esa-sceva/satcom-chunk-collection huggingface dataset:
# RF-Access: Barrier-Free Access Control Systems with UHF RFID
## 3 RF-Access
### Motion Direction Detection
#### 3.2.2 Time-Slot-Based Sensing  
Instead of using RF signals, we attempt to use the application-layer feature--reading records, which are stable and robust to environmental factors. The principle is that when the tag moves from left to right, the left-side antenna is supposed to query the tag earlier than the right-side antenna and vice versa. By observing the timestamps of the reading records of two antennas, it is promising to figure out the moving direction.  
An intuitive solution is to check the first seen time by each antenna. This works in theory but is not robust in real scenarios due to the multi-path effects and the blocking impact of the human body. To address this problem, we resort to all reading records. The solution is to split the reading period into many smaller time slots. For each antenna, we check whether there are some reading records within each time slot. If yes, we consider that this antenna hits the time slot, and the time slot is labeled as a busy slot. The mean of all busy slots is treated as the final reading time of an antenna. By comparing the mean of different antennas, we can figure out the direction.  
More specifically, for the \(m\)-th antenna, we obtain the time-stamp sequence of the reading records \(T^{m}=\left(t_{1}^{m}\ t_{2}^{m}\ \cdots\ t_{n}^{m}\right)\). The time period is:  
\[T_{s}=[\min(T^{m}),max(T^{m})]. \tag{5}\]  
The time period \(T_{s}\) can be divided into \(w\) small time slots with the length of \(\delta\), i.e.,  
Figure 6: RSSI values collected from a running tagged person: (**a**) moving in; (**b**) moving out.  
\[w=\lceil\frac{\max(T^{m})-\min(T^{m})}{\delta}\rceil \tag{6}\]  
The middle \(x_{i}\) of the \(i\)th time slot is:  
\[x_{i}=\min(T^{m})+(i-\frac{1}{2})\delta. \tag{7}\]  
Let the bit vector \(B=\{b_{1},b_{2},...,b_{n}\}\) indicate whether each slot is busy. If \(b_{i}\) is equal to 1, the \(i\)th slot is busy. Otherwise, the \(i\)th slot is idle. We can obtain the mean \(\bar{t}^{m}\) of reading time of the \(m\)-th antenna:  
\[\bar{t}^{m}=\frac{1}{\sum(B)}\sum_{i=1}^{n}x_{i}\times b_{i}. \tag{8}\]  
For two antennas A and B, if \(\bar{t}^{A}<\bar{t}^{B}\), we know that the moving direction is from A to B and vice versa.  
Figure 7 shows the RSSI values when a user moves out with a tag at a constant speed. If the signal-based solution is used, we obtain a wrong answer in this case. For the time slot-based solution, we can obtain \(\bar{t}^{1}=1.78\) s and \(\bar{t}^{2}=1.61\) s, which means that \(\bar{t}^{1}>\bar{t}^{2}\) and further verifies that the moving direction is from antenna 2 to antenna 1, i.e., moving out, where the symbols '\(*\)' and '\(\times\)' in Figure 7 indicate that the tag is queried within a time slot by antenna1 and antenna2, respectively.
# Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?
## 5 Methods for Congestion Learning
### K-Means  
It is one of the most widespread clustering algorithms, due to its simplicity. The algorithm aims to minimize the Within-Cluster Sum of Squares (WCSS). Each cluster is defined by the average value of its samples, which is referred to as centroid. Then, the algorithm seeks to minimize the summation of the squares of distances from each point to its corresponding centroid, as indicated in Equation (1):  
\[argmin(S)\sum_{i=1}^{k}\sum_{x\in S_{i}}||x-\mu_{i}||_{2}^{2} \tag{1}\]  
where \(k\) the number of clusters, \(S\) is the set of samples, \(S_{i}\) are those samples assigned to the \(i^{th}\) cluster, and \(\mu_{i}\) the current centroid of such cluster. \(||\cdot||_{2}\) corresponds to the Euclidean norm. This algorithm has some limitations when the optimal clusters cannot be easily separated. On the other hand, its low computational complexity would simplify its implementation in any device.
## 2 Sample Preparation and Testing Method
### Model Generation of the SRMs
#### 2.1.1 Irregular Rock Block Aggregate Generation  
First of all, the geometries of the aggregate particles need to be generated. Thus, one polygon with random sides will be generated as a boundary of a clump. The method in this study uses a series of random radii and angles in polar coordinates to generate aggregate boundaries based on the Monte Carlo random sampling principle, as shown in Figure 2[34; 35]. Compared with other methods, this method can change the size and shape of an aggregate particle that significantly affects the SRMs mechanical properties via changing radii and angles.
