esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
A 3D scanner based on white LED light, known as the EinScan 3D Scanner, was used in this research. This 3D scanner consists mainly of three components: a turntable, scanner stand, and scanner head, as shown in Figure S1 of the Supplementary File. White LED light is thrown from the scanner head onto the physical object that is placed on the turntable. The round turntable also determines the maximum and minimum size limits of the 3D physical objects to be scanned. The maximum size limit is 200 \(\times\) 200 \(\times\) 200 mm\({}^{3}\). Physical objects with a size of less than 30 \(\times\) 30 \(\times\) 30 mm\({}^{3}\) are not recommended for the EinScan 3D Scanner. Almost any three-dimensional, intricate physical object (which is nonglossy, not shiny, or non-reflective) may be scanned whose geometric dimensions do not exceed the prescribed size limits.  
To keep the experimental methodology simple, a simple box object (i.e., sample one, or S1) and a complex geometrical object (i.e., sample two, or S2) were investigated in this work, as shown in Figures 1 and 2. Engineering drawings with dimensions and tolerances for Figures 1 and 2 are presented as per the guidelines described by the American Society of Mechanical Engineers' standard, known as ASME Y14.5- 2009.  
After the object was bombarded with light patterns, the resulting light patterns were then received by the video camera to finally digitize the physical object, termed the \"3D scanned object\". Before the object was scanned and further used for additive manufacturing, the quality of the 3D scanning was largely affected by the 3D scanning factors and their levels, which were set using EinScan Software. A full factorial design of the experiment (DOE) was conducted in this research, using five 3D scanning factors for samples S1 and S2. These factors can be divided into two categories based on the 3D scanner's operation, i.e., factors set before (pre-scanning factors) and after (post-scanning factors) 3D scanning.  
Figure 1: Geometric dimensions of Sample 1 (S1) (mm).
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 5 Reinforcement Learning
### Neural-Network-Based Function Approximator  
If the initial state-action value function Q\({}_{\pi}\)(s, a) is modified for every time instant 't', then it will converge to Q\({}_{\pi}^{*}\)(s, a) as \(t\rightarrow\infty\). However, this is not easy to achieve. The primary cause for this is due to its implementation in a non-stationary environment. Real-world environments, such as the 5G-UAV integrated network considered in this paper, are often dynamic and may change over time. In such environments, the optimal policy itself may change, making it difficult for the algorithm to converge. Frequent updates of the state  
Figure 8: Representation of Q-table working.  
action values can exacerbate this issue as the learning process might not be able to keep up with the changes in the environment. To counteract this, we use a function approximator.  
At a given time, a neural network with its weights is defined as \(\varphi_{t}\ e\ \mathbb{R}^{uv}\). Also, if \(\varphi_{t}\ :=\ \vec{vcc}(\varphi_{t})\ e\ \mathbb{R}^{uv}\) is defined, a function approximator \(Q_{\pi}(s,a;\varphi_{t})\approx Q_{\pi}{}^{*}\ (s,a)\) is constructed.  
This neural-network-based function approximator forms the DQN. An important component of neural networks is activation functions. The sigmoid function, as shown in (2), is a popular choice [31] for the activation function. The sigmoid function is a mathematical function that maps any real-valued number to a value between 0 and 1. It is defined as  
\[\sigma:x\hookrightarrow 1/(1+e^{-x}) \tag{2}\]  
The deep network is trained by modifying \(\varphi\) for every t to reduce the mean-squared error loss represented by \(K_{t}(\varphi_{t})\):  
\[\underset{\varphi_{t}}{\text{minimise}}\ K_{t}(\varphi_{t}):=E_{s,a}\Big{[}(m _{t}-Q_{\pi}(s,a;\varphi_{t}))^{2}\Big{]} \tag{3}\]  
where  
\(m_{t}:=E_{s^{\prime}}[w_{s,s^{\prime},a}+\delta max_{a^{\prime}}(Q_{\pi},a^{ \prime};\varphi_{t-1})|s_{t},a_{t}]\) is the approximate value of the function at t, when the current state is \(s\) and action \(a\). Interacting with the environment in this manner and making a prediction, comparing it against the correct response, and suffering a loss \(K_{t}(\cdot)\) is termed online training. The associated gNB relays the UAVs' feedback data to a central location for DQN training in online learning [31].  
