esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Crow Search Algorithm for Solving Permutation Flow Shop Scheduling Problems
## 3 Problem Definition  
The PFSP requires running \(n\) jobs on a sequence of \(m\) machines, such that every job must be processed on all machines in the same order; the goal is to minimize the makespan. The detailed description is as follows: Given \(n\) jobs \(\{1,2,\ldots,n\}\), each job is processed on a series of \(s\) machines \(\{1,2,\ldots,s\}\) in a sequence defined by the job permutation. The job permutation \(\pi=\{\pi_{1},\pi_{2},\ldots,\pi_{n}\}\) is calculated by ordering the operations into a permutation. The processing time of job \(i\) on machine \(j\) is denoted by \(p_{\pi_{i},i}\), where each job is permitted to be processed on no more than one machine at a time and each machine can process only one job at a time. The completion time of job \(i\) on the machine is denoted by \(C(\pi_{i},k)\). The objective function for PFSP is expressed in Equation (3).  
\[\begin{array}{ll}C(\pi_{1},1)&=p_{\pi_{1},i},\\ C(\pi_{i},1)&=C(\pi_{i-1},1)+p_{\pi_{i},1},\quad i=2,3,\ldots,n,\\ C(1,\pi_{k})&=C(\pi_{1},k-1)+p_{\pi_{1},k},\quad k=2,3,\ldots,s,\\ C(\pi_{j},k)&=\max\,\{C(\pi_{i},k-1),C(\pi_{i-1},k)\}+p_{\pi_{j},k},\\ &\mbox{for}\quad i=2,3,\ldots,n;\quad k=2,3,\ldots,s.\end{array} \tag{3}\]  
The optimal permutation \(\pi^{*}\) from the set of all different permutations \(F_{m}\) (i.e., the permutation with the minimum makespan) can be found by the optimization in Equation (4):  
\[\begin{array}{ll}\pi^{*}=\arg\min C^{*}(\pi),\quad\forall\pi\;\in\;F_{m},\\ C_{max}(\pi)=C(\pi_{m},s).\end{array} \tag{4}\]
# LoRa Enabled Smart Inverters for Microgrid Scenarios with Widespread Elements
## 3 LoRaWAN Specifications and Applications
### Spread Spectrum Technologies  
LoRa-PHY is based on the chirp spread spectrum (CSS) modulation method [41]. CSS provides a possibility to sacrifice throughput to achieve higher sensitivity for the reception, which directly contributes to wider coverage. In other words, LoRa is capable of creating a long-range physical link by reducing the speed.  
Spread spectrum technologies were initially developed for classified and interference-sensitive applications. Later on, this quality of jamming and interference immunity made them perfect candidates for many-end-users (congested) networks. In general, the spread spectrum uses a much wider band that is required for the transmission [42; 43]. Apart from CSS, there are two other spread spectrum methods. The first is frequency hopping spread spectrum (FHSS), which is immune to detection, and jamming due to the very rapid changing (hopping) of the carrier signal frequency [44]. As the second technique, direct sequence spread spectrum (DSSS), alongside code division multiple access (CDMA), offer similar qualities and concentrate more on allowing multiple transmitters to share a single time slot in the same band [16].
## 8 Performance Evaluation
### Clustering  
The proposed clustering algorithm based on DBScan was compared to the well-known K-means clustering algorithm, as shown in Figure 9. The clustering accuracy achieved 88.2% for one target and 68.8% for two targets. The proposed clustering algorithm was more accurate than the K-means algorithm. To improve DBScan's performance, the weighting parameter \(\alpha\) in Equation (12) must be defined. The large \(\alpha\) causes the object to split into two clusters, whereas the small \(\alpha\) causes loose clusters with many noise points. Practically, we discovered that \(\alpha=0.25\) yielded better clustering performance. Outliers were blended into clusters when \(\alpha=0\). Points related to a certain target were divided into two groups when \(=1\) (standard Euclidean distance).
## 4 Analysis of On-Site Monitoring Results
### Analysis of Monitoring Results and Comparison
#### 4.2.2 Comparison between Numerical Simulation and On-Site Monitoring  
The comparison between numerical simulation and on-site monitoring is depicted in Figure 10. It is clear that the maximum settlement values in the existing underground pedestrian passage occur within the range directly above the centerline of the new shield tunnel. After the completion of the left tunnel, the distribution of settlement of the existing tunnels exhibits a \"V\"-shaped curve.  
