esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 LDCP Problem
### MILP Formulation  
As discussed above, the LDCP problem consists in selecting a PP node for DU processing for each cluster, and a PP node for CU processing for each demand. It is allowable to place DU and CU in the same PP node. The latency of flows realized over the network between the PP nodes selected and the source and destination nodes of the demand, using the routing paths given between these nodes, must be kept below the allowable limit. Moreover, the overall processing load in the PP nodes selected and the traffic volume in network links cannot be greater than the available capacity. Hence, binary variable \(y_{cv},c\in\mathcal{C},v\in\mathcal{V}^{D}\), indicates whether PP node \(v\) is assigned to cluster \(c\) for DU processing. There is a pair of binary variables \(u_{dv}^{D}\) and \(u_{dv}^{C}\), \(d\in\mathcal{D},v\in\mathcal{V}^{P}\), assigned to each demand, where \(u_{dv}^{D}=1\) and \(u_{dv}^{C}=1\) indicate that PP node \(v\) realizes DU/CU processing, respectively, for demand \(d\). Besides, binary variable \(u_{dv}^{CD}\), \(d\in\mathcal{D},v\in\mathcal{V}^{P}\) indicates that both CU and DU of demand \(d\) are placed in the same PP node \(v\). Binary variable \(y_{v},v\in\mathcal{V}^{P}\), denotes the activation of PP node \(v\). In other words, it is equal to 1 if either DU or CU processing is performed in this node. Binary variable \(x_{diffj}\), where \(d\in\mathcal{D},f\in\mathcal{F},i\in\mathcal{V}^{src}(d,f),j\in\mathcal{V}^{ dest}(d,f)\) indicates that flow \(f\) of demand \(d\) is realized between nodes \(i\) and \(j\). Binary variable \(x_{dfe},d\in\mathcal{D},f\in\mathcal{F},e\in\mathcal{E}\) indicates that flow \(f\) of demand \(d\) is routed over link \(e\). Binary variable \(x_{difffe},d\tilde{d}\in\mathcal{D},f,\tilde{f}\in\mathcal{F}\), and \(e\in\mathcal{E}\) indicates that flow \(f\) of demand \(d\) and flow \(\tilde{f}\) of demand \(d\) are both carried over link \(e\). Continuous variable \(w_{df},d\in\mathcal{D},f\in\mathcal{F}\) denotes latency of flow \(f\) belonging to demand \(d\). Continuous variables \(w_{dfe}^{stat}\) and \(w_{dfe}^{dyn}\), \(d\in\mathcal{D},f\in\mathcal{F},e\in\mathcal{E}\) represent, respectively, static and dynamic latency of flow \(f\) of demand \(d\) in link \(e\). Eventually, continuous variables \(w^{HEP}_{affe}\) and \(w^{LP}_{affe}\), \(d\in\mathcal{D},f\in\mathcal{F},e\in\mathcal{E}\) denote the latency of flow \(f\) of demand \(d\) introduced in link \(e\) because of either higher-/equal-priority ( \(w^{HEP}_{affe}\)) and lower-priority flows ( \(w^{LP}_{affe}\)). The notation used in the MILP model is summarized in Table 2.  