The dimension of the input layer is the no. of states \(\ |\mathcal{S}|\ \), while the output layer is set to the no. of actions \(|\mathcal{A}|\). A small depth is chosen for the hidden layer as it has a direct impact on the computational complexity. This is shown in Figure 9.
# An Optimization Methodology for Adapting Legacy SGX Applications to Use Switchless Calls
## 5 Evaluation
### End-to-End Performance Evaluation  
We evaluate the end-to-end performance of SGX-Box, including TLS decryption and pattern matching, when applying switchless calls by varying the number of patterns in the ruleset. We use the DFC [36] string pattern matching algorithm and a custom regular expression matching engine with a commercial ET-Pro ruleset [37] for pattern matching. We measure the throughput for 32 long-running flows where each flow sends 1 KB-sized random packets. Figure 7 shows the end-to-end throughput of (1) using traditional ECALL, (2) naively adopting switchless calls for every ECALL, and (3) adopting switchless calls based on our optimization strategy. As the result shows, naive adoption of switchless  
Figure 5: The comparison of CPU time breakdown between an ECALL and a switchless ECALLcalls significantly degrades the performance of SGX-Box by 80% on average compared to utilizing the traditional ECALL interface. In contrast, our optimization delivers 1% improved performance on average compared to the non-switchless version of SGX-Box. Note that there is no enclave transition during the packet inspection, including exact string matching and regular expression matching, and most of the CPU time is consumed by packet inspection. In other words, it reduces the proportion of overhead that comes from enclave transition, which makes switchless optimization less effective. Therefore, the end-to-end throughput is saturated with the throughput of packet inspection. However, for the case of naive adoption, redundant scheduling of worker threads hinders SGX-Box threads from proceeding with packet inspection, which leads to the pending of the SGX-Box thread.
## 3 Rolling Optimization Algorithm
### Programming Steps  
The pseudo-code of the proposed algorithm is shown in Algorithm 1. Therein, the collision prediction process and avoidance maneuver are in bold. In the beginning, the algorithm searches the candidate points randomly. With the iteration number increasing, the eigenvalues tend to be stable. Shorter trajectories of UAVs are assigned to more probability. That is how the ROA works.  
Figure 5: The process of collision prediction and avoidance maneuver.  
```
Input: start position, destination position, the number of UAVs, the number of random searches in one iteration \(SN\), the number of iteration \(IN\), the expected time \(t\) Output: best trajectory \(T\), distance of the best trajectory \(D\) for\(in=1:IN\)do for\(sn=1:SN\)do while\(time=1:t\)do  Calculate the state space for the next move;  Remove the states disobeying the constraints;  Calculate weight (relevant to distance, angle, eigenvalue) for each state;  Select state as the next positions \(p^{t+1}\) with roulette method; Collision Prediction: whilecollisiondo  calculate the state space \(p^{t+2}\); ifone state is collision-free; then  break; else Collision Avoidance Maneuver:  Set the weight of selected state \(p^{t+1}\) as 0;  Select another state \(p^{t+1}\) with roulette method;  Local update of eigenvalue of waypoints in each trajectory;  Calculate the distance for each trajectory;  Global update of eigenvalue of waypoints for the shortest distance; ifthe shortest distance is shorter than \(D\)then  D = the shortest distance;  T = the shortest trajectory;  return D,T;
```  
**Algorithm 1**Pseudo-code of ROA
## 4 Interference Analysis
### Mathematical Models for Interference Calculation
#### 4.2.3 MVDS Side Lobe Interference  
This subsection will present the MVDS details and the mathematical formula used to calculate the interference power received by GEO satellite S2 due to the radiation from the side lobes of the MVDS transmitting antennas. This model will consider the key parameters influencing this interference, including the MVDS transmitter's power, the antenna's off-axis radiation pattern, the angular separation between the MVDS transmitter and the GEO satellite, and the path loss experienced by the interfering signal as it propagates through the atmosphere and space.  