The maximum measured and simulated settlement in Figure 10a was 15.29 mm and 14.94 mm, respectively. The distribution exhibits a \"V\"-shaped curve, whereas after the completion of the right tunnel, the distribution exhibits a \"W\"-shaped curve. The maximum measured and simulated settlement in Figure 10b is 17.67 mm and 16.06 mm, respectively. In summary, the simulated results align reasonably well with the actual monitoring data,  
Figure 10: Comparison between simulation and actual measurements. (a) Left tunnel construction; (b) right tunnel construction.  
supporting the validity of the numerical simulation and preparing for subsequent safety control measures.
## 5 Field Test Configuration and Results
### Results of Geometric Similarity Evaluation
#### 5.3.2 Results of Scenario Representativeness Evaluation
Particularly in high-speed driving situations, longitudinal following behavior may show markedly different characteristics from low-speed driving, necessitating testing under separate conditions for each speed range to verify representativeness. This inference suggests that the safety and performance characteristics of the longitudinal control algorithms in ADSs may depend on the speed condition. Figure 10: Heatmaps based on same-speed and different-speed comparisons. Figure 11: Geometric similarity for evaluating scenario representativeness. The geometric similarity evaluation methodology proposed herein must be scaled up to achieve comprehensive ADS validation. Specifically, to confirm the representativeness of scenarios, expanded experiments across wider speed ranges and various environmental conditions (road friction, weather, and traffic density) must be conducted. For speed-sensitive behaviors, such as in Scenario D, a more systematic analysis of geometric similarity based on various speed conditions within the same scenario must be performed before evaluating the representativeness of the scenario. In future research, the correlation between geometric similarity and actual ADS behavior should be examined to determine whether ADSs demonstrate similar safety and performance characteristics in scenarios with high similarity indices. This will provide important insights for evaluating the credibility and efficiency of XILS platforms. The results of this study have practical implications for ADS verification frameworks, suggesting that verification systems centered on integrated-behavior scenarios are more efficient and comprehensive than those based on single-behavior scenarios. Based on geometric similarities between scenarios, we highlighted the possibility of optimizing testing procedures by selecting a minimal set of representative scenarios that reflect the characteristics of various other scenarios. However, since this research included limited conditions, the generalizability of the findings must be strengthened through expanded experiments reflecting more diverse driving situations and environmental variables.
## 6 Results
### Security Analysis  
In our two-server setup, we assume that \(CSP_{1}\) and \(CSP_{2}\) are non-collusive. Our security model allows for one or both servers to be malicious, as our verifiable computation scheme can detect the malicious behavior and reject the computation result. However, they cannot cooperate with each other. Under the non-collusion assumption, suppose one of the CSPs is malicious; then, the two CSPs cannot collaborate to perform the correct computation because the other CSP updates its data, causing the temporary ciphertext to not correspond to the temporary key. In this process, the malicious CSP does not learn any valuable information.  
In our system, the function owner cannot obtain any additional information other than the function parameters. \(CSP_{1}\) learns only the original ciphertext, the temporary ciphertext after re-encryption and the input ciphertext. \(CSP_{2}\) learns only the temporary private key, the masked function parameters and the user's input ciphertext. The request user only learns its own input and the computation result. Any public verification party can only learn the input ciphertext, the result ciphertext and the public verification data.  
Next, we present the security analysis of our encryption schemes.  
* HE. The scheme is based on the hardness of the Decisional Composite Residuosity Assumption and Decisional Diffie-Hellman Assumption, which is stated below: [Decisional Composite Residuosity Problem (26)] Given a composite \(n\) and an integer \(z\), decide if \(z\) is an \(n-\)residue modulo \(n^{2}\) or not, namely, if there exists \(y\) such that \(z=y^{n}(modn^{2})\).  
Definition 2: (Decisional Diffie-Hellman Problem (24)) Let \(g\) be an element of prime order in a cyclic group \(\mathbb{G}\). Given \(g^{a},g^{b},h\in\mathbb{G}\), decide whether or not \(h=g^{ab}\).  
Theorem 3.1: _The scheme is semantically secure if and only if the Decisional Composite Residuosity Assumption and Decisional Diffie-Hellman Assumption hold._  
* LHE. The security is established in [3], and we state it below: [LEE is linearly homomorphic as long as \(\max\{|\gamma_{1}|,\ldots,|\gamma_{d}|\}\cdot prn^{1.5}<q/2\).  
Theorem 3.2: _Let \(r=\mathrm{poly}(n)\) and \(q=2^{n^{\varepsilon}}\) for some \(0<\epsilon<1\). The scheme is semantically secure under the worst-case hardness of approximating the shortest vectors on ideal lattices to within a factor of \(O(2^{n^{\varepsilon}})\)._  
* HPRE. It is based on the security of HE.