**LDCP-MILP formulation:**  
\[\text{minimize}\ z=A\cdot\sum_{v\in\mathcal{V}^{P}}y_{v}+\sum_{d\in \mathcal{D}}\sum_{f\in\mathcal{F}}w_{df} \tag{1}\]  
where \(z\) expresses the number of active PP nodes and total network latency, and \(A\) is a weighting coefficient (we assume \(A=10^{5}\)), subject to the constraints:
## Abbreviations  
AF atrial fibrillation AI artificial intelligence bSSFP balanced steady-state free-precession CAD coronary artery disease CAG coronary angiography CHD congenital heart disease CHESS chemically selective saturation CMRA coronary magnetic resonance angiography CS compressed sensing ECG electrocardiogram FISS fast interrupted steady-state fNAV focused navigation FRF free-running framework GIRF gradient impulse response function GRE gradient echo LIBRE lipid-insensitive binomial off-resonant radiofrequency excitation MPR multiplanar reconstruction MRI magnetic resonance imaging PCA principal component analysis PT pilot tone SG self-gating/self-gated SI superior-inferior SNR signal-to-noise ratio SIMBA similarity-driven multi-dimensional binning algorithm XD-GRASP extra-dimensional golden-angle radial sparse parallel
# New Framework for Human Activity Recognition for Wearable Gait Rehabilitation Systems
## 3 Signal Processing Techniques
### Multiresolution Analysis by Wavelet Transform for EMG Signal  
Wavelet transform is one of the most effective signal processing algorithms for electromyography (EMG) signal analysis. The EMG identification system makes extensive use of it. Multiple-level wavelet decomposition is utilized to extract the EMG features [34; 35]. The useful resolution components from the EMG signal were extracted using different levels of different mother wavelets. Noise and unwanted EMG parts can be eliminated through wavelet denoising. The main challenge is to choose the most suitable mother wavelet that suits the hidden target features.
# Gaining Insights into Conceptual Models: A Graph-Theoretic Querying Approach
## 6 OPM Query Implementation
### Path Finding Queries Implementation
#### 6.1.2 Shortest Path Implementation  
[PERSON] published his shortest path algorithm in 1956 [39] while trying to come up with something to show off the new ARMAC computers. He needed to find a problem and a solution that people not familiar with computing can understand, so he designed what is now known as [PERSON]'s algorithm. There are two options to execute the shortest path in Neo4j. One is the native way, directly, using the Cypher language [43], and the other is to use procedures from Neo4j Labs Graph Algorithms library [44]. Since the library was not developed by Neo4j, it is not officially supported by Neo4j. Therefore, our implementation decision was to use the native Cypher method. Code 3 presents the Cypher query for the shortest path.  
**Code 3.** Cypher query used to get the shortest path result  
```
1:MATCH (source: SSS_OPM_THING_TYPE [name:'SS_OPM_THING_TEXT'), (target: $T_OPM_THING_TYPE [name:'ST_OPM_THING_TEXT']),
2:p = shortestpath((source)-[*]->(target))
3:RETURN p
```  
The query finds the shortest path between the specified source and target based on the OPM thing type (object or process) and its name (the text of the OPM Thing), assuming the OPM thing names in the model are unique.
# Cross-Layer-Aided Opportunistic Routing for Sparse Underwater Wireless Sensor Networks
## 6 Performance Evaluation
### Simulation Results  
We first study the impacts of several parameters on CLOR network performance, and then evaluate CLOR against two cross-layer protocols, FBR and CARP, and an enhanced flooding-based protocol, EFIood, in terms of PDR, latency, energy efficiency, and Goodput.
# Counter-Terrorism Video Analysis Using Hash-Based Algorithms
## 6 Implementation and Performance Study
### Pre-Processing Algorithm  
The first proposed algorithm is responsible for converting any given video into a hash sequence. First, it divides the video into frames and selects the correct frame rate per second. If necessary, it rotates the images anti-clockwise so that they become horizontal and flips them so that the darkest half is on the left. Then, it resizes the images to 16 \(\times\) 16 pixels, converts them to black and white, generates the associated hash (Section 6.3) and dismisses the hash if it is constant.  
As a result, the method is resistant to flipped videos (which is a common practice to avoid copyright). We get a great size reduction too, 99.93% in our tests.  
When applied to our database (which contains over 5 million frames), it pre-processed the videos in 7 h, 46 min, and 24 s.
