \([BW(x,y)]\) of the pellets could be defined as:  
\[BW(x,y)=\begin{cases}1&\text{if}\quad f(x,y)\geq T\\ 0&\text{if}\quad f(x,y)<T\end{cases} \tag{1}\]  
where \(f\) is the original image, \(x\), and \(y\) are coordinates of threshold value points, and \(T\) is the threshold value. Subsequently, any image pixel \(\geq 40\) was labeled as 1 (white), while all other pixels were classified  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Ingredients (g kg\({}^{-1}\))** & **TM0** & **TM75** \\ \hline Fish meal & 250 & 62.5 \\ Soybean protein concentrate & 120 & 120 \\ Wheat gluten meal & 150 & 150 \\ Corn meal & 100 & 100 \\ Soybean meal & 150 & 150 \\ Wheat meal & 62 & 54.8 \\ _Tenchio monitor_ meal & 0 & 187.5 \\ Fish oil & 80 & 83 \\ Vegetable oil & 60 & 63 \\ Mineral supplement & 10 & 10 \\ Vitamin supplement & 10 & 10 \\ Methionin & 8 & 9.2 \\ \hline \hline
**Proximate Composition** & & \\ \hline Dry matter (g 100g\({}^{-1}\)) & 90.0 & 90.3 \\ Crude protein (g 100g\({}^{-1}\)) & 44.9 & 44.7 \\ Ether extract (g 100g\({}^{-1}\)) & 17.3 & 17.1 \\ Ash (g 100g\({}^{-1}\)) & 5.4 & 4.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ingredients and composition of TM0 and TM75.  
as a background and were labeled 0 (black). Afterward, pellet diameters were measured (Figure 2). The pixel size of the projected scene was 0.0461 mm. It was calculated from the camera resolution and the distance between the camera and the sample. The accuracy of the diameter measurement is mainly influenced by the pellet edge localization (\(\pm\)0.5 pixel) and the image distortion close to the border of the image. The accuracy was estimated as \(\pm\)0.0461 mm. All images were processed using MATLAB image processing toolbox (MathWorks Inc., Natick, MA, USA).
# Challenges in Physical Layer Security for Visible Light Communication Systems
## 3 Vulnerabilities of Physical Layer Security in VLC Systems
### Beamforming
Although ZF precoding can minimize the SNR at the active eavesdropper site, ZF precoding for the nearby eavesdropper expands the dashed area in which the SNR is higher than that of the legitimate user. This vulnerability comes from the fact that the ZF design purely aims at forcing the active eavesdropper's SNR to zero without consideration for the legitimate user's SNR (or, indeed, SNRs of other unknown receivers in the area). Figure 2 verifies the vulnerability of ZF by depicting the secure outage probability (SOP) as a function of the distance between the legitimate user and the active eavesdropper. Note that a secure outage occurs only when one or more passive eavesdroppers can decode the transmitted message; while the SNR of the active eavesdropper is minimized by the ZF precoder. As the active eavesdropper approaches the legitimate user, the SOP increases, indicating that ZF precoding presents more wiretap opportunities to passive eavesdroppers located in the region. Our recent work [13] proposed an enhanced ZF beamforming scheme to mitigate the weakness from the presence of active and passive eavesdroppers; however, it cannot entirely eliminate the dashed area in which the eavesdroppers can achieve higher SNR. Figure 1: SNR according to receiver locations when zero-forcing (ZF) precoding is utilized. The \(10\times 10\) transmitter array consists of LEDs that are uniformly distributed on a square lattice in a room of \(20\times 20\) m\({}^{2}\). To the author's best knowledge, this weakness is inevitable due to the VLC's intrinsic characteristics that fading does not exist and that a received SNR mainly depends on the geometric properties of the transmitter and receiver.
## 4 Results and Discussion
### Influence of Travelling Speed on the Coating Removal for the Metal Wall  
The travelling speed of the wall-climbing robot can significantly influence the coating removal efficiency. However, if the wall-climbing robot runs too fast, the coating removal rate may be not so satisfactory. So, it is necessary to quantitatively investigate the relationship between the travelling speed and the coating removal rate. In this section, four levels of travelling speeds (1.5, 2.25, 3, and 3.75 m/min) were chosen and compared based on the water jet experiments. Figure 18 shows the surface condition for the four levels with the other experimental condition as follows: the water pressure is 120 MPa, the stand-off distance is 15 mm and the inclined angle is 10 degree. When the travelling speed is lower, the surface condition is cleaner. Figure 19 also shows the measured coating removal rate for the four cases. Results show that the removal rate of the coating increases with the decreased travelling speed. However, it should be noted that lower travelling speed means lower cleaning efficiency as shown in Table 1. So, considering both the efficiency and coating removal rate, the proper travelling speed for this study should be 2.25 m/min.  
Figure 19: Coating removal rate for the three pressure levels.  
Figure 18: Surface condition under various travelling speeds **(a)** initial state; **(b)** 1.5 m/min; **(c)** 2.25 m/min; **(d)** 3 m/min; **(e)** 3.75 m/min.
## 2 Related Work
### Traditional-Based  
Traditional insider threat detection relies on predefined rules and static policies, often employing methods such as log activity monitoring, rule-based IDS, and Endpoint Detection and Response Solutions (EDR) [8; 9; 10]. Anomaly detection tools such as XABA [11] and scoring-based activity log analysis [12] use predefined keywords and scoring mechanisms; however, their dependence on manual thresholds and analyst intervention introduces performance bottlenecks and limits adaptability to novel threats.  
