esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Review of Trajectory Planning
### Approaches Based on Cartesian Motion Planning
Their method was to extract principal motion patterns from recorded data by using fPCA and optimize the weights of a reduced set of these components. fPCA is the functional equivalent of principal-component analysis (PCA) in the temporal domain, and [PERSON] et al. confirmed that kinematic synergies (principal components) could be used for exoskeleton motion planning [45]. They also proved that different principal components contributed to the motion trajectory. Another computational approach based on motion primitives (principal components) was used in [46; 47; 48; 49; 50]. However, these typical computational methods used to generate humanlike motions in the literature are only appropriate for simple point-to-pointmovements with a limited range of motion because they assume a fixed shoulder. Consequently, they are not suitable for most ADLs, during which the center of the shoulder joint moves significantly. Faced with this drawback of the fixed shoulder assumption, [PERSON] et al. proposed a generation method of exoskeleton path that considers the effect of scapulohumeral rhythms. In this work, the authors adopted geodesic curves to generate the joint angular path [27]. Rather than planning the trajectory offline, which optimizes the trajectory to perform a specific task in a structured environment, [PERSON] et al. proposed a method of bounded-jerk online trajectory planning [51]. In their planning method, the generation of upper-limb-exoskeleton trajectory paths relies on determining the immediate motion intention of the user through gaze exploitation. Moreover, their method not only permits online humanlike trajectories but also guarantees the synchronization of joints during multi-DOF movements. Another advantage of such online trajectory planning is its ability to adapt to changes in the environment [7].
# Key Quality Indicators Prediction for Web Browsing with Embedded Filter Feature Selection
## 3 Feature Weighted ML-kNN for KQI Prediction
### KQI Prediction Based on Feature Weighted ML-kNN
\(\mathbb{P}\big{(}H_{j}\big{)}=\frac{s+\sum_{i=1}^{N}y_{i}\in\mathcal{N}}{2s+m \sigma},\ \mathbb{P}\big{(}\overline{H}_{j}\big{)}=1-\mathbb{P}\big{(}H_{j}\big{)}\); (11)
11. Calculate the normalized frequency arrays \(\big{[}f_{j}|r\big{]}\) and \(\big{[}\overline{f}_{j}|r\big{]}\) of \(\mathbf{D}\) by Equation (12) [18]:
12. \(\left\{ \begin{array}{l}f_{j}|r\big{]}=\sum\limits_{i=1}^{N}\big{[}\mathbf{y }_{j}\in\mathbf{Y}_{i}\big{]}\mathbb{I}\delta_{j}(\mathbf{x}_{i})=r\mathbf{I} \\ \overline{f}_{j}|r\big{]}=\sum\limits_{i=1}^{N}\big{[}\mathbf{y}_{j}\
otin \mathbf{Y}_{i}\big{]}\mathbb{I}\delta_{j}(\mathbf{x}_{i})=r\mathbf{I}\end{array}  \right.\), (0 \(\leq r\leq k\)); (12)
13. endfor\(/\)Here \(s\) is a smoothing parameter (\(s=1\) results in Laplace smoothing); \(\delta_{j}(\mathbf{x}_{i})\) is the number of \(\mathbf{x}_{i}\)'s neighbors with positive label \(j\). 14. Identify \(k\)-nearest neighbors \(\mathcal{N}(\mathbf{x}_{0})\) in \(\mathbf{D}\) according to Step 6;
15. Calculate the identical label statistics \(\big{[}C_{j};j=1\sim q\big{]}\) for \(x_{0}\) as in Equation (13):
16. \(C_{j}=\sum_{i=1,\ x_{(}x_{j})\in N(x_{0})}^{k}\big{[}\mathbf{y}_{j}\in \mathbf{Y}_{i}\big{]}\) (13)
17. \(/C_{j}\) is the number of \(x_{0}\)'s neighbors with positive label \(j\). 18. for\(j=1\) to \(q\)do
19. Calculate the likelihood probability \(\mathbb{P}\big{(}C_{j}|H_{j}\big{)}\) and \(\mathbb{P}\big{(}C_{j}|\overline{H}_{j}\big{)}\) of \(x_{0}\) as in Equation (14) [18]:

\[\mathbb{P}\big{(}C_{j}|\overline{H}_{j}\big{)}=\frac{s+\overline{f}\big{[}C_{j} \big{]}}{s(k+1)+\sum\limits_{i=1}^{N}f_{j}|r\big{]}}\,\ \ \big{(}1\leq j\leq q,\ 0\leq C_{j}\leq k\big{)}; \tag{14}\]
20. Predict label \(j\) or \(x\) according to Equation (15):
21. \(y_{0j}=\overline{I}\frac{p(H_{j})C_{j}[H_{j}]}{p(\overline{H}_{j})p(C_{j}| \overline{H}_{j})}>1\mathbf{\hat{k}}\) (15)
