esa-sceva/satcom-chunk-collection huggingface dataset:
# Query-Based Cascade Instance Segmentation Network for Remote Sensing Image Processing
## 3 Experiment and Analysis
### Instance Segmentation Results on NWPU VHR-10
In the first column, QCIS-Net accurately detects and segments even the less prominent aircraft in the middle of the image, while compared methods such as Mask-RCNN and SC-Net exhibit omissions in detecting and segmenting this object, highlighting the strong performance of QCIS-Net. Furthermore, our QCIS-Net also achieves the accurate IS of the GTF and BD categories in the second column. For the bridges with larger aspect ratios in the third column, some compared methods such as MS-RCNN and SC-Net demonstrate suboptimal performance, while QCIS-Net effectively detects and segments them. In the fifth column, the harbor scene experiences missed detections and segmentations in almost all compared algorithms, while our method is able to detect and segment the objects in the image, demonstrating the robust capability of our approach in handling challenging scenes. Overall, the proposed QCIS-Net exhibits excellent performance in optical image IS and object-detection tasks. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Method** & **AP** & **AI** & **BD** & **GTF** & **VC** & **SH** & **TC** & **HB** & **ST** & **BC** & **BR** \\ \hline Mask-RCNN [21] & 68.5 & 78.4 & 77.6 & 87.3 & 56.1 & 60.0 & 77.7 & 51.1 & 80.6 & 77.4 & 37.4 \\ \hline MS-RCNN [52] & 68.8 & 79.0 & 79.9 & 87.7 & 55.7 & 61.1 & 78.0 & 50.7 & 81.8 & 79.8 & 34.4 \\ \hline CM-RCNN [19] & 70.0 & 80.7 & 78.0 & 91.2 & 58.0 & 64.1 & 79.4 & 52.9 & 81.9 & 80.3 & 31.9 \\ \hline HTC [20] & 71.4 & 80.0 & 80.5 & 92.8 & 57.6 & 62.3 & 81.6 & 53.3 & 83.0 & 82.0 & 35.7 \\ \hline SC-Net [53] & 70.8 & 79.0 & 80.9 & 89.1 & 58.3 & 64.8 & 82.6 & 56.2 & 82.3 & 81.0 & 32.0 \\ \hline Insta-Boost [54] & 70.6 & 79.6 & 80.8 & 86.9 & 57.7 & 58.7 & 81.9 & 53.3 & 81.1 & 80.5 & 36.3 \\ \hline QCIS-Net & 77.1 & 82.7 & 84.2 & 95.3 & 66.6 & 71.3 & 84.1 & 65.5 & 84.6 & 86.3 & 45.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Classwise detection results on the NWPU-VHR-10 test set.
# Thermal Evaluation of a Hybrid Dryer with Solar and Geothermal Energy for Agroindustry Application
## 2 Materials and Methods
For this test, 500 W halogen lamps were used, making configurations at a certain distance from the walls to generate irradiation of 300 W/m\({}^{2}\), 400 W/m\({}^{2}\), 500 W/m\({}^{2}\), 600 W/m\({}^{2}\), 700 W/m\({}^{2}\), 800 W/m\({}^{2}\), 900 W/m\({}^{2}\), and 1000 W/m\({}^{2}\). The irradiation that reached the surface of the transparent plates was measured, to do so, it was necessary to make a square mesh 50 cm away for each point. During the test, the variables were recorded every 10 min during 2 h of chamber operation and then at the cooling stage (the luminaires are turned off) of one hour. (f) Environmental conditions  
Ecuador has two seasons: the rainy and humid season from December to May and the dry season from June to November where the annual total rainfall is approximately 1506.5 mm (INAMH). Along the coast of the province, during the dry season (June-November) there are average temperatures from 21.65 \({}^{\circ}\)C to 30.8 \({}^{\circ}\)C with a humidity of 45 to 60%, while in the rainy season (December-May), the average temperature is 30.65 \({}^{\circ}\)C with a humidity of 70 to 99%. From the above, it is necessary to mention that the experimental tests were carried out from March to June. The first test was to analyze the operation of the dryer in active mode (forced