esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model Validation
### Aggregate System Performance
#### 3.2.2 Real-World (Human-Based) Mobility Model  
Following the approach used in the previous analysis, we begin by comparing, in Figure 9a, the model predictions with the simulation outcomes for offloading efficiency, the content latency, and the content copies as a function of the global content request rate (\(\lambda_{0}\)). It is important to note that the real-world mobility dataset exhibits a longer inter-contact time than the synthetic vehicular dataset (2.7 min versus 3.0 s, as shown in Figures 5 and 6). Thus, we consider content request rates that vary in the range \([0.1,1.0]\) req/min instead of \([0.1,1.0]\) req/s. Additionally, both content and sharing timeouts are extended from seconds to minutes.  
Let us first note that, although the inter-contact times do not follow an exponential distribution, there is still a good match between the model predictions and the simulation outcomes, especially for small values of the \(T_{s}\). This confirms the robustness of our modelling approach with respect to the distribution of the content process. Additionally, we observe that offloading efficiency increases with the content request rate, though less rapidly than in the vehicular mobility model. This is most likely due to the fact that the real-world mobility model shows a higher probability of long inter-contact times, making it less likely for some nodes to encounter a node storing a copy of the requested content. Moreover, the results indicate that the model correctly predicts the trend of the content latency, although there is an absolute error of up to 25% for high traffic intensity. Finally, Figure 9c shows that our model slightly overestimates the number of copies that are stored in the devices' local caches when the sharing timeout is high. In conclusion, the analysis of the real-world mobility model seems to confirm that our ODE-based modelling approach provides reliable predictions of the offloading behaviours even when the inter-contact time does not follow an exponential distribution.  
Figure 8: Vehicular mobility model: System performance vs. node density for \(\lambda_{0}=0.5\) req/s. (a) Offloading efficiency (\(\epsilon\)); (b) Content latency (sec); (c) Number of copies.
# Dynamic Behavior Analysis and Stability Control of Tethered Satellite Formation Deployment
## 4 Stable Deployment Laws of Tether Release Rate and Tether Tension Control
### Tether Tension Control  
In order to enhance efficiency, applicability and stability, tether tension control is facilitated by TSS [34]. It is assumed that tether tension is the same at every point of the tether and is equal to the tension at the point of deployment/retrieval. The tether braking mechanism is modeled after the SEDS deployer, which uses a friction brake in order to control tether deployment speed [35]. Tether tension is expressed as follows:  
\[T=\left[T_{0}+{I\rho l}^{2}(1-A_{\text{sol}}l/L_{\text{ref}})^{-E}\right]\exp( f_{\theta}|\theta-\theta_{0}|+2\pi f_{n}n^{\star}) \tag{29}\]  
where \(\dot{\text{J}}\) is the tether release rate. Tether tension control parameters are listed in Table 1.  
A numerical simulation was performed in order to demonstrate control law performance, and simulation parameters are listed in Table 2, where \(\mu_{e}=398,600\) km\({}^{3}\)/s\({}^{2}\) is Earth's gravitational coefficient.  
Figure 8 shows the dynamic response of the TSS deployment process. Figure 8a,b show variations of in-plane and out-of-plane pitch angles and roll angles versus the true anomaly, \(\
u\). It can be concluded that the system approaches the expected angle, 0 rad, after swinging under an initial perturbation. This result illustrates that a controlled deployment process is asymptotically stable and demonstrates the validity of the tether tension control equation (Equation (29)). Figure 8c shows tether length during deployment, which exhibits a smooth deployment curve, and the tether eventually reaches a stable length.  
