esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Motion Pattern Recognition Based on Multilayer Perceptrons
#### 3.1.1 Sliding Window Length is 1024 ms Using All Feature Parameters  
All time-domain and frequency-domain feature parameters were used. With a sliding window length of 1024 ms and a sliding window interval of 50 ms, a feature parameter dataset for training was established. Seventy percent of the dataset was used for training, and 30% was used for testing. In the 70% of the dataset used for training, 90% of the dataset was used for training, and 10% of it was used for verification. The classifier uses a three-layer multilayer perceptron, and the activation function of the hidden layer selects the sigmoid function, while the activation function of the output layer selects the softmax function. A cross-validation network structure was used to change the number of hidden layer nodes from 4 to 60, and a classifier model was trained, respectively. As can be seen from Figure 7a, the overall classification accuracy rate was basically above 90%. When the number of hidden layers was 28, the classifier showed the highest accuracy rate on the verification set, reaching 95.53%. The classification result of a three-layer multilayer perceptron with a sample number of 1200 and a hidden layer nodes of 28 was compared with the true motion model, as shown in Figure 7b. The confusion matrix corresponding to the data in Figure 7b is in Table A1 of Appendix A.  
Figure 8: Performance of a multilayer perceptron classifier with 28 hidden layer nodes. (**a**) Mean square error change during training. It can be seen that as the number of iterations increases, the three curves of training data, validation data, and test data have the same downward trend. The optimal value of the mean square error reached the set mean square error after about 280 iterations. (**b**) Comprehensive error distribution of the classifier. It can be seen that the error distribution of the training data, test data and verification data, the error in the range of -0.429-0.3771 accounts for about 95.4%.  
Figure 7: Recognition accuracy rate using a multilayer perceptron classifier. (**a**) Model accuracy rate under cross validation. (**b**) Comparison of the true value of the validation set and the classification result.  
Figure 8 shows the curve of the mean square error and a histogram of the comprehensive error distribution of the classifier during the training process of the three-layered multilayer perceptron classifier with 28 hidden layer nodes. It can be seen that the error of the verification curve reaches the optimal value at around 280 generations, and the training speed is very fast. The average comprehensive error of the trained classifier is 3.2%.
# What Lies Beneath One's Feet? Terrain Classification Using Inertial Data of Human Walk
## 1 Introduction
* From the experimental results, we found that the lower back location is more suitable for sensor placement than chest for the task of terrain classifications as it produced the highest classification accuracies (details are in Section 4.1).
The proposed algorithm conforms to DVB-S2X with a finer gradation and extension number of modulation and coding modes (MOCODs) to obtain optimal spectrum efficiency for each beam in different time slots. DVB-S2X also guarantees engineering feasibility of the proposed algorithm. (2) Most of the conventional beam hopping resource methods employ two-step strategies. Firstly, obtain the time slots number allocated to each beam. Then, arrange these time slots into beam hopping periods to determine beam illumination order. These two steps are independent when constant channel condition is assumed during transmission. With this assumption, different arrangements of time slots may not affect system performance. However, the effect of time-varying rain attenuation makes the quantity and arrangement of time slots become coupled. In this paper, the proposed method for BH solves the coupled problem, obtaining both the quantity and arrangement of time slots allocated for each beam. (3) With the genetic algorithm, there is no need to perform the derivative or transforming operation on the objective function. Therefore, by adding more constrained conditions, the proposed system model, objective function, and algorithm flow can be employed to solve similar problems in beam hopping system such as resource allocation under the effect of co-channel interference. The following paper is organized as follows. Section 2 describes the beam hopping system model. Section 3 introduces the proposed algorithms. Section 4 analyzes the numerical simulation results. Section 5 presents the conclusions and future works.
