esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
Homogeneity was confirmed in the pre-assessment between groups (\(p>0.05\)). No subjects were excluded. In the pre- and post-comparisons within the groups, there was a significant difference in both groups in the Y-balance test and the toe touch test (\(p<0.05\)). For ROM, only the right dorsiflexion of the NDG demonstrated a significant difference (\(p<0.05\)). For SLR, only the right SLR of the NDG did not show a significant difference (\(p>0.05\)). In the comparison of changes between the two groups before and after the intervention, there was a significant difference only in the left SLR (\(p<0.05\)) (Table 3). In the intragroup comparison, DTG showed a change from 60.88 to 72.47 in the left straight leg raise (SLR), while NDG changed from 66.77 to 72.65. Therefore, in the intergroup comparison of pre-post change, DTG exhibited a result of 11.59, whereas NDG had 5.88. In other words, DTG demonstrated greater improvement in left SLR compared to NDG. There was a significant difference in satisfaction and participation in the lower extremity stretching program between the two groups (\(p<0.05\)) (Table 4). There was no significant difference in \"A1\", \"A2\", \"A5\", \"A7\", \"A9\", and \"A0\" (\(p>0.05\)), but there was a significant difference in \"A3\", \"A4\", \"A6\", and \"A8\" (\(p<0.05\)) (Table 5). In the comparison of satisfaction questionnaire between the groups, DTG scored 41.18 points, whereas NDG scored 36.88 points, indicating higher satisfaction with DTG. Further analysis of the satisfaction survey revealed that DTG outperformed NDG in stretching performance accuracy, change in perception of stretching, and concentration during stretching. Additionally, In the comparison of exercise adherence between groups, DTG participated in exercise more frequently with 8 sessions, while NDG had 5.18 sessions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Mean (M) \(\pm\) Standard Deviation (SD)**} \\ \hline  & **DTG** & **NDG** & **T** & \(p\)**-Value** \\ \hline Satisfaction questionnaire & 41.18 \(\pm\) 4.29 & 36.88 \(\pm\) 4.76 & 2.77 & 0.009 \\ \hline Exercise adherence & 8.00 \(\pm\) 3.97 & 5.18 \(\pm\) 3.71 & 2.14 & 0.04 \\ \hline DTG: Digital Therapeutic Group. & NDG: Non-Digital Therapeutic Group. & & & \\ \end{tabular}
\end{table} 
Table 4: Comparison of satisfaction with and adherence in the stretching program between groups.
# Predictable Trajectory Planning of Industrial Robots with Constraints
## 2 Predictable Trajectory Planning with Constraints
### Predictable Trajectory Planning with End-Effector Constraints
The geodesic equations are  
\[\left\{ \begin{array}{l}\frac{d^{2}u_{i}}{dt^{2}}+\sum\limits_{k,j}{}^{d} \Gamma_{kj}^{i}\frac{du_{k}}{dt}\frac{du_{j}}{dt}=0\\ \frac{d^{2}v_{l}}{dt^{2}}+\sum\limits_{m,b}{}^{o}\Gamma_{bm}^{l}\frac{dv_{m}}{ dt}\frac{dv_{b}}{dt}=0\end{array} \right. \tag{9}\]  
The Christoffel symbols \({}^{d}\Gamma_{kj}^{i}\) and \({}^{o}\Gamma_{mb}^{l}\) are given as  
\[\left\{ \begin{array}{l}{}^{d}\Gamma_{kj}^{i}=\frac{1}{2}\frac{\partial^{d}g _{ik}}{m}g^{ei}(\frac{\partial^{d}g_{ik}}{\partial u_{j}}+\frac{\partial^{d}g_ {ik}}{\partial u_{k}}-\frac{\partial^{d}g_{kj}}{\partial u_{e}})\\ {}^{o}\Gamma_{bm}^{l}=\frac{1}{2}\frac{\partial^{o}g_{ll}}{\partial v_{m}}+ \frac{\partial^{o}g_{ll}}{\partial v_{b}}-\frac{\partial^{o}g_{lm}}{\partial v _{f}})\end{array} \right. \tag{10}\]  
where \({}^{d}g^{ei}\) and \({}^{o}g^{fl}\) are the elements of inverse Riemannian metric coefficient matrices \({}^{d}G^{-1}=\left({}^{d}g_{ij}\right)^{-1}\) and \({}^{o}G^{-1}=\left({}^{o}g_{mn}\right)^{-1}\) respectively [16]. \(\partial\) is the notation of partial differential. The Christoffelsymbols are derived from the position and orientation vectors with end-effector constraints. The joint trajectories are obtained by Equation (9) together with Equations (5) and (7) with boundary conditions  