**Remark 2**.: _Analysis results of tether release rate control and tether tension control laws can provide effective feedback for TSS position and attitude._  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Minimal tension as a result of friction, \(T_{0}\) & 0.01 N \\ Inertial multiplier, \(I\) & 3.1 \\ Tether annulus solidity, \(A_{\text{sol}}\) & 0.89 \\ Area exponent, \(E\) & 1.4 \\ Friction coefficient over the exit guide, \(f_{\theta}\) & 0.18 \\ Zero friction exit angle, \(\theta_{0}\) & 0 \\ Friction coefficient over the brake pole, \(f_{n}\) & 0.05 \\ Number of effective brake turns of the tether, \(n^{*}\) & 1.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tether tension control law.  
Figure 8: Deployment process of TSS: (**a**) tether in-plane angles versus \(\
u\); (**b**) tether out-plane vibration angles versus \(\
u\); (**c**) tether length versus \(\
u\).
## 3 Results and Discussions
### Age of Air Distributions  
The variation in air age along the \(z\)-axis (0.3 m, 1.15 m, and 2 m height from the floor surface) is given in Figure 5. For both device locations, the air age around the air cleaner device was lower than that in other regions. It is expected that the air rate of change near the air cleaner will be faster due to bypass. When the ACD was in the first position, the air age around the wall away from the device was up to 25% higher than the air age around the wall where the device was located. This shows that, even if the flow rate increases, the air will reach, eventually, the wall region away from the device. In the case of the ACD located in the second position, it can be said that the distribution of the air in the room was more homogeneous than in the first case. Therefore, positioning the ACD in the middle of the room will allow the fresh air to reach other parts of the room more quickly.  
Figure 4: Contour plots of velocity, temperature, and air age in the middle of the room while ACD was in position 2.  
It can be said that the locations of the high air age regions did not vary, although the increase in flow rate caused a decrease in the mean air age. With respect to position 1 of the ACD, the air ages at a height of 2 m above the floor were higher than in other regions. It can also be seen that increasing the flow rate did not change the region where the fresh air would reach last, even though the value of the air age decreased. The same is true for the second position of the device. Therefore, in addition to increasing the blowing speed, a change in device position should also be considered in order to improve the air age value of a certain area in the room.  
Table 2 presents the calculated mean and maximum values of air age for all considered cases. It can be seen that increasing mass flow rates resulted in a decrease in the age of air values. Although the air stream divided itself between the two sides of the room and the air velocities slowed down, the center placement of the air cleaner apparently shortened  
Figure 5: Air age distribution on three different XY planes.  
the travel distance of the air. Therefore, the center placement cases show smaller air age values than those observed in the wall-side placement cases.
# Precedence Table Construction Algorithm for CFGs Regardless of Being OPGs
## 4 Approach
### Definitions  
There are the following symbols:  
* A string of terminals \(x\in T^{+}\).
* A string of non-terminals \(\beta\in N^{*}\).
* A sentential form \(\gamma\in(T\cup N)^{*}\).  
The following sets are defined:  
* The left, denoted as \(\mathcal{L}(A)\), is the set of terminal symbols that can appear on the left side of any sentential form as a product of a rightmost derivation from \(A\).  
\[\mathcal{L}(A)=\{a\in T\mid A\stackrel{{*}}{{\Longrightarrow}} \beta a\gamma\}.\]
* The leftmost, denoted as \(\mathcal{L}_{m}(A)\), is the set of terminal symbols that can appear at the end of the left side in any derivation from \(A\), in the absence of other leftmost grammatical symbols (or that derive to \(\varepsilon\) in their absence).  
\[\mathcal{L}_{m}(A)=\{a\in T\mid A\stackrel{{*}}{{ \Longrightarrow}}a\gamma\}.\]
* The right, denoted as \(\mathcal{R}(A)\), is the set of terminal symbols that can appear at the right end of the rightmost derivations from \(A\) or appear further to the right in any production of \(A\).  
\[\mathcal{R}(A)=\{a\in T\mid A\stackrel{{*}}{{ \Longrightarrow}}\gamma a\lor A\longrightarrow\gamma a\beta\}.\]  
According to the above, the following precedence rules are defined:  
* If \(C\longrightarrow\gamma_{1}a\beta B\gamma_{2}\), \(b\in\mathcal{L}(B)\), then \(a\lessdot b\).
