esa-sceva/satcom-chunk-collection huggingface dataset:
# Stable Analysis of Compressive Principal Component Pursuit
## 5 Numerical Results  
In this section, numerical experiments with varieties of the value of parameter \(\sigma\), parameter \(\rho_{s}\), and rank \(r\) are given. For each setting of parameters, we show the average errors over 10 trials. Our implementation was realized with MATLAB. All the computational results were obtained on a desktop computer with a 2.27-GHz CPU (Intel(R) Core(TM) i3) and 2 GB of memory. Without loss of generality, we assume that \(\tau=2\). In [13], the authors certified this result with Accelerated Proximal Gradient (APG) by numerical experiments. In our numerical experiments, we will provide that this result is also proper with Principal Component Pursuit by Alternating Direction Method (PCP-ADM). In our simulations, our matrix is generated by the formulation as: \(M=L_{0}+S_{0}+N_{0}\), and a rank-r matrix \(L_{0}\) is a product \(L_{0}=XY^{T}\), where \(X\) and \(Y\) are \(m\times r\) and \(n\times r\) matrices in which entries are independently sampled from a \(\mathcal{N}(0;1)\) distribution. According to PCP-ADM, we can generate \(S_{0}\) by choosing a support set of size \(k_{s}=\rho_{s}mn\) uniformly at random, and set \(S_{0}=\mathcal{P}_{\Omega}E\). Noise component \(N_{0}\) is generated with entries independently sampled from a \(\mathcal{N}(0;\sigma)\) distribution. Without loss of generality, we set \(m=n=200\) and \(\rho_{s}=0.01\), and other parameters which PCP-ADM requires are the same as parameters of PCP-ADM [10]. Here we briefly interpret PCP-ADM. In [10], in order to stably recover \(\hat{X}=(\hat{L};\hat{S})\), the ADM method operates on the _augmented Lagrangian_  
\[l(L,S,Y) = \|L\|_{*}+\lambda\|S\|_{1}+<Y,M-L-S>+\frac{\mu}{2}\|M-L-S\|_{F}^{2 }\,.\]  
The details of the PCP-ADM can be found in [14; 15].  
In our simulations, the stopping criterion of the PCP-ADM algorithm can be  
\[\frac{\|L+S-M\|_{F}}{\|M\|_{F}}\leq\text{tolerance}\]  
or the maximum iteration number (\(k_{max}=500\)). In order to estimate the errors, we use the root-mean-squared (RMS) error as \(\|\hat{L}-L_{0}\|_{F}/n\), \(\|\hat{S}-S_{0}\|_{F}/n\) for the low-rank component and the sparse component, respectively. Figure 1 shows the RMS errors' variation with different values of \(\sigma^{2}\). It is noted that the RMS error grows approximately linearly with the noise level in Figure 1. This phenomenon verifies Theorem 1 by numerical experiments with PCP-ADM (this phenomenon also exists in [13] with APG, which is very different from PCP-ADM in principle).
# Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning
## 5 Performance Analysis
### Visual Analysis of Feature Space  
In order to visually analyze the feature space of the samples, the number of neurons in the last FC layer is set to 2. Multitone jamming and periodic Gaussian pulse jamming are chosen as unknown classes and the other types as known classes. HCPL and GCPL are retrained using CNN, shown in Figure 6b, as the feature extractor, and the test signal under JNR = 0 dB is the input. Figure 11a,b is the two-dimensional distribution in the feature space using HCPL and GCPL, respectively. The black points represent unknown class signal features, while other different color points represent different known class signal features.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **GCPL** & **CG-Encoder** & **HCPL-CNN** & **HCPL-HAFRNet** \\ \hline Jamming-2(0.397) & **0.8102** & 0.9088 & 0.8132 & 0.8921 \\ \hline Jamming-3(0.293) & 0.8197 & 0.9193 & 0.8346 & **0.9287** \\ \hline Jamming-4(0.216) & 0.8409 & 0.9289 & 0.8677 & **0.9369** \\ \hline Jamming-5(0.155) & 0.8774 & 0.9313 & 0.8888 & **0.9437** \\ \hline Jamming-6(0.106) & 0.8854 & 0.9397 & 0.9071 & **0.9690** \\ \hline Jamming-7(0.065) & 0.9093 & 0.9412 & 0.9268 & **0.9834** \\ \hline Jamming-8(0.030) & 0.9403 & 0.9869 & 0.9710 & **0.9883** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Unknown class rejection performance plateau under the different openness of four methods. The best result in each Dataset is highlighted in bold.  
