esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
We have experimentally investigated the influence of ZnO target morphology on X-ray emission under illumination with several-mJ femtosecond laser pulses focused to intensities \(<10^{17}\) W/cm\({}^{2}\). Replacing flatsurface targets with NW arrays enables enhancement of the hard X-ray flux by 15-22 times in the energy range of 150-300 keV. This enhancement can be explained by a much higher efficiency of laser energy absorption in NW arrays, resulting in a significant increase of the number and energies of hot electrons. In contrast, no significant increase was detected in the \"cold\" Zn K-shell emission, which can be attributed to the lower material density, the different material of the substrate, and reabsorption effects in a larger volume of generation. The polarization dependence of the photon flux supports the assumption that vacuum heating is the main mechanism responsible for X-ray generation. It was shown that the efficiency of X-ray generation in NW arrays is weakly sensitive to laser polarization, in contrast to the flat surface. These experimental results allow the conclusion that there are no strict requirements for incidence geometry, nor for laser beam polarization when disordered NW targets are utilized. Examination of these targets in interactions with non-relativistic laser pulses was an essential step towards gaining a better understanding of the phenomena to be expected at higher intensities. We believe that our results may contribute to the development of efficient hard X-ray sources at kHz repetition rate driven by moderate-intensity lasers and are of great importance for many applications in X-ray spectroscopy.  
[PERSON] and [PERSON] carried out experiments, data processing, and numerical simulations; [PERSON], [PERSON], and [PERSON] contributed to experimental measurements and X-ray spectroscopy; [PERSON], [PERSON], and [PERSON] provided the nanowires and contributed to the discussion of the results; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] contributed to the discussion and interpretation of the results; [PERSON], [PERSON], and [PERSON] wrote the paper with contributions from other authors. [PERSON] contributed to experimental measurements and X-ray spectroscopy  
This work was supported by the Bundesministerium fur Bildung und Forschung (BMBF) projects FKz. 05P15 SJFA1 and 05P15 RGFAA, and under the frame of the Deutsche Forschungsgemeinschaft Forschergruppe FOR1616.  
[PERSON] would like to thank [PERSON] for fruitful discussions and valuable suggestions.  
The authors declare no conflict of interest.  
Figure 7: The background signal from the flat (black line) and nanostructured (ordered NW (red line), disordered NW (blue line)) ZnO targets for p-polarization (**a**) and s-polarization (**b**) detected by the CCD camera (CCD1).
# Understanding Consumer Acceptance for Blockchain-Based Digital Payment Systems in Bhutan
## 6 Discussion
### Practical Implications  
A strategic approach that focuses on consumer education and social influence, together with infrastructure development, is essential for consumer acceptance of the integration of blockchain in Bhutan's digital payment system. Stakeholders in banking institutions, government agencies, and fintech development teams need to develop systematic awareness initiatives that effectively explain the fundamental strengths of blockchain, such as efficiency, transparency, and security, which are essential within Bhutan's distinctive socio-cultural and economic environment. Previous studies [35; 78; 81] emphasized that for successful adoption, stakeholders must ensure regulation alongside trust building and public engagement. Addressing misconceptions and showcasing real-world applicationscan foster confidence in blockchain technology. Public outreach efforts should leverage digital and traditional media, hands-on workshops, and financial literacy programs tailored to diverse demographics, aligning with the findings of [PERSON] et al. [83] on the need for usability-focused education.  
Social trust is a fundamental factor that influences the acceptance of the adoption of technology by consumers in Bhutan's collectivist society, as noted by [PERSON] [80] and [PERSON] et al. [82], who stated that social influence is essential. Endorsements from community leaders and institutional figures, along with sensitization campaigns featuring real-life success stories, can effectively build blockchain acceptance. This aligns with its finding that social proof plays a critical role in influencing consumer decisions. Addressing infrastructural challenges by expanding digital connectivity and improving digital literacy will narrow the technology gap to guarantee universal access. Studies [78, 84] highlighted that financial literacy programs combined with supportive government policies are essential to drive acceptance for adoption. Bhutan can establish a standard for ethical blockchain adoption by incorporating blockchain within its Gross National Happiness framework, along with equitable access and alignment of national values, which will serve as a model for developing economies.