* If \(C\longrightarrow\gamma_{1}A\beta B\gamma_{2}\), \(a\in\mathcal{R}(A)\wedge b\in\mathcal{L}_{m}(B)\wedge\beta\stackrel{{* }}{{\Longrightarrow}}\varepsilon\), then \(a\gtrdot b\).
* If \(C\longrightarrow\gamma_{1}A\beta b\gamma_{2}\), \(a\in\mathcal{R}(A)\wedge\beta\stackrel{{*}}{{\Longrightarrow}}\varepsilon\), then \(a\gtrdot b\).
* If \(C\longrightarrow\gamma_{1}a\beta b\gamma_{2}\), then \(a\doteqdot b\).
# An Approach for Deployment of Service-Oriented Simulation Run-Time Resources
## 6 Experiment Evaluation
### Experiment Results  
We conducted extensive experiments to demonstrate the advantages of LGASRD over comparison methods. Firstly, we presented the results of six algorithms under settings A to L to illustrate that LGASRD has better performance than others over multiple problem scales. Then, we reduced the resources provided by edge servers to verify the effectiveness of the constraint-handling mechanism in LGASRD. Under one experimental setup, each algorithm was run 20 times, and we took the average of these 20 runs.
## 3 Results
### IntErO Implementation on the Three Selected Prototype River Basins in Cluj County
The production of erosion material (W yr) in the three river basins was calculated to be 4610.5, 4429.8 and 25,236.9 m\({}^{3}\) yr\({}^{-1}\), and the coefficient (Ru) of the deposit retention (sediment delivery ration) was calculated as 0.207, 0.326 and 0.175. It implies that 20.7%, 32.6% and 17.5% of the total eroded material reaches the outlet point, whereas the remaining 79.3%, 67.4% and 82.5% is deposited on the basin slopes and within the hydrological drainage system. Calculated real soil losses (Ggod) per year per square kilometer for the studied river basins were estimated to be 44.73, 70.59 and 99.71 m\({}^{3}\) km\({}^{-2}\) yr\({}^{-1}\), which indicates the Batin and Rasca river basins belong to the fifth category, which is a region of weak erosion. The value for the Chinteni river basins belongs to the fourth category, which is a region of moderate erosion [1; 9]. \begin{table}
\begin{tabular}{l c c c c c} \hline
**Input Data** & **Abbe.** & **Unit** & **Batin** & **Rasca** & **Chinteni** \\ \hline River basin area & F & km\({}^{2}\) & 21.29 & 20.47 & 44.17 \\ \hline The length of the watershed & O & km & 19.06 & 26.48 & 39.82 \\ \hline Natural length of the main watercourse & Lv & km & 6.41 & 11.55 & 14.9 \\ \hline The shortest distance between the fountainhead and mouth & Lm & km & 5.81 & 8.88 & 12.72 \\ \hline The total length of the main watercourse with tributaries of 1 and 1 IL class & 2L & km & 21.92 & 22.11 & 20.02 \\ \hline River basin length measured with a series of parallel lines & Lb & km & 6.64 & 10.93 & 17.37 \\ \hline The area of the bigger river basin part & Fv & km\({}^{2}\) & 10.76 & 14.98 & 22.94 \\ \hline The area of the smaller river basin part & Fm & km\({}^{2}\) & 10.54 & 5.49 & 21.23 \\ \hline Altitude of the first contour line & h0 & m & 291 & 500 & 323 \\ \hline Equidistance & Ah & m & 100 & 100 & 100 \\ \hline The lowest river basin elevation & Hmin & m & 291 & 493 & 323 \\ \hline The highest river basin elevation & Hmax & m & 593 & 1396 & 673 \\ \hline A part of the river basin consisting of very permeable products from rocks ( limestone, sand, gravel) & fp & 0.5 & 0 & 0.5 \\ \hline A part of the river basin area consisting of medium-permeable rocks (slates, marks, brownstone) & fpp & 0.5 & 0 & 0.5 \\ \hline A part of the river basin consisting of poor-water-permeability rocks (heavy day, compact eruptive) & fo & 0 & 1 & 0 \\ \hline A part of the river basin under forests & fs & 0.35 & 0.7 & 0.08 \\ \hline A part of the river basin under grass, meadows, pastures and orchards & ft & 0.31 & 0.1 & 0.27 \\ \hline A part of the river basin under bare land, ploughland and ground without grass vegetation & fg & 0.34 & 0.21 & 0.65 \\ \hline The volume of the torrent rain & hb & mm & 29.1 & 37.8 & 30.3 \\ \hline Average annual air temperature & t0 & \({}^{\circ}\)C & 8.1 & 4.7 & 8.4 \\ \hline Average annual precipitation & Hgood & mm & 653 & 939 & 681 \\ \hline \end{tabular}
\end{table} 
Table 1: Input data for the InfEO model.