The lowest satellite elevation observed in Beirut, located at 35 degrees east longitude, has a footprint that covers the city. The satellite's footprint coverage is shown in Figure 2[21]. S2 has a figure of merit \(\left[\frac{\text{G}}{\text{T}}\right]_{\text{S2}}\) of 5.6 dB/K in the coverage region where Lebanon is located.  
To accurately assess the potential for interference from an MVDS system to GEO satellite S2, we analyze the side lobe radiation patterns of a representative 1.2 m parabolic antenna, commonly employed in MVDS transmissions in Lebanon. Figure 3 illustrates the far-field elevation pattern of the antenna at 14 GHz. The antenna exhibits a high directivity with a maximum gain of 41.5 dB at 0.0 degrees and a narrow 3 dB beamwidth of 1.1 degrees. This orientation typically corresponds to the direction of the intended terrestrial coverage area. The side lobe levels are relatively suppressed, with the highest side lobe being approximately \(-11.3\) dB below the main lobe peak, resulting in a gain of around 30.2 dB at approximately \(\pm 2\) degrees off the main beam. This gain level is substantial and can induce interference at the GEO satellite (S2) if the side lobe happens to be directed towards it. While other secondary lobes also contribute to the overall interference potential, for the purpose of establishing a conservative, worst-case interference estimate, we assume that the peak of the first secondary lobe, with a gain of 30.2 dBi, is perfectly aligned with the direction of GEO satellite S2. This assumption facilitates the derivation of robust recommendations and mitigation strategies aimed at minimizing interference from MVDS transmissions.  
The interference radiated from MVDS can be calculated by utilizing Equation (19) [5]:  
\[\mathrm{I}=\frac{\mathrm{P}_{\mathrm{MVDS}}}{4\pi\mathrm{R}^{2}}\mathrm{G}( \theta)_{\mathrm{MVDS}}\,*\,\frac{\lambda^{2}}{4\pi}\,*\,\frac{1}{\mathrm{BW}} \tag{19}\]  
where \(\lambda\) represents the wavelength in free space at the operating frequency, and \(G(\theta)_{MVDS}\) represents the antenna's off-axis performance. These parameters collectively contribute to the formulation of interference calculations and are instrumental in assessing the impact of MVDS transmissions' interference on GEO satellites.
# Solar Potential in Saudi Arabia for Southward-Inclined Flat-Plate Surfaces
## 3 Results
### Evaluation of the PV-GIS Tool  
Given that all data used in this study were downloaded from the PV-GIS platform, the question arises as to the accuracy and reliability of these data. For this reason, various studies have presented validation results for the solar radiation PV-GIS-satellite-derived data by comparing them with ground-based solar radiation measurements from 30 BSRN stations [30; 34; 35]. The reported % differences in the form of \"100\"(PV-GIS-data--station-data)/station-data\" were found to vary between \(-\)14% to +11%. To further demonstrate this, comparison of monthly mean H\({}_{\rm g,0}\) values derived from solar radiation measurements continuously performed at the Actinometric Station of the National Observatory of Athens (ASNOA, 37.97\({}^{\circ}\) N, 23.72\({}^{\circ}\) E, 107 m above sea level) was made with respective values from the PV-GIS platform in the period 2005-2011. The ASNOA site was selected as Saudi Arabia does not possess any permanent solar radiation measuring network nor isolated measuring radiometric stations on a systematic basis. The only station that participated in many international networks (e.g., AERONET, BSRN) is the Solar Village one (18.23\({}^{\circ}\) N, 42.66\({}^{\circ}\) E, 2039 m above sea level) that was in operation during the period March 1998-December 2002, a period that does not include the starting date of PV-GIS (January 2005). Figure 8 shows this comparison. Although there is an excellent agreement qualitatively (R\({}^{2}\) = 0.99), it seems that the PV-GIS-retrieved data overestimate the measured H\({}_{\rm g,0}\) by a factor of 1.1; this ratio is defined by any H\({}_{\rm g,0}\) value on the best-fit line to the corresponding one on the 1:1 line. In other words, the PV-GIS data are +10% higher than the ASNOA values, in accordance to the error range mentioned above. Nevertheless, as this factor is small and R\({}^{2}\) is high, the PV-GIS data are considered acceptable for solar energy analyses.
