esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model
## 4 Experiments Results
### Study of DCNN Parameters  
As mentioned before, the proposed network structure does not have any pooling layer, so the model parameters mainly include the number of network layers and the size and the quantity of filters. We use the filters of size \(3\times 3\), which is used in most cases. We have also experimented the number of filters and the network layers. Table 1 represents the obtained PSNR of the output to enhance Figure 7(a) and LOE of the output to enhance Figure 7(e) after 50 Epochs of training.  
Referring to the method of setting up the number of filters in the SRCNN [28], we have set \(n_{1}=64,n_{i-1}=32\) and \(n_{1}=64\), \(n_{i-1}=64\) in this paper for comparative analysis. Where, \(n_{1}\) represents the number of filters in the first convolution layer and \(n_{i-1}(i\geq 3)\) is the number of filters in all the later convolution layers. The network layers are 5, 7 and 9 in order.  
As shown in Table 1, increasing the number of filters outperform the performance as well, though it is engaged with the computational complexity. We also found that the maximum PSNR of the output image is achieved for the network structure with 7 layers. Also increasing the number of network layers does not necessarily improve the performance, because (1) defects of gradient dispersion, (2) repeating a simple original network may results in unreasonable network architecture.
## 2 Background and Preliminaries
### The Feature Learning Process of CNN
#### 2.2.2 Pooling Layer  
The pooling layer immediately follows the activation layer, and it has two main functions: First, spatially down sampling the image to reduce the size of the feature map as well as keep the number of features in a reasonable range as the number of feature maps increase. Second, the filter of the subsequent convolution layer obtains a larger receptive field and can extract features of a larger size. Average-pooling and max-pooling are two of the most common pooling methods across various tasks. In this research, max-pooling is chosen for the pooling layers, as it is reported particularly suitable for the separation of features that are very sparse [49]. As shown in Figure 5c, the pooling layer can be defined as follows:  
\[\mathbf{X}_{j}^{S_{l}}=f(\beta_{j}^{S_{l}}\ \downarrow(\mathbf{X}_{j}^{C_{l}})+b_{j}^{S_{l}}), \tag{14}\]  
where \(\mathbf{X}_{j}^{S_{l}}\) denotes the \(j\)-th feature map in the \(l\)-th pooling layer; \(\mathbf{X}_{j}^{C_{l}}\) denotes the \(j\)-th feature map generated from \(l\)-th convolution layer; \(\beta_{j}^{S_{l}}\) denotes the \(j\)-th scaling factor of the \(l\)-th pooling layer, \(b_{j}^{S_{l}}\) denotes the \(j\)-th bias of the \(l\)-th pooling layer and \(\cdot\downarrow(\cdot)\) represents the subsampling function.  
Figure 5: Illustration of different CNN components: (**a**) Convolution layer; (**b**) _ReLU_ unit and (**c**) pooling layer.
## 3 In Situ Test with Vibration Exciter
### Sensor Buried  
Currently, research on roadbed excitation tests is more prevalent in the railway sector, while highways typically employ real vehicle tests. To investigate the dynamic characteristics of the road-culvert transition section after the gradient pile-reinforced-concrete slab composite foundation treatment, on-site excitation tests were conducted for the first time in the field of highway engineering, with section B of the transition section being selected for the study. The dynamic response under the excitation load is acquired through the use of a sensor positioned at the location indicated in Figure 4. Two vibration pick-ups and dynamic earth pressure cells were positioned 2 m apart on the top of the asphalt surface to gauge the dynamic displacement, vibration acceleration, and vertical dynamic stress of the upper surface layer. Eight dynamic earth pressure cells and accelerometers were deployed to monitor the vertical dynamic stresses and vibration accelerations within the embankment at depths of 0.9 m, 1.7 m, and 2.5 m from the surface.  
To account for the long-term working stability of the roadbed, settlement monitoring was deemed necessary. A total of five settlement observation points were strategically positioned for this test: four on the top surface of the high-strength bolts at the four corners of the excitation system, and one on the top surface of the shaker. The settlement value is defined as the difference between the settlement observations before and after the excitation test.  