22. endfor
```  
**Algorithm 2** Pseudo code of feature-weighted multi-label k-nearest neighbor (ML-kNN) algorithm.
## 4 Porin-Mediated Prodrug Delivery
### Significance and Scope  
Nitrogen uptake is essential for bacterial growth and virulence, particularly in Gram-negative species, where outer membrane porins serve as primary gateways for small nutrients, such as ammonium [1; 6; 19; 63; 69; 70]. While active transporters, such as AmtB, have been studied extensively, passive diffusion through general porins, like OmpF in _Escherichia coli_ and Omp-Pst2 in _Providencia stuarti_, can become an important route when external ammonium levels are low [5; 6; 7; 24; 25; 26; 28; 38; 41; 50; 71; 72]. Until now, measurements of ammonium uptake have typically used whole-cell assays or radiotracer methods, which cannot distinguish between active and passive pathways [5; 6; 7; 19; 63; 69]. This study closes that gap by directly measuring, for the first time, how much ammonium passes through individual porin channels under controlled ionic conditions [35; 36]. By comparing Omp-Pst2 and OmpF simultaneously, this study reveals how differences in pore size, the arrangement of charged residues, and the narrow \"constriction zone\" affect the rate and selectivity of ammonium diffusion. Generally, porins form water-filled \(\upbeta\)-barrel channels without specific binding sites; therefore, molecules below approximately 600 Da can pass based on their size and charge [36; 48; 49; 72; 73]. In this case, Omp-Pst2's slightly wider channel and unique charge pattern appear may allow more ammonium flux than OmpF. However, detailed, atomic-level confirmation will require future structural or computational studies.  
Under nutrient-limiting conditions, such as those found in host environments where ammonium concentrations drop into the micromolar range, porin-mediated diffusion may become a limiting factor in nitrogen acquisition [1; 29; 63]. Many Gram-negative pathogens respond to nitrogen scarcity by increasing porin expression. Thus, blocking a key porin, such as Omp-Pst2, could slow growth or prevent biofilm formation under these conditions, offering a novel antimicrobial strategy [74; 75].  
Conversely, identifying which porin channels favor ammonium influx suggests a route for drug delivery [16; 17; 18; 21; 61; 76; 77]. Designing antibiotic prodrugs that mimic ammonium or pair with counterions preferred by Omp-Pst2 or OmpF could drive higher drug concentrations into the periplasm of Gram-negative bacteria. This approach could bypass common resistance mechanisms, such as efflux pumps, and improve antibiotic effectiveness.  
Finally, providing quantitative, single-channel measurements of ammonium flux lays the groundwork for more accurate models of how bacteria acquire nitrogen in complex environments. Incorporating these detailed porn data into computational simulations could improve predictions of growth under nutrient-limited conditions, competition among microbial communities, and the development of resistance under selective pressure [1; 60; 64; 65; 68]. Overall, this study provides a new tool for investigating porin function, enables the design of targeted antimicrobials, and closes a longstanding gap in our understanding of bacterial nitrogen uptake.
## 4 Monitoring Strategy Considering Noise Ratio and Coupled Vibration
This includes information on traffic flow magnitude, vehicle weight, and speed. Multilevel thresholds are defined for different traffic scenarios and safety states, serving as criteria to initiate signal filtering and damage detection. Upon detecting damage, the analysis model and monitoring strategy can be regulated, leading to the proposal of a maintenance scheme. The monitoring strategy flowchart is depicted in Figure 2. This approach offers the advantageof filtering out numerous signals with low signal-to-noise ratio or inaccessible data, thereby conserving computing resources. Ultimately, it enables purposeful monitoring based on theoretical analysis.
# Applying Machine Learning to DEEC Protocol: Improved Cluster Formation in Wireless Sensor Networks
## 4 Discussion
- Environmental Noise and Communication Errors: Wireless communication in real environments is subject to interference, signal attenuation, and packet loss. Such noise can result in inaccurate distance measurements, unreliable neighbour discovery, and data loss, all of which can affect the stability and accuracy of clustering. - Hardware Variability: In practice, sensor nodes may have heterogeneous hardware capabilities, including differences in battery life, processing power, and sensing accuracy. These variations can lead to uneven energy consumption and may affect the fairness and efficiency of cluster head selection.