It can be seen from Figure 11a that the KIS features of HCPL appear in the periphery of the feature space, and they avoid a large amount of overlap between the KIS features and UIS features in the inside of the feature space. However, from Figure 11b, the KIS features of GCPL appear in the interior of the feature space, and it leads to a lot of overlap between the UIS features and KIS features; among them, the features of periodic Gaussian pulse jamming overlap those of single-tone jamming. Therefore, the OSR performance of HCPL is better than that of GCPL. It is also worth noting that the features of multitone jamming are closer to those of frequency hopping jamming than the others in GCPL and HCPL. Although, according to the principle of maximum entropy, the unknown class is more likely to embed inside of the feature space, as in periodic Gaussian pulse jamming, because there are certain similarities between frequency hopping jamming and multitone jamming; that is, there are multiple frequencies, and the network is more likely to provide a higher score to the frequency hopping jamming than to unknown multitone jamming.
# Downscaling of Remote Sensing Soil Moisture Products That Integrate Microwave and Optical Data
## 2 Materials and Methods
### Data
#### 2.2.3 In Situ SM  
The verification dataset of the Shandian River Basin comes from the National Tibetan Plateau Data Center. The dataset contains 34 soil moisture monitoring sites. The verification data of Wudaoliang are provided by the Institute of Aerospace Information Innovation, Chinese Academy of Sciences. The dataset contains 10 monitoring sites [29]. The observer acquires soil moisture data at different depths, such as 5 cm and 10 cm, every 15 or 30 min, and uses the soil moisture data measured by the drying method to calibrate the data obtained by the instrument. According to the definition of surface soil moisture, the in situ soil moisture data (in situ SM) with a measured depth of 5 cm were selected as the verification data [3; 30].
## 2 Experimental Section
### Characterization  
The structure was analyzed by X-ray diffraction (Germany Bruker AXS, D8 Advance). The morphology was observed by transmission electron microscopy (Japan JEOL, JEM-2000) and scanning electron microscopy (Holland FEI, SEM 450). The absorption spectra were measured with UV-visible spectrometer (America PerkinElmer, Lambda 35). The optical measurements were performed using a commercial optical microscope system (Japan, Olympus IX73). A Ti:sapphire oscillator (China Atop Electronic Technology Co. Ltd., Vitara) was used for two-photon excitation. The laser beam was focused by an objective (Olympus, NA = 0.65) onto the samples. The reflected signal was collected by the same objective and then directed into a spectrometer (Andor 193i) for spectral measurement, or onto a CCD camera for imaging. The time correlated single photon count (TCSPC) system consisting of a PicoHarp 300 controller, a PDL 800-B reference and a SPDA-15 detector was used for the lifetime measurements. Figure 1 shows the optical path of the photoluminescence test system used in the experiment.  
The experimental results can be fitted to a biexponential decay function:  
\[\mathrm{I}=\mathrm{A_{1}}\exp(-\mathrm{t}/\tau_{1})+\mathrm{A_{2}}\exp(- \mathrm{t}/\tau_{2}) \tag{5}\]  
where \(\tau_{1}\) and \(\tau_{2}\) are the radiative and nonradiative decay lifetime, respectively.
