esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### EEG Data
#### 3.2.1 Motor Imagery  
Motor imagery (MI) is the process of mentally rehearsing a motor action, such as moving a limb, without actually executing it [43]. The BCI Competition IV database 2a [44] comprises EEG data from 9 healthy subjects recorded during an MI paradigm consisting of four different MI tasks, namely, imagining the movement of the left hand, the right hand, both feet, or the tongue. Each trial of the paradigm starts with a fixation cross displayed on a computer screen, along with a beep. At second 2, a visual cue appears on the screen for a period of 1.25 s (an arrow pointing left, right, down, or up, corresponding to one of the four MI tasks). The cue prompts the subject to perform the indicated MI task until the cross vanishes from the screen at second 6. A representation of the paradigm's timecourse is shown in Figure 2A. Each subject performed 144 trials per MI task. The EEG data are acquired at a sampling rate of 250 Hz, from 22 Ag/AgCl electrodes (\(C=22\)) placed according to the international 10/20 system, as depicted in Figure 2B. Next, the data are bandpass-filtered between 0.5 Hz and 100 Hz. A 50 Hz Notch filter is also applied. For each subject, the database contains a training dataset and a testing dataset, obtained following the same experimental paradigm [44]. In this study, we consider a bi-class classification problem involving the left and right hand MI tasks, so we drop the trials associated with the feet and the tongue. Afterward, we also drop the trials marked for rejection in the database itself [44]. Then, for all trials we select a 2 s long time window stretching from second 3 to second 5 (\(M=500\) samples), as schematized in Figure 2A. Finally, we compute the surface Laplacian of each remaining trial through the spherical spline method for source current density estimation, in order to reduce the deleterious effects of volume conduction on connectivity analyses [21; 45; 46].
## 6 Conclusions
Overall, this study offers a new approach and methodology for high-resolution remote sensing water depth inversion in shallow water areas and demonstrates the potential application of GF-1 satellite data in high-resolution water depth inversion. Future efforts could focus on further optimizing the data processing workflow and expanding the study area to promote the application of this research method in water depth mapping. Conceptualization, H.Z. ; methodology, [PERSON], T.H. and H.Z. ; software, [PERSON]; validation, [PERSON]; formal analysis, [PERSON]; data curation, [PERSON] and T.H; writing--original draft preparation, [PERSON]; writing--review and editing, H.Z. ; visualization, [PERSON]; supervision, H.Z. ; project administration, [PERSON], W.C. and D.L. ; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript. This research was funded in part by the National Key Research and Development Program of China, grant number 2021 YFE0117600. The raw data supporting the conclusions of this article will be made available by the authors on request. This research was partially supported by the National Key Research and Development Program (Grant No. 2021 YFE0117600), for which we are grateful. We would also like to express our sincere appreciation to the CRESDA for their support in this study, particularly for providing a substantial amount of GF-1B/C/D imagery data and assistance with image preprocessing. Additionally, we extend our heartfelt thanks to Google for providing the GEE platform and to the European Space Agency for supplying Sentinel-2 satellite imagery data. Finally, we thank the AERONET jointly established by NASA and LOA-PHOTONS (CNRS) for providing the aerosol optical thickness data required for atmospheric correction in this study. The authors declare no conflicts of interest.
## 3 Data Preparation and Experiments Setup  
### Dataset Description  
It is extremely difficult to produce different geometrical parameters of internal cracks using a large number of real coating samples. The coating samples obtained by real experiments often have low precision, low efficiency and high cost. The reason is that in the real experiment, the internal cracks should be added in the coating manufacturing stage. Once the internal cracks are generated in the coating, it is difficult to modify the parameters of the crack. Therefore, a large number of workpieces are needed to generate cracks of different lengths or positions. In addition, it is difficult to control the crack parameters accurately in the real experiment since the procedure is inside the coating. If a numerical TBC and crack model can be established, it will be very convenient to test and develop the performance of the crack detection scheme. Reference [32] developed a 3D finite element heat transfer model to evaluate surface crack depth using lock-in infrared thermography. Similarly, in this paper, microstructure reconstruction software was developed by our team to establish the TBC model [33; 34] with a variety of sizes and locations of internal cracks (see Appendix A). The material of the TBC is 8% mol yttria-doped zirconia (8 YSZ). Nickel base alloy is used as substrate material. There is a 10 um thick bonding layer over the substrate, which is composed of thermally grown oxides (TGO). Table 1 lists the calculation parameters of the model. The initial thermal excitation comes from the air temperature at the top of the coating and the substrate temperature. By applying the calculation parameters, calculation boundaries, flow and thermal boundaries as well as the evolution equation of the temperature fields given in reference [35], the steady-state temperature distributions of the TBC can be then calculated. It should be noted that the numerical coating model is based on the assumption that the pore distribution of the coating remains unchanged in a period of time (This is normal in reality, since the pore location of the coating are determined at the time of manufacture. Only in some cases, such as corrosion, can this characteristic be changed). The dataset contains 245 infrared images of TBCs with resolution of 360 \(\times\) 360 pixels and 116 images with resolution of 720 \(\times\) 300. The porosity is set from 1% to 25% (step size = 1%) [36]. The crack width is set at 4 um and 12 um and the crack length is set from 10 um to 260 um (the crack with a width of 4 um cannot be set longer than 80 um). Ellipsoid cracks are selected as the morphology and the locations of cracks are set randomly.