# Developing a Cybersecurity Training Environment through the Integration of OpenAI and AWS
## 2 Materials and Methods
### Test Data Generation
#### 2.6.1 Type of Data  
The types of data used in the developed testing environment are multifaceted, to provide a comprehensive training and evaluation experience:  
* Network Traffic: Data representing normal and abnormal network traffic are included to train participants to detect suspicious or malicious patterns that may indicate cyberattack activity.
* Security Event Logs: These logs contain detailed information on security events, ranging from failed login attempts to intrusion prevention system alerts, which are vital for forensic analysis and incident response.  
* Vulnerability Data: Data are generated that describe known vulnerabilities, which helps participants understand and mitigate potential weaknesses in systems and applications [39].
* Attack Simulations: Data are created that simulate different types of cyberattacks, such as phishing, ransomware, or zero-day attacks, to prepare professionals to identify and respond to these incidents.
# A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
## 3 Background
### Preliminaries
#### 3.1.1 Voronoi Diagram  
A Voronoi diagram [23] is composed of several geometries in a plane, and each geometric is known as the Voronoi polygon. These Voronoi polygons are generated by a set of generator points, and the generation process is as follows. Given a set of generator points specified beforehand, by making perpendicular bisectors for the straight lines composed of generator points, the whole plane can be divided into several regions. For each point \(x\) in the region \(R\), the distance between \(x\) and generator point \(P_{i}\) is less than that between \(x\) and any other generator points, i.e., \(\forall x\in R(P_{i}),dist(x,P_{i})<dist(x,P_{i})\), where \(P_{i}\
eq P_{i}\). According to the properties of the Voronoi diagram, if fog nodes can be seen as generator points, then a two-dimensional map can be divided into a Voronoi diagram. Figure 1 is an example of a Voronoi diagram based on fog nodes.  
Figure 1: Voronoi diagram based on fog nodes (for an example in a UK location).
## 4 Solution Approach
### Simulation Strategy  
To enhance the stability on the throughput estimation of each solution, we performed the following experiment to study the uncertainty of the simulation and define the simulation strategy to use. To avoid a relevant computational effort of the proposed approach due to simulation, we considered three levels of the number of simulations (Sim_n) equal to 1, 3, and 5 times for each solution, and tested 60 randomly generated solutions. During the evaluation of each solution, the median value among multiple simulation results was used to evaluate the solution, which was the reason why we choose an odd number of times. For each solution, 10.000 replicas were run at the three levels.  
We studied the three distributions of the 10.000 replicas for each of the 60 solutions, and present the detailed data in Appendix A. Apparently, as expected, the variability gradually reduced when the number of simulations on each solution increased, with the maximum range percentages equal to 2.2%, 1.5%, and 1.2% of the production rate, respectively, for \(\text{Sim}_{\text{n}}=1\), \(\text{Sim}_{\text{n}}=3\), and \(\text{Sim}_{\text{n}}=5\). The reduction in the range percentage going from \(\text{Sim}_{\text{n}}=1\) to \(\text{Sim}_{\text{n}}=3\) was much higher than going from \(\text{Sim}_{\text{n}}=3\) to \(\text{Sim}_{\text{n}}=5\). Noting that the additional simulation time required for the above two situations (\(\text{Sim}_{\text{n}}=1\) to \(\text{Sim}_{\text{n}}\) = 3 and \(\text{Sim}_{\text{n}}=3\) to \(\text{Sim}_{\text{n}}=5\)) was the same, we decided to set \(\text{Sim}\_{\text{n}}\) equal to 3 as the number of simulations among which to take the median value to estimate the production rate of each solution. Considering that the uncertainty could be considered symmetrically distributed, it was estimated to be \(\pm 0.75\%\).
