esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Acquisition of Blood Velocity, Image Intensity, and Interface  
As shown in Figure 1(A-iii), the blood velocity in the blood channel and interface in the co-flowing channel were obtained by digital image processing using MATLAB 2022a (Mathworks, Natick, MA, USA). First, a specific region of interest (ROI; 200 \(\upmu\)m \(\times\) 550 \(\upmu\)m) was selected within the blood channel, and blood velocity fields were obtained using time-resolved micro-particle image velocimetry (micro-PIV) [30]. The interrogation window was set to 64 pixel \(\times\) 64 pixel; one pixel corresponded to 3.3 \(\upmu\)m. The window overlap was set to 75%. Based on the analytical formula suggested in a previous study [31], the depth of correlation (DOC) was estimated to be 64.1 \(\upmu\)m. As the DOC was larger than the channel depth (\(h=50\)\(\upmu\)m), it was assumed that the velocity fields remained unchanged along the channel depth. The blood velocity (\(U_{b}\)) was obtained by arithmetically averaging the velocity fields distributed over the ROI. The blood flow rate was then estimated as \(Q_{b}=U_{b}\times A_{c}\), where \(A_{c}\) denotes the cross-sectional area of the rectangular channel (i.e., \(A_{c}\) = width \(\times\) depth). Additionally, based on the same ROI in the blood channel, the image intensity of the blood flow was obtained as \(I_{b}\). Finally, to obtain the interface in the co-flowing channel, a specific ROI (400 \(\upmu\)m \(\times\) 550 \(\upmu\)m) was selected downstream from the junction area of the two streams. A grayscale image was converted into a binary image using the Otsu method [32]. The blood-filled width (\(w_{b}\)) was obtained by averaging variations in the blood-filled width within the ROI. The interface (\(\alpha_{b}\)) was then obtained as \(\alpha_{b}=w_{b}/w\).
###### Abstract
In a GNSS related field, L-S was used for coordinate time series research [31], studies on the tropospheric delay [32], and for earthquakes [33]. The CWT time\(-\)frequency analysis is commonly used for numerous field, e.g., image processing [34], signal filtering [35], astronomy [36], and geophysics [37]. For applications connected with GNSS data processing, CWT is utilised in the time series analysis for denoising and detrending [38], or for GNSS signal multipath analysis [39; 40]. Herein, the authors analysed in a comprehensive way BeiDou satellites clock corrections and their periodic variation using the Lomb-Scargle periodogram [29] and CWT methods [41; 42; 43]. In the article, we present the analysis of recently available data concerning the BeiDou satellite clock. The authors prepared six steps vis a vis the pre-processing and period characteristics for each BDS satellite. In the Material and Methods section, the data used for analysis are described along with the proposed processing methodology. Examples are presented in the Results and Discussion section, and the results for all of the analysed satellites are presented in Appendices C and D. The last section summarizes our analysis and indicates some issues for further analysis.
## 3 Design and Analysis
### Thermal-Via Structure  
To assure the proposed AOC has high heat-dissipation efficiency, the structure of the thermal vias was designed and optimized. Figure 3 shows the cross-sectional view of the thermal vias in a multi-layer substrate. The multi-layer substrate consists of eight organic layers with a total thickness of 0.8 mm. Laser via holes (LVHs) and interstitial via holes (IVHs) are located in the build-up layers (with thickness of 0.05 mm/layer) and the core substrate (with thickness of 0.5 mm), respectively. The LVHs are fabricated by laser-beam machining and filled with copper. The IVHs are fabricated by drilling, and their side walls are formed by copper plating and filled with resin. In addition, a thermal sheet is used to fill in the gap between the substrate and the case. By applying and optimizing this heat-dissipation structure, heat directly and efficiently dissipates to the topside of the case through the thermal vias as shown in Figure 3. Therefore, the proposed AOC allows the optical devices to operate higher temperate.
## 3 Experimental Investigation
### Evaluation of the Initial Numerical Model
#### 4.1.2 Evaluation Based on the Structural Dynamic Properties  
To determine how the developed numerical model describes actual structural behaviour, a comparison of natural frequencies was carried out (Table 6). According to the results of the comparison of the natural frequency values (Table 6), the numerically predicted structural behavior deviates significantly from the actual one. Thus, in the following section the initially developed numerical model is updated in order to better correspond to the actual structural behaviour.