# Interference-Robust Transmission in Wireless Sensor Networks
## 6 Conclusions and Future Works  
In this paper, we have considered the performance improvement of low-power WSNs in practical operation environments, where strong co-channel interference may exist. We have investigated the throughput performance of WSNs in the presence of interference and then designed a transmission scheme to maximize it. We have considered the adjustment of transmission rate and transmission time in response to the change of operation environments. The analytic and simulation results show that the proposed scheme significantly improves throughput, while preserving energy efficiency even in the presence of severe interference. For channel diversity, it may be helpful to use a dynamic channel  
Figure 10: Transmission performance in the presence of mobile interference source. (**a**) Simulation scenario (without any shadowing); (**b**) Throughput according to time when the maximum \(R\) is 2 Mbps; (**c**) Throughput according to time when the maximum \(R\) is limited to 250 Kbps.  
hand-off mechanism (e.g., [40]). The proposed scheme can directly be applied to IEEE 802.15.4e DSME MAC [44] and 15.4g PHY-based [43] WSNs. It can also be applied to a tree or mesh topology with an appropriate link scheduling scheme that can provide robustness to collision between multiple communication links [45; 46].  
This work was supported by the ICT R&D program of MSIP/IITP, Republic of Korea (B0717-16-0025, Design of low-power wireless communication technology that supports massive number of devices in unlicensed spectrum band).  
[PERSON] conceived of the main idea of this paper, performed the mathematical analysis and designed the overall architecture of the proposed scheme. The performance evaluations were conducted by [PERSON]. [PERSON] directed the academic research. The writing of the paper was completed by [PERSON].  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.
## 3 Results
### Analysis of Verification Results of Different Data
#### 3.5.1 Continuous Occlusion Results for the Extended Yale B Database
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Occlusion Rate**} & \multirow{2}{*}{**Image Size**} & \multicolumn{6}{c}{**Method**} \\ \cline{3-8}  & & **LRC** & **SRC** & **CRC** & **ESRC** & **Module LRC** & **BPLRC** \\ \hline \multirow{3}{*}{\(10\%\) (Vertical Occlusion)} & \(15\times 10\) & 60.21 & 47.29 & 43.06 & 33.25 & 4.94 & **64.51** \\  & \(20\times 15\) & 65.15 & 64.83 & 60.05 & 47.45 & 32.46 & **77.27** \\  & \(25\times 20\) & 66.83 & 67.78 & 67.46 & 46.73 & 68.42 & **78.87** \\ \hline \multirow{3}{*}{\(20\%\) (Diagonal Occlusion)} & \(15\times 10\) & 43.46 & 41.63 & 35.89 & 28.79 & 5.02 & **59.57** \\  & \(20\times 15\) & 52.47 & 55.74 & 52.23 & 41.87 & 32.30 & **74.40** \\  & \(25\times 20\) & 57.58 & 59.49 & 61.48 & 40.91 & 67.62 & **76.00** \\ \hline \multirow{3}{*}{\(30\%\)} & \(15\times 10\) & 26.95 & 31.58 & 27.51 & 23.52 & 5.26 & **51.28** \\  & \(20\times 15\) & 40.83 & 45.69 & 40.99 & 33.73 & 30.78 & **71.85** \\  & \(25\times 20\) & 46.33 & 45.61 & 50.80 & 34.53 & 68.66 & **74.00** \\ \hline \multirow{3}{*}{\(40\%\)} & \(15\times 10\) & 21.29 & 25.36 & 22.57 & 19.70 & 4.70 & **40.19** \\  & \(20\times 15\) & 29.11 & 32.70 & 32.69 & 27.43 & 30.46 & **66.59** \\ \cline{1-1}  & \(25\times 20\) & 37.16 & 33.25 & 39.95 & 26.95 & 67.30 & **68.74** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy (%) of different methods when identifying 10%-, 20%-, 30%-, and 40%-occluded face images in the Extended Yale B database. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Occlusion Rate**} & \multirow{2}{*}{**Image Size**} & \multicolumn{6}{c}{**Method**} \\ \cline{3-8}  & & **LRC** & **SRC** & **CRC** & **ESRC** & **Module LRC** & **BPLRC** \\ \hline \multirow{3}{*}{\(10\%\) (Vortical Occlusion)} & \(15\times 10\) & 58.21 & 51.51 & 46.81 & 31.34 & 3.11 & **64.19** \\  & \(20\times 15\) & 65.15 & 66.91 & 63.08 & 45.21 & 54.70 & **70.41** \\  & \(25\times 20\) & 67.62 & 70.41 & 71.05 & 46.73 & 56.86 & **74.72** \\ \cline{1-1}  & \(15\times 10\) & 33.25 & **40.27** & 33.01 & 24.80 & 3.35 & 33.25 \\ \cline{1-1}  & \(20\times 15\) & 40.43 & 51.12 & 54.39 & 39.39 & 42.50 & **75.44** \\ \cline{1-1}  & \(25\times 20\) & 46.25 & 63.32 & 63.32 & 35.33 & 69.06 & **76.79** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy (%) of different methods when identifying 10% vertically and 20% diagonally occluded face images in the Extended Yale B database.
## 4 Experiments and Results
### Error Discussion  
According to the experiments in Section 4.4, we demonstrated the effectiveness of the spatial attention deconvolution block, the parallel convolution block, and two different design approaches for CloudRCNN. By comparing with other representative methods, it is proved that CloudRCNN can achieve more accurate semantic segmentations of satellite cloud images, with the benefit of combining the spatial attention module and the auxiliary branch, which improves the segmentation performance of the foreground and background. Even though CloudRCNN achieves a state-of-the-art semantic segmentation performance on the datasets [14], it still has several typical errors that need to be further corrected.
## 6 Numerical Experiments  
This section describes the numerical experiments we have carried out to test the concept of agile optimization when applied to the TOP. First, we have employed exact methods to solve the optimization problem. This initial experiment allows us to understand the possible limitations of these methods in terms of computational times and the size of the instances they can solve in practical applications. Then, we have employed our parallelized biased-randomized algorithm to generate solutions in real-time. Finally, these solutions have been compared with the optimal or near-optimal ones available in the scientific literature whenever possible.  
Both solving approaches have been tested using the benchmark instances presented in [39], which are available in the repository [[http://www.mech.kuleuven.be/en/cib/op/instances](http://www.mech.kuleuven.be/en/cib/op/instances)]([http://www.mech.kuleuven.be/en/cib/op/instances](http://www.mech.kuleuven.be/en/cib/op/instances)) (accessed on 17 December 2021). This benchmark has been widely used in previous works to test the performance of algorithms aimed at solving the deterministic TOP. The benchmark comprises a total of 354 instances, which are divided into seven different sets (Table 1). The number of nodes, the node locations, and the rewards are identical for all instances within a set. However, the maximum allowed driving range (\(t_{0}\)) and the number of vehicles vary between the instances. The sum of all customers' rewards is shown in column \"Rewards\" in Table 1. The number of vehicles varies between 2 and 4, and the driving range varies between the values tabulated in Table 1. Therefore, from one instance to another, the driving range is increased by the tabulated step. Each instance in a set has a nomenclature \(px.y.z\), where \(x\) denotes the set number, \(y\) is the number of vehicles, and \(z\) symbolizes the maximum driving range.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 6 Application to a Drone Cloud with Ring Topology
### Frequency Hopping Implementation  
Each drone is equipped with an RPU to generate true random frequency hopping sequences. Given the ring topology, the sequence for each link can be independently generated and is specific to the communicating pair of drones. The use of true random numbers ensures that even if an adversary gains access to one link, they cannot predict the hopping pattern of other links.  