# Comparative Study of Indoor Propagation Model Below and Above 6 GHz for 5G Wireless Networks
## 4 Time Dispersion Parameters  
The path loss is the main parameter that can be used to describe the large-scale effects of the propagation channel on the received signal. The RMS delay spread is the main parameter for wideband channel characterization, as it is a good measure of multipath time dispersion. Based on the signal bandwidth, the RMS delay spread provides a good knowledge of the potential severity of inter symbol interference (ISI). The time dispersion characteristics, and analysis of such properties can serve for the indoor mm-wave communications systems design.  
The RMS delay spread is defined as the square root of the second moment of a power delay profile (PDP):  
\[\tau_{rms}=\sqrt{\overline{\tau^{2}}-\left(\tau_{m}\right)^{2}} \tag{7}\]  
where \(\overline{\tau^{2}}\) is the second moment of the PDP and it is given as:  
\[\overline{\tau^{2}}=\frac{\sum_{k}p(\tau_{k})\cdot\left(\tau_{k}\right)^{2}}{ \sum_{k}p(\tau_{k})} \tag{8}\]  
and \(\overline{\tau_{m}}\) is the mean excess delay, also given as:  
\[\overline{\tau_{m}}=\frac{\sum_{k}p(\tau_{k})\cdot\tau_{k}}{\sum_{k}p(\tau_{k })} \tag{9}\]  
where \(p\) and \(\tau\) are defined as the power and delay of the \(k^{th}\) path, respectively.
# Bilateral Teleoperation System with Integrated Position/Force Impedance Control for Assembly Tasks
## 4 Control Algorithms  
To achieve precise and flexible assembly by teleoperation and mitigate severe damage caused by position errors in the inserting process, a master-remote position control, a manual/automatic mixed control based on Cartesian impedance control, and a novel integrated position/force control method taking the demonstration position as the desired position were implemented in this research. A detailed discussion of these control algorithms will be presented in this section.
## 3 Proposed Algorithm for Energy-Efficient Node Mobility
### A Time Interval Calculated According to the Moving Speed and Distance of a Mobile Node
#### 3.2.3 Calculation of the Interval in the Proposed Algorithm  
In the proposed algorithm, we define the interval as the time before a mobile node escapes the transmission range of the parent node. The interval is inversely proportional to the speed of the mobile node and proportional to the distance required for the mobile node to escape the transmission range of the parent node, \(d_{e}\). In other words, the proposed algorithm dynamically adjusts the DIS transmission period based on the speed and distance of the mobile node. However, owing to the random nature of the speed and direction of the mobile node, there can be a time error in escaping the transmission range of the parent node. To overcome this, the proposed algorithm sets the range of intervals as \([\tau/2,\tau]\), and the interval is randomly selected within this range. The detected interval of length \(\tau\) can be calculated using Equation (6) based on Equations (2) and (5), and the interval can be obtained using Equation (7):  
\[\tau=d_{e}/V_{m} \tag{6}\]  
\[I_{MTP}=x,\qquad\text{where}\quad x\in[\tau/2,\tau] \tag{7}\]  
Here, \(x\) is a value randomly selected from \([\tau/2,\tau]\), and \(I_{MTP}\) represents the interval where \(MTP\) means migration time predictable.
# Multi-Tier Heterogeneous Beam Management for Future Indoor FSO Networks
## 3 MTH Beam Management Protocol  
The MTH beam management protocol aims to allow multiple mobile UDs to establish and maintain a connection with fast initial access and seamless connectivity. Accordingly, the protocol must account for pointing, acquisition, and tracking (PAT) at each directional tier; heterogeneous beam refinement and handover across tiers; allocation of beams/resources in multi-UD environments; and zone configuration/deployment strategies. The protocol design must also account for physical parameters including: beam width variation, resolution, range, and transition rates at each tier; number of elements and layout of elements in each tier; and control plane feedback latency.