# Towards Environmentally Friendly Accelerometers Based on Bacterial Cellulose
## 4 Results and Discussion  
### Composite Composition  
The IL uptake was calculated by comparing the weight of the IL-treated sample with the dried ones. The samples were prepared by the same procedure as described before. The following equation was used to evaluate the IL uptake (found to be equal to 34% by weight):  
\[\text{{Ionic liquid uptake}}=\ \frac{W_{wet}-W_{dry}}{W_{dry}}\ \times\ 100 \tag{3}\]  
To evaluate the influence of the spreading treatment on the amount of IL remaining in the BC film, the following experiment was carried out. An IL-soaked BC film was treated by a four-time passage of the dry spreader. After this treatment the amount of IL remaining in the BC film was 11% by weight with respect to the starting dried sample. From this information and through the weighing of the sample on which the PEDOT had been deposited, the amount of PEDOT present in the final device was roughly 7% by weight. Considering that both IL and PEDOT can be extracted from the device by a water treatment, we can affirm that about 82% by weight (corresponding to the BC weight fraction) of the materials can be recycled, thus justifying the environmentally friendly nature of our device.
## 8 Discussion  
This study confirms that MVDS side lobe radiation presents a significant interference threat to GEO satellite links in the shared Ku-band within Lebanon. Our quantitative analysis shows substantial degradation in signal quality metrics (\(\left[\begin{smallmatrix}\mathrm{C}\\ \mathrm{N}\end{smallmatrix}\right]\) and \(\left[\begin{smallmatrix}\mathrm{F}_{\mathrm{h}}\\ \mathrm{N}_{\mathrm{0}}\end{smallmatrix}\right]\)) as MVDS power increases, reaching critical levels even at 0 dBW, highlighting the need for mitigation.  
The findings support the proposed two-step mitigation strategy: reducing MVDS transmit power (compensated by increased receiver LNA gain) and employing an antenna with significantly lower side lobe gain. This combined approach effectively reduces interference impact, facilitating coexistence while maintaining MVDS service quality, as indicated by the preserved receiver output power and improved signal quality thresholds.  
While based on specific modeling assumptions, including worst-case side lobe alignment and clear sky conditions, the results clearly demonstrate the potential for harmful interference and the viability of the proposed mitigation techniques for the Lebanese operational context.
## Materials and Methods
### Last Day (Ldc)--Last Week (Lwc) Consumption
#### 2.2.1 Calculated Load Consumption (I\({}_{\text{cal}}\))  
Daily consumption changes are remarkable indicators, but it is not enough to have a flawless load forecasting. The least squares method is the mathematical equation form that determines and visualizes the relationship in the dataset. Each data reveals the relationship between the known independent and unknown dependent variables.  
The least square method was developed in the late 1700s, and is used for estimating the unknown parameters between data and the model using squared deviations [25]. The least square method is one of the best analytical techniques for extracting valuable information from the dataset [26]. In this paper, day-ahead weekly load changes are used as forecasting parameters. A trend analysis for the day-ahead consumption changes has been calculated using the following mathematical equations:  
\[\text{Least Square Load Trend Equation : }L=a+bx_{i} \tag{1}\] \[\text{Slope : }b=\frac{\sum_{i=1}^{n}x_{i}y_{i}-n\overline{xy}}{ \sum_{i=1}^{n}x_{i}^{2}-n\overline{x}^{2}}\] (2) \[\text{Load Intercept : }a=\overline{y}-b\overline{x} \tag{3}\]  
In Equation (1), \(L\) denotes the estimated hourly electricity load consumption, a denotes the load factor, b denotes the load slope, and \(x_{i}\) denotes the period of load that will be estimated.  
The calculated load curve (I\({}_{\text{CAL}}\)) using the least square method is another important input parameter of the load forecasting system. Load estimation for 1 January 2012, using the data between 25 and 31 December 2011, is seen in Table 1. Each row of the data is the measured electricity load consumption value for the same hour period (12:00 a.m. to 1:00 a.m.).  
The relationship of the weekly-based hourly dataset was analyzed using the least square method, and the load trend line was determined. The target load consumption value was calculated with the equations mentioned above. The hourly load consumption data and calculated values for between 25 December 2011 and 1 January 2012 are seen in Figure 5.  
Figure 4: Normalized (0-255) daily-based hourly electricity load consumption graphic for 2013.
# Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms
## 2 Principles and Methods
### LEO Constellation Patterns
#### 2.1.1 2d-Lfc  
A 2D-LFC is defined with three integer parameters and six continuous ones [31]. The three integer parameters are the number of inertial orbits (\(N_{O}\)), the number of satellites per orbit (\(N_{SO}\)) and the phasing parameter (\(N_{C}\)), which satisfies \(N_{C}\in[0,N_{O}-1]\) and governs the phasing distribution of the satellites in the constellation. The six continuous parameters are the orbit altitude (a), the eccentricity (e), the inclination (i), the argument of perigee (w), the longitude of the ascending node (\(\Omega_{11}\)), and the initial mean anomaly of the first satellite of the constellation (\(M_{11}\)). In particular, the distribution of the satellites in a 2D-LFC corresponds to a lattice in space, where the mean anomaly (M) and the right ascension of the ascending node (\(\Omega\)) of all the satellites of the constellation are uniformly distributed in orbits. The constellation configuration for a 2D-LFC is expressed as follows [31]:  
\[\left[\begin{array}{cc}N_{O}&0\\ N_{C}&N_{SO}\end{array}\right]\left[\begin{array}{c}\Delta\Omega_{ij}\\ \Delta M_{ij}\end{array}\right]=2\pi\left[\begin{array}{c}i-1\\ j-1\end{array}\right] \tag{1}\]  
where \(i=1,\ldots,N_{O}\), \(j=1,\ldots,N_{SO}\); \(N_{C}\in[1,N_{O}]\); \(\Delta\Omega_{ij}=\Omega_{ij}-\Omega_{11}\), \(\Delta M_{ij}=M_{ij}-M_{11}\), among which \(\Omega_{ij}\) and \(M_{ij}\) denote the longitude of the ascending node and the mean anomaly of the \(j\)-th satellite on the \(i\)-th orbital plane, respectively. \(\Delta\Omega_{ij}\) and \(\Delta M_{ij}\) are calculated with Equation (1), based on which \(\Omega_{ij}\) and \(M_{ij}\) can be obtained accordingly with the given \(\Omega_{11}\) and \(M_{11}\).  
In the simulation, circular orbits need to be applied to ensure the uniform and symmetric distribution of the LEO satellites in the 2D-LFC [31], which means that the eccentricity of all the satellites are set to be zero. Some constraints are applied on the variation range of the design parameters for the LEO constellation to avoid unrealistic solutions. The variation ranges of the constellation parameters for the 2D-LFC are listed in Table 1. Due to the regular pattern of the 2D-LFC, it is possible that the satellites will collide in circular orbits. Hence, to avoid the collision of satellites within the 2D-LFC, the constraint on the minimum approach distance between any two satellites is considered, as explained in Reference [33]. In the optimization process, the fitness value will be set to the minimum when this constraint is not satisfied.
## 3 Results and Discussion  
### Specimen Measurements  
Using a micrometer with a millesimal resolution (0.001 mm) from the brand DIGIMESS, the dimensions of the useful region of the printed test specimen (PB) were measured to verify if they met the width and thickness specifications established by ASTM D638. The measurements indicated that the width was 13 \(\pm\) 0.1 mm and the thickness was 3.2 \(\pm\) 0.3 mm. Five measurements were taken in each direction in the useful region, and the obtained values, along with their averages and deviations, confirmed compliance with the dimensions defined by the standard.
## 2 The Proposed Method
### The Parameter Optimization of the SVM and Feature Selection Based on GA
#### 2.3.2 Fitness Function  
Fitness function is an important task of the proposed method. In order to obtain higher accuracy for the SVM classifier, the fitness function should be two attributes: the first one meets the requirements of high performance in the SVM classifier and the second one selects the number of features as infrequently as possible. This means, the higher fitness value represents the lesser number of features and shows a higher accuracy for the classifier. Therefore, in this study, we use a weighted combination  
Figure 3: Design structure of chromosome.  
of selected features with classification accuracy to construct a fitness function. The fitness function is expressed by Equation (8):  
\[\text{fitness}=W_{\text{CA}}\times\text{classification accuracy}+W_{f}\times \left(\sum_{i=1}^{n_{f}}F_{i}\right)^{-1} \tag{8}\]  
where \(W_{\text{CA}}\) denotes the classification accuracy weight, and \(W_{f}\) refers to the weight of the feature subset. In object functions related to the feature subset, \(F_{i}\) indicates the position related to the \(i\)th feature in the binary code of the feature subset, so that \"0\" means feature i is not chosen; \"1\" means feature i is chosen.
