esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Pretraining Phase
#### 3.2.3 Nonlinear Projection Head  
To facilitate contrastive learning, each feature vector \(\mathbf{h}_{t,b}\in\mathbb{R}^{d_{model}}\), extracted from the encoder output \(\mathbf{h}_{reshape}\), is independently transformed by a nonlinear projection head \(g(\cdot)\). This projection maps the representations into a lower-dimensional latent space to enhance the effectiveness of contrastive objectives. The projection head consists of two fully connected layers with a ReLU activation in between:  
\[\mathbf{z}=g(\mathbf{h})=\mathbf{W}^{(2)}\cdot\text{ReLU}(\mathbf{W}^{(1)} \mathbf{h}+\mathbf{b}^{(1)})+\mathbf{b}^{(2)} \tag{13}\]  
where \(\mathbf{W}^{(1)}\in\mathbb{R}^{d_{model}\times d_{model}}\), \(\mathbf{b}^{(1)}\in\mathbb{R}^{d_{model}}\), \(\mathbf{W}^{(2)}\in\mathbb{R}^{d^{\prime}\times d_{model}}\), and \(\mathbf{b}^{(2)}\in\mathbb{R}^{d^{\prime}}\). Here, \(d^{\prime}\) denotes the dimension of the projected latent space, \(\mathbf{z}\in\mathbb{R}^{d^{\prime}}\) is the final output vector corresponding to each feature position. All projection operations are applied independently to each time step and batch element.  
To train the encoder and projection head jointly, we adopt the normalized temperature-scaled cross-entropy (NT-Xent) loss, which encourages the model to bring positive pairs close while pushing apart negative pairs in the projected space. The loss is defined as:  
\[\mathcal{L}=-\frac{1}{2B}\sum_{k=1}^{B}\Bigg{[}\log\frac{\exp(s_{k,k+B}/\tau)} {\sum_{m=1}^{B}1_{[m\
eq k]}\exp(s_{k,m}/\tau)}+\log\frac{\exp(s_{k+B,k}/\tau) }{\sum_{m=1}^{2B}1_{[m\
eq k+B]}\exp(s_{k+B,m}/\tau)}\Bigg{]} \tag{14}\]  
where \(s_{i,j}=\mathbf{z}_{i}^{\top}\mathbf{z}_{j}/(\|\mathbf{z}_{i}\|\|\mathbf{z}_{ j}\|)\) denotes the cosine similarity between embeddings \(z_{i}\) and \(z_{j}\), \(\tau\) is a temperature parameter that controls the concentration of the similarity distribution, and \(1_{m\
eq k}\in\{0,1\}\) is an indicator function evaluating to 1 if \(m\
eq k\). The batch size is denoted by \(B\), and for each anchor \(k\), the corresponding positive sample is at position \(k+B\).
## 4 Performance Analysis of U-Shaped Frame Optimized Structure
### Static Stiffness Check  
During the static analysis step, identical loads and boundary conditions are imposed on both the optimized and empirical design structures, leading to a comparison of their maximum stress values. Figure 11 illustrates the stress distribution maps for the optimized and empirical design structures.  
Finite element analysis results reveal that under identical working conditions and mesh quality, the empirical design structure's maximum stress value stands at 104.4 MPa, whereas the optimized design structure demonstrates a maximum stress of 75.66 MPa, marking a reduction of 28.74 MPa. This indicates that the U-shaped frame, utilizing the optimized design structure, exhibits enhanced static stiffness.
# Demosaicing by Differentiable Deep Restoration
## 3 The Proposed Method
### Joint Learning CFA and Demosaicing Network  
Our deep restoration network is differentiable and can be trained in an end-to-end manner. This brings two advantages. First, the closed-form solver enforces the estimated color image \(I\) to satisfy the physical degradation model. Second, as the CFA \(H\) is explicitly included in the restoration formulation, the back-propagated gradient can quickly reach the CFA without having to go through the entire demosaicing network. In this way, our method can provide better supervision to train the CFA. In comparison, the pioneer methods [2,3] in jointly optimizing CFA and demosaicing networks could suffer from the notorious gradient vanishing problem as the back-propagation has to pass through the demosaicing network before reaching the CFA.