## Appendix A Calculation of the Cross-Meridian Width of a MODIS Swath Gap  
The cross-meridian width of a MODIS swath gap at a given latitude is calculated as follows:  
\[d_{\lambda}=\frac{(L_{\lambda}-L_{M})}{n}\] (A1)  
where, given a latitude \(\lambda\), \(d_{\lambda}\) is the cross-meridian gap width (units of km), \(L_{\lambda}\) and \(L_{M}\) are the circumference of the cycle of latitude (at \(\lambda\)) and the length of Aqua MODIS daily coverage along the cycle of latitude, respectively. \(n\) is number of Aqua orbits in 24 h (\(n\) = 24 h \(\times\) 60 min/98.9 min = 14.56), where the 98.9 min is the mean orbital period of MODIS. The circumference of the cycle of latitude (at \(\lambda\)) is calculated as:  
\[L_{\lambda}=2\pi R_{\lambda}\] (A2)  
\[R_{\lambda}=\cos(\lambda)\sqrt{\frac{\left(a^{2}\cos(\lambda)\right)^{2}+ \left(b^{2}\sin(\lambda)\right)^{2}}{\left(a\cos(\lambda)\right)^{2}+\left(b \sin(\lambda)\right)^{2}}}\] (A3)  
where \(L_{\lambda}\) is the same as in A1, \(R_{\lambda}\) is radius of the cycle of latitude (at \(\lambda\)), and \(\pi\) is the constant Pi. \(a\) (\(a\) = 6378.1 km) and \(b\) (b = 6356.8 km) are the equatorial radius of the earth and polar radius of the earth, respectively. The length of Aqua MODIS daily coverage along the cycle of latitude is calculated as:  
\[L_{M}=2d_{l}n\] (A4)  
\[d_{l}=\frac{d_{s}}{\cos(\omega)}\] (A5)  
where \(L_{\lambda}\) and \(n\) are the same as in A1. \(d_{l}\) is the length of a cross-track half of MODIS swath (or length from nadir to the scan edge, \(d_{s}\) = 2340 km/2 = 1170 km) projected along the cycle of latitude, and \(\omega\) is the angle between the ascending orbit track of MODIS Aqua and the north direction (\(\omega\) = 98.2\({}^{\circ}\) - 90\({}^{\circ}\) = 8.2\({}^{\circ}\)), as illustrated in Figure A1. Note that MODIS Aqua orbit has an inclination of 98.2\({}^{\circ}\).  
Figure A1: Illustration of calculating the projected length of a MODIS swath along the cycle of latitude. The orange arrow represents between the ascending orbit track of MODIS Aqua and the blue line shows a cross-track half of MODIS swath.
## 3 Results
### Case Study: Real NeRF in Unreal Engine  
This case study explores the visualization of a real NeRF model in the Unreal Engine. The study involves a comparison between the actual NeRF obtained from the LUMA AI plugin and a mesh-based model. The results of this comparison are depicted in Figure 17, shedding light on the performance and visual representation of a NeRF model within the Unreal Engine environment.  
The superior visualization quality achieved with NeRFs in Unreal (as shown in Figure 17b) compared to its mesh approximation (depicted in Figure 17a) underscores its potential for creating realistic objects and immersive environments. This comparison distinctly showcases the effectiveness of NeRF technique in managing lighting conditions and reflective surfaces, thereby contributing to a significantly more authentic representation compared to traditional mesh-based models. The study offers valuable insights into the suitability of NeRFs for applications where accurate lighting and reflections are pivotal.  
However, the success of the NeRF technique also emphasizes the importance of thoughtful consideration and resource planning when implementing it in projects with limited computational resources.
# Round-Trip Time Ranging to Wi-Fi Access Points Beats GNSS Localization
## 9 Which of the APs Should Be Used for Ranging?
If BSSIDs with different frequencies and bandwidths are used, then one may favor those with higher bandwidth since the RTT range is more accurate with higher bandwidths. Finally, to avoid becoming stuck in a situation where a set of APs yields no response (perhaps because the currently estimated position of the UE is outdated), it is useful to add a random component to the overall quality criterion. This ensures that successive ranging requests will have different AP composition even when there has been no response (and hence no change in the quality factors). The random component is also useful when there are just a few responses. The weight of the random component can be made to decline with the number of APs responding, so that it is quite large when there are no responses (or when there are just one or two), but does not have much of a disturbing effect when several APs are providing good data. In summary, the selection criterion may be based on the following: (i) distance; (ii) time since last seen; (iii) signal strength (faded since last seen); (iv) whether the frequency is known; (v) directional factor; (vi) prior quality factor (weight); and (vii) a random perturbation;  
Some other factors that could be taken into account in the quality factor calculation include the frequency band, the Wi-Fi standard supported by the AP, the selection of preamble, and whether the Service Set Identifier (SSID) is hidden. Further, ranging with APs operating in the Dynamic Frequency Selection (DFS) part of the 5 GHz band may be slower on some platforms, so one may want to assign a lower weight to those APs. Also, since switching the frequency that the radio operates on takes time, there may be some advantage to picking a set of APs that operate on the same small set of different frequencies. Overall, while some strategy is needed to avoid becoming stuck in a situation where no APs respond to successive ranging requests, the exact details appear to be less important.