convection). The main variables recorded were the chamber temperature, temperature at the collector outlet, ambient temperature, and global irradiation that reached the transparent plates for a 10 h operating time. For the second test, the operation of the aerothermal system during the day and night was evaluated in order to determine the temperature of the exchanger's stabilization and the temperature input of the soil. For this purpose, we placed type K thermocouples to record the temperatures of the soil at depths of 0.25 cm, 50 cm, 75 cm, and 100 cm. Third test: During the experimental testing, the following parameters were measured: inlet and outlet temperature of the buried exchanger \(T_{e,aerothermal}\); \(T_{s,aerothermal}\) (\({}^{\circ}\)C); relative humidity (%) and ambient temperature (\({}^{\circ}\)C); the inlet and outlet temperature of the solar collector \(T_{e,col}\), \(T_{s,col}\) (\({}^{\circ}\)C); hot fluid velocity (m/s) at the exchanger outlet and in the drying chamber; chamber heating time(s); and global radiation on the ground surface, collector, and drying chamber (W/m\({}^{2}\)). Additional energy input generated through an electrical resistance of 1000 W was also measured. On the other hand, parameters such as the energy consumption, voltage, and current supplied by the photovoltaic system were also recorded. It should be noted that the fan and the source of the control system was powered by photovoltaic energy. Finally, a test was performed on the hybrid dryer by coupling an electrical resistance as a complementary source for air heating.
## 3 Results and Discussion
### Experimental Verification
#### 3.1.2 Luminescent Properties  
Laser engraving is applied on the fabric surface to achieve intricate pattern design. Based on the light-transmitting characteristics of POFs, these patterns could present a  
\begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{2}{c}{**24-Bit Data**} & 0-7 & 8-15 & 16-23 \\ \hline
**Portion** & Red & Green & Blue \\ \hline \multirow{3}{*}{**LED**} & Red & 11111111 & 0000000 & 0000000 \\  & Green & 00000000 & 1111111 & 00000000 \\  & Blue & 00000000 & 0000000 & 11111111 \\ \hline \end{tabular}
\end{table}
Table 2: Control data for different light colors.  
Figure 9: Fabric’s sensing area pressing response: (a) 20 cycles; (b) 1000 cycles.  
bright luminescent effect when the fabric is connected to the light source. The brightness values of this POF fabric sample after coupling with light sources were measured below to show its luminescent effect across the surface.  
The TES-137 Luminance Meter (Taishi Electronics Industry Co., Ltd., Taipei, China) can be used to measure brightness values of the luminescent fabric's surface. The fabric is placed flat in a dark room and connected to a light source. The luminance meter's photo detector captures real-time brightness values of the fabric's luminescence. The multiple-point sampling method is employed during the process to ensure the feasibility of the final results.  
The measurement involved sampling multiple points in the intricate patterned area (laser-engraved) and the other area (non-engraved) of the fabric. The box plot of luminescence values for each area is shown in Figure 10. The luminescence value of the intricate patterned area is significantly higher than the other area. The average values of luminescence in the intricate patterned area and the other area are about 62.06 and 21.21 cd/m\({}^{2}\), respectively. The measurement results provide evidence for the bright luminescent effect of the intricate pattern in this study.
