esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study Results
### Results of BPriS Method Utilization with Holistic Approach
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**PBP** & **PBP Type** & **First Priority SF** & **Second Priority SF** & **Precondition SF** \\ \hline \multirow{6}{*}{Finance (Billing)} & & & & Coding tables data input: types of documents, type of payment, banks, patients, patient’s employment companies \\  & Input & Billing data input & Bill print & Employment companies \\  & & & & Data input from previous business process: medical \\  & & & & records—out of scope \\ \hline Quality management and reporting (in financial domain) & Processing & Monthly billing report processing & Monthly billing report print & Billing data input \\ \hline \multirow{6}{*}{Finance (Billing)} & & & & \\  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \end{tabular}
\end{table} 
Table 5: Mapping table as a result of BPriS method in the case study project. Table 6 presents results of phase 4 and consists of columns related to primitive business processes (PBP), software functions (SF), order of work items application (OR) and work item (WI). In this case study, multi-layered architecture is selected as a basis for work items list pattern from initial examples of work items patterns derived from most commonly used software architectures presented at Table 3. In this multi-layered WILP, there are four layers and in the BPriS methodology it is proposed that these layers should be developed with a bottom-up approach. Therefore, DataLayer with CRUD operations should be developed first, then service layer, business logic layer and finally presentation layer. At the level of set of software functions that support each business process, first priority in implementation, i.e., work items list is assigned to precondition software functions, then the \"first priority\" software functions that directly support the business process and finally the \"second priority\" software functions should be implemented. In this particular example, for the first business process (Finance--\"Billing\"), the order or software functions development should be (1) coding tables software functions development (example for coding table \"types of documents\" and others), (2) billing data input, (3) Bill print. The same principle is used with the second business process in this example, except that the precondition software function was already listed to be implemented, so only the \"first priority\" software function of report calculation and \"second priority\" software function of report print were included in Table 6 with appropriate DA-FOWIL items.
## 5 Experimental Results and Analyses
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Problems** & **ddfw** & **Paws** & **Yalsat** & **probSAT** & **ddfw+dynInitWeight** & **Score2 SAT** & **Ratio** \\ \hline
4 blocks.cn & 8.71 & 2.67 & 1.67 & 0.28 & 0.45 & 0.51 & 19 \\ ais12.cnf & 0.65 & 0.10 & 1.66 & 7.10 & 0.09 & **0.02** & 7.2 \\ BMS-499 & 0.17 & 0.04 & **0.00** & 0.06 & **0.00** & **0.00** & \(\approx\)20 \\ bw\_large.c & 0.00 & 0.63 & 5.81 & 1.99 & **0.00** & 0.25 & n/a \\ bw\_large.d & 0.19 & 1.22 & 9.22 & 7.71 & **0.09** & 1.7 & 2.1 \\ CBS & 0.04 & 0.10 & 0.04 & **0.03** & 0.00 & 0.00 & \(\approx\)4 \\ f800-hard & 0.56 & 0.46 & 1.47 & 0.47 & **0.13** & 2.94 & 4.3 \\ f1600-hard & 6.15 & 5.02 & 5.18 & 2.74 & **0.52** & 3.79 & 11.8 \\