# Antioxidants
## Results  
### Characterization of Hemp Inflorescence Extract  
#### 3.1.1 Total Phenolic Content (TPC), Terpene Profile, and Cannabidiol Content  
The total phenolic compounds were determined spectrophotometrically. The hemp ethanolic extract displayed a TPC of 1.80 \(\pm\) 0.25 mg GAE/mL (equal to 19.73 \(\pm\) 3.04 mg GAE/g inflorescence). In addition, the terpenes and CBD were simultaneously determined. Seventeen terpenes were identified and quantified (Figure 1) with a total amount of 6.00 \(\pm\) 0.73 mg/mL of extract. The monoterenes were 48.76% of total terpenes, while sesquiterpenes and diterpenes represented 47.65% and 3.59%, respectively. Among the monoterenes, \(\beta\)-pinene, limonene, \(\alpha\)-pinene, linalool, \(\gamma\)-terpineol, borneol, fenchol, camphor, terpinolene, and menthol were identified. Sesquiterpenes included cedrol, \(\beta\)-carophyllene, \(\alpha\)-bisabolol, eudesmol, \(\alpha\)-humulene, and cis-nerolidol. Phytol was the only diterpene identified. The most abundant compound was \(\beta\)-pinene (1.69 \(\pm\) 0.45 mg/mL; 28.17% of the total), followed by cedrol (0.87 \(\pm\) 0.07 mg/mL; 14.5% of the total) and \(\beta\)-carophyllene (0.72 \(\pm\) 0.06 mg/mL; 12% of the total). The terpene profile is detailed in Table S1 (Supplementary Materials).  
In addition, the content of cannabidiol (CBD; the main phytocannabinoid reported in hemp) was equal to 4.99% (_w/w_) \(\pm\) 1.24%.  
Figure 1: Terpene profile of hemp inflorescence ethanolic extract. Values are reported as the mean \(\pm\) standard deviation (\(n=3\)).
## 1 Introduction  
Quantum Key Distribution (QKD) is a revolutionary security protocol which generates unlimited amounts of symmetric keying material between two geographically separated parties. Unlike conventional key distribution techniques, the security of QKD rests on the laws of quantum mechanics and not on computational complexity [1]. In theory, these attributes make QKD well suited for high-security applications such as banking, government, and military environments. However, implementation non-idealities and practical engineering limitations in commercially viable QKD systems (i.e., those which balance cost, performance, and security towards affordability) can negatively impact system performance and security [2]. For example, while commercial QKD systems often employ the decoy state protocol to mitigate vulnerabilities in non-ideal photon sources [3; 4], its implementation security is poorly understood. Note, the decoy state protocol is well studied with respect to secure key rate generation (see background for details).  
This work uniquely studies the decoy state protocol's implementation security in commercially viable QKD systems by modeling, simulating, and analyzing decoy state protocol configurations. The results of this study: (i) demonstrate the effectiveness of the several common decoy state enabled QKD system configurations to detect Photon Number Splitting (PNS) attacks; (ii) optimize the decoy state protocol to maximum quantum throughput; (iii) offer implementation security recommendations for QKD system designers and users; and (iv) present a repeatable methodology for analyzing the performance and security of QKD systems. This article is an extension of the Author's previous works [5; 6; 7; 8; 9].  
This article is organized as follows: First, an introduction to QKD is provided with an emphasis on security vulnerabilities, the PNS attack, and the decoy state protocol. Note, Section 2 is intended for those unfamiliar with QKD and the decoy state protocol. If the reader is familiar with these protocols, they should proceed directly to the next Section. In Section 3, the research method is explained, including a comprehensive listing of fielded decoy state enabled QKD systems, the Researcher's experimental design, and the QKD system-level model. Section 4 details the decoy state protocol's ability to detect PNS attacks across 40 common decoy state protocol configurations. Next, an optimization of the protocol is presented and demonstrated through model and simulation. Lastly, several implementation security recommendations are offered. Section 5 presents conclusions and future work. For security specialists desiring to further understand QKD, please see [5; 10; 11]. For comprehensive physics-based reviews of QKD, please see [1; 4].