# A Noniterative Simultaneous Rigid Registration Method for Serial Sections of Biological Tissues +
## 4 Experiments
### Comparison with state-of-the-art methods
Coinciding with the conclusion of the last experiment, our method achieves the least error in the least amount of time. Figure 6 shows a visualization of the registration results. As Figure 6 shows, our method achieves the best result and does not cause error accumulation and propagation. Moreover, it generates the clearest average image compared to the others. Some methods that previously obtained poor performances perform better on these data, such as BUnwarpJ and CW_R_color; however, the performance of our method remains stable. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **SSIM** & **Time** \\ \hline BUnwarpJ(fast) [10] & 0.6523 \(\pm\) 0.1235 & 3.6 min \\ BUnwarpJ(accurate) [10] & 0.7037 \(\pm\) 1.2717 & 4.3 min \\ LeastSquare [8] & 0.8046 \(\pm\) 0.1578 & 11.729 s \\ Elastic Method [12] & 0.6118 \(\pm\) 0.0968 & 1.4 min \\ CW\_R\_color [3] & 0.6721 \(\pm\) 0.9013 & 36.4 min \\ Pairwise method [23] & 0.7331 \(\pm\) 0.1283 & 0.937 s \\ \hline NSRR (ours) & 0.8297 \(\pm\) 0.0706 & 0.2813 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of accuracy and speed in Zebrafish SEM data. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **EPE** & **Time** \\ \hline BUnwarp[](fast) [10] & 16.6243 & 3.4 min \\ BUnwarp[](accurate) [10] & 14.3140 & 5.5 min \\ LeastSquare [8] & 15.4013 & 5.462 s \\ Elastic Method [12] & 17.6141 & 10.323 s \\ CW\_R\_color [3] & 12.7754 & 8.1 min \\ Pairwise method [23] & 14.5277 & 0.368 s \\ \hline NSRR (ours) & 11.1903 & 0.289 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of accuracy and speed in _Drosophila_ brain FIB-SEM data. Figure 5: An example of our experimental results on the zebrafish SEM data. To provide a clear demonstration of the registration effects, we averaged the registered sequence and show the results in the last column.
# GPS Multipath Analysis Using Fresnel Zones
## 2 Multipath Theory  
In this section, the theoretical basics of GPS multipath propagation and Fresnel zones are presented, closely following the comprehensive and detailed descriptions documented in [18; 35; 36]. In Section 2.1, the process of multipath interference is explained and in Section 2.2, a method for the determination of the attenuation factor \(\alpha\) is described. This is followed by an explanation of the Fresnel zone theory in Section 2.3 and the determination of reflection points in Section 2.4. Finally, theoretical prerequisites for multipath occurrence are defined in Section 2.5.