The sensor was buried as follows: firstly, prior to burying the sensor, site formation is performed using an excavator. Subsequently, in accordance with the sensor burial program, the sensor wire protection trench is excavated. This protection trench is sequentially connected, forming a progressively wider and deeper groove based on the actual hole position of the sensor. Following the completion of the protective trench construction, the sensor is buried. By employing fiber-optic sensors, losses associated with low-frequency pickups can be effectively mitigated [34]. One of the signal cable protections involves the use of PVC pipe. It is important to note the configuration of different PVC pipe segments, with the outermost PVC pipe extending beyond the roadbed to prevent potential drainage  
Figure 4: Schematic of instrumentations.  
damage. The final step of the burial process includes the initial measurement reading and backfilling with mulch to ensure proper instrument functionality.
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 8 Future Directions in 6G Technology Development
### Harmonized Networks  
Future harmonization is another important area to be considered during prospective 6G research. Harmonization involves incorporating nanonetworks, nodes (requiring frequencies higher than 100 GHz), self-running D2D, and CF-MIMO. Better radio access visualization techniques can be used to achieve this harmonization in 6G research, since these techniques support programmability as well as abstraction, leading to network harmonization. Network harmonization is one of the most anticipated features of the advanced service-based 6G networks and must be attributed to adaptability, re-ease of use, and optimal assistance independence. This is only possible when 6G is equipped with something beyond the traditional cloud-based network capacities of 5G. In other words, 6G is expected to offer server-less architectures or other such computing paradigms by modifying the legacy mobile architectures in terms of signaling techniques and interfaces. Network harmonization must also offer high reliability, accountability, availability, and liability to ensure proper execution. All the above-mentioned features can be incorporated in network harmonization through communication-control co-design [136; 137] and spatiotemporal network design [138; 139] or other cross-layer techniques as well as computing techniques such as blockchain [140]. Moreover, forecasting trajectory, resource, and spectrum usage is also found to yield better harmonization results.
## 4 Materials and Methods
### Synthesis of Microparticles PLGA-CIN Microparticles and Production Efficiency  
The MPs were prepared by a single emulsion solvent evaporation technique. Briefly, for organic phase preparation 25 mg of poly(lactide-co-glycolide) (PLGA) (L-lactideglycolide 75:25,molecular weight 66-107 kDa, Sigma-Aldrich) and 40% \(v/w\) of CIN (density of 1.05 g/cm\({}^{3}\)) were dissolved in 1 mL of dichloromethane (DCM). The organic phase was emulsified to an aqueous phase (PVA 0.3% \(w/v\) in 10 mL of distilled water) through sonication in a probe sonicator (Vibra, Bioblock Scientific, France) for 5 min with 70% voltage efficiency at 25 \({}^{\circ}\)C. To allow organic phase evaporation, the emulsion was stirred (700 rpm for 3 h) at room temperature. Once synthesized, PLGA-CIN MPs were washed twice in distilled water using an ultracentrifuge (Avanti J-25 Beckman, California) (16,000 rpm for 30 min at 10 \({}^{\circ}\)C). Finally, the MPs resuspended in distilled water were kept at \(-20\)\({}^{\circ}\)C overnight and freeze-dried at \(-80\)\({}^{\circ}\)C under vacuum for 48h. Dried MPs were collected and stored at room temperature. The production efficiency was calculated according to Equation (1).  
\[\text{Production~{}efficiency}\left(\%\right)=\frac{\text{amount~{}of~{} dried~{}MPs}}{\text{initial~{}PLGA~{}amount}}\times 100 \tag{1}\]
# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 5 Delay Minimization and Resource Allocation Algorithms
### Selected Papers  
Among the technological enablers of ANM, MEC is heavily adopted in the selected works as it contributes to delay minimization [71; 72] and offloading. In [71], a FiWi enhanced two-level edge computing concept is developed to guarantee low end-to-end latency with offloading capabilities. The UEs send their computation offloading tasks to their associated ONU-APs in this the user-driven approach. Ref. [72] introduces a cooperative offloading strategy that allows users and MEC servers to iterate backhaul and user offloading probabilities until the minimized delay converges to a near-optimal solution. MEC is also seen as an enabler to reach URLLC target of 1 ms end-to-end delay for a tactile internet application in [48], and an artificial neural network is used at the network edge to minimize delay together with the offloading scheme used in [72]. Delay minimization in optical and mmWave radio networks is also considered in the scope of QoS restrictions in [67; 68]. The delay restrictions of different services are used as constraint in the optimization problem in [67]. The problem is decomposed using Lagrange duality theory and solved with a distributed iterative algorithm to maximize the utility of all users and providing a better average response time. A resource management scheme for FiWi fronthaul is presented in [68] in which time slots are allocated in a way that the packets of a QoS class with higher priority are sent using more time slots to reduce the delay of the high QoS traffic. In order to realize an optical and mmWave radio network, the relation between delay and beamforming is studied in the literature [69; 70]. An iterative algorithm is proposed for the beamforming at the central processor and the RRHs to minimize the fronthaul transmission delay for mmWave C-RAN in [69]. The computational capabilities of MEC can be used for beamforming as well, such as the joint beamforming and resource allocation algorithm presented in [70] for system delay minimization. The proposed dual decomposition-based distributed algorithm also has an information exchange mechanism
# Retrospective Spectrum-Conversion Method Based on Time-Modulated Van Atta Array
## 5 Spectrum-Conversion Method
### Amplitude Coefficient \(x\)  
Assuming that the modulation frequency of AFSS for a single-frequency signal is 50 MHz, the modulation frequency for LFM signal is 5 MHz, and the duty cycle \(\alpha=0.4\), Figure 17 shows the simulation results under different amplitude coefficients \(\mathrm{x}\).  