## 2 Materials and Methods
### Sample Preparation
#### 2.1.2 Anti-Reflective Coating  
After the precision cleaning, an anti-reflective coating was deposited onto the substrates by means of ion beam sputtering using a Veeco Spector 1.5 system located in a cleanroom environment (ISO 5-7). As layer materials, SiO\({}_{2}\) (\(n\) = 1.482 @ 1064 nm) and Ta\({}_{2}\)O\({}_{5}\) (\(n\) = 2.087 @ 1064 nm) were used. The multi-layer AR coating was designed for 1064 nm and normal incidence (angle of incidence AOI = 0\({}^{\circ}\)) starting with a 2 QWT-layer SiO\({}_{2}\) (sub/2L 0.64H 0.44L 0.23H 0.59L/air). Before coating, the coating chamber was pre-heated to 105 \({}^{\circ}\)C, and the temperature was kept constant during the thin film deposition. To avoid any influence of layer thickness distribution in the coating chamber, the substrates were placed at equal positions in the substrate holder. Figure 1 shows the AR-coated test sample's measured reflectivity with R < 0.1% at 1064 nm.
# Performance Optimization of Object Tracking Algorithms in OpenCV on GPUs
## 3 Analysis of OpenCV Object Tracking Algorithms
### Object Detection Algorithm
#### 3.1.1 Execution Flow  
Object detection in OpenCV detects objects from images by utilizing information about the target objects. Its execution flow is shown in Figure 5a. The host program receives frames from video, images, and webcams to detect objects. To facilitate image detection, three functions, cvtColor, resize, and equalizeHist, preprocess the input frames. cvtColor converts the input frames into grayscale images. resize modifies the image size, and equalizeHist equalizes the histogram. It finally calls one of the two classifiers, Haar and LBP, which are the core algorithms of object detection.  
Figure 5: Flow chart of OpenCV computer vision algorithms. (**a**) Object detection algorithm. (**b**) Optical flow algorithm.  
Since OpenCV adopts transparent APIs [12], the host functions invoke the corresponding OpenCL kernels if the OpenCL platform is available on a system. The OpenCL kernels associated with the host functions are also depicted in the figure. Hence, cvtColor does not work on the CPU, but runs the RGB2 Gray kernel on OpenCL computing devices. Furthermore, resize executes either the resizeLN or resizeSampler kernel. If the OpenCL sampler is available, it performs resizeSampler; otherwise, it performs resizeLN. equalizeHist calls multiple OpenCL kernels internally, but we omit detailed descriptions.  
After the preprocessing, detectMultiScale finally calls the actual object detection algorithm. Using the Haar class launches the runHaarClassifier kernel, and using the LBP class executes the runLBPClassifierStumpSimple kernel. After the above process, the object detection result is obtained from the input image, such as size and coordinates.
## 2 Data and Overall System
### Signal Processing Framework  
A complete and standard framework was employed in this study. The overall framework included the following steps (shown in Figure 2): raw data acquisition, pre-processing (including data truncation, baseline wandering removal, and cycle alignment), algorithm implementation, signal reconstruction, and display. Data from different pulse-taking depths were truncated and selected before pre-processing. Then, a Butterworth high-pass filter (cut-off frequency: 0.5 Hz) was performed to remove the low-frequency noise of the measured multi-channel signals, and the baseline drift was mainly eliminated by cubic spline estimation [27]. Multi-channel raw data were partitioned into segments, each corresponding to a complete heartbeat cycle. For every channel, these segments were scaled and aligned along the time axis and composed into a group. After pre-processing, multiple groups  
Figure 1: A three-dimensional pulse image (3 DPI) at peak amplitude. Upper left: stereoscopic 3 DPI; upper right: planar 3 DPI; lower: data acquisition by a PPS tactile sensor.  
of one-dimensional temporal pulse waves were sent into the algorithm implementation module. The algorithm implementation module proposes a signal de-noising and feature extraction algorithm with interchannel weights to decompose input data matrices into signal and noise matrices. Subsequently, to evaluate the performance of the proposed cross-channel DWRPCA algorithm, we analyzed the signal features and noises extracted from the original signals from the aspects of the time domain and frequency domain. Finally, the filtered signals were reconstructed into a complete multi-channel signal with less noise and displayed in the form of 3 DPI.