## 2 Materials and Methods
### Field Experiment  
The experiment was conducted with a randomized complete block design (RCBD) with three replications, three types of laser, four irradiation times, and two levels of laser intensity with control (without laser treatment) (24 treatment \(\times\) 3 replications). Field area was 6 m \(\times\) 25 m = 300 m\({}^{2}\), the total number of irradiated and planted seeds was 1200, and, after two weeks, the seeds thinned to 300 plants. The experimental field area was plowed in the fall. Fields were cultivated, disced, furrowed, and then plotted in early July 2018 before souring the seeds. Soil samples were collected randomly from the field and then transferred to the lab to measure the physical-chemical parameters (Table 1). Fields were fertilized with 520 kg/ha compost (46:18 N:P\({}_{2}\)O\({}_{5}\)), and, before souring, the soil was fertilized by urea of 436 kg/ha (half at 14 days after souring and the rest at the flowering stage). The seeds were collected from the Office of Agriculture Research, and the variety used in this experiment was cultivar Baghdad 3; all seeds were filtered to have the same size. Before souring, maize seeds were irradiated by three types of laser (red, green, and blue laser) for different exposure times--45, 65, 85, and 105 s--and with two level of laser intensity (4 and 2 mW/cm\({}^{2}\)); additionally, seeds without irradiation were used as control plants. After laser treatment, the seeds were transferred to the field and sourn at 4-5 cm depth with plant spacing of 30 cm length and 60 cm width. Irrigation was adjusted based on 80 mm of evaporation from pan class A and every five days during crop growth period. Weeds were hand-removed during growing seasons, and diazinon pesticide was used to control insects (1.5 kg/ha), applied 20 and 35 days after souring. Above ground, parts of the crop were harvested at 110 days (at maturity) from souring.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline pH & 7.2 \\ Electrical conductivity meq/100g soil & 3.8 \\ Potassium (mg/L) & 1.6 \\ Phosphorus (mg/kg soil) & 43.2 \\ Silt (g/kg soil) & 28.9 \\ Clay (g/kg soil) & 38.5 \\ Sand (g/kg soil) & 32.6 \\ Nitrate (\%) & 0.0017 \\ Ammonia nitrogen (\%) & 0.009 \\ Texture & Silty clay \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial physical and chemical properties of the experimental soil.  
Figure 3: UV–visible analysis for two statuses of maize seed (dry (**A**) and wet (**B**)) by wavelength, ranging from 190–1100 nm.
## 3 Results
### Precision Analysis
A statistically significant difference was found in 13 out of 26 measurements (\(p<0.05\)). In general, the dentist and student, both without the aid of AI, had more significant differences in precision compared to the rest of the examiner groups. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Measurements** & **Orthodonist** & **Orthodonist** & **Resident** & **Resident** & **Denist** & **Denist** & **Student** & **AI** & **AI** & **Mean** & **Mean** & **with AI** \\ \hline N/-Snf (mm) & 0.75 & 0.93 & 0.85 & 0.97 & 0.67 & 0.98 & 0.85 & 0.97 & 0.99 & 0.78 & 0.96 \\ Ser-Mv (mm) & 0.95 & 0.97 & 0.97 & 0.96 & 0.95 & 0.82 & 0.85 & 0.94 & 1.00 & 0.93 & 0.92 \\ N-ANS (mm) & 0.94 & 0.92 & 0.75 & 0.98 & 0.56 & 0.99 & 0.81 & 0.94 & 0.89 & 0.77 & 0.96 \\ AN-Max (mm) & 0.98 & 0.99 & 0.99 & 0.99 & 0.99 & 1.00 & 0.98 & 0.99 & 0.98 & 0.99 & 0.99 \\ Ar-Go (mm) & 0.96 & 0.92 & 0.93 & 0.99 & 0.81 & 0.97 & 0.91 & 0.99 & 0.98 & 0.90 & 0.97 \\ NA-Apo (\({}^{\circ}\)) & 0.83 & 0.87 & 0.84 & 0.70 & 0.59 & 1.00 & 0.13 & 0.90 & 0.91 & 0.60 & 0.87 \\ FH-Npo (\({}^{\circ}\)) & 0.81 & 0.95 & 0.88 & 0.95 & 0.71 & 0.94 & 0.65 & 0.94 & 0.96 & 0.76 & 0.95 \\ \hline Mean & 0.92 & 0.95 & 0.92 & 0.94 & 0.81 & 0.97 & 0.77 & 0.95 & 0.97 & 0.86 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c} \hline \hline