Similarly, the amplitude coefficient \(\mathrm{x}\) mainly affects the amplitude coefficient of the peak output. With the increase in \(\mathrm{x}\), the zero-order peak value continues to increase. On the contrary, the peak amplitude of other orders decreases. However, in general, the total energy of the signal increases as \(\mathrm{x}\) increases, so the average level of the signal matched filter output is raised.  
Figure 16: The spectral modulation results at different duty cycles \(\alpha\). (**a**) \(\alpha=0.4\); (**b**) \(\alpha=0.5\); (**c**) \(\alpha=0.6\).  
Figure 15: The spectral modulation results at different frequencies \(\mathrm{f_{s}}\). (**a**) \(\mathrm{f_{s}}=25\) MHz; (**b**) \(\mathrm{f_{s}}=50\) MHz; (**c**) \(\mathrm{f_{s}}=100\) MHz.  
Among the three modulation parameters studied above, the modulation frequency and duty cycle put forward requirements for the switching speed of the SPST switch, and the amplitude coefficient x put forward requirements for the amplitude difference between the two states of the Van Atta array. To reach x = 0.1, the amplitude difference between the two states needs to reach more than 10 dB. With the 5 dB amplitude difference that the current Van Atta array can reach, x can reach 0.32. Therefore, in order to achieve better modulation effects, it is necessary to increase the switching speed of the SPST switch and the RCS contrast between the two states of the Van Atta array.
## 5 Conclusions  
Here, a system for capturing 360-degree light field information of one object in one shot and optically reconstructing it was proposed. To process 2D light field image to depth profile with texture, a depth extraction method based on the optical flow for light field camera (DOLF) was introduced. Maximum optical flow value and a mask for the value were introduced according to relative position difference between sub-images in light field. For 360-degree capturing, a compact and inexpensive studio that was composed of two mirrors and a light field camera was used. As a result, we captured light field information around a subject in one-shot. Experimental and simulation results were presented to support the proposed system and analysis. The resolution of the used light field was 3864 \(\times\) 2681 and the resolution of each sub-image and depth map was 552 \(\times\) 383. Holographic display with anamorphic Fourier optical system and DMD was used to optically reconstruct the captured light field information.  
Conceptualization, Y.J. and J.C.; Methodology, Y.J.; Software, Y.J. and S.M.; Validation, Y.J., J.J. and G.L.; Formal Analysis, Y.J. and G.L.; Writing-Original Draft Preparation, Y.J.; Writing-Review & Editing, Y.J. and B.L.; Visualization, Y.J. and J.J.; Supervision, B.L.  
This research was funded by Projects for Research and Development of Police Science and Technology under Center for Research and Development of Police science and Technology and Korean National Police Agency (PA-H000001).  
Figure 10: Optical reconstruction of captured information with holograms. Captured hologram of simulated light field focused on **(a)** Front (Video S1. 3D reconstruction of simulated object) and **(b)** Rear. Captured hologram of the captured light field focused on **(c)** front (Video S2. 3D reconstruction of captured object) and **(d)** rear.  
Figure 9: Top view of the holographic display system with anamorphic optical transformation model. DMD: digital micromirror device.