# A Boosted 3-D PCA Algorithm Based on Efficient Analysis Method
## 3 Experiment and Analysis  
In this section, the proposed detection system is comparatively experimented with the latest seven detectors and three benchmark datasets. To validate our proposed system, we have tested it on four publicly available sequences, which are PET'09 S3.MF (multiple flow) and PET'09 S0.CC (city center) from the PET benchmark [20], \"Caltech Person\" dataset from the California Institute of Technology dataset [21], and the INRIA Person dataset [22]. The first two datasets are consecutive frames captured by one fixed camera, while the sequences of the latter two datasets are chosen from different scenarios. For the urban center sequence, the first 100 frames are selected for testing, as the amount of this sequence is large, while all sequences of the other three datasets are adopted, during this experiment. The Caltech dataset consists of approximately 10 h of video of the vehicle operating in an urban traffic environment. For the INRIA dataset, parts of these images are resized that these pedestrians have a similar size to the training datasets. Pedestrian objects in this data set include a wide variety of scales.  
Training datasets are benchmark datasets selected from the INRIA, Caltech, and PETS2000/2001 Pedestrian dataset, with an image size of 64 \(\times\) 128 pixels. Datasets apply the k-fold cross-validation method for balancing data that occurs during learning. Furthermore, the data augmentation method is performed for input of the detection system as shown in Figure 5, and the experiment is performed by inputting it into the system.  
Experiments on the INRIA dataset perform comparative evaluations with four detectors: CAP [23], HOG [22], GAB [24], CWPAC, CW2 DPCA [25] and Y-PD [26]. As shown as in Table 1, the results were derived, and higher accuracy were derived than linear-based detection systems. In addition, the proposed method was improved by 1.3% over the Y-PD results with the YOLOv2 algorithm.  
Figure 4: Cross-validation segmentation algorithm for solving data balancing problems.  
In addition, the following experiments were conducted for comparative evaluation with PCA-based detectors: It was evaluated against three detectors, PCA, CWPCA, and CW2 DPCA, and PCA was performed on a Caltech dataset basis, and CWPCA and CW2 DPCA were performed on a PETS2000/2001 dataset basis. As shown in Table 1, the results were derived, and accuracy improvements such as Caltech: 1.84%; PETS2000/2001: 14.77/15.23%; and 2.5/1.07% were confirmed, respectively. PCA and CWPCA are detectors consisting of PCA with the same backbone, but there is a large difference in their respective comparative performance with the proposed algorithm. As shown in Table 1, we analyzed as the performance difference caused by different datasets. The Caltech dataset is a collection of images, and PETS dataset is a continuous collection of images, resulting in large performance differences due to its complexity of recognition and various object features.
## 4 Additional Clarification on Computational Complexity
### Hybrid JADEDO Complexity  
Sequential or Staged Execution  
The hybrid approach begins by running JADE for a specified number of iterations (JADE_iter), then transitions to DO (or \"DOA\") for another prescribed number of iterations (DO_iter). Let  
\[T_{\text{JADE}}=\text{JADE\_iter},\quad T_{\text{DO}}=\text{DO\_iter},\quad T_{ \text{hybrid}}=T_{\text{JADE}}+T_{\text{DO}}.\]  
Hence, the total complexity of the hybrid is as follows:  
\[\mathcal{O}\big{(}N\times T_{\text{JADE}}\big{)}\ +\ \mathcal{O}\big{(}N\times T_{ \text{DO}}\big{)}\ =\ \mathcal{O}\big{(}N\times(T_{\text{JADE}}+T_{\text{DO}})\big{)}.\]  
If a total iteration budget \(T\) is predetermined and divided between JADE and DO (for example, 50 iterations each for \(T=100\)), the overall computational complexity remains \(\mathcal{O}(N\times T)\).  