###### Abstract  
In this work, an improved dynamic convolutional neural network (DCNN) model to accurately identify the risk of a forest fire was established based on the traditional DCNN model. First, the DCNN network model was trained in combination with transfer learning, and multiple pre-trained DCNN models were used to extract features from forest fire images. Second, principal component analysis (PCA) reconstruction technology was used in the appropriate subspace. The constructed 15-layer forest fire risk identification DCNN model named \"DCN_Fire\" could accurately identify core fire insurance areas. Moreover, the original and enhanced image data sets were used to evaluate the impact of data enhancement on the model's accuracy. The traditional DCNN model was improved and the recognition speed and accuracy were compared and analyzed with the other three DCNN model algorithms with different architectures. The difficulty of using DCNN to monitor forest fire risk was solved, and the model's detection accuracy was further improved. The true positive rate was 7.41% and the false positive rate was 4.8%. When verifying the impact of different batch sizes and loss rates on verification accuracy, the loss rate of the DCN_Fire model of 0.5 and the batch size of 50 provided the optimal value for verification accuracy (0.983). The analysis results showed that the improved DCNN model had excellent recognition speed and accuracy and could accurately recognize and classify the risk of a forest fire under natural light conditions, thereby providing a technical reference for preventing and tackling forest fires.  
forest fire; deep learning; transfer learning; convolutional neural network +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Case Situation Analysis
#### 2.1.1 Image Collection  
The quality of the AOI images collected for training the model was largely related to the model's self-learning and defect detection abilities as well as the model of the video camera, the shooting angle, the scanning speed, the focal length, and the magnification, etc. In this study, the AOI acquisition parameters were a line scan camera with 7500 pixels, the illuminance of 65,100 lux, and the scanning speed of 50 mm/s, respectively. There is a total of 1000 images including standard products and defects for training, validation and testing.
# Communication Architecture for Grid Integration of Cyber Physical Wind Energy Systems
## 4 Simulation Results
### Wind Farm Results  
We configured the WF communication network based on the data from real large-scale WF projects (Greater Gabbard WF in UK [16], Horn Rev WF in Denmark [15], and Yeung Heung in South Korea [17]). The network topology was configured as cascaded architecture. A main ethernet switch was located on the offshore platform. Dedicated optical fibers were connected between the main ethernet switch and the nearest wind turbines. The connectivity between turbines was provided through the connection between ethernet switches installed at the base of each turbine.  
Figure 11 shows the total traffic received at the control center (SCADA server). The traffic received for analogue measurements, status information, foundation measurements, and protection information was 4,510,880 bytes/s (225,544 \(\times\) 20), 1160 bytes/s (58 \(\times\) 20), 28,680 bytes/s (1434 \(\times\) 20), and 1,536,320 bytes/s (76,816 \(\times\) 20), respectively. Figure 12 shows the average ETE delay of real-time monitoring data for all wind turbine applications. The average ETE delay was about 7.83 ms and 0.78 ms for link capacities of 100 Mbps and 1 Gbps, respectively. For the 20 wind turbines in the WF, Table 11 shows the maximum and minimum ETE delays for SCADA, MU-IED, and CB-ID between the wind turbines and the control center.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **\# Sensor Nodes** & **Idle Mode** & **Operation Mode** \\ \hline Case1\(\times\) & 108 & 0.53 ms & 1.1 ms \\ Case2\(\times\) & 216 & 0.65 ms & 1.77 ms \\ Case3\(\times\) & 324 & 0.69 ms & 3.08 ms \\ \hline \hline \end{tabular}
\end{table}
Table 10: ETE delay inside WTs with different numbers of sensor nodes.  
Figure 10: (**a**) Total received traffic at WTC for different operation mode; (**b**) ETE delay for the internal communication inside wind turbine.
## 2 Cepstrum Analysis for Wheel Flat Detection
### Analysis of Data Tested by Roller Rig  
In the actual operation, passengers ride on in service. Thus, it is difficult to conduct measurement tests using a wheel with flats. Therefore, the vibration test was conducted using a roller rig in the Korea Railroad Research Institute.  
The measurement test using a roller rig is shown in Figure 15. The wheel test conditions are 20 km/h in uniform velocity and a flat of 50 mm at the left wheel. In addition, the data were obtained by installing a three-axis accelerometer in the axle box. The tests were conducted more than three times to obtain good data. The test results are shown in Figure 16. Figure 16a shows all the acceleration signals measured in the two 3-axis accelerometers. The flat signals were measured even on the right wheel without a flat. However, the largest vibration signal was measured on the left wheel z-axis, which is most related to the flat.  
Figure 11: Cepstrum results of a single flat in the angle domain.  
Figure 12: Acceleration sensor installation.  