**Measurements** & **F-Value** & **\(p\)-Value** & **Pairwise Comparison** \\ \hline SNA (\({}^{\circ}\)) & 1.31 & 0.24 \\ SN-Palatal Plane (\({}^{\circ}\)) & 1.97 & 0.05 \\ SNB (\({}^{\circ}\)) & 3.98 & \textless{}0.01 & \(1<5,2<5,3<5,4<5,7<5,8<5,5>6,5>9\) \\ FMA (\({}^{\circ}\)) & 9.18 & \textless{}0.01 & \(1>7,2>7,3>7,4>7,7<8,7<5,7<6,7<9\) \\ SN-MP (\({}^{\circ}\)) & 5.51 & \textless{}0.01 & \(1>3,1>5,2<5,3<4,3<8,3>5,3<6,\) \\ \(3<9,4>5,7>5,8>5,6<5>9\) \\ Y-Axis (\({}^{\circ}\)) & 6.83 & \textless{}0.01 & \(1>5,2>5,3>5,4>5,7>5,8>5,5<6,5>9\) \\ \(1<8,2<7,2<8,3>6,4>4,8,7<8,8>5,\) \\  & & & \(8>6,8>9\) \\ ANS-PNS (mm) & 2.71 & 0.01 & \(7<5,7<6,7<9\) \\ Co-Gn (mm) & 2.4 & 0.02 & \(1<2,1<5,2>7,3<5,4<5,7<5,5>6,5>9\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of the one-way ANOVA analysis with a random effect used to test for the differences in precision between the methods. Figure 2: Mean intraclass correlation coefficient for each examiner group and percentages of improvement in precision.
## 3 Calculation
From the equation shown in Figure 8c, the speed to accumulate voids number decreases as equivalent aperture increases. The cumulative function of the voids number correlates well with the Exp function with R\({}^{2}\) of 0.99, which could be used to calculate the voids number of different sizes or accumulated voids number with a certain equivalent aperture range. On the other hand, the relationship between the accumulated void areas and equivalent aperture range is shown in Figure 9a,b. As Figure 9a shows, each section of OGFC sample -I has a good consistency with others, with minor fluctuation on the equivalent aperture ranging from 6-8 mm, 8-10 mm and 10-12 mm. For the OGFC sample -II in Figure 9b, a similar trend could be found, while for the equivalent aperture ranging from 6-8 mm, 8-10 mm, and 10-12 mm, a more remarkable  
Figure 8: Relationship between accumulated number of voids and the equivalent aperture, (**a**) OGFC sample I, (**b**) OGFC sample II, (**c**) Average value of OGFC samples. fluctuation could be noted. Figure 9c shows the relationship between average accumulated voids areas and equivalent aperture range, the fluctuation on the equivalent aperture ranging from 6-8 mm, 8-10 mm, and 10-12 mm is more apparent. As in vertical sections, the number of voids whose equivalent aperture ranges from 12-14 mm is few and there are no voids with the equivalent aperture ranging from 14-16 mm, it is understandable that there is no large variation in accumulated voids areas. However, as aforementioned, as equivalent aperture increases, the number of voids would decrease, while the trend of accumulated voids areas differs from the accumulated voids number. Additionally, as the equivalent aperture range increases, the speed to accumulate voids areas would firstly increase then decrease, and the inflection point is obtained by calculated the point when the second derivative of the equation reaches 0. The result of the inflection point is about 8 mm, which means that the speed to accumulate voids area becomes the highest when the equivalent aperture is 8 mm, indicating that those air voids account for the largest part of the total area. Before equivalent aperture reaches 8 mm, the speed to accumulate number of voids decreases while the speed to accumulate voids area would increase. It means that before the equivalent aperture reaches 8 mm, the contribution to the total voids area would become greater as the equivalent aperture increases, and then the contribution to the total voids area would decrease. The cumulative function of voids area fits DoseResp function well with 0.99 R\({}^{2}\), which could be used to calculate the proportion of voids area with a specific size or the accumulated voids area with a certain equivalent aperture range along the vertical direction. Figure 9: Relationship between accumulated voids area and the equivalent aperture, (**a**) OGFC sample I, (**b**) OGFC sample II, (**c**) Average value of OGFC samples.
