esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
In future work, we aim to perform additional experiments and comparison with super-resolution techniques and will provide more extensive comparison of model performance, model parameters, and the overall quality of upscaled images with other networks like generative models [30, 31, 32, 59], CNN [16, 60], and visual transformers [61, 62, 63], as well as comparing the impact of different training structures (supervised, unsupervised, and semi-supervised), rather than the actual layers of the model. In addition to the above, we currently use publicly available MRI datasets and have not added experimental data from a new set of cohorts. This does not affect the study, as this work does not introduce any new neural network architecture. Rather, in this in silico work, we tested whether three neural architectures that are significantly different from each other were able to upscale images with significant difference. Our experiments support the conclusion that a different neural network can upscale images that visually appear similar to each other regardless of the actual layers of the architecture. This study supports the notion that attention must be paid to how the actual data are collected. All three UNets upscaled LQ MRI to a different degree, indicating that one must be cautious of how ML is applied, interpreted, and used practically. In the With-Prior scenario, LQ images can be reliably upscaled; this contributes to the tradeoff between acquisition time and image quality of X-nuclei or spectroscopic imaging. In the WithOut-Prior case, since small and low contrast lesions may be lost, we may need to consider new approaches that entail the concurrent customization of deep learning and acquisition protocols. Overall, the results reflect what is expected from MR physics and known to the community: in single-contrast images, if a structure is not there you cannot recover it, unless you have some complementary contrast information. The critical aspect is what data are available: resolution, contrast SNR and especially the presence or not of same subject complementary information. This pilot study further supports the notion that, in certain cases, the particular layers and architecture of UNet may not be important. Effort may, rather, focus on alternative metrics of desired image features such as cost functions, hyperparameters and training protocols in machine learning. Furthermore, the results point to the incorporation of the most obvious and ultimate, in our opinion, criterion in ML pipeline and application: the end-user, i.e., radiologists or other specialists, to ensure and access the merit of such techniques in the clinical realm.
## 1 Introduction  
Wireless Power Transfers (WPTs) have been a recurrent topic in the last decades generating numerous practical and commercial applications [1, 2]. WPT typically ranges between 20 dBm and \(-30\) dBm. Ambient RF-EH is situated in the ultra-low power extreme of this range, operating around and even below \(-30\) dBm [3, 4, 5, 6, 7]. The possibility of feeding low-power devices by scavenging ambient RF energy is attractive for powering autonomous sensors [8] and IoTs general applications [9], especially in urban environments. More specifically, ambient RF harvesting has been recently evaluated for feeding Low Power Wide Area Networks (LPWANs) [3, 10], wireless underground sensor networks (UWC) [11], or batteries sensors [12]. As a consequence of the low power level, ambient RF-EH systems tend to operate in broadband or multiband configurations to enhance the system's performance [13, 14, 15, 12]. The impedance matching between the antenna and the rectifier constitutes an important challenge in the case of multiband or broadband applications; however, there are promising approaches based on metamaterials [15]. The efficiency of an RF-EH system (\(\eta_{RF}\)) can be expressed as the product of the RF-EH constituent functional block efficiencies, as shown in Equation (1):  
\[\eta_{RF}=\eta_{ant}\cdot\eta_{rec}\cdot\eta_{EMS}\cdot\eta_{sto} \tag{1}\]  
where \(\eta_{ant}\) denotes antenna efficiency, \(\eta_{rec}\) denotes rectifier efficiency, \(\eta_{EMS}\) denotes energy management system efficiency, and \(\eta_{sto}\) denotes efficiency of the storage element. \(H_{rec}\) is defined as the ratio between the effective input power delivered to the rectifier by the antenna (\(P_{in}\)) and the power (\(P_{R_{L}}\)) delivered to the load resistor (\(R_{L}\)), as shown in Equation (2).  
\[\eta_{rec}=\frac{P_{R_{L}}}{P_{in}} \tag{2}\]  
three rectifiers with higher efficiencies in ultra-low power conditions. Section 4 shows the fabrication and characterization of an ultra-low power RF rectifier operating in the WIFI band, pointing out some issues to consider in the implementation process. This work points out some practical and theoretical issues that should be considered when designing an ultra-low power RF rectifier for ambient RF harvesting systems.
# Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction
## 2 Method
### Convolutional Feature Extraction  
The CNN model is to extract features from input GNSS time series through convolution and pooling processes, and then combine and classify them through multiple layers of neural networks. The CNN model typically consists of multiple layers, including convolutional layers, pooling layers, and fully connected layers. This section consists of a convolutional layer and a pooling layer.  
The convolutional layer extracts characteristics from input time series through the convolutional process. By sliding the convolutional kernel over the input, it effectively captures local features from different positions, thus comprehensively representing the spatial structure information of the input. Its formula is as follows:  
\[a_{j}^{l+1}=ReLU(\sum_{f=1}^{F^{l}}K_{j,f}^{l}a_{j}^{l}+b_{j}^{l}) \tag{1}\]  
where \(l\) is the number of layers, \(a_{j}^{l+1}\) is the \(j\)th characteristic of the \(l+1\) layer, \(K\) is the convolutional kernel, \(b\) is the bias, \(1\leq f\leq F^{l}\), \(F^{l}\) is the characteristic number of \(l\) layer, and \(ReLU()\) is the activation function. The convolutional layer generates multiple feature maps using multiple convolutional kernels. Each feature map represents distinct features within the input GNSS time series, while the same convolutional kernel uses the same weights at different positions.  
The pooling layer downsamples the feature maps of the convolutional layer, thereby reducing their size and subsequently lowering the computational complexity of the model.  
Figure 1: The flowcharts of the CNN-GRU model.  
The proposed method employs the max-pooling function to downsample the feature maps, as shown in the following formula:  
\[x_{i}^{l+1}=max_{k=1}^{r}\Big{(}x_{i+k}^{l}\Big{)} \tag{2}\]  
where \(x_{i}^{l+1}\) is the \(i\)th neuron of the \(l+1\) layer, \(x_{i+k}^{l}\) is the \(i+k\) neuron of the \(l\) layer, and \(1\leq k\leq r\), \(r\) is the size of the pooling layer. The max-pooling function reduces the dimensionality of the dataset in the preceding layer by retaining the most significant features. Additionally, the pooling layer alleviates overfitting issues and enhances the generalization ability of the proposed model.
###### Abstract  
Many medical imaging data, especially the magnetic resonance imaging (MRI) data, usually have a small sample size, but a large number of features. How to reduce effectively the data dimension and locate accurately the biomarkers from such kinds of data are quite crucial for diagnosis and further precision medicine. In this paper, we propose a hybrid feature selection method based on machine learning and traditional statistical approaches and explore the brain abnormalities of schizophrenia by using the functional and structural MRI data. The results show that the abnormal brain regions are mainly distributed in the supramarginal gyrus, cingulate gyrus, frontal gyrus, precuneus and caudate, and the abnormal functional connections are related to the caudate nucleus, insula and rolandic operculum. In addition, some complex network analyses based on graph theory are utilized on the functional connection data, and the results demonstrate that the located abnormal functional connections in brain can distinguish schizophrenia patients from healthy controls. The identified abnormalities in brain with schizophrenia by the proposed hybrid feature selection method show that there do exist some abnormal brain regions and abnormal disruption of the network segregation and network integration for schizophrenia, and these changes may lead to inaccurate and inefficient information processing and synthesis in the brain, which provide further evidence for the cognitive dysmetria of schizophrenia.  