###### Abstract  
The reconfigurable intelligent surface (RIS) and rate-splitting multiple access (RSMA) are considered as promising technologies for the beyond Fifth-Generation (B5G) and Sixth-Generation (6G) wireless systems by controlling the propagation environment, which attenuates the transmitted signal, and by managing the interference by splitting the user message into common and private messages. Because conventional RIS elements have each impedance connected to the ground, the sum-rate performance improvement of the RIS is limited. Therefore, the new RISs, which have impedance elements connected to each other, have been proposed recently. To be more adaptive to each channel, the optimization of the grouping of the RIS elements is required. Furthermore, since the solution of the optimal rate-splitting (RS) power-splitting ratio is complex, the value should be simply optimized to be more practical in the wireless system. In this paper, the grouping scheme of the RIS elements according to the user scheduling and the solution of the RS power-splitting ratio based on fractional programming (FP) are proposed. The simulation results showed that the proposed RIS-assisted RSMA system achieved a high sum-rate performance compared to the conventional RIS-assisted spatial-division multiple access (SDMA) system. Therefore, the proposed scheme can perform adaptively for the channel and has a flexible interference management. Furthermore, it can be a more suitable technique for B5G and 6G.  
reconfigurable intelligent surface; rate-splitting multiple access; mmWave; MIMO 2023  
2233934
## 3 Results and Discussion
### Simulated Results  
Figure 7 shows the magnetic field distribution at position X = 0 m for all the nano fab-based permutations and ideal permutation cases. At X = 0 m and Y = 0 m, one can observe a very high magnetic field in the range of \(-\)5 m to +5 m along the Y-axis. Ideal permutation case 1 shows a lower magnetic field along the \(Y\)-axis than any other permutation setup. The results were obtained without any shielding system.  
Figures 8 and 9 show the magnetic field distribution of the nano fab through the simulation results. The conditions of the simulation environment were current I = 15 A and 750 A at X = 0 m. We chose these values to ensure that the ideal permutation showed  
Figure 6: Magnetic field distribution of nano fab using an ideal power cable permutation system with a three-phase, four-cable (R, S, T, and N) system (as shown in Figure 4).  
Figure 7: The magnetic field at X = 0 with simulated existing case and prediction of the best 1-D permutation results.  
the best results throughout the system with the minimum error rate possible. The results of both systems were obtained without considering any shielding effect. In Figures 8 and 9, the magnetic field distribution for the nano fab-based permutation shows better results than the ideal permutation system at X = 0 m and until Y = 0.25 m.  
The magnetic field values shown in Figures 10 and 11 were measured at Y = 2.3 m and Y = 3 m (Y is the distance from the cable tray and inside cable tray width) by varying the X-axis. At Y = 2.3 m, the nano fab base permutation setup showed a magnetic field  
Figure 8: Magnetic field distribution of nano fab at position X = 0 m and current level I = 750 A.  
Figure 9: Magnetic field distribution of nano fab at position X = 0 m and current level I = 15 A.  
value of 5.1 mG\(\sim\)6.3 mG whereas the ideal permutation system showed the value of 0.19 mG\(\sim\)0.21 mG. The results indicate the effective mitigation of the magnetic field at 2.3 m floor range from the cable trap for the ideal permutation case.
