esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Hand-Launched Solar-Powered Unmanned Aerial Vehicle (UAV) System for Plateau
###### Abstract  
This paper describes our work on a small, hand-launched, solar-powered unmanned aerial vehicle (UAV) suitable for low temperatures and high altitudes, which has the perpetual flight potential for conservation missions for rare animals in the plateau area in winter. Firstly, the conceptual design method of a small, solar-powered UAV based on energy balance is proposed, which is suitable for flight in high-altitude and low-temperature area. The solar irradiance model, which can reflect the geographical location and time, was used. Based on the low-temperature discharge test of the battery, a battery weight model considering the influence of low temperature on the battery performance was proposed. Secondly, this paper introduces the detailed design of solar UAV for plateau area, including layout design, structure design, load, and avionics. To increase the proportion of solar cells covered, the ailerons were removed and a rudder was used to control both roll and yaw. Then, the dynamics model of an aileron-free layout UAV was developed, and the differences in maneuverability and stability of aileron-free UAV in plateau and plain areas were analyzed. The control law and trajectory tracking control law were designed for the aileron-free UAV. Finally, the flight test was conducted in Qiangtang, Tibet, at an altitude of 4500 m, China's first solar-powered UAV to take off and land above 4500 m on the plateau in winter (\(-30\,^{\circ}\)C). The test data showed the success of the scheme, validated the conceptual design method and the success of the control system for aileron-free UAV, and analyzed the feasibility of perpetual flight carrying different loads according to the flight energy consumption data.  
solar-powered UAV; perpetual flight; conceptual design; aileron-free; flight control system; flight test +
Footnote †: journal: Applied Sciences
# A Multi-Source Separation Approach Based on DOA Cue and DNN
## 5 Conclusions  
In this paper, a novel multi-source separation approach based on a DOA cue and a DNN is presented. Unlike the existing SCNR-based separation method, an NMF-based dereverberation algorithm is employed first to remove the reverberant component which can degrade the quality of the separated signal. Next, the multiple-sound-source separation framework is divided into two parts: sparse and non-sparse component point recovery. For sparse component points, a DOA cue is used to determine the dominant sound source to recover the sound source components. To achieve a better perceptual quality for the separated sound signals, a DNN is utilized to complete non-sparse component point recovery. Then, signal matching and post-processing are used to obtain the separated signals. Evaluation results show that the proposed \"dereverberation and separation\" framework can achieve a satisfactory quality of separated signal. Objective and subjective experiments show that the proposed separation approach outperforms the existing approaches especially in reverberant conditions. In the future, the current system needs to be further extended to speech separation conditions with more sound sources and background noise.  
Data curation, M.J.; Funding acquisition, M.J.; Investigation, Y.Z. and X.J.; Methodology, Y.Z. and M.J.; Software, Y.Z.; Supervision, M.J.; Writing--original draft, T.-W.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Cooperative Research Project of BJUT-NTUT, grant number No. NTUT-BJUT-110-05. And The APC was funded by the Cooperative Research Project of BJUT-NTUT.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Figure 14: MUSHRA test results under the reverberant case with 95% confidence intervals.  
**Acknowledgments:** The authors would like to thank the reviewers for their helpful comments. This work was supported by the Cooperative Research Project of BJUT-NTUT (No. NTUT-BJUT-110-05).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Test Results
### Horizontal Displacement  
In PIV analysis, the horizontal components of the displacement vector were calculated for each soil patch [35]. The horizontal displacement field of the slurry is presented in Figure 10. Because the flocculated slurry had fully consolidated by 96 h, no horizontal displacement was observed in Figure 10h,l,p.  