The true random numbers \(R_{o}\) generated by the RPUs are mapped to frequency values \(f_{o}\) within the communication band allocated for the drone cloud. The frequency for the \(o\)-th interval is defined as: \(f_{o}=f_{\text{min}}+(f_{\text{max}}-f_{\text{min}})\frac{R_{o}}{F_{\text{max}}}\), where \(f_{\text{min}}\) and \(f_{\text{max}}\) are the minimum and maximum frequencies available, and \(R_{\text{max}}\) is the maximum output of the RPU.
# A Jamming-Resilient and Scalable Broadcasting Algorithm for Multiple Access Channel Networks
## 3 Technical Preliminaries
The random waiting is chosen from a window range that is determined based on the number of collisions and the backoff function: \(W_{i}=W_{0}\cdot 2^{i}\) where \(W_{0}\) is the initial window size and \(i\) is the backoff stage number. The parameter \(W_{0}\) specifies the minimum window range from which the waiting time is selected, and it is set to a predefined value in the IEEE 802.11 standard.
# Generative AI in Medicine and Healthcare: Promises, Opportunities and Challenges
## 3 Current Efforts of Applying Generative AI and LLMs in Medicine and Healthcare
### Patient Engagement  
Hippocratic AI [34] focuses on creating an LLM tailored for healthcare. It aims to offer one that is patient-centred, prioritising empathy, care, compassion, and generation of patient-friendly responses, enhancing patient engagement and outreach. This important notion of 'generative AI empathy' has been demonstrated in a study by [PERSON] et al., who reported that LLM-powered chatbot (ChatGPT) responses were preferred over physician responses and rated significantly higher for empathy [35].  
By focusing on non-diagnostic, patient-facing applications, Hippocratic AI values patient safety while improving healthcare access and outcomes. Hippocratic AI proves beneficial in augmenting administrative tasks and handling complexities like medical coding and license exams. Moreover, its compliance certifications demonstrate reliability in maintaining healthcare standards. In clinical settings, the model's exceptional performance on various medical certification exams confirms its real-world applicability. By providing accurate and empathetic support to healthcare professionals, Hippocratic AI enriches patient care, fostering a trustful and efficient healthcare environment.  
Gridspace [36] is an enterprise solution powered by generative AI that automates patient outreach by handling phone calls, answering questions, and performing administrative tasks. It enables scalable, 24/7 accessible, cost-effective patient engagement, receiving inbound calls and phoning patients. Gridspace automates routine administrative tasks such as appointment scheduling, patient reminders, insurance verification, and more. By offloading these inquiries to voice bots, healthcare professionals can save time and focus on critical patient care tasks. Furthermore, Gridspace can triage and direct patient inquiries in real-time, providing valuable insights. Its applications exemplify the potential of Generative AI in transforming patient interactions, streamlining administrative processes, and enhancing overall healthcare efficiency and patient satisfaction.
# Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning
## 2 Literature Review
### CNN and Transformers for Image Recognition  
CNN can be employed for traffic sign recognition and general object recognition (refer to the details of these two datasets in Section 3.3). Transformers are actively used in the field of image processing. However, there are various studies that show that CNN is better in performance compared to transformers. The authors experiment with seven CNNs and five vision transformers based on datasets. They proved that transformers are not as competitive as CNNs at classifying traffic signs. The German, Indian, and Chinese traffic sign datasets, respectively, show performance gaps of 12.81%, 2.01%, and 4.37% [48]. Another study was carried out for the first time, in which eight different vision transformers were validated on three real-world traffic sign datasets. Based on their experimental results, the best vision transformer performs between the performance of pre-trained DenseNet and DenseNet trained from scratch. Aside from that, transformer's best vision model generally takes less time to train than DenseNet's [49]. However, the transformer having the higher model capacity takes a higher computational load than CNN. All of the approaches mentioned in this section used neither traditional nor embedded denoising techniques for analyzingrecognition accuracy. Since the image recognition performance of CNN is better than that of the transformer, as evidenced by [48; 49], it creates an appealing reason for us to select CNN for our performance study.  