## 4 Results and Discussion
### Sliding Experiment  
Dimple structures in the proposed method were formed on the laminated sheet to hold and stabilize the droplets. It was difficult to discuss the effect of the dimple structure with a conventional method using the contact angle because the material composition of the sheet was the same as the flat surface and the contact angle was also the same. Hence, we measured the sliding angles to derive the adhesion energy of the surface and estimated the stability of the droplet on the dimple structure. Figure 7 shows the results of the sliding experiment in which devices with and without the dimple structure were compared. As shown in Figure 7A, the sliding angles decreased as the droplets increased, and the angles of the surface with the dimple structures were larger than those of the flat surface. From this sliding angle, we derived the adhesion energy of each surface. The result showed that the value of the surface with dimple structures was approximately 1.21 mJ/m\({}^{2}\) higher than that of the flat surface (Figure 7B). On the dimple structure, which had a large adhesion energy, it can be considered that the potential energy was relatively low (energetically stable), which means that the droplet was stable. Additionally, the small error bar, which represented the standard deviation of the five results, showed stable data with low variation. This low variation in the adhesion energy ensured reliable droplet motion, independent of differences in experimental conditions.  
Figure 6: Droplet migration pathway on the EWOD device. (**A**) Trace map of the droplets on a surface with a dimple structure. (**B**) Trace map of the droplets on a surface without a dimple structure. The volumes of the droplets were 10 \(\upmu\)L and 20 \(\upmu\)L. The median point of the droplets was plotted every second. Each coordinate plane was 12 mm square, and the coordinate origin was at the center of the six tiles. The dotted lines represent the boundaries of the electrodes and each of the four trials is indicated in a different shape and color.
###### Abstract
Recently, the impressive achievement of deep architectures on computer vision tasks such as object recognition [26; 27], object detection [28] and action recognition [29] have shown the significance of convolution neural network (CNN) in the image domain. Most methods utilize the deep networks as features extraction strategy and then train the pattern recognition model. Inspired by the successful application of CNN in image processing, we proposed an auto features extraction strategy based on CNN (CNN-AFE). The key idea behind our method is to transform the time series into pictures so as to make full use of the advantages of CNN. The significant contribution of the paper is to put forward a deep learning-based auto features extraction strategy in the e-tongue system for tea classification. Furthermore, the proposed model is an approximate end-to-end features extraction and pattern recognition method, which reduces manual operation and improves efficiency. Figure 2 shows the implementation process of this work. First, sensors response of the e-tongue was converted to time-frequency maps by STFT. Second, the CNN extracted features automatically with time-frequency maps as input. Finally, the features extraction and classification results were carried out under a general shallow CNN architecture. Compared with other methods, the proposed method avoided manual features selection with training of network parameters. The proposed method has advantages in two aspects. On the one hand, the remaining features extraction steps are completed in network training automatically after transforming the sampling signal into time-frequency images with STFT. On the other hand, the CNN-AFE combines features extraction and pattern recognition into a whole. In terms of traditional algorithms, features extraction and pattern recognition are two separate parts. First, they apply experience-based features extraction method to obtain features, and then they choose different classifiers, such as SVM, \(k\)-NN and RF to complete pattern recognition. However, the CNN-AFE is an approximate end-to-end features extraction and pattern recognition method, which is conducive to improve the efficiency and accuracy of pattern recognition. Comprehensively, the proposed method not only has high accuracy, but also omits the inconvenience of manual data selection, which is applicable to most data scenarios. In this study, we adopt tea database for classification, tea samples are collected by an e-tongue system we designed. The proposed method was compared with other state-of-art approaches, and showed superior performance, which is nearly 99.9% classification accuracy. We infer that the CNN-AFE is suitable for liquid classification. In the rest of this paper, we arrange the content as follows: Section 2 provides a brief description about the e-tongue system, introduces experiment settings and details of the proposed method. In Section 3, we demonstrate the experimental results for evaluations upon our own database. Finally, we summarize this study in Section 4.