Following the initiation of the vacuum preloading test, a distinct horizontal displacement zone appeared near the PVD. The horizontal displacement of the slurry initially increased along the radial direction from the PVD, reaching a peak value, and then gradually decreased. As the test progressed, the displacement values continued to decline, and a low-displacement zone (d\({}_{\mathrm{x}}\) < 1 mm) formed near the PVD, indicating that the slurry in this region had completed consolidation. By comparing Figure 10a-d with Figure 10e-p, it is evident that flocculation treatment accelerated slurry consolidation. Among the tested flocculants, the lime-flocculated slurry exhibited the fastest consolidation rate. Additionally, during the vacuum preloading test, the peak horizontal displacement values for lime-flocculated slurry, APAM-flocculated slurry, dual use PAC and APAM-flocculated slurry, and untreated slurry were 10 mm, 17 mm, 22 mm, and 18 mm, respectively. The horizontal displacement of the lime-flocculated slurry was significantly lower than that of the other slurries, indicating that lime flocculation effectively reduced lateral deformation of the soil during vacuum preloading.  
Figure 10: Horizontal displacement field of slurry. (**a**) Untreated slurry, 0–3 h; (**b**) untreated slurry, 3–24 h; (**c**) untreated slurry, 24–96 h; (**d**) untreated slurry, 96–168 h; (**e**) lime-flocculated slurry, 0–3 h; (**f**) lime-flocculated slurry, 3–24 h; (**g**) lime-flocculated slurry, 24–96 h; (**h**) lime-flocculated slurry, 96–168 h; (**i**) APAM-flocculated slurry, 0–3 h; (**j**) APAM-flocculated slurry, 3–24 h; (**k**) APAM-flocculated slurry, 24–96 h; (**l**) APAM-flocculated slurry, 96–168 h; (**m**) dual use of PAC and APAM-flocculated slurry, 0–3 h; (**n**) dual use of PAC and APAM-flocculated slurry, 3–24 h; (**o**) dual use of PAC and APAM-flocculated slurry, 24–96 h; (**p**) dual use of PAC and APAM-flocculated slurry, 96–168 h.
## 4 Experiments and Results
### Results  
To evaluate the performance of the proposed methods, we use the BreaKHis dataset for a classification case with two classes: benign and malignant. Three different configurations of the proposed methods produce different classification results, as shown in Tables 3 and 4. We compare the classification accuracies of the proposed methods with the classification accuracy reports obtained both with variance and without any variance (dependent on the authors). However, in the case of the accuracies reported with variance, these values are obtained by computing the average rate over five trials according to the experimental protocol of [5; 37; 6  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Magnification Factors**} & \multicolumn{4}{c}{**Improvement**} \\ \cline{3-10}
**Methods** & **Authors** & **40\(\times\)** & **100\(\times\)** & **200\(\times\)** & **400\(\times\)** & **Average** & **Proposed** & **Proposed** & **Proposed** \\  & & & & & & & **Method 1** & **Method 2** & **Method 3** \\ \hline PTFAS1 & [5] & \(83.8\pm 4.1\) & \(82.1\pm 4.9\) & \(85.1\pm 3.1\) & \(82.3\pm 3.8\) & \(83.33\) & \(5.87\) & \(11.70\) & \(13.32\) \\ CNN1 & [65] & \(83.08\pm 2.08\) & \(83.17\pm 3.51\) & \(84.63\pm 2.72\) & \(82.10\pm 4.42\) & \(83.25\) & \(5.95\) & \(11.78\) & \(13.40\) \\ CNN2 & [64] & \(89.6\pm 