## 5 Discussion
### Research Gaps
#### 5.1.3 AI Techniques' Challenges and Dataset Shortage  
Based on the analysis of the third research question, several challenges have been identified using AI techniques to identify APTs in smartphone sensors. Nevertheless, AI techniques do not stop zero-day and advanced threats. Various researchers have adopted an array of AI approaches, including machine learning, genetic algorithms, game theory, deep learning, and neural networks. The AI is built on learning from previous examples of malicious software, specifically the malicious software's appearance and behavior.  
Novel threats have arrived in the form of zero-day exploits and sophisticated attacks. Currently, APTs are equipped with unique evasion strategies and new methods to activate APIs, besides inventive access approaches to system resources. Although certain APT activities may be sufficiently similar to prior occurrences for Als to detect them, entirely novel approaches have no such past event. Real defense against advanced and sophisticated threats should not rely on previous infections or attacks [196].  
Most research has used AI techniques to enhance malware detection's efficiency or accuracy. The priority of cyber threat intelligence (CTI) has recently turned to preventing accidents. Detecting an attacker's malicious behavior is a different essential technique. Determining whether the discovered result may be expressed as a criterion for responding to a threat is a significant obstacle [26]. Nevertheless, it must have access to appropriate datasets to investigate APTs on smartphone sensors.  
The lack of smartphone datasets is apparent from the existing literature. Resultantly, evaluating and detecting security risks and threats are unfeasible without relevant datasets. This situation has proven to be a major challenge in implementing AI for cyber security [197].
## 3 Numerical Examples
### Numerical Examples of Homogeneous Material
Next, the mesh density N1 \(\times\) N2 is fixed at 100 \(\times\) 200 to analyze the convergence of the refined mesh of the crack tip. The effect of Nr varying from 2 to 50 on the stress intensity factor is examined. As shown in Figure 7b, when Nr > 50, the computed value stabilizes, with an error of less than 1% compared to the theoretical solution. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**N1 \(\times\) N2** & **8 \(\times\) 16** & **10 \(\times\) 20** & **34 \(\times\) 66** & **100 \(\times\) 200** & **166 \(\times\) 334** \\ \hline \(K_{\rm I}/K_{0}\) & 2.797 & 2.801 & 2.819 & 2.820 & 2.821 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Normalized SIFs for different mesh sizes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\beta\) [\({}^{\circ}\)] & **Type** & **Present** & **Theoretical** & **ABAQUS** & **Error A \({}^{\ddagger}\) [\%]** & **Error B \({}^{\ddagger}\) [\%]** \\ \hline
22.5 & \(K_{\rm I}/K_{0}\) & 0.745 & 0.735 & 0.745 & 1.4 & 0 \\  & \(K_{\rm II}/K_{0}\) & 0.279 & 0.272 & 0.280 & 2.6 & 0.4 \\
45 & \(K_{\rm I}/K_{0}\) &  
Through the verification of these two aspects presented above, it can be demonstrated that the present method possesses good mesh convergence in calculating fracture parameters at the crack tip. Since the interaction integral is obtained by superimposing J-integrals, it should satisfy the domain-independence for different integration domains. To verify the domain-independence of the present method, the mesh and example in Figure 4 are used to calculate the stress intensity factors for different integral domains. As shown in Figure 8, \(R\)tip is the element length at the crack tip and \(R\)c is the radius of the circular integration domain. The stress intensity factor is calculated for five integration domains with \(R\)c/\(R\)tip ratios of 5, 8, 10, 15, and 20. The normalized factor is selected as \(K_{0}~{}=~{}\sigma\sqrt{\pi a}\). Table 4 lists the normalized stress intensity factors obtained from these domains. The relative error is defined as follows:  
\[E_{\rm rr}~{}=~{}\left|\frac{K_{\rm max}-K_{\rm min}}{K_{\rm mean}}\right| \times 100\%, \tag{29}\]  
In the equation, \(K_{\rm max}\), \(K_{\rm min}\), and \(K_{\rm mean}\) represent the maximum, minimum, and average values of the stress intensity factor, respectively. The calculation of Table 5 shows that the relative error of the stress intensity factor from different integration domains is within \(\pm 0.11\%\). This indicates that the interaction integral computed by the present method has good domain-independence.
# Industry 4.0 and World Class Manufacturing Integration: 100 Technologies for a WCM-I4.0 Matrix
## 7 Discussion: I4.0 and WCM Synergy
### Industry 4.0 Impact on Professional Maintenance Pillars  
I4.0 includes the concepts of monitoring, control and implementation, prediction of results, autonomous calculation and classification, concepts that are well suited to the needs of the PM pillar.  