## 2 Sequential Coupling Model  
### Hypothesis and Simplification  
This study constructs the CFD-rotordynamics sequential coupling numerical model of centrifugal pump mainly based on following hypotheses and simplifications:  
(1) The coupling between flow field and rotordynamics belongs to one-way coupling. It means that only influences of flow induced exciting force on the vibration behavior of the rotor system were considered, but interferences of the deformation and whirl orbits of impeller to the flow field are ignored. According to the calculation and experimental results of a centrifugal pump made by [PERSON] et al. [PERSON] et al. (2014, 2015), compared with the two-way coupling method with consideration of impeller deformation, the calculated radial force based on one-way coupling shows little differences in numerical values. In addition, the vibration displacements of impeller predicted by both one-way coupling and two-way coupling show very small errors with experimental results. However, compared with the two-way coupling method, the one-way coupling way can save as much as 75% computing time and achieves stronger convergence ability during numerical solving.  
(2) In flow field analysis, internal leakage of fluid media through sealing rings was neglected.  
(3) In rotordynamics analysis, mass distribution was hypothesized uniform on the impeller, namely the unbalanced mass force of the rotating impeller was treated to be zero. In addition, hole and slot structures of the main shaft are ignored. Therefore, the rotor system is considered as a rotationally symmetric structure composed of some mass points of impellers, a main shaft, and its bearings.
## 2 Materials and Methods
### Vendor Evaluation:
The total cost of ownership (TCO) is a decisive factor; hence, consider all costs, such as initial implementation, ongoing maintenance, licensing, and scaling or customization. A comprehensive cost-benefit analysis ensures that the vendor's solutions meet the organization's budget constraints and offer a favorable return on investment (ROI).
## 1 Introduction
The results, however, demonstrated the need for a careful evaluation of the sorption systems in relation to the operational features of the ship, i.e., days at sea, dimension of the cold room, and so on. What the previous literature fails to report is the possibility of combining the sorption system with other systems on-board for providing refrigeration and air conditioning with the highest efficiency possible and at different temperature levels. Instead, in the present work, different solutions are proposed and analysed, trying to highlight how the integration of the sorption units for synergic operation with other thermal systems can boost the efficiency and increase the reliability of the energy system. In such a context, the present work aims to give an innovative overview on the feasibility of waste-heat powered systems in fishing vessels, which include not only sorption refrigerators but hybrid solutions that couple sorption and compression systems, analysed for the first time for application on-board. This paper is structured as follows: in Section 2, the methodology is presented; the different cases selected for the application are described in Section 3--particular focus was put on choosing different types of fishing vessels and different typical routes; in Section 4, the sorption systems introduced in the analysis are described; Section 5 presents the schematics for the integration of such systems on-board; Section 6 presents the results of the energy analysis, whereas Section 7 summarises the environmental analysis carried out. Finally, a discussion on the future trends and possibilities for the application of innovative energy systems is reported.
## 4 Potential IIoT Applications
### Agriculture and Farming Applications  
The deployment of NTNs in the agriculture and farming sector mainly targets the efficient management of water resources and irrigation systems and the monitoring of weather and climatological conditions. To succeed in these objectives, multiple sensor nodes should be deployed at different points across farming land and large remote plantations. In this direction, a LoRaWAN gateway can provide ubiquitous connectivity to these sensors and send data via satellites or aerial platforms to central cloud platforms that will then analyze this data and guide the agriculture and farming activity. This procedure intends to optimize water usage and irrigation schedules for precision agriculture, ensures consistent soil-water content for all plants, enables intelligent resource management in a particular area, and decreases the amount of land required for production purposes. Using NTNs, farming operations can also be monitored all the way from harvest to delivery [79, 80]. Therefore, farmers can make better decisions about the planting and harvesting of crops. Moreover, a long-range and reliable network for the tracking of livestock and keeping sight of their location, health, and safety is also viable by using tracking devices on each one of the animals.