## 4 Application of the Hierarchical Trajectory Clustering Algorithm
Following the initial clustering, sub-route clustering was applied based on predefined reanalysis and adoption criteria. Cluster 1 yielded two sub-clusters, Cluster 1-1 and Cluster 1-2, which included noise clusters and were adopted. Similarly, Cluster 2 adopted sub-clusters Cluster 2-1 and Cluster 2-2, which also contained a noise cluster. Cluster 3 underwent two rounds of sub-clustering, leading to the adoption of a noise cluster and sub-cluster, Cluster 3-1-1. Cluster 4 did not meet the sub-route clustering criteria and remained a single cluster from the initial DBSCAN results. Finally, Cluster 5 was divided into sub-clusters Cluster 5-1 and Cluster 5-2, which included a noise cluster. The algorithm concludes by forming eight distinct route clusters, excluding the noise clusters. By analyzing the subdivided sub-routes, it was observed that despite the availability of recommended routes for entering or exiting Mokpo waters, many ships navigate closer to the shore to save time. Based on this, information can be obtained regarding the main types of vessels that sail closer to land to reduce travel time or the purposes for which inbound and outbound vessels primarily use such routes. Similarly, distinguishing between routes 5-1 and 5-2 can identify the characteristics of ships choosing these routes. This information can be communicated to VTS operators and navigators, serving as key data for preventing ship accidents, and is expected to be useful as fundamental data for improving maritime traffic conditions. Figure 16: Final result of Hierarchical Trajectory Clustering Algorithm. (**a**) 776 ship trajectories. (**b**) Final noise Cluster. (**c**) Results of reflecting the final eight clusters in the survey area.
# Metal Element Traces Sampled from Peri-Urban Road Verge Particulate Matter
## 3 Results and Discussion
### Determination of Metal Concentrations from Dust Samples  
Metals were found in the dust samples, with a different concentration level between the two areas of sampling.  
Analysis of the dust samples taken from the road verge has detected Na (sodium), Mg (magnesium), Al (aluminum), Si (silicon), Pb (lead), K (potassium), Ca (calcium), and Fe (iron) chemical elements in different concentrations, as shown in Figure 3.  
Figure 3: Si concentrations found in the road verge samples.  
Figure 2: Placement of the spectrometer for sampling within the two measurement episodes.  
Not all chemical elements are present in each sample; Pb was detected in only one of the samples, and K was present in six of the seven total samples.  
Of the chemical elements present in different concentration across all samples, the highest concentration is of Si, ranging from 4.44% to 12.46% composition (detailed in Figure 3), while the other metal elements present concentrations between 0% and 3.29%, as shown in Figure 4.  
Looking to the dust samples taken from the garden area with dense vegetation, the metal concentrations are varied, without any presence of Pb. The rest of the metals have varied concentrations, as shown in Figure 5, but with a reduced concentration.  
In one of the samples obtained from the road verge, lead (Pb) was detected, possibly attributable to residual soil deposits. The reasoning is explained in the introduction part of this article, with the literature highlighting how Pb is deposited and why it is found in the soil.  
All metals present in the garden area samples are naturally found in soil, with increased concentrations for Al and Si due to their usage as fertilizer compounds in the garden.  
An explanation for why Si is found in higher concentrations in the road verge could be trucks and construction vehicles passing through the area. The residential area is constantly growing and developing with new residential homes; therefore, a lot of construction sites are present. Silicon is usually found in dust particles around construction. Dust containing silicon is spread by the tires of vehicles that have passed through areas exposed to construction materials such as cement. Cement dust can be easily transported and spread in different areas. The levels of silica exposure in occupational settings are comprehensively documented and globally estimated. Silica dust is responsible for causing debilitating conditions such as silicosis and lethal lung diseases. Consequently, numerous countries across the globe have established air quality standards tailored to occupational environments, which are focused on regulating the percentage of silica present in the ambient air. A multitude of operations, such as drilling, blasting, crushing, screening, transportation, pulverization, galvanizing, and various metalworking processes, generate significant dust emissions, including the presence of silica in the air [32].  
Figure 4: Metals present in the analyzed dust samples from the road verge.