## 3 Data, Modelling Methods and Analysis Tools
### Agent Model for Malarial Spreading
Our model is inspired by Mozambique's seasonality [18; 28], considering 150 days for the duration of its high-transmission season--see Figure 2.  
Relevant details as well as the flowchart describing the computer implementation of the agent-based model are given in Ref. [3]. That model was also used as support for an additional study concerning the presence of heterogeneity in malaria transmission along with the use of ivermectin [4]. Using the present model we analyzed the behavior of the human-mosquito coupled system, resulting from a complex interaction between the two compartments. Human-to-mosquito transmission efficiency (\(w_{h}\)) was used to define the probability of a sustained presence of gametocytemia in human blood circulation, as well as the survival probability of infected mosquitoes beyond latency. These aspects are considered critical in disease transmission. Our model simulations use gametocytemia as an independent variable affecting human-to-mosquito transmission. Different levels of gametocytemia define different stages of disease transmission efficiency. Theoretical gametocytemia reduction is considered equivalent to an effective treatment with gametoidal agents, such as primaquine or methylene blue, in a fraction of the human population.
## 6 Simulation Results  
This section presents the simulation results obtained to validate the theoretical derivations, and to determine the requirement of the SWIPT- and ABCS-enabled hybrid switch as per the values presented in Table 1.  
It is considered that \(S\) moves through the warehouse floor and transmits data to \(R\) while being at specific locations given in Figure 4. For simplicity, we assume that all the positions presented in the figure are within the warehouse floor and no walls or other barriers are present in these specific locations. Also, it is assumed that \(S\) stays stationary while transmitting, and once the transmission is completed, it moves to the next position.  
The following simulations of the system are conducted with respect to the \(x,y\) coordinates of these transmission points. The results are demonstrated with variations in \(x\) coordinates along specific \(y\) coordinates, and for each transmission node, for a better comparison, we consider transmission using an AF relay model without any EH process, transmission using ABCS alone with EH, transmission using SWIPT-enabled relay scheme with EH, and transmission using the proposed algorithm with EH.
## 3 Space Segment
### On-Board Calibration Facilities  
During EnMAP operations, a series of spectral and radiometric characterization activities will be carried out taking advantage of on-board calibration facilities. Detector linearity calibration and suspicious pixel mapping will be performed using light-emitting diodes (LEDs) mounted in front of the detectors in conjunction with the ability of the focal planes to control integration time in a wide parameter range. Spectral calibration on-orbit will be performed using the spectral mode of the on-board calibration device where a rare earth-doped diffuser is used to illuminate the spectrometer entrance slits with a radiance spectrum with well-defined and stable spectral structures. The shift in spectral parameters up to second order can be retrieved by comparing the predicted system performance with on-board measurements. The calibration system based on an Ulbricht integrating sphere will be used in its radiometric mode to create repeatable and smooth spectral radiance for assessing the radiometric properties of the spectrometers. This facility allows the monitoring of the instrument at small time intervals in order to confirm the validity of the radiometric calibration. Primary radiometric calibration will be achieved by the use of a full-aperture diffuser system with a well-characterized bidirectional reflectance distribution function. A mechanism introduces the diffuser into the optical beam and opens the diffuser protection hatch to allow for direct Sun illumination, thereby generating a well-known spectral radiance at the entrance pupil of the instrument. Finally, the shutter calibration mechanism allows the blocking of all light from entering the spectrometers in order to perform frequent dark signal calibrations. The same mechanism switches the optical path from the telescope view (Earth observation  
Figure 6: Red-green-blue composites from a simulated EnMAP dataset over Barrax (Spain) illustrating the conversion from digital numbers (**left**) to radiometrically-calibrated radiance data (**right**). The switch between high and low radiometric gain modes is visible in the digital number image as yellowish and pinkish patches.  
and Sun calibration) to the Ulbricht integrating sphere (spectral calibration and radiometric validation). A schematic view of on-board calibration devices is displayed in Figure 7.