# Rain Monitoring with Polarimetric GNSS Signals: Ground-Based Experimental Research
## 2 Experiments and Data
### Data Processing Algorithm
#### 2.3.2 Method to Solve Hardware Effect and Phase Ambiguity  
The hardware effect, namely the effect of transmitter and receiver, is basically the same during a process of receiving GPS signals. Thus, \(\Delta d\) is a constant for an observation process. The ambiguity is also constant during a continuous period of data record, and then that \(\Delta b\) is constant [24]. Due to the fact that the observation of phase is not absolute, but relative to the initial value, the GPS receiver cannot get real phase value [28]. Furthermore, when its track is lost, the GPS receiver starts again with a new arbitrary initial value. Therefore, the longest continuous sequence in every day is used.  
The orbital period of a GPS satellite is one sidereal day, which means that the satellite will be in the same position after a sidereal day. Thus, we can convert time series of observing data into elevation or azimuth series. Then, the same elevation or azimuth can be selected to ensure consistency of the initial data in every day. This transformation makes the data only depends on the position of GPS satellite. Since the absolute values of each data sequence depend on its initial value, each sequence minus its own mean is considered in order to compare different observing data [24]. At the same time, the hardware effect and phase ambiguity can be cancelled out using the following equation:  
\[\Delta\phi^{\prime}(\beta)=\Delta\phi(\beta)-\left\langle\Delta\phi(\beta)\right\rangle \tag{5}\]  
where \(\beta\) represents the trajectory of the GPS satellite defined by its elevation and azimuth.
# Joint Interference and Phase Alignment among Data Streams in Multicell MIMO Broadcasting  
[PERSON] 1  
[PERSON] 1  
[PERSON] 2  
1 Department of Electrical Engineering, University of Engineering & Technology, Peshawar 814 KPK, Pakistan; [EMAIL_ADDRESS] (W.S.); [EMAIL_ADDRESS] (S.W.S.)
2 Integrated Management Coastal Research Institute, Universitat Politecnica de Valencia, C/ Paranimf n\({}^{\circ}\) 1, Gandia, 46730 Valencia, Grao de Gandia, Spain
3 Institute of Telecommunications and Multimedia Applications (iTEAM), Universitat Politecnica de Valencia, Camino Vera n/n, 46022 Valencia, Spain; [EMAIL_ADDRESS]  
2  
[PERSON] 3  
334 Correspondence: [EMAIL_ADDRESS] Tel.: +34-609-54-90-4334  
maximizing the SINR of the receiver at the uplink communication. Next, the water-filling algorithm is deployed for power distribution due to which more power falls on better subchannels. Finally, the received power of the target data stream is increased using the phase alignment to rotate the interference between data streams into the signal space of the target receive data stream. Simulations results show that the proposed algorithm has obvious performance enhancement and outperforms the exiting IA algorithms. The main contributions of the paper are as follows:  
* Proposed joint IA and Phase Alignment algorithm that effectively improve the performance of MIMO systems
* Deployed Bayesian estimation for efficient utilization of the IA algorithm
* Improved the downlink and uplink SINR of the MIMO system by incorporating such algorithm
* Reduced the cochannel interference to adequate level
* Improved the system capacity, bit error rate (BER), and reliability  
The rest of the paper is organized as follows. Section 2 provides the literature review of the proposed research work. Section 3 discusses the system model. Section 4 gives the Bayesian modeling of the delay error CSI. Section 5 explains the Interference Alignment algorithm for MIMO-BC. Section 6 presents the algorithm performance analysis. Section 7 provides the simulations results and analysis while Section 8 concludes the paper.