As mentioned earlier, many approaches exist in the current literature to denoise images using median, Gaussian, and bilateral filters. However, a comparative study of the recognition accuracy of CNNs considering the environmental and camera impacts with traditional and embedded approaches has not been carried out yet. In this article, we aim to investigate the impact of image denoising on the performance of CNN for both traditional denoising (denoising as a pre-processing step) and embedded denoising compared with a baseline approach [18] that does not apply denoising in CNN. Most of these techniques employ the denoising approach as a layer of the CNN model [50; 51; 52] rather than using traditional filtering (Gaussian and median) techniques developed specifically using CNN models [44]. Another major merit of embedding the denoising approach as a layer of CNN is that it sheds light on the best way to embed the denoising technique in any CNN-based image processing technique. Therefore, in this comparative study, following the general and widely adopted approaches, we embed denoising techniques as a layer in the CNN model.
# Mechanical Performance Analysis and Experimental Study of Four-Star-Type Crank-Linkage Mechanism
## 2 Numerical Calculation of Crank Linkage
### Force Analysis of Crank Linkage
#### 2.2.4 Friction and Multiple Piston Forces  
The shaft-side gas is the atmospheric pressure. The frictional force \(F_{f}\) is always in the opposite direction of the movement of piston, and the indicated power is expressed in \(N_{id}\); then, we obtain the following:  
\[\lambda_{v}=1-S_{c}\bigg{(}\frac{\varepsilon_{s}}{\varepsilon_{d}}\varepsilon^ {\frac{1}{2}}-1\bigg{)} \tag{11}\]  
where \(\varepsilon_{s}\) is the compressibility coefficient of the gas in the nominal suction condition, \(\varepsilon_{d}\) is the compressibility coefficient of the gas in the nominal exhaust condition, \(m\) is the polytropic coefficient, \(\varepsilon\) is the pressure ratio, and \(\lambda_{v}\) is the volume coefficient.  
\[N_{id}=1.634P_{s}V_{t}\lambda_{v}\frac{\kappa}{\kappa-1}\bigg{[}\bigg{(}\frac{ P_{d}^{\prime}}{P_{s}^{\prime}}\bigg{)}^{\frac{\kappa}{\kappa-1}}-1\bigg{]}+ \frac{\varepsilon_{s}+\varepsilon_{d}}{2\varepsilon_{s}} \tag{12}\]  
where \(V_{t}\) is the stroke volume of the piston, and \(\kappa\) is the adiabatic coefficient.  
\[F_{f}=0.6N_{id}\bigg{(}\frac{1}{\eta_{m}}-1\bigg{)}\frac{60\times 102}{4\omega r} \tag{13}\]  
where \(\eta_{m}\) is the mechanical efficiency. Referring to the relevant data [15], \(\eta_{m}=0.78\) and \(\varepsilon_{s}=\varepsilon_{d}\) are taken.  
The reciprocating inertia force along the centerline of the cylinder \(F_{ls}\), the gas force \(F_{g}\), and the friction force \(F_{f}\) are collectively referred to as the multiple piston force, \(F_{p}\), which is the following:  
\[F_{p}=F_{ls}+F_{g}+F_{f} \tag{14}\]  
The side thrust of the piston \(F_{h}\) can be expressed as given below:  
\[F_{h}=F_{p}\frac{\lambda\sin\theta}{\sqrt{1-\lambda^{2}\sin^{2}\theta}} \tag{15}\]  
The variation in the multiple piston force and piston side thrust with crank angle is shown in Figure 5. As reciprocating inertia force and friction force are considered, the piston force fluctuates in both suction and exhaust phases. When the exhaust valve is open, the multiple piston force and the piston side thrust achieve the maximum value at the same time. When the intake valve is opened, the multiple piston force achieves the reverse maximum, while the reverse maximum of the piston side thrust is delayed.  
Figure 4: Variation curve of gas force on piston with crank angle.