## 3 Results
### Habitat Quality Change
#### 3.2.4 Future Habitat Quality Projection  
The InVEST method was employed for assessing habitat quality in accordance with the land use projection results in 2050 (Figure 10). While acknowledging the uncertainty of future changes to the transport network, we focused on major roads as they represented the primary infrastructure impact on habitat quality, an approach validated by previous research.  
Our analysis shows a decline in excellent- and good-quality habitats within natural areas, while very poor-quality habitats will increase around urban and high-altitude zones. The Qinghai-Tibet Plateau demonstrated enhanced habitat quality. From 2020 to 2050, excellent habitats displayed an \"island effect\" in coastal areas, while medium-quality habitats formed a belt-like pattern across central China.  
We used the aforementioned methodology to analyze the habitat quality levels in 2050 (Table 8). The 2050 projection reveals concerning patterns in different land use classes. Cropland will be dominated by medium-quality habitats, with no excellent- or good-quality areas remaining. Unused land showed polarization between minimal good-quality areas and extensive poor-quality regions, indicating challenges for ecological preservation.  
Figure 9: Area of habitat quality levels by land use classes (unit: 10,000 km\({}^{2}\)).  
This projected degradation of habitat quality poses a significant threat to biodiversity, particularly in the urban-rural transition zones, where habitat fragmentation is most severe. The conversion of excellent- and good-quality habitats could have a significant impact on species migration corridors and ecosystem stability, with forests and grasslands experiencing the most significant losses in terms of both area and quality. These changes are primarily driven by urban expansion and insufficient ecological restoration.
## 3 Results
#### 3.1.1 Comparison of Proposed and Realized Training Volume through PASm-12 (Q1)
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Athlete** & **Training Zone** & **Heart Rate (bpm)** & **Tempo/Speed (min/km)** \\ \hline \multirow{8}{*}{S1} & RL/LDL & 125 & slower than 6:32 \\  & MDL & 126–144 & 6:32–5:30 \\  & TDL & 145–155 & 5:30–5:01 \\  & ETL & 156–162 & 5:01–4:41 \\  & ITL & 163–168 & 4:41–4:24 \\  & SB & **169–max.** & faster than 4:24 \\ \hline \multirow{8}{*}{S2} & RL/LDL & 162 & slower than 5:33 \\  & MDL & 163–169 & 5:33–4:55 \\  & TDL & 170–175 & 4:55–4:24 \\  & ETL & 176–178 & 4:24–4:06 \\  & ITL & 179–181 & 4:06–3:58 \\  & SB & **182–max.** & faster than 3:58 \\ \hline \multirow{8}{*}{S3} & RL/LDL & 134 & slower than 5:40 \\  & MDL & 135–148 & 5:40–4:48 \\  & TDL & 149–158 & 4:48–4:20 \\  & ETL & 159–166 & 4:20–4:01 \\  & ITL & 167–173 & 4:01–3:39 \\  & SB & **174–max.** & faster than 3:39 \\ \hline \multirow{8}{*}{S4} & RL/LDL & 90 & slower than 9:39 \\  & MDL & 91–115 & 9:39–7:03 \\  & TDL & 116–126 & 7:03–6:30 \\  & ETL & 127–145 & 6:30–5:07 \\  & ITL & 146–151 & 5:07–4:48 \\  & SB & **152–max.** & faster than 4:48 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Training zones and maximum heart rate determined through the Bruce test for female runners S1–S6. Based on initial laboratory data, we proposed and then monitored PASm-12's training intensity for each athlete. Each mesocycle and microcycle had pre-set training intensity values (proposed values) applied in practice (realized values) through PASm-12.