# Donning/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control
## Materials and Methods
### Performance Metrics  
To quantify the performance during the online test phase, we measured four quantitative metrics. The completion rate (\(CR\)) was defined as the number of hits \(H\) over the total amount of targets \(N\) expressed in a percentage scale %:  
\[CR(\%)=100\cdot\frac{H}{N} \tag{14}\]  
We defined previously a hit such as reaching the target and staying inside for two seconds, everything in less than 10 s.  
The path efficiency (\(PE\)) indicated if the distance traveled by the participant during the experiment was close to the optimal path, i.e., the shortest one. The PE was defined as the ratio in % of the sum of distances between targets and the real distance traveled by the subject:  
\[PE(\%)=100\cdot\frac{\sum\limits_{i=1}^{N}\lVert\mathbf{d}_{i}-\mathbf{d}_{i -1}\rVert}{\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{I_{i}}\lVert\mathbf{y}_{i} (j)-\mathbf{y}_{i}(j-1)\rVert} \tag{15}\]  
where \(N\) is the number of targets, and \(J_{i}\) is the number of samples to get from target \(i\) to target \(i+1\). For the first target \(i=1\), we assumed that \(\mathbf{d}_{0}=(0,0)\) and \(\mathbf{y}_{1}(0)=(0,0)\) (the starting point for all the test experiments was the origin of coordinates). The starting point for the next segment was equal to the final position of the actual one, i.e., \(\mathbf{y}_{i+1}(0)=\mathbf{y}_{i}(J_{i})\). A larger value of PE implies a more linear trajectory in the 2D space. Linear trajectories in the 2D space are directly correlated with the activation of combined DoFs movements. Therefore, the PE can be used also as an indirect measurement of the simultaneous multiple degree of freedom control.  
Figure 4: The three arm positions used as training arm positions. Each position involves different muscle activation and has different effects from gravity and fatigue. P1: Arm fully extended pointing down with the wrist. P2: Arm pointing front with the wrist and elbow flexed 90 degrees. P3: Arm fully extended pointing front.  
The completion time (\(CT\)) metric measured the efficiency in the time domain. It was calculated as the time average in seconds to reach a target:  
\[CT=\frac{\sum\limits_{i=1}^{N}t_{i}}{N} \tag{16}\]  
where \(t_{i}\) is the time in seconds to travel from target \(i-1\) to target \(i\). For the missed targets (the subject was not able to reach the target before 10 s and kept into the target for at least two consecutive seconds), \(t_{i}=10\) in Equation (16).  
To measure the stability of the system to accomplish the targets hit condition, we defined the attempt ratio (\(AR\)). It was defined as the average ratio between the number of times the subject enters the hit targets \(E\) and the number of hits \(H\).  
\[AR=\frac{E}{H} \tag{17}\]  
By definition, \(AR\geq 1\); the lower the AR value, the lower the attempts needed to hit the targets. A value close to 1 means that the system was very stable, since the first time that he got into the target he was able to stay for the two required seconds to be considered a hit.
# Mechanistic Insights into Fish Spoilage and Integrated Preservation Technologies
## 2 Mechanisms of Spoilage
### Microbial Spoilage
#### 2.3.3 Spoilage Manifestations and Sensory Implications  
The accumulation of these metabolites leads to pronounced sensory deterioration. Elevated levels of TMA, ammonia, amines, H\({}_{2}\)S, aldehydes, and organic acids produce foil odors (fishy, ammonia-like, sulfide), off-flavors (bitter, putrid), discoloration, mucous exudation, and muscle softening [48; 49]. Storage conditions modulate which SSOs dominate: chilled storage (0-4 \({}^{\circ}\)C) favors Pseudomonas and Shewanella; lightly salted fillets select for salt-tolerant lactic acid bacteria; and modified atmosphere packaging (high CO\({}_{2}\)) suppresses aerobes but may permit the growth of Photobacterium phosphoreum under temperature abuse [49; 50]. These changes markedly shorten shelf life and undermine fish quality and safety.  
A critical spoilage marker is trimethylamine (TMA), which results from the bacterial reduction of trimethylamine oxide (TMAO), a compound fish use as an osmoregulant. Bacteria such as Shewanella putrefaciens, Aeromonas spp., and P. phosphorem can reduce TMAO to TMA, producing ammonia-like off-flavors [47]. The production of TMA and other spoilage compounds accelerates the deterioration process, making the fish unsuitable for consumption.  