2000 & 3.92 & 87.80 & 0.56 & **0.42** & 0.95 & 6.45 & 4.1 \\ fla-barthe & 0.23 & 0.12 & 0.41 & 0.10 & **0.07** & 0.09 & 3.3 \\ flat200-har & 0.30 & 0.83 & 8.72 & 1.82 & **0.03** & timed out & 10 \\ g125.17.cn & 0.55 & **0.02** & 8.75 & timed out & 0.09 & 3.57 & 6.1 \\ g125.18.cn & 0.02 & 0.02 & 0.02 & 11.67 & **0.01** & 0.45 & 2 \\ huge.cnf & 0.17 & 0.02 & 0.01 & 0.00 & 0.00 & 0.00 & \(\approx\)17 \\ logistics. & 0.00 & 7.25 & 0.52 & 0.07 & **0.00** & 0.03 & n/a \\ u400-hard & 3.13 & 0.72 & 9.01 & 2.12 & 0.73 & **0.41** & 4.3 \\ uni-K5-v200 & 3.34 & 21.30 & 4.37 & 2.20 & **0.65** & 1.28 & 5.1 \\ uni-K7-v98 & 20.30 & 26.50 & 32.55 & 18.66 & **4.56** & 6.76 & 4.5 \\ uni-K7-v102 & 90.25 & 81.30 & 110.51 & 85.46 & **42.15** & 63.51 & 2.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results for the DIMACS problems set. uf400-hard, where the performance was an order of magnitude better. Also, DDFW+Dynamic initial weight assignment performed four times better than DDFW on the unified k5-v200 and k7-v98 and twice better on the k7-v102 problem. Moreover, DDFW+Dynamic initial weight assignment, when compared to other solvers, performed better in 50% of the overall problem sets and performed similarly with the other solvers on the other problem sets. Our dynamic mechanism's significant performance enhancement could be noticed on large problems such as the g125.17 and problems known to be hardest to solve, such as the f1600-hard, f800-hard, the uf400-hard, the bw_large.d and the uniform problems k5 and k7. Which support our preliminary assessment such that small clauses with either, small or large neighborhood are hard to be satisfied, and take a longer time to build weights. So when they are dynamically assigned the initial weight as per our mechanism, the performance is enhanced.
# Wideband Eight-Antenna Array Designs for 5G Smartphone Applications
## 4 Hand Effect on the Proposed Eight-Antenna Array
### Single-Hand Mode (SHM)  
The relative positions of the user's hand in SHM and the smartphone drawn in the simulation software 2021 HFSS (High-Frequency Structure Simulator) are shown in Figure 15. The reflection coefficient outcome of SHM is illustrated in Figure 16a. It can be observed that the antenna bands tend to shift towards lower frequencies. However, most antennas still cover the bands-of-interest, with only a few antennas not fully covering the target bands. Figure 16b shows the total efficiency of the proposed antenna affected by the single-hand mode. Among them, Ant.1 and Ant.5, which are farther from the hand, are less influenced by hand interference (efficiency > 60%). Other antennas such as Ant.2, Ant.3, Ant.4, Ant.6, and Ant.8 exhibit efficiencies above 30%, while Ant.6 shows an efficiency  
Figure 14: Calculated channel capacity (CC).  
Figure 13: ECC values of proposed array (Ant.1–Ant.4 only): (**a**) simulation and (**b**) measurement.  
between 20% and 25%. In this mode, Ant.6 consistently shows poorer antenna efficiency. The reason for this is that Ant.6 is located at the position of the thumb c  
Figure 16: Single-hand model (**a**) reflection coefficients and (**b**) total efficiency.  
Figure 15: Diagram of the single-hand model relative to the smartphone via HFSS.
## 2 Materials and Methods
### Quantification of Ascorbic Acid  
The pH of 10 mL of orange juice was adjusted to 2.5 with o-phosphoric acid (85% solution). The mixture was centrifuged for 5 min at 4000 rpm, and the supernatant was filtered at 0.2 \(\upmu\)m (regenerated cellulose, with a diameter of 15 mm) and used for direct analysis with HPLC or stored immediately at \(-18\)\({}^{\circ}\)C for a maximum of ten days.  
For the bananas, the extraction medium consisted of 3% meta-phosphoric acid, 0.05% EDTA disodium salt, and 8.4% glacial acetic acid, in distilled water. Of the peeled banana pieces, 15 g were mixed with 25 mL extraction medium for 30-60 s using a Polytron (PT 10-35 GT, Kinematica AG; Luzern, Switzerland) while being cooled with ice. The mixture was shaken in a bench shaker for 5 min at 800 rpm under light protection, using a pre-cooled block before being centrifuged for 3 min at 4000 rpm. The supernatant was stored in the dark on ice, while the residue was extracted for a second time with 10 mL of extraction medium (bench shaker, 800 rpm, 5 min). After centrifugation for 5 min at 4000 rpm, the two supernatants were combined, filtered at 0.2 \(\upmu\)m (syringe filter, regenerated cellulose, with a diameter of 15 mm), and analysed directly by HPLC or stored immediately at \(-18\)\({}^{\circ}\)C for a maximum of ten days.  