# An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series
## 2 Materials and Methods
### Methods
#### 2.2.3 Time-Series Shapelet Detection  
Time-series shapelet detection includes an exhaustive search to generate a set of candidates and quality measurements to detect the best shapelet [20; 53]. For a given annual NDVI time series \(\rm T=\{t_{1},\;t_{2},\ldots,t_{30}\}\) of the forest object, its candidate shapelet can be defined by the starting point \(\rm p\) and length \(\rm l\), \(\rm S_{(p,l)}=\Big{\{}t_{p},t_{p+1},\;\ldots,\;t_{p+1-1}\Big{\}}(min\leq 1 \leq max)\), whereas the corresponding remainder of the time series is defined as the non-shapelet \(\rm N_{(p,l)}=\Big{\{}\ldots,t_{p-1},\;\ldots,\;t_{p+l},\ldots\Big{\}}(min\leq 1 \leq max)\). We learned from local forestry experts that, in order to improve the survival rate of planted forests, trees were predominantly established through planting instead of seeding, and it took at least 4 years for the canopy cover to reach a relatively high level. Thus, the \(\rm min\) of the shapelet was set to 4 (\(\rm max=26\)) (1) to guarantee that the overwhelming majority of the planting initiatives could be examined and (2) to avoid misinterpreting changes induced by non-planting disturbances (e.g., clouds and cloud shadows) as planting activities.  
[PERSON] and [PERSON] [21] explained that the best shapelet divided a dataset into two groups as purely as possible. Time-series data were separated into two segments, shapelet and non-shapelet, so that the best shapelet in this study should have had the ability to minimize the intra-segment differences while maximizing the inter-segment differences, which helps to  
Figure 4: Sensitivity analysis of the scale parameter (size) measured by the area-weighted local variance (ALV), Moran’s I of layers (MIL), and weighted score (WS).  
highlight the discrepancy between the pre- and post-planting stages. An indicator termed GAP (Formula (4)) was applied to evaluate the quality of candidates meeting the length criteria [54], and the best shapelet was defined as the candidate \(\mathrm{S_{(p,l)}}\) with the maximum value of GAP:  
\[\mathrm{GAP_{(p,l)}}=\mathrm{m_{N_{(p,l)}}}-\mathrm{std_{N_{(p,l)}}}-\left( \mathrm{m_{S_{(p,l)}}}-\mathrm{std_{S_{(p,l)}}}\right) \tag{4}\]  
where \(\mathrm{m_{N_{(p,l)}}}\) and \(\mathrm{m_{S_{(p,l)}}}\) represent the mean of the non-shapelet and shapelet, respectively; \(\mathrm{std_{N_{(p,l)}}}\) and \(\mathrm{std_{S_{(p,l)}}}\) represent their standard deviations.
## 3 Results and Discussion
### Rice Flour Microstructure  
The morphology of the rice flour particles from the different extrusion parameters are shown in the scanning electron microscope (SEM) images (Figure S1). Non-extruded native rice starch has a heterogeneous morphology with visible starch granules, as shown in Figure S1A,B. The shear forces and high temperature during extrusion caused gelatinization of the starch, which in turn created a rough and broken structure. A similar phenomenon was observed by [PERSON] et al. [38] in extruded corn starch. It is well known that thermal treatments can modify starch granules and denature proteins, and the extent of starch granule morphological changes depends mainly on the processing conditions, such as temperature and moisture. Extrusion performed below gelatinization temperature, preserving the integrity of the starch granules, and treatments carried out above gelatinization temperature induce structural alterations, destroying the molecular order of the starch granule [7]. Moisture contained in the feed material is the main factor responsible for its vitrification during extrusion [39]. Generally, the higher the humidity, the smaller the cells formed after the extrusion process and the more uniform the structure of the resulting products. These changes were observed regardless of particle size (Figure S1C-J). Importantly, temperature also affected the morphology of the extruded flours. The obtained structure was more homogeneous with increasing temperature (from 80 to 120 \({}^{\circ}\)C). As indicated by [PERSON] et al. [40], starch gelatinization begins at about 70 to 80 \({}^{\circ}\)C, depending on the botanical origin of the starch. At this temperature, in the presence of water, starch gels form homogeneous gels, which is consistent with the morphological changes we observed.