In fact, the implementation of I4.0 technologies can bring significant improvements to PM pillar performance:  
* MTTR reduction: Machines can autonomously warn the maintainer about the presence of the fault by reporting descriptive information of the faulty component; thanks to Machine Learning techniques, machines can learn from previous faults and remember their precedent behavior; virtual training can reduce MTTR and augmented reality can guide replacement operations;
* PM Calendar execution: The maintenance technician examining the various machines in few seconds can get information on the status of the PM Calendar thanks to augmented reality or to a device connected to the network; the Standard Maintenance Procedures (SMP) can be tested in a virtual environment or can be guided by means of augmented reality glasses;
* Predictive Maintenance: Through suitably positioned CPS and software able to perform computations on complex predictive mathematical models or with the help of Neural Networks and Deep Learning algorithms, it is now possible to have real-time information on the health status of the component, showing the maintainer the estimated residual life via mobile device or through augmented reality glasses; it is possible to receive notifications about the current status of the component, and an alarm before an expected failure.  
Summing up, the result is a possible decrease in maintenance costs (in accordance with the logic of the last steps of the pillar) due to the presence of precise and reliable information that guarantees the minimum sum of spare parts and failure costs.
# A Comparative Study on the Schedulability of the EDZL Scheduling Algorithm on Multiprocessors
## 4 Comparing EDZL and EDF\({}^{(k)}\)
### Success Ratio  
This section compares the performance of EDZL and EDF\({}^{(k)}\) scheduling in terms of the number of successfully scheduled test instances. In soft real-time systems, a deadline miss does not cause system failure but merely degrades the system's operation. In such systems, a test instance that does not pass the schedulability test might be executed. In this experiment, all test instances are simulated without a schedulability test. For this experiment, we extended the custom simulator (explained in Section 3.3) to perform EDF\({}^{(k)}\) scheduling. And the simulation was performed with the same dataset used in Section 3. Then, we compared the results with the EDZL scheduling simulation results.  
To begin with, we compare the performance of the EDZL and EDF\({}^{(k)}\) algorithms from a quantitative perspective. Similar to the evaluation in Section 3.1, we count the number of test instances successfully scheduled by EDZL and EDF\({}^{(k)}\), respectively, for each task utilization interval. Then, we calculate their ratio, which we define as _success ratio_. Figures 7-10 show the success ratio when \(m=2,3,4,5\), respectively. The figures demonstrate that EDZL can successfully schedule more high-utilization task sets than EDF\({}^{(k)}\). For example, when \(m=4\), EDF\({}^{(k)}\) begins to decrease at a utilization of about 3.2, EDZL begins to decrease at a utilization of about 3.8.  
As mentioned in Section 3, the number of all test instances is 1,000,752,406 and EDZL successfully scheduled 990,451,970 instances out of these. In our EDF\({}^{(k)}\) scheduling simulations, EDF\({}^{(k)}\) successfully scheduled 919,989,568 instances. EDZL can successfully schedule 7.0% more instances than EDF\({}^{(k)}\). We also discover the dominance between the EDZL and EDF\({}^{(k)}\) algorithms. We investigate the simulation results of both algorithms and we found out that the two algorithms do not dominate each other. Task set \(\{(5,8),(1,2),(3,6),(3,8)\}\) can be successfully scheduled by EDF\({}^{(k)}\) on two processors, i.e., all deadlines are met for a hyper-period in the simulation. However, EDZL fails to successfully schedule this task set; in its EDZL scheduling simulation, a deadline miss occurs at time 24. Thus, EDZL does not dominate EDF\({}^{(k)}\). On the other hand, task set \(\{(2,3),(3,5),(1,3),(2,6)\}\) can be successfully scheduled by EDZL on two processors, but not by EDF\({}^{(k)}\). EDF\({}^{(k)}\) does not dominate EDZL.  
Figure 8: Success ratio when \(m=3\).  
Figure 7: Success ratio when \(m=2\).  
Figure 9: Success ratio when \(m=4\).