Figure 13: The test measurement result for a high irregularity section of a rail.  
Figure 14: Cepstrum results depending on the absence (the upper) or presence (the below) of a wheel flat.  
Figure 15: Measurement test using a roller rig (wheel with a 50 mm flat).  
Figure 16: Vibration signals on the 50 mm flat wheel at a speed of 20 km/h.  
Figure 16b is the z-axis acceleration of the left wheel. A periodic harmonic component of about 20g occurred at vertical (z-axis) acceleration.  
Figure 17 shows the results of a cepstrum analysis using the flat signal shown in Figure 16b. As a result of the cepstrum analysis, it was confirmed that peak values are occurred in the harmonic components. Therefore, based on the results of Figure 17, it is confirmed that the cepstrum analysis method is appropriate for not only simulation but also measurement data.
## 5 Industry Applications and Manufacturers
### Autonomous Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs)  
Autonomous guided vehicles (AGVs) and autonomous mobile robots (AMRs) are essential tools for modern industrial automation and logistics. Both technologies streamline material handling, inventory management, and intra-facility transportation. While they share the common goal of autonomous navigation, AGVs and AMRs differ significantly in their operational methods, flexibility, and deployment scenarios. These distinctions set them apart from the autonomous vehicles often seen on roads and highways.  
This section is summarized in Table 10, which presents a comparison between automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) in the context of industrial automation. The figure highlights the key differences in functionality, flexibility, and technological integration, providing a clear overview of their respective roles and advantages in modern industrial applications  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature** & **(AGVs)** & **(AMRs)** \\ \hline \multirow{2}{*}{**Navigation**} & Relies on predefined paths (e.g., QR codes) & Utilizes advanced sensors (e.g., LiDAR, SLAM, cameras) for dynamic pathfinding \\ \hline \multirow{2}{*}{**Flexibility**} & Limited to structured environments & Adapts to dynamic, unstructured environments \\ \hline \multirow{2}{*}{**Deployment**} & Requires installation of infrastructure (tracks, markers) & No additional infrastructure needed; operates in existing layouts \\ \hline \multirow{2}{*}{**Obstacle Handling**} & Stops or requires manual intervention when encountering obstacles & Navigates around obstacles autonomously \\ \hline \multirow{2}{*}{**Use Cases**} & Repetitive tasks in static environments (e.g., manufacturing, assembly lines) & Complex tasks in dynamic environments (e.g., e-commerce warehouses) \\ \hline \multirow{2}{*}{**Scalability**} & Limited by the need for fixed pathways & Scalable and adaptable to growing or changing operations \\ \hline \multirow{2}{*}{**Technology Cost**} & Lower initial cost due to simpler technology & Higher initial investment but better ROI over time \\ \hline \multirow{2}{*}{**Examples**} & Conveyor replacements, pallet transportation & Inventory management, order picking \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of AGVs and AMRs in industrial automation inspired by [118].  
Figure 19: Summary of the content in Section 5.
# Target Detection for Coloring and Ripening Potted Dwarf Apple Fruits Based on Improved YOLOv7-RSEs
## 2 Materials and Methods  
### Dataset Collection  
This work focuses on researching the new variety of Ruixiang red apple with dwarf anvil, using pot planting in a sunlit greenhouse. The sunlit greenhouse spans 4000 square meters and houses 1500 potted dwarf anvil fruit trees. The rows are 3 m apart, with trees spaced 2 m apart within each row. The difference between dwarf rootstock fruit trees and traditionally planted fruit trees can be visually compared through Figure 1. The datasetwas collected at the fruit tree dwarf rootstock planting base of Shanxi Agricultural University, located in Xiaowang Village, Nandali Township, Xia County, Yuncheng City, Shanxi Province, with geographic coordinates ranging from \(111^{\circ}02^{\prime}\) to \(111^{\circ}41^{\prime}\) east longitude and \(34^{\circ}55^{\prime}\) to \(35^{\circ}19^{\prime}\) north latitude. The collection period was from September 15 to 30 October 2023, using a Canon 700D camera. The images were captured in various weather and light conditions (sunny or cloudy) and at different distances--far (100-150 cm) and close (50-100 cm)--as shown in Figure 2. The apple fruit's reproductive period includes flowering, fruit drop, expansion, coloring, and ripening stages. This collection focuses on the coloring and ripening stage of the fruit. It is designed for fruits freshly taken out of the film bag, transitioning from green to yellow to red as they ripen. The fruit should have a diameter between 70 and 100 mm. During orchard production, it is crucial to monitor fruit color and ripeness in real time, automate picking, and accurately predict yield to boost efficiency and profits. Yuncheng, the research area, experiences year-round monsoon influence and has a warm temperate continental climate. It receives an average of 525 mm rainfall annually, enjoys 2350 h of sunshine, maintains an average yearly temperature of 13.3 \({}^{\circ}\)C, and has a frost-free period lasting 212 days. In recent years, through robust agricultural industry restructuring, the apple cultivation area has surged to 2.12 million mu, yielding over 1.8 billion kilograms annually, establishing itself as a prime national hub for high-quality apple production.  