# Event-Based Path-Planning and Path-Following
A wide diversity of methods have been developed in order to solve the path-following problem, among which are backstepping method [22; 23; 24], observer-based method [25], nonlinear PID control [26], adaptive control [27], model predictive control [21; 28], sliding mode control [5; 29], and fuzzy control [30]. In the context of the simultaneous solution of path-planning and path-following problems, we note the studies presented in [5; 20; 21; 31]. The majority of these studies are aimed at combating uncertainties, disturbances, and control constraints. However, in addition to taking into account these negative factors, a path-following controller should allow for its simple implementation in the on-board control system, which is usually designed as a digital one, and for the fast calculation of control signals. Tuning control parameters in path-following controllers to ensure desired or optimal tracking performance is an important step in designing path-following controllers. The contributions of this paper are as follows. We propose a new formulation of the path planning problem, in which the AUV's primary goal is to sail along a given path passing through a sequence of waypoints. To the best of the authors' knowledge, such a path planning formulation has not been considered before. To solve the problem, we develop an event-based framework that includes two levels: the upper one for real-time path planning and the lower one for path-following. A discrete-event system, which is a basis of the upper level, is designed in order to detect situations that require building a new path and changing the active waypoint. Two path-planning algorithms are proposed. The first one, based on refined data from forward-looking sonar (FLS), plans a path that ensures safe obstacle avoidance, and the second one generates a path that returns the AUV to the reference path. Paths that are built by these algorithms meet the minimum turning radius constraint. A robust path-following controller is designed using the gain scheduling control methodology and sublinear vector Lyapunov functions. When synthesizing the controller gains, in contrast to most known studies, we aim to minimize the AUV's positioning errors on the path and take into account the control saturation, measurement errors, and variability of the path curvature. The designed controller can be easily implemented in digital control platforms, which are traditional for AUVs. The remainder of this paper is organized, as follows. The problem statement is presented in Section 2. The main results of the work are presented in Section 3. In particular, Section 3.1 presents the architecture of the overall control system. The path-following controller design is discussed in Section 3.2. Section 3.3 presents an event-based approach for path-planning in an unknown environment, including path planning algorithms and a discrete event system in order to determine the moments to run them. In Section 4, the simulation results are provided in order to illustrate the performance of the proposed approach. Finally, Section 5 discusses the obtained results.
# Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network
## 5 Conclusions  
SSPU-FENet, a self-supervised network with feature enhancement and perturbation learning modules, is proposed to generate geometric information-rich dense upsampled point clouds. This network captures global structural details, ensuring clear boundaries and optimal coordinate reconstruction. Experimental results demonstrate its superior upsampling performance compared to state-of-art methods, demonstrating its potential in dealing with CAD models. However, despite its promising performance, SSPU-FENet still faces challenges when processing complex point clouds. These challenges arise from the inherent difficulties in handling noisy, incomplete, or irregularly shaped point clouds, which are common in real-world applications. Therefore, further enhancements and refinements are needed to improve the network capability and generalization. For future work, we plan to extend its ability to handle arbitrary proportions of upsampling by creating a more flexible architecture, as well as minimizing manual human interference by building a fully unsupervised network. These efforts will expand the applicability of the network and reduce user workload while improving efficiency and accuracy. Meanwhile, we will transfer the network to the task of point cloud completion, especially for missing point  
Figure 14: Visualization of mesh reconstruction compared to other methods.  
clouds or point clouds with big holes, in order to improve the generalization ability of the network. In conclusion, while SSPU-FENet has achieved promising results in point cloud upsampling, there is still improvement in handling complex point clouds. By focusing on enhancing the robustness, flexibility, and automation of the network, we hope to develop a more powerful and generalized tool for point cloud upsampling in a variety of applications.  
Conceptualization, S.Q.; methodology, S.Q. and Y.J.; validation, S.Q.; writing--original draft preparation, S.Q.; writing--review and editing, S.Q., Y.J. and H.H. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Key R & D Programs of Zhejiang Province (No. 2023C01224), the National Nature Science Foundation of China (No. 61702458), the Natural Science Foundation of Huzhou city, China (Np. 2022 YZ15), the Zhejiang University of Water Resources and Electric Power Excellent Course Project (No. 2023-108) and the Huzhou University Excellent Graduate Course Project (No. YJGX24003).  
Not applicable.  
All data that support the findings of this study are included in this manuscript.  
The authors report there are no competing interests to declare.