* VerM. It is based on the hardness of the Discrete Logarithm Assumption [9]. It will pass the verification by mistake with a probability of \(1/q\) at most, which is negligible when \(q\) is a \(\lambda\)-bit prime. It is stated below:  
**Definition 3**: _(Discrete Logarithm Problem) Let \(g\) be an element of prime order in a cyclic group \(\mathbb{G}\). Given \(h\in\mathbb{G}\), compute \(x\) such that \(h=g^{x}\)._  
**Theorem 4**: _The scheme is semantically secure if and only if the Discrete Logarithm Assumption holds._
# Fractal Features of Muscle to Quantify Fatty Infiltration in Aging and Pathology
## 2 Materials and Methods
### Image Processing and Analysis
#### 2.2.1 Analysis of MR Images  
The algorithms developed for the characterization of lumbar paraspinal muscles start with an image area selection by a semi-automatic procedure to define the region of interest (ROI) corresponding to the lumbar paraspinal muscle group considered. This ROI was used to delimit lean muscle area (contractile area) and the surrounding fatty muscle area. A simple count of pixel numbers belonging to these ROIs was performed for muscle composition estimation by classic indices such as cross-sectional area of the whole muscle mass (TotCSA), lean muscle mass (LeanCSA) and fatty muscle mass (FatCSA). The percentage of lean and fatty mass, and the ratio between lean and fatty mass (Lean/Fat ratio) were also calculated.  
LeanCSA was considered to quantify fatty infiltration by three independent indices:  
* \(\rho\): the ratio between lean (contractile) and fatty (non-contractile) portions of muscle tissue belonging to the lean muscle area (LeanCSA);
* \(\beta\): the lacunarity of lean muscle area, where fatty infiltration is assumed as lacunae;
* \(\mu\): the succolarity of LeanCSA, where fatty infiltration is assumed as the empty space within the muscle tissue which is percolated by a hypothetical fluid crossing the set in four directions (down to up \(\uparrow\); left to right \(\rightarrow\); up to down \(\downarrow\); right to left \(\leftarrow\)).  
The computational procedures to obtain these indices from LeanCSA are described below. These indices were compared with the classic indices of muscle composition described above. Figure 1 shows details of paraspinal muscle MRI from subjects of different age where the age-related increase in fatty infiltration is well observable. Figure 2 (left) shows a sketch of ROIs' selection for both muscle composition and fatty infiltration estimates.  
Figure 1: Paraspinal muscle fatty infiltration. Examples of paraspinal muscle MRI from subjects of different age and physio-pathological status: (**top**) young, 31 years; (**middle**) old, 71 years; (**bottom**) osteoporotic, 71 years. The age-related increasing branching of fatty infiltration with a fractal tree like pattern is more evident in osteoporosis than in ’healthy’ aging.
# Spatiotemporal Variation of Land Surface Temperature Retrieved from FY-3D MERSI-II Data in Pakistan
## 2 The Study Region and the Data  
### The Study Region  
Pakistan is counted as an agricultural country with differential topography and climatology. Figure 1 shows spatial distribution of landcover types, elevation and five subregions of the country. According to the land utilization report generated by the Statistics Bureau of Pakistan [20], an area of 34.15 million hectares (Mha) is cultivable/agricultural land, while the rest of the 23.60 Mha area are not available for cultivation. Two-thirds of the area in the country lies in an arid zone, which provokes slight climatic changes such as an increase of floods, heatwaves, and droughts, etc. [13]. With regard to climate, during the winter season, the average temperature recorded is between 2 and 23 \({}^{\circ}\)C throughout Northern Pakistan (Upper Indus Basin), while it is 14-20 \({}^{\circ}\)C in Southern Pakistan (Lower Indus Basin) [21,22]. During the summer season, the average maximum temperature can reach up to a range of 42-44 \({}^{\circ}\)C in the southern areas, whereas the northern areas can experience an average temperature of between 23 and 49 \({}^{\circ}\)C [21]. The total annual precipitation varies from greater than 2000 mm in the north to less than 250 mm in the south [21,23]. Pakistan has two major precipitation systems that produce rainfall. The monsoon system from the Bay of Bengal releases rain in the southeast and east from June, and continue until September [24]. Western disturbances from December to March brought precipitation to northern and western areas of Pakistan [24].