\[\left\{ \begin{array}{c}u_{i}(t_{0})={}^{0}u_{i}\\ v_{l}(t_{0})={}^{0}v_{l}\\ u_{i}(t_{0}+t_{w})={}^{f}u_{i}\\ v_{l}(t_{0}+t_{w})={}^{f}v_{l}\end{array} \right. \tag{11}\]  
where \(t_{0}\), \(\left\{{}^{0}u_{i},{}^{0}v_{l}\right\}\) and \(\left\{{}^{f}u_{i},{}^{f}v_{l}\right\}\) are the start time, the start and end joint positions, respectively. In other words, the direct output of the geodesic method is joint trajectories. As we can see above, trajectory planning with the end-effector constraints is given. The prediction of the end-effector motion properties can be made before the calculation of the geodesic method. The prediction is demonstrated in Section 3.1 in a simple way. The reason why the trajectories can be predicted is that they are geodesic trajectories. Geodesic has many merits, such as the shortest path, constant velocity.
## 3 Results and Discussion
### Ecological Conditions of the Caspian Sea
#### 3.3.2 Heavy Metal Contamination
In 2008-2010, during complex ecological expeditions and investigations, the accumulation of a number of heavy metals in the organs and tissues of fish from the open part of the Kazakh sector of the Caspian Sea was studied. The results obtained are generally consistent with the above mentioned data for 2003-2005. The level of heavy metal accumulation is generally uneven both in the water area of the north-eastern part of the Caspian Sea and in fish species. At certain points of the lake, there is a higher accumulation level, which is 10, 19 and 23 mg/kg for _Acipenseridae_, _Clupeidae_ and _Rutilus rutilus_, respectively. The maximum accumulation of zinc was detected in _Acipenseridae_ and _Rutilus rutilus_ caught in the south-eastern section of the Northern Caspian Sea, i.e., in areas of active use of oil and gas fields. This is shown in Figure 16 as an example for the roach. Cadmium accumulates in tissues in a low amount. 20% of _Acipenseridae_ and 50% of _Rutilus rutilus_ of all the samples taken did not contain cadmium; its content in the muscles of all studied fish species varied from 0.1 to 0.3 mg/kg, in a single case-up to 0.6 mg/kg. The presence of lead was registered in all samples taken for analysis. Its concentration in fish muscles varies within narrow limits: in _Acipenseridae_ and _Rutilus rutilus_ 0.2-0.5 mg/kg and 0.2-0.7 mg/kg, respectively, in _Clupeidae_ 0.3-0.8 mg/kg. The highest accumulation of lead is recorded in the muscles of fish species in strong contaminated areas of the Caspian Sea. The distribution pattern for _Clupeidae_ is shown in Figure 16.  
The analysis of the presented illustrations clearly shows that the characteristic feature of the distribution of all the considered elements is the increased accumulation in certain parts of the considered Caspian Sea area. Namely, in the areas affected by the flows of the Volga River and Ural River, in the south-eastern part, where a number of large oil and gas field facilities are located, as well as in the zone of the Kulalinskiy threshold, by which the contaminated water mass, in the direction to the Caspian Sea current characteristics for this section, spreads towards the Middle Caspian Sea. A similar pattern of distribution in this lake area is typical for other toxic compounds, including petroleum hydrocarbons, as mentioned above. Figure 16: Zinc and lead accumulation levels in muscular of fish species in certain parts of the north-eastern Caspian Sea. The water quality of the Kazakh sector of the Caspian Sea, according to the state monitoring in recent years, is characterized by different levels of contamination from <<Standard-clean>> to <<Moderately contaminated>>.