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 7 Conclusions
Therefore, more case studies need to be conducted on short payload transmission over WiGig to better understand the relative impact of the RX DBB and to propose adequate latency mitigation mechanisms. Furthermore, transmitting the entire payload using only one of the 15 discussed MCSs results in a limited amount of discrete data transmission latency and reliability outcomes. Splitting the payload in arbitrary lengths and transmitting them at different MCSs could provide fine-grained control over latency and BER. Conceptualization, A.M., L.D.S. and L.V.d.P. ; Data curation, A.M. and D.D. ; Formal analysis, A.M.; Investigation, A.M. and D.D. ; Methodology, A.M.; Software, A.M.; Supervision, L.D.S. and L.V.d.P. ; Validation, A.M.; Visualization, A.M.; Writing--original draft, A.M., D.D., L.D.S. and L.V.d.P. ; Writing--review & editing, A.M., D.D., L.V.d.P. All authors have read and agreed to the published version of the manuscript. This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreements: MINTS MSCA-ITN, No 861222; REINDEER RIA, No 101013425. The data presented in the manuscript are reproducible using the simulation framework, found at [[https://github.com/PhyPy-802](https://github.com/PhyPy-802) dot11 ad]([https://github.com/PhyPy-802](https://github.com/PhyPy-802) dot11 ad) (accessed on 31 May 2021). The authors declare no conflict of interest.
## 4 Experiments
### Application and Datasets
#### 4.2.2 Smart Phone Human Activity Recognition Dataset  
The UCI HAR from the smartphone dataset consists of data from 30 users performing daily activities from a waist-mounted smartphone with embedded inertial sensors. The sensor signals (accelerometer and gyroscope) were preprocessed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 s and 50% overlap (128 readings/window). Some of the activities include sitting, standing, walking, lying, and so on. It is a six-category and well-balanced dataset.
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
## 4 Application to GPS and Galileo Signals
### Comparison of Galileo E1 and E5 Signals Acquisition
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Comparison** & **Fixed Element** & \(-\)**140 dBm** & \(-\)**145 dBm** & \(-\)**150 dBm** & \(-\)**155 dBm** & \(-\)**160 dBm** \\ \hline  & \(T_{\mathrm{C}}=100\) ms (no assistance) & 100 & 100 & 60 & 54 & 46 \\  & \(100\) & 100 & 60 & 48 & 44 \\ Impact of & \(T_{\mathrm{C}}\) unlimited (no assistance) & 100 & 100 & 75 & 70 & 68 \\ using E5: E5 & \(T_{\mathrm{C}}=100\) ms (assistance) & 100 & 100 & 50 & 47 & 43 \\  & \(100\) & 50 & 50 & 46 & 42 \\  & \(T_{\mathrm{C}}\) unlimited(assistance) & 57 & 61 & 62 & 63 & 63 \\  & \(T_{\mathrm{C}}\) unlimited(assistance) & 64 & 61 & 63 & 63 & 63 \\ \hline  & E1 signal 4.096 (no assistance) & 100 & 100 & 80 & 42 & 17 \\  & \(100\) & 100 & 60 & 29 & 10 \\  & E1 signal 6.144 (no assistance) & 100 & 100 & 80 & 40 & 17 \\  & \(100\) & 100 & 60 & 28 & 10 \\ Impact of \(T_{\mathrm{C}}\): & E5 signal (no assistance) & 100 & 100 & 100 & 54 & 26 \\ Unlimited & & 100 & 100 & 67 & 40 & 16 \\  & \(28\) & 80 & 62 & 41 & 18 \\ verses 100 ms & E1 signal 4.096 (assistance) & 44 & 70 & 54 & 29 & 11 \\  & \(28\) & 80 & 63 & 41 & 18 \\  & \(28\) & 44 & 70 & 55 & 28 & 11 \\  & \(28\) & 86 & 49 & 77 & 54 & 26 \\ \hline  & E1 signal 4.096 (\(T_{\mathrm{C}}=100\) ms) & 100 & 100 & 80 & 79 & 77 \\  & \(100\) & 100 & 80 & 76 & 76 \\ Impact of & \(100\) & 100 & 80 & 76 & 76 \\ assistance: & E5 signal (\(T_{\mathrm{C}}=100\) ms) & 100 & 100 & 67 & 69 & 72 \\ Assistance & & 100 & 50 & 67 & 73 & 73 \\ verses no & E1 signal 4.096 (\(T_{\mathrm{C}}\) unlimited) & 28 & 80 & 62 & 78 & 79 \\ assistance & & 44 & 70 & 72 & 76 & 80 \\  & E1 signal 6.144 (\(T_{\mathrm{C}}\) unlimited) & 28 & 80 & 63 & 79 & 80 \\  & \(44\) & 70 & 73 & 77 & 80 \\  & \(44\) & 70 & 73 & 77 & 80 \\  & \(45\) & 51 & 70 & 73 & 73 \\  & \(46\) & 51 & 70 & 73 & 75 \\  & \(47\) & 51 & 70 & 73 & 75 \\  & \(47\) & 51 & 70 & 73 & 75 \\  & \(48\) & 52 & 70 & 73 & 75 \\  & \(49\) & 51 & 70 & 73 & 75 \\  & \(47\) & 52 & 70 & 73 & 75 \\  & \(49\) & 53 & 70 & 73 & 75 \\  & \(40\) & 54 & 53 & 70 & 73 & 75 \\  & \(40\) & 55 & 55 & 55 & 55 & 55 \\  & \(41\) & 55 & 55 & 55 & 55 & 5 Now, we can take into account the average number of frequency bins searched until detection to obtain the final ratios of complexity. Table 21 shows the ratio of number of frequency bins between the signals. As for the GPS, when there is non-coherent integration and that the coherent integration time is the same for both signals, the ratio depends only on the width of the search space, hence the ratio of 0.8. Then, with coherent integration only, since both signals are not limited in the coherent integration time (unlike the comparison of GPS L1 C/ A and L5), the ratio is in favor of the E5 signal because it requires a smaller integration time thanks to its higher power. Finally, applying these ratios to Table 20 gives Table 22 that shows the final complexity ratio, and it can be seen that even if the ratios are lower than Table 20, the E5 signal is still more complex than the E1.
## 3 Results
The POD\({}_{\text{Y}}\) appropriate for the linear regression of the response were POD\({}_{7}\) for _F. crenata_, POD\({}_{4}\) for _Q. serrata_, POD\({}_{8}\) for _Q. mongolica_ var. _crispula_ and POD\({}_{3}\) for _B. platyphyllla_ var. _japonica_. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Tree Species**} & \multirow{2}{*}{**No. of**} & \multicolumn{6}{c}{**Y**} \\ \cline{3-13}  & & **Plot** & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline _F. crenata_ & \(n=8\) & 0.710 & 0.747 & 0.805 & 0.850 & 0.881 & 0.903 & 0.917 & 0.921 & 0.918 & 0.903 & 0.878 \\  & & ** & ** & ** & ** & ** & ** & ** & ** & ** & ** & ** & ** \\ \hline _Q. serrata_ & \(n=16\) & 0.724 & 0.776 & 0.839 & 0.879 & 0.893 & 0.884 & 0.858 & 0.817 & 0.764 & 0.704 & 0.653 \\  & & ** & ** & ** & ** & ** & ** & ** & ** & ** & ** & ** & ** \\ \hline _Q. mongolica_ & \(n=16\) & 0.697 & 0.730 & 0.780 & 0.824 & 0.861 & 0.889 & 0.909 & 0.921 & 0.925 & 0.918 & 0.900 \\ var. _crispula_ & & ** & ** & ** & ** & ** & ** & ** & ** & ** & ** & ** & ** \\ \hline _B. platyphyllla_ & \(n=36\) & 0.738 & 0.754 & 0.768 & 0.775 & 0.774 & 0.767 & 0.755 & 0.740 & 0.722 & 0.703 & 0.681 \\ var. _japonica_ & & ** & ** & ** & ** & ** & ** & ** & ** & ** & ** & ** \\ \hline \hline \end{tabular} Statistical significance for \(R^{2}\) is shown: **\(p<0.01\)** and **\(\pi\)**\(p<0.001\).  