Storage conditions further shape which specific spoilage organisms (SSOs) dominate fish spoilage and the speed at which it progresses. As shown in Table 1, under chilled storage (0-4 \({}^{\circ}\)C), psychotrophic Gram-negative bacteria (primarily _Pseudomonas_ spp. and _Shewanella_ spp.) proliferate, exploiting low temperatures to produce trimethylamine and hydrogen sulfide that drive off-odors [48]. In salted or brining conditions, elevated NaCl reduces water activity, selectively favoring halotolerant lactic acid bacteria that generate organic acids and diacetyl, imparting sour and buttery off-flavors [49]. Meanwhile, modified atmosphere packaging (MAP) with high CO\({}_{2}\) concentrations suppresses aerobic spoilage organisms like _Pseudomonas_, but if temperatures rise above optimal levels, anaerobic SSOs such as _Photobacterium phosphorem_ can flourish in low-oxygen environments, accelerating trimethylamine accumulation and spoilage [50].
# Calibration and Analysis of Seeding Parameters of Soaked _Cyperus esculentus_ L. Seeds
## 2 Materials and Methods
### Determination of Physical Parameters of soaked C. esculentus Seeds  
#### 2.2.1 Measurement of Triaxial Dimensions and Density of Soaked _C. esculentus_ Seeds  
One hundred soaked _C. esculentus_ seeds were randomly selected, and their length (L), width (W), and thickness (T) were measured using a vernier caliper (accuracy: 0.01 mm).  
Figure 1: Pre-treatment for soaking of _C. esculentus_ (The soaking began at 19:00 on 8 November 2024).  
The average values of equivalent diameter (D) and sphericity (\(\Phi\)) were 12.92 mm and 92%, respectively. The average values for the length (L), width (W), and thickness (T) of the seeds were 14.07 mm, 13.95 mm, and 10.99 mm, respectively. The three-dimensional characteristics of the _C. esculentus_ seeds are shown in Figure 2.  
\[D=\sqrt[3]{LWT} \tag{1}\]  
\[\Phi=\frac{D}{L} \tag{2}\]  
The triaxial dimensions of _C. esculentus_ seeds after soaking were analyzed using Origin 2024 and were found to be approximately normally distributed, as shown in Figure 3.  
Most of the _C. esculentus_ seeds were elliptical. The saturation degree of _C. esculentus_ seeds can be determined by measuring the density, which can also be used as an important parameter for modeling. The DH-300X density measuring instrument was used in the experiment, with measurements repeated in 10 groups. The average density value obtained was 1.185 kg/m\({}^{3}\).
###### Abstract  
In order to facilitate the local sharing of renewable energy, an energy sharing management method of multiple microgrids (MGs) with a battery energy storage system (BESS) and renewable energy sources (RESs) is developed. First, a virtual entity named the energy sharing provider (ESP), which acts as an agent for MGs, is introduced to minimize the power loss cost. Second, a distributed optimal model and a two-level iterative algorithm for the MGs and ESP are proposed, which minimize the total operation cost including purchasing electricity cost, energy storage cost and power loss cost. Based on the energy sharing framework, considering the local objectives of MGs and the objective of ESP, the optimal scheduling can be achieved through the bidirectional interaction between MGs and ESP. During the optimization, the shared information between MGs and ESP is limited to expected exchange power, which protects the privacy of MGs and ESP. Finally, the effectiveness of the proposed model and algorithm in different scenarios is verified through a case study.  
interconnected microgrids; distributed optimization; energy sharing; alternating direction method of multipliers (ADMM); power loss +
Footnote †: journal: Applied Soft. 2018, 8, 590; doi:10.3390/app8040590
# CMOS Fixed Pattern Noise Removal Based on Low Rank Sparse Variational Method
## 5 Experimental Results and Discussions
### Discussion  
#### 5.6.1 Parameter Selection  
In Model (7), adjustment of parameters \(\alpha_{1},\alpha_{2},\alpha_{3},\alpha_{4},\gamma,\tau\) are involved. The basic principle of parameter adjustment is that as the variance of stripe noise increases, the value of \(\gamma\) needs to be increased accordingly. The best PSNR value is obtained by adjusting \(\gamma\) between [0.65-0.85], when \(\sigma\) changes between [5:20] ranges, according to experience. The adjustment for \(\tau\) is generally between [0.5-0.75], based on experience. The adjustment of \(\alpha_{2}\) is critical, because its values vary widely with the intensity of images and stripes. To simplify the adjustment of \(\alpha_{2}\), we used an adaptive strategy. The basic steps are as follows:  
First, the Fourier transform of the noise image Y in each column is calculated, as shown in Formula (22).  
\[\mathrm{F_{i}}=\mathcal{F}(\mathrm{Y_{i}}) \tag{22}\]  
where F represents the forward Fourier transform operator, Y represents the input noise image, and i represents the index of the column.  