# On the Safe Deployment of Matrix Multiplication in Massively Parallel Safety-Related Systems
## 4 Evaluation
### Diagnostic Coverage  
We have performed a fault-injection campaign (following the same methods used in reference [14]) in input matrices \(A\) and \(B\) in all bit positions before launching the execution in the GPU to evaluate the achievable DC with every single-diagnostic mechanism implementation and the combinations included in our catalog. Initially, we perform the matrix-matrix multiplication without fault-injections, and we store the ES computed as the reference or golden value. Then, we perform the single-bit fault-injection campaign and the ESs comparison with the golden value for each execution. Finally, we quantify the DC as the number of detected faults relative to the total number of injected faults.  
Table 3 gathers the results of our fault-injection campaign implemented in GPU together with the DC of the sequential and AVX-based implementations extracted from reference [14], excluding the combinations that reach 100% in all cases. This table shows that GPU-based implementations achieve a higher DC than the less parallelized implementations for external (E) implementations. This occurs due to the specific implementation details of the MMM for the GPU, where the entire matrix is decomposed into block tiles that independently compute partial matrix multiplications. In this implementation, the number  
Figure 6: Performance impact with -O3 optimization.  
of values protected in the most external loop increases with a consequent increment in the achievable DC.  
We observe the same trend in the intermediate implementations in square matrices with dimensions greater than 20 and unbalanced matrices L2 and L3, with an exception in the XOR implementation in L3, which was motivated by the nature of XOR which does not detect even single-bit errors. In the rest of the matrices, the DC of AVX is superior to that achieved in GPUs. The reason lies in the AVX-based implementation that protects eight values in the intermediate loop and, in small matrices, the number of values protected is larger than those for the GPU.  
Internal loop implementations reach 100% DC in all matrix dimensions with one's complement, Fletcher, and CRC. XOR implementation has identical DC to sequential and AVX in all dimensions except in the L3 matrix. DC drops because our GPU-based implementation for these matrix sizes divides the matrix-matrix multiplication into an even number of computations performed by each thread, and this checksum fails to detect even failures. Two's complement provides similar results to the other implementations.  
Finally, all combinations of checksums reach the maximum DC, excluding the ones presented in Table 3 that still provide a high DC. In the GPU-based implementation, the combinations, including Fletcher in the intermediate loop, do not provide as much DC as the other implementations. This relates to the location of the Fletcher checksum computation in the code, which could not be kept identical to the other implementations in the GPU-based implementation.
## 6 AI-Based Approaches for Autonomous UAV Swarm Systems
### AI-Enhanced Data Processing for Swarm UAV Applications
#### 6.1.4 Search and Rescue Operations Using AI-Equipped UAV Swarms  
Rescue operations using UAVs with integrated AI technology are of great interest to experiment with. This highlights the possibility of solving complex or even impossible tasks for humans. The main goal of search and rescue missions remains to locate the target quickly and accurately, followed by critical actions such as information exchange and timely delivery, all of which must be met within strict time constraints. In [145], the authors present an advanced AI-enabled UAV swarm system with high accuracy and fast response time. The system is equipped with modern sensor technology, advanced image recognition algorithms, and autonomous navigation capabilities. The methodology of the proposed method includes multi-faceted data collection techniques, including surveying, data mining, IoT sensors, and video analytics. Machine learning and deep learning models are then applied to process and analyze these data, enabling real-time image recognition for precise target identification. AI-optimized algorithms significantly reduce response times and increase mission success rates, accelerating emergency response efforts in complex environments.  
Search and rescue missions during and after disasters are often labor-intensive and costly. Quickly locating injured people helps minimize loss of life and property. The authors in [146] proposed a technique that combines behavioral-based artificial intelligence with cooperative swarm behavior. The UAV swarm is controlled by integrated sets of reactive behaviors such as collision avoidance, battery recharging, formation control, and altitude maintenance. In addition, various search methods are used to optimize the coverage of cameras and heart rate sensors mounted on the UAVs. The authors implemented a simulation of a UAV swarm (with up to 20 UAVs). The simulation scenario uses real-world location data, including post-disaster satellite imagery, real-world locations of damaged buildings, and victim locations. The results of measuring the effectiveness of the approach by recording the detection rate over time of survivors showed a 90% detection rate in less than 24 h in the simulation.  