\end{table} 
Table 4: The \(R^{2}\) value for the linear regression line between relative value of leaf-level \(\Sigma P_{\text{n\_est}}\) and phytotoxic O\({}_{3}\) dose (POD\({}_{\text{Y}}\), Y: flux threshold) in the leaves of _F. crenata_, _Q. serrata_, _Q. mongolica_ var. _crispula_ and _B. platyphyllla_ var. _japonica_ seedlings. Figure 2: Relationships between relative \(A_{\text{sat}}\) (**a–d**) or \(\Sigma P_{\text{n\_est}}\) (**e–h**) and POD without flux threshold (POD\({}_{0}\)) in the leaves of _F. crenata_ (**a**,**e**), _Q. serrata_ (**b**,**f**), _Q. mongolica_ var. _crispula_ (**c**,**g**) and _B. platyphyllla_ var. _japonica_ (**d**,**h**). Open circles indicate 1 st flush or early leaves, and open diamonds indicate 2 nd flush or late leaves. The Weibull function was fitted to the response, and the coefficient of determination (\(R^{2}\)) is indicated in each figure. Data in (**a**)–(**d**) were from Table S2 (POD\({}_{0}\)) and S7 (\(A_{\text{sat}}\)), and in (**e**)–(**h**) were from Table S4 (POD\({}_{0}\) and \(\Sigma P_{\text{n\_est}}\)). In Figure 3, the relationships between the relative value of \(\Sigma P_{\text{n\_est}}\) and that of \(\Sigma A_{\text{sat}}\times\text{days}\) in the leaves of _F. crentata_, _Q. serrata_, _Q. mongolica_ var. _crispula_ and _B. platyphylla_ var. _japonica_ are shown. There was a significant positive linear correlation between the relative value of \(\Sigma P_{\text{n\_est}}\) and that of \(\Sigma A_{\text{sat}}\times\text{days}\).
## 6 Evaluation and Analysis
### Performance Metrics  
* _Delay_: the average time for a frame/packet containing sensor data published by a sensor node to arrive at the VB.
* _Throughput_: network capacity in bits per second based on published sensor data that successfully arrived at the VB.
* _Energy cost per delivered frame/packet_: ratio of total energy consumed by all nodes to the number of successfully delivered published sensor data frames to the VB.
* _Packet delivery ratio (PDR)_: ratio of the number of packets received by the VB to the total data packets sent by all sensor nodes.  
* _Communication overhead_: number of control frames/packets sent during the sensor data publishing from the sensor nodes to the VB member nodes.
## 2 Interference Signal Models
### Specific Steps of the Proposed Algorithm
#### 3.2.3 Steps for Iterations  
1. Treat the mean \(A^{\prime}(k)\) of the \(p-1\) segments of length \(l\) and the mean \(A^{\prime\prime}(k^{\prime})\) of the \(n\) segments of length \(q\) as the interference set. Compare all data in the interference set with the initial threshold. Data smaller than the initial threshold are classified into the noise set \(I_{m}\), while the remaining data are classified into the interference set \(J_{m}\).
2. Use the noise set \(I_{m}\) to calculate the next threshold. Data in the interference set \(J_{m}\) that are smaller than the new threshold are moved to the noise set, updating it to \(I_{m+1}\), while the remaining data stay in the interference set, updating it to \(J_{m+1}\).
3. Repeat Step 2 until no new data are moved from the interference set to the noise set or the maximum number of iterations is reached, at which point the iteration stops.
4. Compare the amplitude of the initial data of length \(N\) with the final calculated threshold. Frequencies with amplitudes greater than this threshold are identified as interference frequencies, and the interference set \(J_{m}\) is output.  
Figure 2: Flowchart of calculating relatively flat noise data segment \(R\).  
The process for iterations is illustrated in Figure 4.  
Figure 3: Flowchart of initial threshold calculation.