m pacs: **Keywords:** magnetic resonance imaging schizophrenia; feature selection; brain abnormalities; biomarkers
## 4 Discussion
Therefore, it is conceivable that the interference of one of the above steps reduces fibrogenesis. Herein, we explored intracellular pathways that mediate the effects of 4-MSP on HSCs, and focused on PDGF-BB-triggered HSC growth and TGF-\(\beta\)1-induced HSC activation. TGF-\(\beta\)1/Smad pathway is the major signaling involved in the regulation of ECM synthesis, accumulation, and degradation under the progression of liver fibrosis, which further initiates the liver fibrogenesis. Previous studies showed that TGF-\(\beta\)1 is a major factor mediating in the liver fibrosis process of both experimental models and clinical patients with liver diseases [44]. TGF-\(\beta\)1 binds to its receptors and simultaneously phosphorylates the Smad2/3 proteins that further lead to the production of hetero-oligomeric complex with Smad4. Then, this complex is translocated from the cell cytosol into the nuclei of HSCs. This process further induces gene expression of collagen and promotes hepatic fibrosis [45]. Previous studies indicated that mice with Smad3 knockout were protected from the toxicant-induced liver fibrosis [46; 47]. TGF-\(\beta\) signaling comprises the C-terminal and linker regions phosphorylation of Smad2 and Smad3 [48]. In Figure 1, we showed that 4-MSP selectively decreases TGF-\(\beta\)1-induced Smad2/3 C-terminal region phosphorylation (p-Smad2(Ser465/467)/p-Smad3(Ser423/425)), \(\alpha\)-SMA, and Col1A2 expression. The inhibition of C-terminal phosphorylation is very important for blocking the consequent Smad4 binding and extracellular matrix proteins transcription in the nucleus [48]. The linker region phosphorylation of Smad protein can be regulated by several kinases, such as PI3K/mTORC2, ERK, and JNK [49; 50; 51; 52]. In Figure 2, we showed that 4-MSP can decrease the PDGF-BB-induced MEK/ERK, JNK, Akt, and p7056K phosphorylation. Accordingly, we proposed that 4-MSP may also have the ability to inhibit the phosphorylation of the Smad linker region. Another potent stimulator that regulates HSC proliferation is PDGF-BB. PDGF-BB is able to activate MAPK and Akt signaling during liver fibrogenesis [17]. Moreover, the MAPK/ERK pathway activation is closely correlated with a promotion of cell growth, differentiation, and migration of HSCs [53; 54]. In addition, PI3K/Akt and the downstream molecule p7056K promote cell proliferation, protein synthesis, and collagen gene expression in HSCs [55; 56]. Inhibition of either of the MAPK and PI3K/Akt pathways suppressed HSC activation and liver fibrosis [57; 58]. Besides this, previous studies showed that PDGF-BB secreted from HSCs would stimulate the HCC cell proliferation through activation of the PI3K/Akt signaling, whereas inhibiting the PDGF-BB or PI3K/Akt signaling would enhance cell apoptosis [59]. In this present study, we have shown that compound 4-MSP has beneficial suppressive effects on both the MAPK and Akt signaling pathways in HSCs treated with PDGF-BB.
# Satellite IoT Edge Intelligent Computing: A Research on Architecture
## 3 Satellite IoT Edge Intelligent Computing Architecture
### Summary of Satellite IoT Edge Intelligent Computing Architecture  
In summary, using the combination of the cross-layer satellite IoT edge intelligent computing architecture and the parameter server algorithm is simpler in the process of gradient sharing. Further, the satellite IoT cloud node cluster can provide highly available support for parameter management. Meanwhile, the architecture has higher requirements on the network, and the communication cost is higher in the process of parameter sharing. Therefore, this architecture is more suitable for intelligent computing tasks with less computation and smaller scale. In contrast, the architecture using the Ring Allreduce algorithm is friendlier in terms of network bandwidth requirements. This is at the expense of a more complex gradient sharing algorithm. Therefore, the architecture is more suitable for the intelligent computing tasks with complex computation.  
In general, deploying the IoT edge intelligent computing architecture on a satellite cluster is feasible and meaningful. The intelligent processing on the edge side can greatly reduce the time cost caused by network transmission during the process of converting from original data to valid information, which can realize rapid response and fully utilize the storage and computing capabilities of the satellite nodes. This technology has a very broad development prospect in the application of satellite constellation communication, navigation, and remote-sensing observation.
## 3 Results and Discussion
### Electrical Characterization of Fabricated Devices  
To test the outcome of the silicidation process, electrical characterization of devices based on single unoxidized NWs patterned in both \(<\)100\(>\) and \(<\)110\(>\) orientations was carried out by back-gating. The back-gate voltage (V\({}_{\text{bg}}\)) was swept between \(-\)40 to 40 V in a butterfly loop (0 to 40 V, 40 to \(-\)40 V, and \(-\)40 to 0 V). The drain to source voltage (V\({}_{\text{ds}}\)) was varied from 0.25 to 1 V. The devices exhibit large hysteresis as the nanowires are not passivated. We extracted a single sweep from the transfer characteristics, as illustrated in Figure 10. The minimum of the curves was shifted to left by 17 V to center the curves around 0 V. An ambipolar behavior of devices was found. The currents in \(<\)110\(>\)-oriented devices are higher compared to the currents in \(<\)100\(>\) devices. At V\({}_{\text{ds}}\) = 1 V, the values of \(n\)- and \(p\)- currents in \(<\)110\(>\) devices are 40.7 and 450.0 nA, while in \(<\)100\(>\) the values are 16.8 and 207.0 nA, respectively. The shift of original curves away from 0 V is attributed to a built-in potential on the NW surface in ambient conditions in the absence of passivation with, for example, an oxidation layer [34]. Unipolar behavior in these devices can be attained by using multiple gate electrodes [9], while \(p/n\) current symmetry, which is required for energy efficient functioning of circuits based on RFETs, can be tuned by oxidation induced stress [9; 14].