## 3 Results and Discussion of Biosensor Formation and Its Validation
### Comparison to ELISA  
A necessary element of the verification of a newly developed analytical method is a comparison with a recognized standard method. We used commercially available ELISA tests as a standard method. These had the following LOD and LOQ parameters: for VEGF-A, LOD = 9 pg/mL, LOQ = 27 pg/mL; for FGF-2, LOD = 3 pg/mL, LOQ = 9 pg/mL. Using the newly developed method, simultaneous quantification of VEGF-A and FGF-2 was performed in the serum of children with diagnosed leukaemia (ALL type) and in the blood serum of the control group. A total of 10 samples (4 leukaemia samples [L] and 6 control samples [C]) were tested. The results were compared with those obtained with the commercial ELISA tests. The data are summarized in Table 6.  
The Mann-Whitney U test was performed to characterize the differences in concentrations of the studied analytes between the developed analytical method and the ELISA test. The lack of statistical significance of the statistical test performed for the developed method in relation to the ELISA method proves the lack of significant differences between the concentrations obtained with the SPRi biosensors and ELISA. Figure 10 in the Appendix A shows graphs of the results of the Mann-Whitney U test.
# Assessment of Carbon Emissions at the Logistics and Transportation Stage of Prefabricated Buildings
## 5 Discussion  
Figure 1 presents the carbon emissions of general building materials, precast loading and unloading, and off-site transportation of precast components, where the carbon emissions of off-site transportation of precast components correspond to a full-load rate of 70%. The graph shows that the carbon emission level of the transportation stage of prefabricated components accounts for a larger proportion of the carbon emissions of the transportation stage of assembled buildings, at 76%. The transportation of building materials and loading and unloading of prefabricated components account for smaller proportions, at 16.71% and 7.25%, respectively. As can be deduced from the carbon emissions proportions in the figure, it is clear that the accuracy of the carbon emission calculations for the whole transportation phase depends on how well the transportation of the prefabricated components is calculated.  
In Figure 2, it can be seen that the carbon emission level of the precast components decreases with an increase in the full-load rate, showing a non-linear trend. Compared with a full-load rate of 50%, the carbon emission levels in the transportation phase of the assembled building could be reduced by 54.32% at a full load of 100%. The figure indicates that different full-load rates have a large effect on the carbon emission level of prefabricated components during the transportation phase. This demonstrates the importance of taking the full-load rate into account when determining the value of the carbon emission factor. It also proves that the formula used for calculating the carbon emission level during the transportation phase of prefabricated components was accurate.
## 3 Monitoring and Analysis of Subsidence in Shield Tunneling Under-Passing the Bridge and Culvert
### Monitoring and Analysis of Subsidence in Bridge and Culvert  
When monitoring the abutment subsidence, the data of each monitoring point were collected 12 times, once a day, in the process of the shield tunneling under-passing of the culvert. The subsidence curve was plotted with the monitoring data of the three measuring points at the left and right abutments 1, 8, and 10, as shown in Figure 14. The curve indicates that neither continuous sinking nor rising occurs at the abutment in the shield-construction process. The maximum subsidence value is 1.98 mm. With the shield tunneling, after the third and ninth day of monitoring, the abutments were lifted to varying degrees, with the lifting amount not exceeding 1 mm. The second lifting amount was smaller than the first, and the maximum floating value of the position was 0.57 mm. The maximum relative settlement between the monitoring points with the same serial number on the left and right abutments was 1.29 mm, exerting no effect on the safety of the bridge structure. In general, the greater the soft-rock height ratio of the shield-heading face at the location of the culvert, the larger the subsidence value of the culvert. However, during the process of shield-driving through the pile foundation, the influence of stress release caused by the tunnelling on the culvert was greater than that of the change in the height ratio of the soft rock on the excavation face. Therefore, the final settlement value of the monitoring point \"L-10\" was larger than that of the monitoring point \"L-08,\" even though the height ratio of the soft rock on the tunnel-excavation face corresponding to the monitoring point \"L-08\" on the left abutment was higher than that of the monitoring point \"L-10.\"  
Figure 14: Abutment settlement curve.  
Figure 13: Final settlement curve of longitudinal surface of left tunnel.