Figure 18: Denoising effect of various methods in the area of sunspot. (**a**) Noise Image; (**b**) WAFT; (**c**) UVT; (**d**) ASSTV; (**e**) VSNR; (**f**) SILR; (**g**) \(\ell_{0}\) sparse; (**h**) LRSUTV; (**i**) CFPN noise estimated by LRSUTV method; (**j**) mean cross track of images (**a**,**h**).  
Second, update the regular parameter \(\alpha_{2}\).  
\[\alpha_{2}=\frac{\|\frac{\partial F_{1:}}{\partial x}\|_{11}}{10^{5}}\cdot\alpha_ {1} \tag{23}\]  
where \(\frac{\partial F_{1:}}{\partial x}\) represents the horizontal differentiation of the DC component in F. The algorithm automatically calculates the relationship between \(\alpha_{2}\) and the fidelity term regularization factor \(\alpha_{1}\), based on the intensity of the current stripe noise. The \(\alpha_{1}\) coefficient is set to a fixed value of \(\alpha_{1}=20\) in the LRSUTV algorithm. The algorithm can automatically enhance the \(\alpha_{2}\) parameter if the intensity of the stripe noise is too high, which will cause the stripe suppression process to dominate in the iteration process. As the intensity of the stripe decreases, the \(\|\frac{\partial F_{1:}}{\partial x}\|_{11}\) value will become smaller and, thus, the fidelity process will dominate. As a rule of thumb, the values of \(\alpha_{3}\) and \(\alpha_{4}\) are set at [15:25] and [1.5:2.5], respectively. At this time, the denoised image has the optimal PSNR value. To simplify the parameter adjustment process, we generally set \(\alpha_{2}=0.1\), \(\alpha_{3}=0.1\) and \(\alpha_{4}=0.1\).  
The parameters in Table 5 are the optimal parameters for various methods for Figure 9a. Figure 9a contains stripe noise with intensity_\(\sigma=10\) and random noise with intensity_\(\sigma=5\). By setting the above parameters, each method obtains the best PSNR for Figure 9a. The above optimization parameters are used for different pictures and different noise intensities used in the experiment.
## 2 System Model  
The user equipment (UE) captures the SSS in 5G system and the MVF consist of four radio measurements of synchronization signal including reference signal received power (RSRP), reference signal received quality (RSRQ), received signal strength indication (RSSI) and signal-to-noise and interference ratio (SINR) [24]. The indoor positioning system based on MVF includes an offline stage and online stage, as shown in the Figure 1.  
Figure 1: Indoor positioning system with CNN and path-loss model based on multivariable fingerprints.  
In the offline stage, the positioning area is evenly divided into two-dimensional rectangular grid reference points. At each reference point, UE is used to capture SSS containing multiple radio measurements. The measurements are used to construct observation matrix. Then, the observation matrix is preprocessed, and the data packets with missing values are eliminated. We use Kalman filter algorithm to smooth the data, reduce the noise and obtain the MVF. With the sliding window, the preprocessed observation matrix is transformed into the observation image. The fingerprinting database is obtained by combining the observation image and the coordinates of the corresponding reference points. The fingerprinting database is used to train a CNN location classification model. The MVF and the distance between reference points and base station are used to train the path-loss model.  
In the online stage, the test points are evenly selected in the positioning area. UE is used to capture SSS containing multiple radio measurements and construct observation matrix at each test point. Similarly, the data are preprocessed and obtain the MVF. With the sliding window, the preprocessed observation matrix is transformed into the observation image. Then, the observation image is provided to the CNN location classification model trained in the offline stage for pattern matching. The output of the CNN model is the probability that the test point belongs to each reference point. Use the criterion to judge the test point whether it fits the path-loss model. If the test point satisfy the criterion, we will combine the path-loss model and CNN to obtain the positioning results. Otherwise, the CNN model will work alone.