6.5\) & \(85.0\pm 4.8\) & \(84.0\pm 3.2\) & \(80.80\pm 3.1\) & \(84.80\) & \(4.35\) & \(10.18\) & \(11.80\) \\ DeCAF-CNN & [29] & \(84.6\pm 2.9\) & \(84.8\pm 4.2\) & \(84.2\pm 1.7\) & \(81.6\pm 3.7\) & \(83.80\) & \(5.40\) & \(11.23\) & \(12.85\) \\ VLAD-SVM & [24] & \(91.8\) & \(92.2\) & \(91.6\) & \(90.5\) & \(91.53\) & \(-2.33\) & \(3.50\) & \(5.12\) \\ CNN3 & [66] & \(90.4\pm 1.5\) & \(86.3\pm 3.3\) & \(83.1\pm 2.2\) & \(81.3\pm 3.5\) & \(85.28\) & \(3.92\) & \(9.75\) & \(11.37\) \\ PPTAS2 & [25] & \(89.1\) & \(87.3\) & \(91.0\) & \(86.6\) & \(88.50\) & \(0.70\) & \(6.53\) & \(8.15\) \\ CNN4 & [67] & \(94.4\) & \(95.9\) & \(\mathbf{97.1}\) & \(\mathbf{96.0}\) & \(95.85\) & \(-6.65\) & \(-0.82\) & \(0.80\) \\ MIL & [37] & \(87.8\pm 5.6\) & \(85.6\pm 4.3\) & \(81.7\pm 4.4\) & \(82.9\pm 4.1\) & \(84.50\) & \(4.70\) & \(10.53\) & \(12.15\) \\ DCGAN & [38] & \(96.5\) & \(94.0\) & \(95.5\) & \(93.0\) & \(94.75\) & \(-5.55\) & \(0.28\) & \(1.90\) \\ IDSNet & [40] & \(89.5\pm 2.0\) & \(87.5\pm 2.9\) & \(90.0\pm 5.3\) & \(84.0\pm 2.9\) & \(87.75\) & \(1.45\) & \(7.28\) & \(8.9\) \\ ResNet50-KWELM & [39] & \(88.36\) & \(87.14\) & \(90.02\) & \(84.16\) & \(87.42\) & \(1.78\) & \(7.61\) & \(9.23\) \\ Xception-SVM & [41] & \(96.25\) & \(96.25\) & \(95.74\) & \(94.11\) & \(95.59\) & \(-6.39\) & \(-0.56\) & \(1.06\) \\ Deep semantic-GLCM & [42] & \(96.75\) & \(95.21\) & \(2.18\pm 2.9\) & \(96.57\pm 1.82\) & \(93.15\pm 2.30\) & \(95.42\) & \(-6.22\) & \(-0.4\) & \(1.23\) \\ Proposed method 1 & \(91.5\pm 2.29\) & \(89.1\pm 3.9\) & \(88.8\pm 4.36\) & \(87.4\pm 4.77\) & \(89.20\) & - & - & - \\ Proposed method 2 & \(95.8\pm 0.39\) & \(94.1\pm 0.45\) & \(94.9\pm 0.3\) & \(95.3\pm 0.34\) & \(95.03\) & - & - &
## 3 Results
### Vegetation Recovery in the Vegetation Damage Area
#### 3.3.4 The Relationship between Vegetation Recovery and Soil Texture  
The clay content in the topsoil of the study area was divided into eight grades using cluster analysis (Table 4).  
The relationship between vegetation recovery and clay content in the topsoil is not obvious, but it was observed that the proportion of vegetation with no recovery trended down with increasing clay levels. When the clay content was 0%, the proportion of areas not recovering was 47.26%. When clay content was greater than 0%, the proportion of vegetation not recovering fluctuated slightly. (Figure 8).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Grade**} & \multirow{2}{*}{\
\begin{tabular}{c} **Clay** \\ **Content** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Total Area** \\ **(ha)** \\ \end{tabular} } & \multicolumn{6}{c}{**Vegetation Recovery Types**} \\ \cline{4-9}  & & & **Damage** & **Not Recovering** & **Recovering** & **Recovering** & **Recovered** \\  & & **Area (ha)** & **(\%)** & **Slightly (\%)** & **Slightly (\%)** & **S**(\%)** \\ \hline I & 0 & 7099 & 3630 & 47.26 & 5.13 & 8.50 & 39.12 \\ II & 6–15 & 503,924 & 91,668 & 13.23 & 9.58 & 17.93 & 59.26 \\ III & 18–19 & 121,035 & 25,247 & 6.55 & 22.46 & 54.32 & 16.67 \\ IV & 20 & 978,085 & 136,466 & 5.94 & 13.02 & 34.02 & 47.02 \\ V & 21 & 636,578 & 82,080 & 10.72 & 13.44 & 42.77 & 33.06 \\ VI & 22 & 851,507 & 118,671 & 5.90 & 17.96 & 44.17 & 31.97 \\ VII & 23 & 107,063 & 11,132 & 12.18 & 16.05 & 38.70 & 33.07 \\ VIII & 39–46 & 111,603 & 6793 & 7.94 & 8.60 & 29.53 & 53.93 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Clay grades in the topsoil and vegetation recovery.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Intensity**} & \multirow{2}{*}{