# Study on the Effect of Rock Mass Structure on CO2 Transient Fissure Excavation
## 3 Influence of Angles between Different Structural Surfaces (Test 1)
### Analysis of Cracking Effect
#### 3.3.5 The Loose Coefficient  
The coefficient of looseness is deemed the basis for rock cleaning and transportation after cracking. Based on the site measurement and the image division in the later period, the corresponding blast heap volume of the No. 1-4 cracking hole is 7.34 m\({}^{3}\), 25.70 m\({}^{3}\), 20.46 m\({}^{3}\), and 13.64 m\({}^{3}\), respectively. There is a gap between the broken stones, so the total volume of blast heap formed by each cracking pipe is more than the total square amount of effective fracking. The loosening coefficient of each hole blasting pile after cracking is shown in Table 4: the coefficient of looseness of the blast heap of each hole after cracking is more than 1.2, which certifies that, after the cracking, the rock is good in looseness degree, which is conducive to cleaning and carrying on the site.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**The Number of the Hole** & **The Loose Coefficient** \\ \hline NO.1 & 1.62 \\ NO.2 & 1.24 \\ NO.3 & 1.40 \\ NO.4 & 1.58 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Loose coefficient statistics.  
Figure 8: Size of block rock measured on-site (intercept part).  
3.3.6. Statistics of Flying Stone Distance  
Since the rock at the bottom is crushed and deformed, the overlaid rock breaks away from the rock under the high-pressure swelling gas and collapses, tumbles down, and separates from the rock to form the flying stone. The farthest distance of the flying stone from the No. 1 hole is 11.76 m, and the rock size is 0.49 m; the farthest distance of the flying stone from the No. 2 hole is 15.60 m, and the rock size is 0.44 m; the farthest distance of the flying stone from the No. 3 hole is 34.20 m, the flying stone size is 1.10 m, and flying stone volume reaches 0.38 m\({}^{3}\). The flying stone is deemed the one with the maximum volume. The nearest distance of the flying stone from the No. 4 hole is 7.44 m, and the rock size is 0.33 m.  
For this test only, the flying stone distance of the vertical hole layout is the largest (as shown in Figure 9), and that of the reverse hole layout is the smallest.
# Renal Ultrasound Elastography: A Review of the Previous Reports on Chronic Kidney Diseases
## 5 Ultrasound Elastography of Transplanted Kidneys  
Interstitial fibrosis, tubular atrophy, and glomerular sclerosis are important factors strongly associated with graft loss in transplanted kidneys [39]. The assessment of these pathological changes in transplanted kidneys relies on protocol biopsies; non-invasive modalities have not been established.  
Several studies have investigated the usefulness of ultrasound elastography in transplanted kidneys. However, the relationship between the pathological changes, particularly in interstitial fibrosis and elasticity, is controversial. [PERSON] et al. performed SWE in 18 patients who underwent a renal transplantation and investigated the association between renal elasticity and the Banff score or interstitial fibrosis [40]. They found a positive correlation between the SWV and the grade of fibrosis and the Banff score. Another study by [PERSON] et al. also investigated the relationship between the SWV and the grade of fibrosis in patients who underwent a kidney transplantation. In 30 patients with renal transplants, the SWVs showed no significant differences with respect to the degree of fibrosis [41]. They further confirmed their observation by precisely eliminating the influence of ultrasound transducer compression. As a result, they revealed no correlation between the SWV and fibrosis [42]. The discrepancies between these reports could be explained by the variations in the histological changes and the time since the transplantation. The histological changes observed in the early phase are characterized by fibrogenesis and tubulointerstitial alterations due to immunologic reactions whereas interstitial fibrosis, tubular atrophy, and glomerular sclerosis can be observed in the late phase [43]. Most patients recruited in the two studies by [PERSON] et al. were within one year after transplantation, which might reflect the early phase of the histological changes. Indeed, a stronger correlation between the chronic phase changes in the kidney and the SWV has been shown in a larger cohort [42]. Thus, the duration after transplantation and the heterogeneity of the histological changes should be considered when performing and interpreting the ultrasound elastography results. The variability in the SWE values obtained from transplanted kidneys has been discussed previously [44]. In a single-center prospective study on patients who underwent a renal transplantation, the SWE values in patients with renal allograft dysfunction were significantly higher than those in patients with a stable renal function. However, there was a significantly higher variability in patients with allograft dysfunction. A study by [PERSON] et al. compared the diagnostic abilities of SWE and magnetic resonance elastography for detecting renal allograft dysfunction [45]. SWE could not differentiate between patients with a stable renal function and allograft dysfunction; in contrast, magnetic resonance elastography was a sensitive modality to predict allograft loss.