## 3 Results
### Vortex Strength  
As commented on at the beginning of Section 3, the vortex peak vorticity is one of the main characteristics of a vortex, so the maximum value of the streamwise vorticity was studied for cases and incident angles at a plane placed to \(5\delta\) distance from the TE of the VG, see Figure 9a. Additionally, the strength of the vortex was evaluated in the present study by the circulation (\(\Gamma_{5}\)) defined by Equation (4), see [PERSON] et al. [(33)]. It was calculated by finding the peak vorticity over the area surrounding the vortex core in cross-flow plane positions normal to the wall and at the distance of \(5\delta\) downstream of the VG trailing edge. Figure 9b presents the values of the circulation against the device heights.  
\[\Gamma_{5}\ =\ \frac{\omega_{peak}\cdot\pi\cdot R_{0.5}}{0.693} \tag{4}\]  
The circulation measured at \(5\delta\) roughly varies for cases where device heights are larger than 0.4H. Furthermore, the highest values are obtained for \(\beta=18^{\circ}\) and \(\beta=20^{\circ}\) incident angle cases, following a similar trend in these two incident angles. In agreement with the values obtained for wall shear stress in Figure 8c,d, where the highest values are for \(\beta=18^{\circ}\) and \(\beta=20^{\circ}\) and 0.4H and 0.6H VG heights, Figure 9b shows that the highest circulation values are reached mostly for the same incident angles and heights. Regarding this issue, 0.4H and 0.6H VG heights and \(\beta=18^{\circ}\) and \(\beta=20^{\circ}\) incident angles show the best performance in terms of vortex strength and generation of wall shear stress.
## 3 A Prototype System for Pedestrian Positioning
### 3D Map of an Urban Terrain  
A precise 3D map of an urban terrain was provided by the local cartographic office of Lodz (MODGiK). The map is divided in layers containing: roads, buildings, entrances to buildings, steps, fences, lawns, trees, bushes, ponds, lamp posts and many other data not used in the project. The accuracy of building deployment is in an order of single centimetres and the buildings' heights were measured with a bit worse accuracy. The radius of trees' crowns was assumed 3 m and the radius of lamp posts 0.5 m.
## 2 Theoretical Foundation
### Multi-Objective Sparrow Optimization Algorithm with Non-Dominated Sorting  
The search performance of the optimization algorithm directly determines the extent to which the optimization results can approach the global optimal solution. The sparrow search algorithm (SSA) is a metaheuristic algorithm that was proposed in 2020. It is known for its simple structure, ease of implementation, and strong local search capabilities. SSA has shown significant advantages in solving single-objective optimization problems, as demonstrated by benchmark function tests [30; 31; 32]. However, it was originally designed for single-objective optimization problems. To extend its use to multi-objective optimization, non-dominated sorting is required to compare fitness values. In summary, this article builds upon the improvement strategy of a single optimal point proposed in reference [32]. It introduces a variable spiral factor and combines it with a per dimension lens imaging learning strategy to enhance the early algorithm's exploration ability and escape local extreme points. Additionally, to solve the multi-objective optimization problem more effectively, the article introduces the non-dominated sorting method, which enables the original search algorithm to search for the Pareto optimal frontier instead of a single optimal point. The pseudocode for the algorithm is presented in Figure 3. The MOSSA algorithm utilized in this article has been implemented using MATLAB scripts [33]. Using the DK and SK surrogate models, the article seeks the Pareto optimal solution to the multi-objective optimization problem of the pulse correction projectile through MOSSA.