Ascorbic acid quantification was conducted with an Agilent 1260 Infinity Binary LC system coupled with a 1260 DAD detector. The separation of the analytes was performed using an Agilent Poroshell 120 EC-C18 (2.1 \(\times\) 5 mm, 2.7\(\upmu\)m) column. The flow rate was set at 1.5 mL/min and the column temperature at 20 \({}^{\circ}\)C. The mobile phase A consisted of 20 mM monobasic potassium phosphate buffer (pH 2.5) and the mobile phase B was a mixture of 60% methanol and 40% acetonitrile. The gradient elution was as follows: 0-0.5 min, 5% B; 0.6-3 min, 25% B; 3.1-3.9 min, 70% B; and 4-5 min, 5% B. The re-equilibration time was two minutes. The injection volume was 4 \(\upmu\)L. UV detection was performed at 243.5 nm (band width of 4 nm).  
For the quantification, a reference standard of ascorbic acid was used (L-Ascorbic acid, Sigma Aldrich, Buchs, Switzerland). The calibration curve was built using the peak area at the corresponding wavelength under the chromatographic conditions described above. The calibration was analysed for each batch of analysis. The following linear regressions were used for quantification:  
Linear range: 5-750 \(\upmu\)g/mL.  
Equations:  
\[y =7.7351\ x+66.792\ \Big{(}\mathrm{R}^{2}\ =\ 0.9975;\ \mathrm{oranges}\Big{)}\] \[y =8.3842\ x+0.6953\ \Big{(}\mathrm{R}^{2}\ =\ 0.9998;\ \mathrm{bananas}\Big{)}\]
# A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks
## 3 The Improved Clustering Scheme
### Cluster Formation
#### 3.2.1 Neighborhood Discovery
Due to the difference of the maximum transmission range of each node, we consider the average approximate maximum transmission range in its 1-hop neighborhood (e.g., for node \(i\), denoted as \(R_{i,av}\)) to reduce the difference of maximum clustering range that each node can employ. Through executing Algorithm 1, each node (e.g., \(i\)) knows not only its approximate maximum transmission range but also those of its neighbors. Therefore, it can estimate the average approximate maximum transmission range in its 1-hop neighborhood by means of the following formula:  
\[R_{i,av}=\frac{d_{i,max}+\sum_{j\in L_{i,av}}d_{j,max}}{|L_{i,nei}|+1} \tag{5}\]  
In Equation (5), \(1L_{i,nei}\,|\) denotes the number of the elements in \(L_{i,nei}\). By selecting a discount \(\mu\), a clustering radius for any node (e.g., \(i\)) is computed by the following formula:  
\[R_{i,intra}=\mu\cdot R_{i,av} \tag{6}\]  
According to Theorem 1 in [3] (i.e., the graph of MCHs generated by the CMIMO protocol is connected if \(R_{inter}=3R_{intra}\)), the discount \(\mu\) may be roughly set to 1/3. However, in general, \(\mu\) is a free tunable parameter. For example, in the following simulation setting, when \(\mu\) is set to 0.6, full network connectivity cannot be ensured.
## 2 Materials and Methods
### Measurement Systems
#### 2.4.3 Tracking  
Seasonal external load data from team training were collected by using the Apex Pro system (sampling rates: GPS: 18 Hz, GNSS: 10 Hz, triaxial accelerometer: 952 Hz, gyroscope: 952 Hz, magnetometer: 10 Hz; STATSports Group Limited (Newry, North Ireland)). Team's home match data was optically analyzed by the Chiron Hego system (Chiron Hego, Cologne, Germany). Data from additional national-team camps and matches were provided by the respective national teams.  