\begin{tabular}{c} **Total Area** \\ **(ha)** \\ \end{tabular} } & \multicolumn{6}{c}{**Vegetation Recovery Types**} \\ \cline{3-6}  & & **Damage** & **Not Recovering** & **Recovering** & **Recovering** & **Recovered** \\  & & **Area (ha)** & **(\%)** & **Slightly (\%)** & **S**(\%)** & **S**(\%)** \\ \hline XI & 217,523 & 83,076 & 10.65 & 24.80 & 42.40 & 22.15 \\ X & 345,750 & 95,789 & 4.87 & 18.21 & 45.80 & 31.13 \\ IX & 648,980 & 63,497 & 5.96 & 13.74 & 40.15 & 40.15 \\ VIII & 1,429,477 & 150,025 & 7.89 & 8.39 & 34.50 & 49.23 \\ VII & 673,543 & 83,276 & 15.07 & 9.31 & 17.17 & 58.45 \\ VI & 1621 & 25 & 100.00 & 0.00 & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The relationship between vegetation recovery and earthquake intensity.
## 2 System Description
### Fourth-Order Model Considering Only Phase-Locked Loop Dynamics  
Ref. [25] considers the PLL bandwidth that is significantly smaller than the bandwidth of the LC filter and AC network transient, PR-based current control dynamics, and the DSOGI//PNSC dynamics. Therefore, the impact of the LC filter, AC network transients, PR-based current control dynamics, and DSOGI/PNSC dynamics on the transient characteristics of the system is much smaller compared to the PLL dynamics. As a result, Ref. [25] neglects the effects of the LC filter, AC network transients, PR-based current control dynamics, and DSOGI/PNSC dynamics, leading to the equivalent circuit shown in Figure 5. Then, a fourth-order synchronization model that only considers PLL dynamics is constructed to analyze the transient synchronization stability of the system during asymmetric faults. The  
Figure 4: The control structure of PR-based dual-sequence current control.  
Figure 3: The control structure of PLL\({}^{+}\) and PLL\({}^{-}\): (**a**) PLL\({}^{+}\); (**b**) PLL\({}^{-}\).  
detailed mathematical expressions and derivation process of the fourth-order model can be found in [25] and are not provided in this paper.
## 5 Results
### OHC and EEI as Complementary Measurements
These discrepancies may reflect differences in the response timescales of the ocean compared to the atmosphere, where satellite-based EEI observations display enhanced short-term variability (on monthly to annual timescales), driven by fluctuations in cloud cover, volcanic aerosol loading, ENSO phases, and other radiative feedback processes, as well as potential errors inherent in instrumental measurements. Together, OHC measurements from the Argo float network and EEI estimates from satellite observations provide complementary insights into ERB, enhancing our ability to monitor, understand, and predict changes in the climate system. A comparison of their temporal evolutions highlights how these datasets offer consistent, yet distinct, information  
Figure 8: 12-month running means of EEI anomalies from Uvsq-Sat and CERES (EBAF and SSFIdeg products), alongside \(\partial\)OHC/\(\partial t\) (data series spanning from 2013 to May 2024). on Earth's energy dynamics. Maintaining the continuity of both observation systems is essential for accurately tracking the progression of ongoing climate change.