The above studies show a strong development trend of AI-based UAV swarm control solutions. The advantages achieved include the ability to optimize resources and suitability for distributed systems. Technology trends show the potential of multi-technology solutions (including UAVs, AI, and IoT). However, in the actual implementation process, it is necessary to pay attention to implementation costs (due to hardware requirements of UAVs and sensors) and scalability (some methods have not been verified on a large scale).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
## 3 Results and Discussion
### Results of Measurements of the Temperature of Brake Discs during Continuous Braking  
The temperature of brake discs during continuous braking is shown in Figure 10. The temperature was measured at the beginning of measuring and then every 5 min. Low-priced brake discs are in black colour, mid-priced are in green and high-priced brake discs are in red colour, as in the previous measurement. The brake discs with full thickness are displayed by a solid line and brake discs with minimum thickness are displayed by a broken line.  
During continuous braking and wheel rotating at a low speed of 10 km.h\({}^{-1}\), the temperature of the low-priced brake discs with minimum thickness reached 339 \({}^{\circ}\)C, and  
Figure 10: Temperature of brake discs during continuous braking.  
Figure 9: Other vehicle parts and brake discs heating up.  
the temperature of the mid-price brake discs with original thickness reached 240 \({}^{\circ}\)C, which is 29% lower. In addition to the price of brake discs, the thickness of brake discs evinced significantly as well (Figure 10). The mid-priced brake disc with maximum thickness heated to 240 \({}^{\circ}\)C after 15 min of braking with a force of 2500 N, whereas with minimum thickness it heated up to 320 \({}^{\circ}\)C. Thus, the reduction in the brake disc's thickness caused an increase in temperature by 33%, almost to the level of the low-priced brake disc with maximum thickness. A significant difference in temperatures due to the thickness of the brake discs was measured with the high-priced brake discs as well.  
The order of brake discs according to their temperature during continuous braking at low speed (Figure 10) was slightly different from the order of brake discs according to their temperature measured during braking at high speeds (Figure 8). The change in the order is predominantly caused by an increasing effect of brake discs' thickness on their temperature. The publication [106] mentioned that the temperature of drilled brake discs during continuous braking at the speed of 60 km.h\({}^{-1}\) is 24% lower than the temperature of the solid brake disc. The measurement results in this publication show again that in addition to brake disc drilling, thermal properties of a brake disc's material are also important [29; 107; 108]. Thus, a drilled brake disc does not ensure a lower temperature during continuous braking at low speed in comparison with a solid brake disc.
## 4 Implementing L2S-M in a Cloud-Native Platform for Intra-Site Connectivity: K8s Case
### Virtual Network Management Flow
#### 4.2.2 Attachment of a Pod into a Virtual Network  
The attachment of a pod into one, or more, virtual networks using L2S-M follows this structure as seen in Figure 5:  
1. When a pod wants to be deployed in the cluster associated with one (or several) virtual network, it will introduce the corresponding annotation in its descriptor, using the standard Multus annotation format. The user will then use kubectl to deploy the pod, generating a creation event in the K8s cluster.
2. The L2S-M will pick up the event and check whether the pod has the corresponding annotation and if so, it will check each annotation element to see if it corresponds to a virtual network NAD from its database. Once it matches, L2S-M checks in its database the free vEth in the node where the deployment is being performed (these data are retrieved using the K8s API), writing an entry in the database for that interface with the name of the pod and the virtual network that this interface is associated with.
3. The L2S-M operator updates the deployment with the new interface annotation, instructing the Multus agent in the node of the vEth pair interface that will be aggregated to the pod. Once this operation is completed, the pod finishes its deployment phase attached to the OVS switch of the node.
4. After the deployment, the L2S-M operator sends the SDN controller the new attachment of the pod, notifying the controller that the new port of the switch is associated to a new virtual network. With this information, the SDN controller can configure all the switches of the overlay with the corresponding rules to exclusively forward packets between the members of the virtual network. This behaviour is up to the application running in the SDN controller, which is in charge of finding the appropriate path  
Figure 6: Deleting a pod from a virtual network in L2S-M.  
between the pods and configuring the forwarding rules of the switches. One way to perform this could be using intent-based connectivity in such a way that L2S-M provides the MAC address of the members of the virtual network to the SDN controller using intents so that the controller can properly configure the paths between them.