## 5 Implementation and Experiment
### Experimental Result
#### 5.3.1 Finding the Fault in the Disconnected Sequence (Fault Index #1)  
This fault is when the input normally enters the function, but the input is not passed to the output. In other words, the updated patterns of the memory according to the operation of the function are not connected to the sequences. In Table 1, fault index #1 is a malfunction due to consecutive commands input to the front turn signal function. That is, when a function is in operation, a command for another operation is input, and a fault occurs. We conducted a test to turn on/off each operation in the unit test. In the integration test, consecutive instructions were input at intervals of 500 ms. The result is shown in Figure 12. In the figure, the IDUR of 166-171 normally shows the pattern, and the IDUR of 216-218 is the fourth sequence, but is missed. Because the frame interval is 10 ms, the 50-frame difference is 500 ms. Therefore, in the following operation command, the previous input operates normally, and the subsequent input shows that a fault has occurred.  
According to Algorithm 1, sequence #4 of the front turn signal is the fault-occurrence sequence (R1), #3 is the suspect sequence (R2), and the other sequences are the related sequence (R3). At this time, it can be seen that the proposed method focuses on sequential patterns rather than continuity in 167 and 217 frames that are excluded. As a result, according to Table 2, the fault candidates of the fault index #1 is R1 (#8619), R2 (#8611, #8612, #8613) and R3 (#8650, #10677, #10681, #8603, #8624, #8625, #8626). In fact, the fault we injected is mutated to '=' with '!=' in the assignment operation of the variable assigned to #8619, Rank R1, according to the OEBA operator.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Node** & **Classifier** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score** \\ \hline \multirow{3}{*}{1} & App/Infra & 96.06 & 96.07 & 96.40 & 96.23 \\  & Unit Function & 97.92 & 98.47 & 98.12 & 98.29 \\  & Featured Frame & 98.90 & 99.13 & 98.70 & 98.92 \\ \hline \multirow{3}{*}{2} & App/Infra & 100.00 & 100.00 & 100.00 & 100.00 \\  & Unit Function & 97.99 & 97.96 & 97.82 & 97.89 \\  & Featured Frame & 98.80 & 97.09 & 96.94 & 97.02 \\ \hline \multirow{3}{*}{3} & App/Infra & 98.44 & 98.39 & 98.57 & 98.48 \\  & Unit Function & 96.84 & 97.44 & 97.39 & 97.41 \\ \cline{1-1}  & Featured Frame & 98.44 & 97.86 & 98.66 & 98.26 \\ \hline \multicolumn{3}{c}{Average (\%)} & 98.15 & 98.04 & 98.07 & 98.06 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training results of the classifiers using a convolutional neural network (CNN).
## 2 Materials and Methods
### Scanning Electron Microscope (SEM) and Energy Dispersive Spectroscopy (EDX)  
The eggshell specimens were taken using a Mira3 scanning electron microscope by Tescan Orsay Holding, a.s. (Brno, Czech Republic). The eggshell specimens (\(n\) = 10 eggs \(\times\) 5 replicates) were washed with ultra-high purity water and then dried under ambient conditions at 20 \(\pm\) 1 \({}^{\circ}\)C for two days. The dried eggshells were manually broken into small pieces of 0.4 \(\times\) 0.4 cm, mounted onto SEM pin stubs using double-sided adhesive carbon discs, and coated with a gold-palladium 15 nm thick layer using Leica EM ACE600 vacuum sputter coater (Leica Microsystems, Wien, Austria). The eggshell and membrane thickness measurements were completed at three regions of the egg, i.e., the large end, equator, and small end. The conditions were adjusted to operate under high vacuum mode utilizing the Secondary Electron (SE) and Back-scattered Electron (BSE) detectors. The morphology of the eggshell was examined, raising magnification up to 2000\(\times\) for a detailed dimension measurement and elemental composition analysis operating at 15 kV acceleration voltage. An elemental composition analysis has been conducted employing INCA x-act LN2-free Analytical Silicon Drift Detector with PentaFET(r) Precision energy-dispersive X-ray spectrometer by Oxford Instruments Inc. (Abingdon, United Kingdom). A 15 mm working distance with dead time from 40 to 60% was adjusted.