# HS-SPME Gas Chromatography Approach for Underivatized Acrylamide Determination in Biscuits
## 2 Materials and Methods
### GC-qMS Conditions  
For AA detection in biscuits the HS-SPME fiber with a CAR/PDMS coating was manually introduced into the GC injection port at 250 \({}^{\circ}\)C (equipped with a glass liner, 0.75 mm I.D.) and kept for 3 min for desorption. The desorbed volatile compounds were separated in an Agilent Technologies 6890N Network gas chromatographer equipped with a DB-FFAP column (60 m \(\times\) 0.25 mm I.D. \(\times\) 0.25 \(\upmu\)m film thickness) supplied by Agilent J&W Scientific (Folsom, CA, USA) connected to an Agilent 5973N quadrupole mass selective detector. Helium (Air Liquid, Portugal) was used as the carrier gas at a flow rate of 1.7 mL/min. The injection was performed in splitless mode. The GC oven program temperature was the same as described in Section 2.6. For the MS system, the temperatures of the transfer line, quadrupole and ionization source were 250 \({}^{\circ}\)C, 150 \({}^{\circ}\)C, and 250 \({}^{\circ}\)C, respectively. Electron impact mass spectra were recorded at 70 eV and the ionization current was about 30 \(\upmu\)A. A delay time of 7 min was used. Under these chromatographic conditions,  
Figure 1: Schematic representation for the HS-SPME procedure applied to extract AA from wheat biscuits. The HS-SPME procedure includes 4 steps: 1—AA water extraction from the solid matrix, 15 min); 2—addition of propanol to enhance AA volatilization to the vial headspace (+15 min); 3—thermostatization of the mixture (+15 min); 4—SPME fiber exposure to the vial headspace (+45 min). The clock represents the time evolution along the 4 steps sequence (total of 90 min) is included.  
the run was 18 min. The acquisition was performed in an ion extraction chromatography (IEC) mode (71 and 55 \(m/z\), for AA monitoring, with higher proportion for 71).
# Blind Recognition of Forward Error Correction Codes Based on Recurrent Neural Network
## 4 Simulation Results
### Performance of Training Set Size  
The size of training samples is an important parameter of neural network and often has a great impact on the accuracy of network training. This experiment takes this into account formally and carries out code type recognition on nine different sizes of samples from 64K to 16M to analyze its influence. The Figure 9 shows how the curve of model performance influenced by the size of training samples. When the sample size is too low, for example, when S = 64K, the model recognition accuracy fluctuates significantly and the recognition accuracy is not high, fluctuating around 80%. With the increase of the size of samples, the identification accuracy is also increasing and the curve tends to be stable. However, when the sample size S exceeds 4M, the sample accuracy has reached 99% and the recognition rate does not fluctuate greatly with the change of SNR and no significant improvement has been observed.
# Residual Echo Suppression Considering Harmonic Distortion and Temporal Correlation
## 2 Problem Formulation  
Let \(x(t)\) denote the far-end signal and let \(s(t)\) and \(n(t)\) denote near-end speech and background noise with time index \(t\). AEC output signal \(e(t)\) and the microphone signal \(y(t)\) are expressed as follows:  
\[y(t) =d(t)+s(t)+n(t), \tag{1}\] \[e(t) =\left(d(t)-\hat{d}(t)\right)+s(t)+n(t)=d_{r}(t)+s(t)+n(t), \tag{2}\]  
in which \(d(t)\) is the echo signal, \(\hat{d}(t)\) is the linear echo estimate produced by an AEC filter, and \(d_{r}(t)\) is the residual echo. The residual echo is always left in the output signal of the AEC, due to the nonlinearity arising from the power amps, loudspeakers, nonlinear echo path, and imperfect AEC. In the frequency domain, these equations become:  
\[Y(m,f) =D(m,f)+S(m,f)+N(m,f), \tag{3}\] \[E(m,f) =\left(D(m,f)-\hat{D}(m,f)\right)+S(m,f)+N(m,f)\] \[=D_{r}(m,f)+S(m,f)+N(m,f), \tag{4}\]where \(Y(m,f)\), \(E(m,f)\), \(D(m,f)\), \(\hat{D}(m,f)\), \(D_{r}(m,f)\), \(S(m,f)\), and \(N(m,f)\) are the STFT coefficients of \(y(t)\), \(e(t)\), \(x(t)\), \(d(t)\), \(d_{r}(t)\), \(s(t)\), and \(n(t)\) in the frame \(m\) and frequency \(f\), respectively. The goal of the RES is to estimate and remove the residual echo, \(D_{r}(m,f)\), from the available signals, such as the far-end reference signal, \(X(m,f)\), the linear echo estimate, \(\hat{D}(m,f)\), and microphone signals, \(Y(m,f)\), in the past and current frames in all frequency bins. A block diagram of an AEC system with a RES module is shown in Figure 1.