## 3 Available Techniques to Reduce the Bandwidth of the 360-Degree Video
### Tiling
#### 3.2.7 Sidelink-Aided Multiquality Tiled  
Dai, Yue [39] adapt sidelink is a modification of the basic LTE standard that enables device-to-device (D2D) communication in 360-degree streaming without the use of a base station. Allocate tile weight based on long-term weight (how often the tile was visited) and short-term weight (tile distance from the FOV). To find suboptimal solutions with minimal computational cost, a two-stage optimization technique is used to pick sidelink Receivers and Senders in stage 1 and allocate bandwidth and select tile quality level in stage 2.
# Monitoring Pediatric Head CT Scan Dose Levels:
## 3 Results and Discussion
In the age group of > 6 years, the European guidelines' DRL is 650 mGy- cm, while in Abu Dhabi, in the two age groups of \(\geq\)5y < 10 y and \(\geq\)10 y < 16 y, the local DRLs are 441 mGy cm and 568 mGy-cm, respectively. The comparison of DRL values among different countries is important for identifying the potential areas for improvement and for ensuring that the radiation doses delivered to patients are in line with international standards. While this study reveals that the LDRL values for CT brain procedures in Abu Dhabi are lower than those found in other selected countries, it is important to note that there may be variations in DRL values between different countries due to differences in population characteristics, imaging practices, and regulatory requirements. Although diagnostic reference levels (DRLs) provide guidance on the acceptable levels of radiation doses for specific imaging procedures, they do not provide a complete picture of the quality of pediatric CT imagi  
Figure 2: **(a)** Computed tomography DRLs (CTDIVol) for pediatric head CT scans; (b) computed tomography DRLs (DLP) for pediatric head CT scans. To ensure a valid comparison, we selected those studies (references [17, 18, 19, 20, 21]) that have the same age groups as those of our study. \begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{5}{c}{**European Guidelines’ DRL**} & \multicolumn{3}{c}{**Abu Dhabi LDRL**} \\ \hline
**Age Group** & **CTDIVol (mGy)** & **DLP (mGy-cm)** & **Age Group** & **CTDIVol (mGy)** & **DLP (mGy-cm)** \\ \hline
0 \textless{} 3 m & 24 & 300 & \textless{}1 y & 18.4 & 304 \\ \hline
3 m \textless{} 1 y & 28 & 385 & \textgreater{}1 y \textless{}5 y & 21.1 & 385 \\ \hline
1 \textless{} 6 y & 40 & 505 & \textgreater{}5 y \textless{}10 y & 23.4 & 441 \\ \hline \textgreater{}6 y & 50 & 650 & \textgreater{}10 y \textless{}16 y & 27.8 & 568 \\ \hline \end{tabular}
\end{table} 
Table 3: Pediatric head CT local DRLs in Abu Dhabi in comparison with European DRLs; “y” represents year, and “m” represents months. such as the appropriateness of imaging referrals, the use of dose-reduction techniques, and the quality of image interpretation can all impact the overall effectiveness of pediatric CT imaging. This requires collaboration between clinicians, radiologists, and technologists to ensure that pediatric CT imaging is performed in a safe, effective, and appropriate manner. Overall, the results of this study are promising and provide significant reassurance to healthcare providers and patients in the region. The ongoing monitoring and refinement of imaging protocols will help to ensure that radiation doses remain as low as possible while still providing the necessary diagnostic information. Finally, although the total number of patients considered in this study is not high, the number of patients in each age group is still larger than 10, which is the minimum number required to draw any concrete conclusion [12]. Thus, the results are statistically significant.
## 3 Results and Discussion
### Reduction in Deactivation Rate from Oils  
The effects of EOs on AMPAR's desensitization kinetics motivated the investigation of their influence on AMPAR deactivation (Figure 3). The average deactivation time for GluA1, GluA2, GluA1/2 and GluA2/3 recorded at; \(2.3\pm 0.1\) ms, \(2.1\pm 0.1\) ms, \(2.5\pm 0.2\) ms, \(2.5\pm 0.3\) ms, denoting an average rate of \(0.43\) ms\({}^{-1}\), \(0.48\) ms\({}^{-1}\), \(0.40\) ms\({}^{-1}\), \(0.40\) ms\({}^{-1}\), respectively. Upon treating the cells with Citronellol oil, the deactivation rates decreased for all AMPARs subunits. Hence, the deactivation was recorded at \(3.7\pm 0.2\) ms (\(0.27\) ms\({}^{-1}\)), \(3.2\pm 0.3\) ms (\(0.31\) ms\({}^{-1}\)), \(3.4\pm 0.2\) ms (\(0.29\) ms\({}^{-1}\)) and \(2.9\pm 0.4\) ms (\(0.34\) ms\({}^{-1}\)) for GluA1, GluA2, GluA1/2 and GluA2/3, respectively. Likewise, upon treating the cells with Geraniol oil, the deactivation for GluA1, GluA2, GluA1/2, and GluA2/3 increased to the following: \(3.2\pm 0.4\) ms (\(0.31\) ms\({}^{-1}\)), \(2.8\pm 0.1\) ms (\(0.36\) ms\({}^{-1}\)), \(3.1\pm 0.5\) ms (\(0.32\) ms\({}^{-1}\)), and \(2.7\pm 0.5\) ms (\(0.37\) ms\({}^{-1}\)).  