As defined in previous research [13], ACWR, originally developed as a training load metric, was calculated as the ratio of the mean high metabolic load distance (HMLD) of the last seven days to the mean HMLD of the last 28 days (ACWR\({}_{\text{HMLD}}\)). Acute\({}_{\text{HMLD}}\) (average of the last 7 days) and chronicHMLD (average of the last 28 days) are therefore included in this parameter. Only the coupled ACWR was calculated, as the coupled and uncoupled ACWR calculations showed an almost perfect relationship [26].  
The documented non-contact injuries were used to evaluate the predictive ability of ACWR\({}_{\text{HMLD}}\), AW, and CW. In a second step, the same calculation was performed for the EP-related parameters (ACWR\({}_{\text{HMLD}}\)/EP, AW\({}_{\text{HMLD}}\)/EP, CW\({}_{\text{HMLD}}\)/EP). Weekly ACWR\({}_{\text{HMLD}}\) was used to compare and analyze seasonal changes in the training load of the athletes. Dueto the significantly higher AUC values and for content reasons (dependency between load and EP), only the results of the second calculation are presented.
# A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT Devices
## 3 Vulnerability Analysis, Discovery, Detection, and Mitigation  
At this stage, there is no precise classification of IoT security. In addition, the core of security research is vulnerability. Therefore, we focus on the device's vulnerability. Around its life cycle, the research process is divided into three stages: discovery, detection, and mitigation. Because of the particularity of IoT security, it is impossible to have standard interfaces to support analysis. Thus, research on the basic analytical framework of IoT is also valuable to research content. To comprehensively review IoT security technologies, we summarize by the following four aspects: (1) Research on the basic framework of vulnerability analysis, which performs firmware simulation to help analyze IoT security issues. (2) Research on vulnerability discovers the technology, which studies methods to discover unknown vulnerabilities in IoT devices. (3) Research on vulnerability detection, which studies methods to detect known vulnerabilities based on the features and signatures of existing vulnerabilities. (4) Research on vulnerability mitigation, which studies methods to automatically fix vulnerability or access control methods to limit malicious behavior. In addition, this section mainly summarizes the IoT vulnerability analysis technologies, which require a series of pre-conditions, such as firmware extraction [42]. Thus, we mark the technical requirements, but do not sum them up.
# Elevation Angle Characterization for LEO Satellites: First and Second Order Statistics
## Appendix A  
Figure 11a-f contain the mean error for each elevation angle CDF. Those errors were obtained by comparing the proposed methodology results against the results obtained through simulations. The same format than in Figure 9c,d is applied here; only showing the ES's located below the satellite orbit inclinations at each column.
## 2 Materials and Methods
### Hardware Development and Layer Description  
We distribute 11 sensors on 5 physical layers. A detailed description of sensors, companies, and technical specifications is given in Table 3.  
Figure 1: General strategy of operation.MCU: Microcontroller Unit.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline
**Sensor/Component** & **Company** & **Layer** & **Category** & **Parameter** & **T. Int. (ms)** & **Interface** & **Pow. Supply (V)** & **Pow. Cons. (\#W)** & **Resolution** \\ \hline
3 SP\_NO2\_5F & Spec & & & NO\({}_{2}\) & 1000 & & & 10.0-50.0 & 0.1 ppm \\ \hline
3 SP\_CO\_1000 & Spec & & & CO & 1000 & & & 10.0-50.0 & 5 ppm \\ \hline MCF6041 & Microchip & & & & & & 1.6-6 & 1.98 & \\ \cline{3-7} ISL60002 DII320Z & Intersil & & & & & 2.8-5.5 & 1.15 & & \\ \cline{3-7} LMP91000 & TI & & & NA & & 2.8-5.5 & 10 & \\ \hline \end{tabular}
\end{table}
Table 3: Sensors and components of the prototype. Note: ppm: part per million, HW: hardware, IIC: Inter-integrated circuit, ADC: analogue-to-digital converter, T: time, Int: interval, Pow: power, Cons: consumption.