## 6 Experimental Work
### Analysis of CS-SR through Energy Consumption  
The energy consumption required for finding the optimal service can be calculated through the execution time. Therefore, the amount of energy consumed using a constant of proportionality calculated using an execution time is obtained in Equation (10). Table 7 shows the energy consumption in (Joules) for each of the algorithms for the service [10; 11; 12; 13; 14; 15; 160].  
\[\text{E}=\int_{n=1}^{N}\alpha\,*\,\text{R.T.} \tag{10}\]  
where E is the energy consumption, T is the execution time, and \(\alpha\) is the constant of proportionality, in this case, \(\alpha=1\).  
The above Tables 5-7 shows that for m = 2, A.H.P. has the highest energy consumption and highest execution time, and CS-SR has the lowest energy consumption and lowest execution time. In addition, for M = 5, the CS-SR approach outperforms the other three existing approaches in terms of energy consumption and execution time.  
The following Figure 4 is a graph plotted for the energy consumption for four approaches that shows that A.H.P. consumes the maximum energy for both m = 2 and m = 5. CS-SR approach is found as an efficient approach as compared to the other three approaches.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S. No.** & **M = 2** & **M = 5** \\ \hline AHP & 49.35 & 55.8 \\ \hline ENS-SS & 39.7 & 47.7 \\ \hline BWM & 37.1 & 41.1 \\ \hline CS-SR & 32.6 & 35.4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Energy consumption.  
Figure 3: Execution time for bi-objective [M = 2].  
The experiment was conducted on CS-SR, A.H.P., and ENS_SS by using a sample size of services from [10; 11; 12; 13; 14; 15; 160] with 10 jumps. The total execution time taken by all three algorithms is shown in Figures 3 and 5 and measured against service. The experiments conducted for two attributes in Figure 3 and five attributes in Figure 5, the execution time is evaluated for both the scenario with an increasing number of services. The experiment results in Figure 3 depict the ENS-SS approach is capable of responding initially to the smaller number of services, but the execution time increases later. The A.H.P. approach is also efficient with a lesser number of services but not with a large number of services, and it needs more time to respond and find service. The CS-SR selects are found to be efficient as compared to A.H.P. and ENS-SS in finding an optimal service by reducing execution time.  
Moreover, CS-SR is energy efficient in comparison with A.H.P. and ENS-SS as it reduces energy consumption in context to Jules. The proposed service selection and recommendation model supports QoS along with ensuring reduced execution time and  
Figure 4: Energy consumption.  
Figure 5: Execution time for multi-objective [M = 5].  
use of idle cloud servers. Henceforth, the CS-SR model is proficient to total execution time helps in creating an energy-efficient and economically sustainable cloud ecosystem.
## 4 Discussion
### Identification and Suggestions for Key Regulatory Areas
#### 4.3.1 Spatial Distribution of EQI Changes  
The spatial distribution of EQI changes is illustrated in Figure 11. It is evident that, for all three study areas, the EQI change pattern is dominated by Slight Improvement (SI) and  
Figure 10: Results of correlation tests for various factors in three urban agglomerations in 2001 and 2020. The symbols ***, **, and * display three significant level markers with \(p<0.001\), \(p<0.01\), and \(p<0.05\), respectively. (**a–c**) Correlation coefficients between selected factors and EQI in 2001 of BTH, CYRD, and PRD, respectively. (**d–f**) Correlation coefficients between selected factors and EQI in 2020 of BTH, CYRD, and PRD, respectively.  
Slight Deterioration (SD) when excluding Inapparent Change (IC) pixels. Specifically, SD has the largest proportion among pixels with changes and is distributed in the east of BTH (Qinhuangdao, Tianjin, and Tangshan), the north of CYRD (along the Yangtze River and near Shanghai), and southeastern PRD. SI mainly occurs in western BTH, coastal CYRD, and west and southwestern PRD. The distribution of pixels with increasing EQI aligns with the ecological protected area, while the distribution of pixels with decreasing EQI corresponds to the direction of urban expansion [40]. Details regarding the specific areas and percentages of EQI changes for each category can be found in Table S4.