Figure 2: Effect of oils and compounds on AMPAR desensitization. (**A**). Desensitization from glutamate alone (black), glutamate + Citronellol (white) and from glutamate + Geraniol (grey). (**B**). Pair of representative whole-cell current traces from GluA1 channels (most significant) expressed in HEK-293 cells in the absence (left) and presence (right) of Citronellol oil. The recording was conducted upon 500 ms application of 10 mM glutamate to whole-cell recording from HEK293 cells expressing various AMPARs subunits. Data shown are mean \(\pm\) SEM; n = 4 (number of patch cells in the whole-cell configuration). A one-way ANOVA test was used to measure the significance: * \(p\) < 0.05; ** \(p\) < 0.01; *** \(p\) < 0.001; ns, not significant.
# Real-Time Contrail Monitoring and Mitigation Using CubeSat Constellations
## 2 Contrail Formation and Environmental Impact  
### Theoretical Background of Contrail Formation  
Contrails are formed when the hot, humid exhaust gases from aircraft engines mix with the cold, low-pressure air at high altitudes, causing the water vapor to condense and freeze into ice crystals that appear as a visible trail behind the aircraft. The Schmidt-Appleman criterion provides the theoretical framework for contrail formation, specifying that ambient temperatures below \(-40\,^{\circ}\)C and high relative humidity with respect to ice are necessary [16]. The exact thresholds depend on factors such as the aircraft's exhaust properties, including water vapor and particulates, and engine efficiency [17; 18].  
The critical temperature for contrail formation, \(T_{crit}\), is influenced by variables like ambient pressure, the specific heat of air, and the fuel's combustion characteristics. This allows for predictive modeling of contrail formation based on real-time atmospheric data. Other factors, such as fuel type and engine technology, also play significant roles in contrail formation [18]. While more efficient engines generate fewer soot particles, they may produce more water vapor, increasing the likelihood of contrail formation under certain conditions. These insights are crucial for improving predictive capabilities and devising strategies to minimize contrail-related climate impacts [19].
# Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
## 5 Proposed Classification
### Classification of RL-Based Routing Protocols Based on the Data Dissemination Process
Then, the geocast packet is delivered to the UAVs in that area. In this method, a geocast group includes a set of nodes in a particular geographic area. To determine the members of this group, each node must know its geographical location on the network. Therefore, they need a positioning system [64; 65; 66].
## 3 Optimized Dual-Channel Data Storage and Access Method
### De-Cross Access and Caching Method  
#### 3.3.1 Range Data Access and Caching  
In the range access mode, range lines are accessed one by one according to their order in the SAR data matrix. As the burst length is eight, each access can obtain data of two range lines. Since a sub-matrix datum with a size of \(N_{a}\times N_{r}\) is mapped to a row of DDR3, there are \(N_{a}\) data in each row of DDR3 at the same range line, and the data of two adjacent range lines are cross-stored in DDR3. When reading the data, read \(4N_{a}\) consecutive data in a row of DDR3 (the \(4N_{a}\) data belong to four consecutive range lines in the SAR data matrix), and then jump to the next row to read the remaining data on the four range lines until all data on these four lines have been read, as shown in Figure 9. When the range data are written back to DDR3 after being processed by the processing engine, the same address change method is used.  
Since the data of two adjacent range lines are cross-stored in DDR3, among the eight data obtained by each burst transmission, two adjacent data belong to different range lines. When performing data caching, the burst data corresponding to the first two range lines needs to be decross-mapped to RAM0 and RAM1, respectively, and the burst data corresponding to the next two range lines need to be decross-mapped to RAM2 and RAM3, respectively, as shown in Figure 10. Therefore, that the data storage order in the cache RAMs is consistent with the SAR data matrix, which is more convenient for the processing engine.  
Figure 9: Data access address change method in range direction. (**a**) The four range lines to be accessed in the SAR data matrix; (**b**) the physical addresses of the four range lines to be accessed in DDR3 (each cell represents eight burst transmission data in DDR3; the red dotted line represents the data access sequence in DDR3).