## 4 Results and Discussion  
In this study, the saturated imaging method was used to image the defect samples in the black stripes of binary structured backlight, and the saturated images were cropped to obtain defective region samples with sizes of \(48\times 58\). An example is shown in Figure 7a,b. All these samples form the unlabeled sample set \(\mathcal{X}=\{x_{i}\}_{i=1}^{n}\). Subsequently, a laser confocal microscope was used to measure the defect shape attributes. The main settings are an objective lens magnification of \(10\times\), horizontal and vertical measurement ranges of \(0-1417\)\(\upmu\)m and \(0-1062\)\(\upmu\)m, respectively, a depth measurement repeatability of \(0.1\)\(\upmu\)m, and a width measurement repeatability of \(0.2\)\(\upmu\)m. The measured values were used as ground truth to analyze our estimation method. The defect shape attributes were labeled manually. Without loss of generality, only depth and width were considered in our experiment. Figure 7c illustrates the manual labeling to obtain label set \(\mathcal{L}=\{l_{i}\}_{i=1}^{n}\). Finally, 113 samples were obtained (The dataset can be found in the following page. [[https://doi.org/10.4121/uuid:9b562](https://doi.org/10.4121/uuid:9b562) ad4-6f92-48 ba-8883-95921d3b0 efb]([https://doi.org/10.4121/uuid:9b562](https://doi.org/10.4121/uuid:9b562) ad4-6f92-48 ba-8883-95921d3b0 efb)). The minimum and maximum depths of defects in these samples are \(2.0\)\(\upmu\)m and \(18.5\)\(\upmu\)m, respectively. The minimum and maximum widths of defects are \(250\)\(\upmu\)m and \(500\)\(\upmu\)m, respectively.  
Figure 7: Sample data acquisition. (**a**), (**b**) Example of a saturated image of a defective region, (**c**) Manual labeling method for measuring the shape attributes on laser confocal microscopy images.
# Electrospinning of High-Performance Nanofibres: State of the Art and Insights into the Path Forward
## 3 Electrospinning of High-Performance Polymers
### Meta-Aramid
M-aramid nanofibrous membranes were investigated as a precursor to N-halamine, for the production of an antimicrobial water filter [8]. Here, m-aramid nanofibres were submerged in a solution of diluted bleach to form an N-halamine structure. The chlorinated membrane was able to be recharged with 98% efficacy. Moreover, the membrane was able to fully remove both _Escherichia coli_ and _Staphylococcus aureus_ from water through a non-pressure-driven filtration process without contaminating the water with chlorine. M-aramid nanofibres were also produced with a helical morphology through co-electrospinning with an m-aramid core and thermoplastic polyurethane outer shell [46]. The production of helical nanofibres can be used in applications where high fibre resiliency is needed. M-aramid was also electrospun as a composite with SiO\({}_{2}\) nanoparticles in order to improve the conductivity and electrolyte uptake of the membrane [7]. The presence of SiO\({}_{2}\) nanoparticles increased the wettability, porosity, breaking strength, conductivity, and dimensional stability. Silver nanoparticles have also been added to m-aramid nanofibrons membranes in order to produce a high-performance flexible pressure sensor [53]. The m-aramid membrane contributed excellent mechanical and breathable properties to the sensor, making it suitable for wearable applications. In summary, M-aramid nanofibrons membranes have been successfully produced via electrospinning using a variety of methods. The optimization of the electrospinning process through solvent, salt content, polymer concentration, and electric field behaviour has been demonstrated [9; 11; 46]. Moreover, methods to improve the mechanical properties and thermal stability of the nanofibres continue to improve [7; 45; 49; 50]. M-aramid nanofibres are now being studied for specific applications, and the increasing interest in m-aramid nanofibrous composites is allowing for the enhancement of the nanofibrous membrane properties.