Comparison: If the same total number of function evaluations is assigned, namely \(T_{\text{hybrid}}=T_{\text{DO}}=T_{\text{JADE}}\), then the hybrid's computational complexity is effectively equivalent to running either DO or JADE alone for \(T\) iterations. Utilizing two methods within a single total budget does not increase the asymptotic cost, as each iteration of DO or JADE remains \(\mathcal{O}(N)\), and the total iteration budget \(T\) is merely divided into two segments.  
Under a fixed total iteration budget \(T\), the proposed hybrid algorithm maintains the same asymptotic complexity, \(\mathcal{O}(N\times T)\), as DO or JADE independently. There is no additional computational cost; rather, the hybrid typically achieves improved convergence for the same number of function evaluations by leveraging JADE's adaptive exploration and DO's exploitation capabilities, without altering the theoretical complexity class.  
In the JADEDO algorithm, the dandelion optimizer (DO) stages--rising, decline, and landing--are executed once per iteration within the main evolutionary loop. Specifically, inside the while loop that runs until Max_iter is reached, these stages occur after the mutation, crossover, selection, and adaptation steps of JADE.  
Once the new population is formed through JADE's procedures, the algorithm integrates DO as follows:  
* **Rising stage:** The population is perturbed based on the parameter \(\alpha\) and randomly generated values that influence individual movements.
* **Decline stage:** The population is refined further by incorporating the population mean and Gaussian noise, improving exploration.
* **Landing stage:** A Levy flight-based step length is applied around the best individual, enhancing local exploitation.  
Since these DO stages are embedded directly within each iteration, they do not rely on additional conditions or triggers. Instead, they are systematically applied in every generation following JADE's selection process. Consequently, the algorithm benefits from JADE's global exploration and DO's fine-tuned exploitation throughout the entire optimization procedure.
# Multi-Layer QoE Learning System Implemented by Fiberhost
## 5 Implementation of Unsupervised Learning
### Outline of Unsupervised Part of System  
In a distributed TV system, the main problem in video assessment is the lack of labels showing whether the evaluation is intended to use supervised machine learning techniques. Specifically, the evaluation will be relying on data collected from set-top boxes through telemetry protocols such as MQTT. The type of data could provide the details associated with the video content that is currently being played. In the TV system environment, it relates to the TV broadcast channel, video-on-demand content, or other services available from the decoder. We decided that it was possible to collect metrics related to the level of the video stream being played, stalls, channel switches, and usage of decoder features that include npvr, catchup, startover, as well as timestamps. We linked them to one another and grouped them accordingly to have an overview of the level of video quality played on the set-top boxes. Figure 10 shows an outline of the system designed to collect the necessary metrics from the decoders located in customers' homes. The part related to unsupervised learning, which was used in the first stage to make an overall assessment of the content being played, is highlighted in red. The MQTT protocol was used by the decoders to send data to the database cluster. A compute server used queuing mechanisms to retrieve data related to the playback of video streams, stalls, time stamps, the use of decoder features and other respective parameters from the worker nodes at regular intervals. The data were cleaned. Features were extracted and combined into new metrics, as described in Section 5.2. Once the target features were obtained, they were clustered in a continuous mode. For this, a two-stage algorithm based on DSTream [41] and a hierarchical algorithm were used. In the first phase, a synopsis of the stream was computed. This synopsis was updated whenever a new input occurs. The second phase was to agglomerate the collected stream summary and obtain the target associations. DSTream and agglomeration algorithms were chosen due to their speed and flexibility [24]. To reflect the Absolute Category Rating (ACR) and Degradation Category Rating (DCR) scales shown in Table 4, and described in ITU-T Recommendation P.913 [42]; the number of groups was set to five. A score of 5 indicates the best possible perceived video quality on the ACR scale. It corresponds to an imperceptible level of degradation on the DCR scale. A score of 1 on the ACR is the lowest possible video quality and corresponds to the maximum degradation on the DCR scale.  
Figure 9: Multi-path QoE assessment.