Signature-based IDS, such as SNORT, Suricata, and Zeek rely on matching known malicious patterns, but are vulnerable to zero-day attacks [13; 14; 15; 16; 17; 18; 19]. These IDS tools have limited capabilities against previously unseen threats, as they rely on a fixed database of known signatures. While combining IDS tools with other network analysis tools such as Wireshark can enhance detection efficiency, reliance on predefined rules limits adaptability [20].  
Approaches such as Corporate Insider Threat Detection (CITD) [21] and tree-structured activity profiling [22] aim to reduce false positives by incorporating analyst feedback. However, manual intervention introduces challenges around scaling and reduces the efficacy of real-time analysis. Adversarial Risk Analysis (ARA) models [23; 24] provide a structured approach for insider threat detection; however, their static nature limits adaptability. Methods based on recording user inputs, such as keyloggers [25], face privacy concerns and reduced efficiency. The two-step insider detection approach proposed by [26] further illustrates the challenges of balancing accuracy and adaptability in traditional methods.  
Overall, traditional methods suffer from high false positives, static adaptability, and dependence on human intervention, making them less effective for dynamic and evolving threats.
## 3 Results
### Research Results and Analysis of Carbon Emission Risk of Land Use
#### 3.3.1 Analysis of Calculation Results of Carbon Emission Risk of Land Use
To accommodate urban development, vast tracts of land were developed, leading to a substantial reduction in natural ecological areas with strong carbon sink capabilities, such as agricultural land, forests, and wetlands. This degradation of the ecological system significantly impaired its capacity to absorb and regulate carbon emissions. The carbon emissions generated in these regions had approached or even exceeded the carbon absorption capacity of the local ecological system, posing an extremely severe challenge to the environmental sustainability of the entire region. The severe-risk zone included one county or district, with the proportion of the number of counties and districts being 4.55%. The county or district in this zone faced the highest land use carbon emission risk. In order to pursue rapid economic development, natural resources had been over-exploited. For example, the disorderly mining of mineral resources not only damaged the surface vegetation but also led to a series of ecological problems such as soil erosion, destroying the originally relatively stable ecological environment. Additionally, in the absence of proper planning and comprehensive environmental protection measures, industrial production has been characterized by large-scale, unregulated expansion. Many high-pollution and energy-intensive enterprises have recklessly discharged pollutants, leading to unchecked energy consumption and a dramatic increase in carbon emissions. Consequently, a severe imbalance in the carbon cycle has emerged. Urgent measures must be implemented to mitigate carbon emission risk and restore the ecological balance of the region. From 2010 to 2015, in the study area, two counties were transferred from micro-risk areas, two counties from low-risk areas, five counties from moderate-risk areas, four counties from high-risk areas, and one county from severe-risk areas, with obvious changes. From 2015 to 2020, there were minor adjustments, with one county moving from the moderate risk category and another shifting from the severe risk category, resulting in minimal overall change. From 2010 to 2020, the proportion of micro-risk areas decreased from 27.27% to 18.18%, while low-risk areas increased from 9.09% to 18.18%. Additionally, the proportion of moderate-risk areas dropped from 40.91% to 22.73%, and the share of high-risk areas rose significantly from 18.18%. This increase in high-risk areas can be attributed to the rising carbon emissions resulting from construction, transportation, and other urbanization-related activities. This trend highlights the need for the government to enhance emission reduction policies and implement ecological restoration measures in these regions to address the challenges posed by carbon emissions from land use.
## 2 Materials and Methods  
### Service Quality in the Wireless Industry  
In terms of factors that determine service quality, [PERSON] and [PERSON] [8] defined service quality as the proximity of the service provided to the customers' expectations, and [PERSON], et al. [9] suggested measurement of service quality as the gap between customer's pre-service expectation and performance based on the SERVQUAL model. However, in the case of communication service, [PERSON] and [PERSON] [10] identified that service quality can be measured sufficiently by only looking at performance. Following [PERSON] and [PERSON] [10], this study proposes to use performance, without customers' pre-service expectations, as output factor.  
On the other hand, this study identifies _purchase experience_ and _customer care_, highlighted by [PERSON] and [PERSON] [11], as the outcomes (outputs) of service quality. These two elements are related to customer satisfaction, which is affected by service quality and contribute to the loyalty of customers as well as the product or service's profitability [12; 13]. A number of scholars have reported that better service quality leads to greater customer satisfaction to improve market share and significantly affect customers' intention to revisit [14; 15; 16]. To increase customer satisfaction, wire carriers have been allocating a good deal of their budgets to sales promotion [17] as an effective sales tool in reflection of greater market competition and customers' growing preference for cheaper services.  
At the same time, scholars have pointed out that customers' preference towards sales promotion benefits can differ by customer [18], that is, not all customers think cheaper is better. [PERSON], et al. [19] argued that non-instrumental, empirical, and emotional benefits are central in sales promotion. Purchase experience and customer care influence service quality by providing customers benefits beyond price: purchase experience by enhancing all touchpoints between the customer and the brand and customer care by building customer trust. Thus, [PERSON], et al. [20] stipulated that these two elements are important service quality factors that enable firms to maintain customer preference and loyalty in the long run. In other words, the expanding or preserving market share entails the management of purchase experience and customer care at the service interface to manage and improve service quality [21].  
The indices used for purchase experience and customer care have been extracted from J.D. Power's \"U.S. Wireless Customer Care Full-Service Performance Study\" and \"U.S. Full-Service Wireless Purchase Experience Study\". J.D. Power is a global market research company that conducts surveys on customer satisfaction, product quality, and buyer behavior and provides indices on related topics.