# An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization
## 3 Proposed Method
### Updating \(S\)  
The variable \(\mathbf{S}\) is updated while minimizing over \(h(\mathbf{L},\mathbf{S},\mathbf{Z},\mathbf{\Lambda}_{1},\mathbf{\Lambda}_{2})\) with variables \((\mathbf{L},\mathbf{Z},\mathbf{\Lambda}_{1},\mathbf{\Lambda}_{2})\) fixed:  
\[\begin{split}\mathbf{S}^{i+1}&=\operatorname*{arg\, min}_{\mathbf{S}}h\Big{(}\mathbf{L}^{i+1},\mathbf{S},\mathbf{Z}^{i}, \mathbf{\Lambda}_{1}^{i},\mathbf{\Lambda}_{2}^{i}\Big{)}\\ &=\operatorname*{arg\,min}_{\mathbf{S}}\lambda\|\mathbf{S}\|_{1}+ \Big{\langle}\mathbf{\Lambda}_{1}^{i},\mathbf{D}-\mathbf{L}^{i+1}-\mathbf{S} \Big{\rangle}+\frac{\mu}{2}\Big{\|}\mathbf{D}-\mathbf{L}^{i+1}-\mathbf{S} \Big{\|}_{\text{F}}^{2}\\ &=\operatorname*{arg\,min}_{\mathbf{S}}\lambda\|\mathbf{S}\|_{1}+ \frac{\mu^{i}}{2}\bigg{\|}\mathbf{S}-\left(\mathbf{D}-\mathbf{L}^{i+1}+\frac{ \mathbf{\Lambda}_{1}^{i}}{\mu^{i}}\right)\bigg{\|}_{\text{F}}^{2}\end{split} \tag{15}\]  
where \(\mathbf{S}\) is the matrix of the sparse foreground part. The foreground is equivalent to an \(l_{1}\) regularization. The solution is as follows:  
\[\mathbf{S}^{i+1}=T_{\frac{\lambda}{2\mu^{i}}}\left[\mathbf{D}-\mathbf{L}^{i+1 }+\frac{\mathbf{\Lambda}_{1}^{i}}{\mu^{i}}\right] \tag{16}\]  
and here, \(T_{\frac{\lambda}{2\mu^{i}}}\) is the contraction operator which is defined as follows:  
\[T_{\theta}(x)=\operatorname*{sgn}(x)\cdot\max(|x|-\theta,0) \tag{17}\]  
with the function \(\operatorname*{sgn}(\cdot)\) returning the sign of the given operand.
## 3 Results  
### Simulation Setup  
In this paper, a simulation study has been carried out to evaluate the performance of the proposed method. The simulation procedure consists of two stages, namely, the first one is related to creating the dataset and the second one aims to detect and classify the LL faults (see Figure 4). In the first stage, the simulation platform has been developed in MATLAB, which was used for I-V curve analysis in order to extract the features. To this end, as previously mentioned, a PV array has been built in a MATLAB/Simulink environment using the configuration presented in Figure 1, which includes 3 PV strings with 10 PV modules in each string. Therefore, to create the dataset, the LL faults and normal events are simulated under various conditions (see Section 3.2). After building the dataset, LL faults have been detected and classified using the SVM classifier. Here, in order to improve the accuracy of the LL fault diagnosis, two layers have been defined. The first layer identifies the fault condition, which is a binary classification problem, whereas the second layer classifies LL faults based on mismatch levels, which is a multi-class classification problem. In these two layers, several common assumptions have been considered as follows:  
1. The original dataset is divided into two datasets, which include the training and validation dataset. The percentage of the dataset that is used to train the models is 80%, and 20% is held back as a validation dataset.
2. The Cross Validation (CV) technique has been used in the learning process of the SVM classifier in order to obtain a more reliable estimation for the classifier performance. In this study, the 5-fold CV process has been used to assess each layer.
3. The classification accuracy metric has been introduced to assess the performance of the layers, which is defined based on the confusion matrix, see Table 3[30]. This metric is formulated as Equation (10): \[\text{accuracy}= \frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+\text{FN}}\] (10) where TP (true positive) is the case for identification of a data sample correctly, TN (true negative) is the case for rejection of a data sample correctly, FP (false positive) represents the case which is incorrectly identified, and FN (false negative) is the case which is incorrectly rejected.  
* In this research, we have set the search range of parameter \(\gamma\) to [0.0001, 10], whereas parameter \(C\) is in the range of 0.1 to 1000. Moreover, the parameters of the genetic algorithm have been tuned as follows: the population size of 100 is selected, the mutation rate and crossover rate are equal to 0.6 and 0.1, and the maximum iteration number is set to 30. It should be noted that the termination criterion is set to the generation number.