This paper used an on-site data collection approach to tackle uneven sample distribution, enhance dataset diversity, and reduce bias in model training. We focused on target detection technology for tasks like machine inspection and mechanical fruit picking, where identifying ripe fruits at different distances and executing picking operations in close proximity are crucial. We gathered apple images from various time slots, distances, and angles, totaling 1325 images.
# Flying Free: A Research Overview of Deep Learning in Drone Navigation Autonomy
## 3 Results
### Expanded Navigation  
Expanded navigation covers elements of autonomy that we suggest are second-level navigation autonomy features, relative to those of Section 3.2, and will be addressed at a later stage than the core features of basic navigation. These features would increase the operational capacity of a drone autonomy project that already covers some features of basic navigation; the following features are tabulated in Table 3.  
* **Path Generation (PG):** The drone attempts to generate or optimize a pathway to a given location, the application of the generated pathway can vary depending on the goal of the project (e.g., pathways for safety or pathways for efficiency).
* **Environment Distinction (ED):** The drone can distinguish or take advantage of features of an uncommon use case environment, such as forests, rural areas or mountainous regions. Urban and indoor environments have been excluded from this criteria.
* **Non-Planar Movement (NPM):** The implemented navigational policy makes use of full three-dimensional movement strategies enabling the drone to navigate above or below obstacles as well as around them.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Paper** & **Year** & **Citations** & **AM** & **CA** & **ATL** \\ \hline [PERSON] et al. [9] & 2020 & 34 & Yes & Yes & No \\ [PERSON] et al. [10] & 2020 & 11 & No & Yes & No \\ [PERSON] et al. [11] & 2020 & 8 & No & No & No \\ [PERSON] et al. [12] & 2020 & 6 & No & No & No \\ [PERSON] et al. [13] & 2020 & 6 & Yes & Yes & No \\ [PERSON] et al. [14] & 2020 & 5 & Yes & Yes & No \\ [PERSON] et al. [15] & 2020 & 4 & No & No & No \\ [PERSON] et al. [16] & 2019 & 55 & No & No & No \\ [PERSON] et al. [17] & 2019 & 50 & Yes & Yes & No \\ [PERSON] et al. [7] & 2019 & 43 & Yes & Yes & No \\ [PERSON] et al. [18] & 2019 & 19 & No & No & No \\ [PERSON] et al. [19] & 2019 & 11 & No & No & No \\ [PERSON] et al. [20] & 2019 & 9 & No & Yes & No \\ [PERSON] et al. [21] & 2019 & 8 & No & No & No \\ [PERSON] et al. [22] & 2018 & 158 & Yes & Yes & No \\ [PERSON] et al. [23] & 2018 & 60 & Yes & Yes & No \\ [PERSON] et al. [24] & 2018 & 58 & No & No & No \\ [PERSON] et al. [25] & 2018 & 57 & Yes & No & No \\ [PERSON] et al. [26] & 2018 & 23 & Yes & Yes & No \\ [PERSON] et al. [27] & 2018 & 14 & No & No & Yes \\ [PERSON] et al. [28] & 2018 & 14 & Yes & Yes & No \\ [PERSON] et al. [29] & 2017 & 165 & Yes & Yes & No \\ [PERSON] et al. [30] & 2017 & 98 & Yes & Yes & No \\ [PERSON] et al. [31] & 2017 & 88 & Yes & Yes & No \\ [PERSON] et al. [32] & 2017 & 43 & No & No & No \\ [PERSON] et al. [33] & 2017 & 31 & No & No & No \\ [PERSON] et al. [34] & 2017 & 25 & No & No & No \\ [PERSON] et al. [35] & 2017 & 11 & No & No & Yes \\ [PERSON] et al. [36] & 2016 & 424 & Yes & Yes & No \\ [PERSON] et al. [37] & 2016 & 263 & Yes & Yes & No \\ [PERSON] et al. [38] & 2016 & 26 & Yes & Yes & No \\ [PERSON] et al. [39] & 2016 & 3 & No & No & No \\ \hline \end{tabular}
\end{table}
Table 2: The most cited entries in the research pool as of 18 March 2021 in the context of Basic Navigation features.