## Appendix A The Fractional Model of the Cross-Correlation Equation
### Signal Matching Model  
The fractional Cross-correlation model we discuss in this paper was developed originally for detecting digital watermarks embedded using the following rule:  
\[y_{i}=x_{i}+gw_{i} \tag{13}\]  
From the embedding rule defined by (13), we get for Gaussian channels \(x_{i}=y_{i}-gw_{i}\in N(\mu,\sigma)\), so, the likelihood function is:  
\[L(\theta)=\prod_{i=1}^{N}\frac{1}{\sqrt{2\pi\sigma^{2}}}\exp\left[-\frac{1}{2 }\left(\frac{y_{i}-gw_{i}-\mu}{\sigma}\right)^{2}\right]=\frac{1}{(2\pi\sigma ^{2})^{\frac{N}{2}}}\exp\left[-\frac{1}{2}\sum_{i=1}^{N}\left(\frac{y_{i}-gw_ {i}-\mu}{\sigma}\right)^{2}\right] \tag{14}\]  
To simplify, logarithm is taken to get:  
\[L=-\frac{N}{2}\ln(2\pi\sigma^{2})-\frac{1}{2}\sum_{i=1}^{N}\left(\frac{y_{i} -gw_{i}-\mu}{\sigma}\right)^{2} \tag{15}\]  
Now, using algebraic manipulations, we reduce (15) to:  
\[L=-\frac{N\ln(2\pi\sigma^{2})+N-1}{2}+\frac{g}{\sigma^{2}}\sum_{i=1}^{N}y_{i} w_{i}-\frac{Ng^{2}}{2\sigma^{2}} \tag{16}\]  
Unlike classical approaches which take the derivative to maximize the last likelihood function, a fractional derivative is computed instead using (11) to get:\[\frac{\partial^{\alpha}L}{\partial g^{\alpha}}=-\frac{N\ln(2\pi\sigma^{2})+N-1}{2 \Gamma(1-\alpha)}g^{-\alpha}+\frac{g^{1-\alpha}}{\Gamma(2-\alpha)\sigma^{2}} \sum_{i=1}^{N}y_{i}w_{i}-\frac{Ng^{2-\alpha}}{\Gamma(3-\alpha)\sigma^{2}} \tag{10}\]  
Now, to advance in computing the maximum of (10), we let it be zero, in addition, we multiply by \(g^{\alpha}\) and get the following equation:  
\[0=-\frac{N\ln(2\pi\sigma^{2})+N-1}{2\Gamma(1-\alpha)}+\frac{g}{\Gamma(2- \alpha)\sigma^{2}}\sum_{i=1}^{N}y_{i}w_{i}-\frac{Ng^{2}}{\Gamma(3-\alpha)\sigma ^{2}} \tag{11}\]  
It is a quadratic equation for \(g\), and thus, the estimator for the embedding gain is:  
\[\hat{g}=\frac{\Gamma(3-\alpha)}{2\Gamma(2-\alpha)}\frac{1}{N}\sum_{i=1}^{N}y_ {i}^{*}w_{i}+\frac{\Gamma(3-\alpha)\sigma^{2}}{2N}\sqrt{\frac{1}{\Gamma^{2}( 2-\alpha)\sigma^{4}}\left(\sum_{i=1}^{N}y_{i}^{*}w_{i}\right)^{2}-\frac{2N[2 NH-1]}{\Gamma(3-\alpha)\Gamma(1-\alpha)\sigma^{2}}} \tag{12}\]  
where \(H=\ln(\sqrt{2\pi\sigma^{2}\text{e}})\). This fractional model generalizes the cross-correlation formula as one gets Equation (1) by letting \(\alpha=1\) which is the derivative of order 1, or in other words, the classical approach to compute a first order derivative. [PERSON] et al. [5] determined that the minimum variance of this equation is reached for \(\alpha=\frac{1}{2}\); thus, using this value, we get \(\Gamma(2-\alpha)=\frac{\sqrt{\pi}}{2}\), \(\Gamma(1-\alpha)=\sqrt{\pi}\), and \(\Gamma(3-\alpha)=\frac{3}{4}\sqrt{\pi}\), leading to the following equation:  
\[\hat{g}=\frac{3}{4}\frac{1}{N}\sum_{i=1}^{N}y_{i}^{*}w_{i}+\frac{3}{4}\frac{1 }{N}\sqrt{\left(\sum_{i=1}^{N}y_{i}^{*}w_{i}\right)^{2}-\frac{2}{3}\sigma^{2} N[2 NH-1]} \tag{13}\]  
Through experimentation, it was noted that (13) is complex-valued when \(y\) and \(w\) are matching signals.