# Performance Evaluation of IEEE 802.11 ah Networks With High-Throughput Bidirectional Traffic
## 1 Introduction
Available RAW (re)configuration algorithms are only applicable to WSN scenarios given that they are based on uplink throughput and/or energy optimization. Moreover, MAC mechanisms for downlink scheduling have been barely investigated, let alone optimized. Therefore, we investigated the combined performance of RAW and TIM with UL/DL traffic patterns. We varied density, Modulation and Coding Schemes (MCSs) and configurations of RAW, TIM and Transmission Control Protocol (TCP) through more than 40,000 simulations in the ns-3 network simulator in two scenarios: (1) Video streaming that needs reliable and high-throughput (Section 4.2) and (2) Reliable monitoring scenario (Section 4.3) where each packet is acknowledged at the application layer. The first scenario implies a set of motion cameras that stream if they detect motion, whereas the second implies a very dense network of reliable sensors. Reliability in both scenarios is achieved through the use of TCP. We shortened the Round Trip Time (RTT) by introducingthe _immediate reply_ mechanism. We also demonstrated that IEEE 802.11 ah can support legacy Internet traffic by way of verbose TCP that is rarely suitable for IoT applications. TCP acknowledgements represent DL traffic in both scenarios. In the first scenario, 92% of the total traffic in simulated networks was UL, whereas the rest (8%) was DL. In the second scenario, 67% of the total traffic was UL and 33% was DL. By enabling the described experiments and assessing the results, this article makes the following key contributions:  
* Detailed analysis of the influence of RAW and TIM grouping (in parallel) on attainable scalability, throughput, latency and energy-efficiency considering bidirectional communication,
* Insights in the trade-off between scalability and throughput, along with configuration best-practices for achieving the desired performance,
* An immediate reply mechanism at the Access Point to reduce downlink latency,
* An extension of the ns-3 IEEE 802.11 ah module [4], namely implementation of: (1) Delivery TIM (DTIM), (2) TIM segmentation, (3) non-Cross-Slot Boundary (non-CSB), (4) transmission scheduling for DL frames and (4) immediate reply mechanism. The remainder of this article is structured as follows. Section 2 introduces the related research on IEEE 802.11 ah. Section 3 gives an overview of the 802.11 ah MAC features relevant for this paper. Furthermore, Section 4 presents performed experiments, namely a video streaming scenario (Section 4.2) and a reliable monitoring scenario (Section 4.3). Setups of the two experiments and obtained results are discussed in the corresponding sections. Section 4 also presents the simulation environment (Section 4.1), theoretical limits (Section 4.4) and a discussion on delay tolerance of TCP (Section 4.5). Finally, conclusions are summarized in Section 5.
# A Low-Power WLAN Communication Scheme for IoT WLAN Devices Using Wake-Up Receivers
## 4 Proposed System for Massive Uplink Scenario
### Wake-Up Frame and Data Transmission for Triggering Uplink Transmission from Multiple Users
#### 4.1.1 Aggregation of Uplink Data with the Acknowledgment of Trigger Frame (Unicast-MAC)  
In single-user transmission, the data transmission procedure causes the two-channel access process after the wake-up frame transmission. To reduce the additional contention delay in data transmission, the STA that receives the single-user trigger may respond with an acknowledgment frame aggregated with the data frame, as shown in Figure 8. In this procedure, the single-user trigger includes vendor-specific information to aggregate the acknowledgment frame with the requested data frame. The STA that receives the single-user trigger with the information sends an acknowledgment frame aggregated with the data frame. After the reception of the aggregated frame, the AP sends the acknowledgment frame to indicate reception, and it repeats this procedure for all of the STAs. In this scheme, there is contention for the transmission only when transmitting the single-user trigger, and no additional contention is required to transmit the data frame.
# Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System
## 4 Data Processing
### Video Data Processing
#### 4.1.2 Image-Based Rendering  
The integration of 3 DRS refinement models with UAV aerial video faces many challenges, such as scale matching and lighting consistency [33]. We address the problems by creating a virtual camera using Blender 4.0 software. Its positioning and motion trajectory were designed to follow a predefined path, ensuring close alignment with the actual camera's movement.  