# System Design and Reliability Improvement of Wireless Sensor Network in Plant Factory Scenario
## 3 Results and Discussions  
### Analysis of Link Quality Prediction Model Results  
From the link quality tests at different heights, as the distance increased, the path loss increased (Figure 3). Among these four heights, 0.7 m had the greatest impact on link quality, which was likely due to the interference from agricultural facilities and vegetation, as the height of cultivation beds was 0.7 m. Within the first 15 m, path loss increased relatively rapidly from 37 dB to 93 dB (151% increase) compared to the path loss increase from 93 dB to 99 dB (6% increase) as the distance increasing from 15 m to 50 m. To ensure the reliability of the wireless sensor network (WSN) strategy, this study focused on optimizing the system's configuration. A node height of 0.7 m was selected as the appropriate setup. This configuration aimed to ensure that environmental monitoring data accurately reflect the current growth status and environmental conditions of the plants. Based on this setup, the reliability of the WSN in the plant factory was thoroughly studied and analyzed.  
Generic functional form was applied to fit the correlation between path loss and distance, and the functional expression of the link quality prediction model is \(y=(38.20017)x^{0.12671}+36.96638\), as shown in Figure 4. The coefficient of determination (R\({}^{2}\)) is 0.9962, indicating that the proposed link quality prediction model can accurately predict the path loss based on inter-node distance. This model can be effectively applied to subsequent network coverage optimization strategies and the design of clustering-based routing protocols.  
Figure 3: Relationship between path loss and distance for four node heights.  
The study on the statistical analysis of link quality metrics conducted by [PERSON] et al. [31] demonstrated that the physical layer metric RSSI was able to effectively identify high-quality links. The findings indicated that the average RSSI value of high-quality links had an at least 80% probability of being less than \(-88\) dB, whereas the average RSSI values of medium- and poor-quality links were all greater than \(-88\) dB. Therefore, the link quality prediction model developed in this study considered the inter-node communication distance with path loss less than 88 dB as a good quality link. Communication links with a distance of less than 10 m between nodes were considered to be of good quality based on the link quality prediction model.
## 3 Proposed Modified Sine Cosine Algorithm (MSCA)
### Adjustment of Sine Cosine Algorithm (SCA) Using Latin Hypercube Sampling (LHS)
```
01: Input: The population \(X=\{X_{1},X_{2},\ldots,X_{D}\}\), the constant magnitude \(a\)
02: Output: \(X_{best}\) and the updated population \(X\prime=\{X_{1}^{\prime},X_{2}^{\prime},\ldots,X_{N}^{\prime}\}\)
03: Generate a random set of population \(X\)
04: Evaluate the fitness of each and every population
05: **while do**
06: Set initial \(r_{1}\) using Equation (2)
07: Update the best solution
08: **for** (\(t<\) maximum number of generation)
09: Perform Latin Hypercube Sampling
10: Update \(r_{1},r_{2},r_{3}\) and \(r_{4}\)
11: Update the position of the search agents (population) using (1), (2) and (3)
12: Evaluate the fitness of each and every search agent
13: **If** fitness (current) < fitness (previous) then
14: Update the best solution
15: **Else if** fitness (current) = fitness (previous) and Number of features selected (current) < Number of features selected (previous) then
16: Update the best solution and its position, \(P_{i}^{t}=X_{best}\)
17: **End**
18: Replace the least fit search agent with LHS and update the new population
19: **End**
20: **End**
21: Return the updated solution, \(X\) and the best result \(X_{best}\)
```  
**Algorithm 1** Pseudo-code of MSCA Algorithm.