## 4 Conclusions  
This study proposes using a specially designed feeding device to produce conductive corn yarns with PSA roving as the coated material and metallic wires as the core material. The conductive corn yarns are made into conductive fabrics and then processed with superhydrophobic treatment, forming superhydrophobic/flame retardant/electromagnetic shielding fabrics. The test results show that treated with waterproof 2P, the fabric CAs reached 155\({}^{\circ}\) and the EMI SE is also increased accordingly. The EMI SE value of PSA-SS-1' and PSA-SS-2' is 34.08 dB and 34.42 dB with electromagnetic waves of 0-3000 MHz. Comparing to the control groups, the EMI SE value of PSA-SS-1' and PSA-SS-2' is 0.82 dB and 1.92 dB higher. The shielding effectiveness of 99.9 % proves that the conductive fabrics are suitable for use in harsh conditions such as humid, acid and alkali environments. Based on the permittivity results, when composed of conductive yarns for both the warp and weft yarns, the conductive fabrics have a polarization ability and greater loss ability against electromagnetic waves than those made of only weft yarns being conductive yarns in an external electric field. The fabrics shield electromagnetic waves by means of reflection. After the hydrophobic treatment with waterproof 2P, the PSA fiber retains its inherent thermal properties demonstrated by a burned length of the conductive fabrics being shorter than 40 mm.  
In this study, the concepts and designs for the experiment are supervised by [PERSON] and [PERSON]; Experiment and data processing are conducted by [PERSON]; Text composition and results analysis are performed by [PERSON] and [PERSON]; The experimental result is examined by [PERSON], [PERSON] and [PERSON].  
This work was supported by National Natural Science Foundation of China [grant numbers 51503145, 21806121, 11702187]; the Natural Science Foundation of Tianjin City [grant numbers 18]CQNJC03400, 17 JCQNJC08000]; the Natural Science Foundation of Fujian Province [grant numbers 2018]01504, 201801505]; the Opening Project of Green Dyeing and Finishing Engineering Research Center of Fujian University (2017001A, 2017001B, 2017002B and 2017004B), and the Program for Innovative Research Team in University of Tianjin [grant number TD13-5043].  
The authors declare no conflict of interest.
# A Convolutional Neural Network SAR Image Denoising Algorithm Based on Self-Learning Strategies
## 2 SAR Image Denoising Algorithm Based on Self-Learning Strategy DnCNN  
### Algorithm Design  
Based on the idea of self-learning strategy [26] and dual-path structure on denoising [27], this article modifies and designs the traditional DnCNN model to fully utilize the image features of SAR images in terms of denoising effect. We add Gaussian noise to img.H to generate img.L, which involves preparing a set of noisy image datasets and corresponding noise free image datasets. These noisy datasets can be obtained by simulating the addition of noise on the corresponding noise free image datasets. Specifically, we added Gaussian noise to the clean simulation datasets to obtain corresponding noisy image datasets. During the training process, the input noisy image needs to be fed into the network for forward propagation to obtain the output denoised image. Then, compare the output denoised image with the corresponding noiseless image and calculate the value of the loss function. Next, use the backpropagation algorithm to update the network parameters to minimize the value of the loss function. This process requires multiple iterations until the model converges. The loss function used for training is the Mean Square Error (MSE) loss function. During the training process, the model parameters with the best training effect were selected and tested on the simulation dataset. It can be intuitively seen that the denoising effect has been greatly improved compared to previous methods, and the Gaussian noise added to the image itself has been basically completely removed.
## 3 Results and Discussion  
### Characterization of CNCs Properties  
#### 3.1.1 Light Scattering Characterization  
\(\mathrm{CNC_{SO3H}}\) showed smaller hydrodynamic diameter and \(Z\) potential than those of \(\mathrm{CNC_{COOH}}\) and \(\mathrm{CNC_{COOH}}\), however, the three types of CNCs dispersions showed similar values of polydispersity index, proving that crystals dimensions were well distributed in the water media (Table 1).