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 2 Metal Sulfide Thin Films
### Nickel Sulfide Films  
Preparation of p-type NiS films took place on a single crystal Si (111) wafer [67]. The band gap and the activation energy values were measured to be 0.45 and 0.15 eV, respectively. The band gap values of NiS films produced by using thiourea were 3.12, 2.93, and 2.81 eV when the deposition cycles were 4, 6, and 8, respectively [68]. The band gap values of Ni\({}_{3}\)S\({}_{4}\) films were 2.8 and 2.15 eV for 6 and 8 cycles, respectively. The depositions of NiS films took place by using nickel (II) nitrate (Ni(NO\({}_{3}\))\({}_{2}\)) and sodium sulfide at 27 \({}^{\circ}\)C, under unstirred conditions [69]. The XRD studies revealed these films were hexagonal phases, with film thicknesses of 350 nm. Two strong peaks (3480 and 1985 cm\({}^{-1}\)) could be observed in the FTIR spectrum, indicating C-H stretching modes. Based on the optical absorption investigations, the average optical transmittance and reflectance were 0.62% and 1.1%, respectively.
## 5 Conclusions  
SLM technology is being widely used in different sectors as an interesting manufacturing technique to create prototypes and/or small productions of complex products. Hence, it appears to be an appropriate technique for the \"ad-hoc\" manufacture of porous media, particularly wicks for the heat pipe and loop heat pipe industry, where outstanding capillarity as well as high permeability is required. This technology generates fully controlled internal passages inside the wick's body, with the desired geometry and pore size according to detailed specifications. A new SLM wick was manufactured and both the capillary action and permeability were addressed through different tests. The results have been compared to those obtained for wicks manufactured with conventional techniques: sintered powder and meshes (widely used nowadays in LHP manufacturing).  
The first test was performed to measure the liquid pumping capacity and capillary action of wicks created by different techniques (sintering, mesh, SLM). The wick specifications were a speed of advance of 0.031 mm/s at a height of 30 mm imposed by a particular 80 W LHP application. It was concluded that the faster the ascent of the fluid, the better the wick performance. With regard to capillary action, the SLM wick (scaffold) presented a 23% faster response than the sintered powder, although the stainless-steel mesh offered the fastest response.  
Regarding permeability, scaffold samples presented values of two orders of magnitude higher than that of the sintered powder, showing its lower resistance to fluid flow. In this study, the hydraulic behavior of the scaffold design was superior to the behavior of sintering copper, suggesting that it is more suitable for implementation in the 80 W LHP application.  
Finally, it has been concluded that SLM technology can easily and precisely create the wick structure that best fits the heat pipe requirements in terms of fluid pumping, capillary action, and permeability.  
[PERSON] and [PERSON] conceived and designed the whole experimental procedure and assisted with planning trials and the interpretation of results. [PERSON] and [PERSON] provided constructive instructions in the methodology used for the preparation and post-processing of this study, supervised the manuscript, and were responsible for funding acquisition.  
This research received no external funding.  
The authors are very grateful to the Navarre Government for its continuous support of the research and development program at NAITEC, and to the Basque Government for support through projects IT781-13 and IT1314-19, respectively.  
The authors declare no conflict of interest.  
A\({}_{1}\), A\({}_{2}\)Constants of the fluid for Equations (3) and (4) and Tables 3-5, respectively  
AM Additive manufacturing  
CAD Computer aided design  
d Diameter (mm)  
D Average pore size (\(\mu\)m)  
EDS Energy dispersive spectroscopy  
H Maximum height of capillary (m)  
h\({}_{\mathrm{fv}}\) Vaporization latent heat (kJ/kg)  
IR Infra Red  
L Permeability length (mm)  
L Happ Loop heat pipe  
PC Personal Computer  
PM Porous media
## 6 Discussion, a Perspective, and an Outlook for Future Research
### Business Continuity  
Concerning BC, an overview of the various concepts of BC is presented in Table 3, considering the focus or object that characterises the concept. This analysis aims to provide the various perspectives by which the literature defines BC. Thus, \"business continuity\" is associated with several research objects.  
Because of the variety of research objects exemplified in Table 3, the definition of BC is not unique. BC is a relatively modern management discipline derived from disaster recovery in the 1980s, which began in the mid-1970s [60]. Regarding the perspective of resilience and the organisation's capacity to deal with a disaster, some authors refer to this capacity in their definition.  
Figure 7: Number of publications by extraction data question. Source: own development (2023).  
The studies in Table 3 present concepts of resilience, disaster, incident, crisis, and emergency as differentiators of the organisation's response in terms of installed capacity, necessary preparation and readiness, and inherent risk. These differences highlight the need for different strategies for different planning and affect the organisation's BC differently.