# Optimisation of Synchronous Grouting Mix Ratio for Shield Tunnels
## 3 Results and Discussion
### Uniform Test
#### 3.2.2 Effect of Slurry Rheology on Bleeding and Segregation Rates
When the yield shear stress \(\tau_{0}\) is 0 Pa \(\leq\tau_{0}\) < 5 Pa and 20 Pa \(\leq\tau_{0}\) < 25 Pa and the viscosity coefficient \(K\) is 800 \(\times\) 10\({}^{-4}\) Pa\(\cdot\)s \(\leq\)\(K\) < 1000 \(\times\) 10\({}^{-4}\) Pa\(\cdot\)s and 0 Pa\(\cdot\)s \(\leq\)\(K\) < 200 \(\times\) 10\({}^{-4}\) Pa\(\cdot\)s, the segregation rate can be controlled in the range of 11.92-13.9%.
## 3 Results
### Temporal Characteristics of Parameter SA  
The total sensitivity index variation of the primary model parameters with the DOY for two outputs, namely GPP and NEE during the entire growing season was shown in Figures 3 and 4.  
Figure 2: MSIs and TSIs of 16 parameters with different outputs (The parameters’ range between the minimum and maximum from Table 1): (**a**) Gross primary productivity (GPP) as output and (**b**) net ecosystem carbon exchange (NEE) as output.  
Figure 4: Temporal TSI variation of the main parameters from DOY10 to DOY340 when model output was NEE. (**a**) TSI of \(E_{V\_m}\)_vs._ DOY; (**b**) TSI of \(ER_{0}\)_vs._ DOY; (**c**) TSI of \(Q_{10}\)_vs._ DOY; (**d**) TSI of \(f_{G}\)_vs._ DOY.  
Figure 3: Temporal Variation in parameter TSI from DOY10 to DOY340 when model output was GPP. (**a**) TSI of \(a_{\text{q}}\)_vs._ DOY; (**b**) TSI of \(E_{V\_m}\)_vs._ DOY; (**c**) TSI of \(f_{G}\)_vs._ DOY; (**d**) TSI of \(D_{0}\)_vs._ DOY.  
As for GPP as shown in Figure 3, the TSI of \(E_{\mathrm{V}m}\) and \(D_{0}\) obviously changed over time. TSI of \(E_{Vm}\) decreased from DOY10 to DOY150, and was less than 0.01 from DOY160 to DOY240, and then increased from DOY260 to DOY340. SIs of \(D_{0}\) was less than 0.01 before DOY100, and increased to the maximum at DOY220, and then declined. The SIs of the main parameters \(\alpha_{q}\) and \(f_{ci}\) vary weakly with DOY, and TSI of \(V_{m}^{25}\) did not change with DOY, So TSI of \(V_{m}^{25}\) was not shown in Figure 3. As for NEE (Figure 4), \(ER_{0}\) and \(Q_{10}\) played key roles, and the variation in them correlated with time. TSI of \(ER_{0}\) and \(Q_{10}\) were very small between DOY110 and DOY210, and increased quickly to 0.423 and 0.463, respectively.  
The analysis of temporal change of SIs showed that the parameters were not invariable and changed prominently in different growth periods.  
The study found that the variation in sensitivity had a trade-off effect. The \(\alpha_{q}\), \(f_{ci}\), and \(E_{Vm}\) declined with time, whereas \(ER_{0}\) and \(Q_{10}\) increased and became more sensitive from DOY110 to DOY210. After DOY210, \(ER_{0}\) and \(Q_{10}\) declined, and \(f_{ci}\), \(\mathrm{E}_{\mathrm{V}m}\) and \(\alpha_{q}\) increased.