## 1 Introduction
In this work, 2D codes and higher order modulation formats (i.e., m-order QAM) are combined into a hybrid IM/DD Fast OFDM-CDMA (IM/DD Fast-OFDM-CDMA) system for network access offering security and high spectral efficiency. To this end, a novel analytic model is developed to analyze system performance that includes key limitation parameters, such as OFDM subcarrier overlapping, peak-to-average power ratio (PAPR) and multiple access interference (MAI). Additionally, a 2D OCDMA code (2D-HC) is used in the proposed hybrid system at a signal bit-rate of 40 Gb/s and over 80 km of standard single-mode fiber (SSMF) link length, that has a split ratio of 1:16 and is amplifier-free. In our previous work reported in Refs. [15, 23], we presented a numerical performance analysis of 40 Gb/s for the AO-OFDMA-OCDMA and OCDMA-WDM PON configurations, respectively. Numerical demonstrations revealed that the maximum achievable transmission-reach of the IM/DD Fast-OFDM-CDMA with 2D-HC coding for network access is 142 km, compared to 82 km and 108 km for the OCDMA-WDM and IM/DD AO-OFDM-CDMA PON configurations, respectively. To the best of our knowledge, it is the first paper to propose a hybrid system named IM/DD Fast-OFDM-CDMA for an amplifier-free access network, characterized by the inherent OCDMA security feature (i.e., by preserving the integrity component), Fast-OFDM's high spectral efficiency and IM/DD's cost-effective solution. This paper is organized as follows: Section 1 presents the proposed IM/DD Fast-OFDM-CDMA system architecture for network access. In Section 2, the system model is analytically investigated. In Section 3, numerical simulation results are presented in terms of BER, Q-factor and power budget analysis. The last part deals with a budget analysis comparison.
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies
### Minimizing Transmission Energy Costs  
Continuous health monitoring involves collecting large amounts of data, which can be stored locally or transmitted to a higher level for further storage and analysis. However, transmitting continuous streams of physiological data at high data rates and frequent intervals costs energy consumption, which needs to be minimized to extend the battery life of WMDs. The energy consumption is directly proportional to the volume of data and the frequency of transmission. Consequently, high data volumes and frequent transmissions can quickly drain the battery. To address this issue, researchers have developed several approaches focused on the idea of data compression and power-aware transmission, aiming to reduce the data volume, lower the data rates, and decrease the transmission frequency, thereby minimizing energy consumption. Different data compression approaches, their energy minimization methods, and their associated advantages are further explained in the following sections and also summarized in Table 4.
# Cooperative Power-Domain NOMA Systems: An Overview
## 2 Non-Orthogonal Multiple Access
### Basic Principle of NOMA
#### 2.2.2 Successive Interference Cancellation  
The mapped signal is then broadcast across the same time and frequency resources as the original signal. The required signals are detected at the receiver using the SIC detection approach, which is possible by providing substantial power allocation differences between users. The CWIC and SLIC are the two types of interference cancellation explored in NOMA. To keep CWIC's performance closer to that of ideal SIC, its power allocation factor must be more than or equal to 0.65 for the user having bad channel conditions [51]. An CWIC receiver is able to reduce the interference between users. SLIC receivers, on the other hand,  
Figure 4: Hierarchical constellation diagram of superposition coding example.  
decreases the complexity of NOMA receivers, but suffers significant performance loss if the weak user's power allocation factor is not high enough. The SIC process of a conventional NOMA system is shown in Figure 5.
## 2 System Model
### Energy Consumption Model  
The energy consumption model is inspired from [24] where it is modeled as a combination of the energy consumption resulting from communication, UAV hovering, and UAV mobility.  
Figure 1: Considered scenario depicting a ground macro BS that provides a wide-range coverage and UAV BS that provides additional capacity to the cellular network. A no-fly zone (NFZ), over which the UAV BSs are prohibited to fly, is also illustrated.  
The communication energy is needed to communicate with the ground users; i.e., transmit/receive the signals to/from the users. As such, the communication energy consumption of UAV \(E_{\text{C}}\) can be calculated as follows:  
\[E_{\text{C}}(t_{j})=(n_{u,t_{j}}P_{\text{t}}+P_{\text{cu}})t_{\text{cm}}, \tag{5}\]  
where \(P_{\text{t}}\) is transmission power, \(P_{\text{cu}}\) is the on-board circuit power, \(t_{\text{cm}}\) is the duration to communication of UAV to user \(j\), and \(n_{u,t_{j}}\) is the number of users served by UAV during time slot \(t_{j}\).