First, the UAV's cruise video is imported into the work area using Blender's motion-tracking function. The video frame rate is adjusted to match the original recording, and scene frames are set to ensure the video length corresponds to the total number of frames. A pre-read strategy is used to load the video into memory and the motion mode is changed to \"position + rotation + zoom\".  
At least eight feature points are identified in dynamic scenes. A prominent building number is selected as the first tracking point and tracked forward using the motion tracking algorithm. If tracking fails in subsequent frames, the algorithm reverts to the previous frame, expands the search area, and resumes tracking. This is repeated until all eight tracking points are obtained.  
Next, the UAV camera parameters are input into the software, and key frames A and B are set to correspond to the first and 369 th frames of the video, respectively. Thus, the camera motion in 3D space can be solved by camera-solving techniques. Subsequently, the tracking scene is set up, applying motion trajectory to the 3D viewport. The 3D model (in.obj format) is imported, and motion tracking is enabled to visualize the eight feature points in 3D space. Three feature points are selected on the 3D model ground and aligned with the ground in the video, and the positional and rotational coordinates of the detailed 3D model are adjusted to a perfect fit with the road in the video. Figure 5 shows an example of image-based rendering processed by this method.  
Finally, the output file format is set to FFmpeg, and transparency settings are configured. During rendering, all background layers except the 3D model are hidden. Select the rendering animation, complete the image-based rendering synthesis, and then obtain the final rendering output. The resulting rendered video integrates UAV emergency rescue and regular aerial footage with image-based rendering of 3D models, providing a more realistic visual effect [34]. This process enables long-distance deformation monitoring of hidden danger points and provides valuable decision-making assistance.
# Efficient Secure Routing Mechanisms for the Low-Powered IoT Network: A Literature Review
## 11 Comparative Analysis  
Several approaches [44; 50; 57; 58; 59] have been developed in the literature related to routing mechanisms and protocols. Table 6 shows a comparative analysis, which shows that our proposed work discusses several factors, whereas others only discuss a few. Most existing works have not discussed anomaly types, challenges, advantages, and restraints regarding specific techniques. Information about attacks is also not addressed by most of the researchers. Finally, the total score has been calculated to measure the highest factor rate for computing purposes. However, this work is novel in terms of the factors mentioned earlier, as other existing research has not been as comprehensive work, especially in terms of IoT-LPN research challenges, LPN architecture, and LPN application. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Factors**} & \multirow{2}{*}{\begin{tabular}{c} **[PERSON]** \\ **[7]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **[PERSON] and** \\  & & **[PERSON] et al.** \\  & & **[45]** \\  & & **[44]** \\ \hline \begin{tabular}{c} LPN \\ architecture \\ \end{tabular} & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & 1 \\ \hline \begin{tabular}{c} LPN \\ application \\ \end{tabular} & 0 & 0 & 0 & 0 & 1 & 0 & 1 & 1 & 1 \\ \hline \begin{tabular}{c} Physical layer \\ attacks \\ \end{tabular} & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 \\ \hline \begin{tabular}{c} Network \\ layer attacks \\ \end{tabular} & 0 & 1 & 1 & 0 & 1 & 1 & 1 & 1 & 1 \\ \hline \begin{tabular}{c} Software \\ attacks \\ \end{tabular} & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 \\ \hline \begin{tabular}{c} Data attacks \\ \end{tabular} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\ \hline \begin{tabular}{c} WSN issues \\ and challenges \\ \end{tabular} & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 1 & 1 & 1 \\ \hline \
\begin{tabular}{c} IoT-LPN \\ research \\ challenges \\ \end{tabular} & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & 1 \\ \hline
\begin{tabular}{c} Security \\ objectives/factor \\ \end{tabular} & 1 & 0 & 1 & 1 & 0 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative analysis with existing research.