# A Correlation-Based Sensing Scheme for Outlier Detection in Cognitive Radio Networks
## 3 Proposed Scheme  
In this paper, we proposed a correlation-based approach to identify the legitimate SUs and outliers. The box-whisker plot was introduced to classify the legitimate SUs from the outliers. In the proposed scheme, each SU senses the spectrum by utilizing the energy-detection technique and compares the received signal strength with a threshold value. On the basis of the sensing results, the SUs send a hard decision of \(1\) or \(-1\) to the FC, which can be given as  
\[Z_{j}(i)=\left\{\begin{array}{ccc}1&;&if\ E_{j}(i)\geq\gamma_{j}\\ \\ -1&;&Otherwise\end{array}\right., \tag{4}\]  
where \(E_{j}(i)\) is the energy received by the \(j\)th SU in the \(i\)th sensing interval, \(\gamma_{j}\)is the value of the threshold set for the \(j\)th SU, and \(Z_{j}(i)\) is the \(j\)th SU decision of the PU signal in the \(i\)th sensing interval, representing \(1\) if the \(E_{j}(i)\) is greater than the threshold value and \(-1\) if energy of the received signal \(E_{j}(i)\) is smaller than the threshold value. The FC collects spectrum sensing information of the individual SU results with its own local decision as  
\[Z=\left[\begin{array}{cccccc}z_{11}&z_{12}&z_{13}&z_{14}&\cdots&z_{1M}\\ z_{21}&z_{22}&z_{23}&z_{24}&\cdots&z_{2M}\\ z_{31}&z_{32}&z_{33}&z_{34}&\cdots&z_{3M}\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ z_{N1}&z_{N2}&z_{N3}&z_{N4}&\cdots&z_{NM}\end{array}\right]\, \tag{5}\]where \(Z\) represents the sensing energy accumulated in the database of the FC by all the SUs' hard-decision values. In Equation (5), the rows represent the sensing intervals, and the columns represent the SUs' energy responses under each sensing interval. \(M\) denotes the total number of SUs including the normal SUs, the outlier/MU, and the FC information, and \(N\) is the number of sensing intervals. Furthermore, correlation was used as a tool for the detection of the most harmful and difficult detect OMU and ROMU users.  
The correlation coefficients for the two samples \(X\) and \(Y\) can be determined as  
\[\begin{array}{cc}r_{XY}&=r_{XY=}\frac{\Sigma_{p}(X_{p}-\overline{X})(Y_{p}- \overline{Y})}{\sqrt{\Sigma_{p}(X_{p}-\overline  
In order to estimate how much the individual sensing results of each SU, \(z_{ij}\), are behaving differently from the average value of other users' results, \(m_{ij}\), we considered the following  
\[\Delta d_{ij}=\left[\begin{array}{cccc}\Delta d_{ij}=z_{ij}-m_{ij}\\ \Delta d_{11}&\Delta d_{12}&\ldots&\Delta d_{1M}\\ \Delta d_{21}&\Delta d_{22}&\ldots&\Delta d_{2M}\\ \ldots&\ldots&\ldots&\ldots\\ \Delta d_{N1}&\Delta d_{N2}&\ldots&\Delta d_{NM}\end{array}\right]\, \tag{8}\]  
where \(\Delta d_{ij}\) is the difference in the sensing results of the/\(i\)th SU in the \(i\)thsensing interval,\(z_{ij}\) is the individual sensing result of the \(j\)th SU in the \(i\)th sensing interval, and \(m_{ij}\)is the average sensing result of the SUs other than the \(j\)th SU.
# An Efficient Scalable Scheduling MAC Protocol for Underwater Sensor Networks +
## 5 Efficient Depth-Based MAC Protocol
### Number of Slots Analysis  
ED-MAC can support broadcast transmissions in which any two nodes that are strictly more than two-hops away from each other can transmit simultaneously without interfering. ED-MAC is said to be collision free if and only if any two nodes, that are two-hop neighbours, do not have the same slot.  
Figure 8: New node \(M\) joins the network.  
**Definition 1**.: _Let \(G=(V,E)\) be an undirected graph representing the network topology. \(V\) includes all the nodes in the network while \(E\) represents all edges between two neighbouring nodes._  
**Definition 2**.: _The number of h-hop neighbours of a vertex \(v\), denoted by \(d_{h}(v)\), is called the h-hop degree of \(v\). The maximum h-hop degree vertex in a graph \(G\), \(\Delta_{h}(G)\), defines the h-hop graph degree._  
**Theorem 1**.: _If \(\Delta_{2}(G)=d\), all nodes in \(G\) can be scheduled with \(d+1\) slots without having slot overlap between any two-hop neighbouring pairs._  
Proof.: Since each node has at most \(d\) two-hop neighbours, there is always at least one free slot in the range \(\{1,...,d+1\}\).  
For the network design shown in Figure 9, in this figure, node \(w\) has a number of \(N\) neighbours (in this case \(N=6\), numbered 1 through 6 in the figure). Each of them is connected through \(w\) as a neighbourhood. Thus, the upper bound can be handled by \(w=\{d+1\}\), where \(d=6\). Since some of these have \(N\) neighbours which are two-hop neighbours of \(w\) as shown in Figure 10, this can also be handled by assigning two-hop distance slots.  
It should be noticed that \(d+1\) is only an upper bound because it might be possible to schedule nodes with fewer slots.  
Figure 10. Network layout with two-hop neighbours of \(w\).  
Figure 9. Network design.