esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Galvanostatic Cycling--Capacity Retention  
Besides SEM, light microscopy (LM) was performed for controlling the ablation depth and surface quality (Figure 2). For this purpose, the top view and the cross section view of both unstructured and  
Figure 1: Example of a constant current pulse applied for a known interval of time \(\tau\), followed by a relaxation time, while measuring the corresponding potential.  
structured cathodes were analyzed for unstructured and laser structured samples. The ablation depth is almost equal to the layer thickness and the laser ablation process does not damage the current collector.  
Cells rate capability was determined by applying successively increased C-rates from C/10 up to 2C (Figure 3). Galvanostatic testing measurements were performed in a voltage range between 3.0 and 4.2 V vs Li/Li\({}^{+}\). It was observed that the capacity of the reference-cell is significantly decreased, i.e., at C/2, its retained capacity is only at 60%, and, furthermore, at 1C and 2C such cell shows no capacity anymore. At 2C, the cell with the laser structured electrode still retains about 60% of its initial discharge capacity. As a final cycling step, the C-rate was set back to C/10 to determine whether any cell degradation occurred during these galvanostatic measurements. A small capacity loss was observed at the end of the measurements: the cell capacity decreased by 6% and 8% for the laser-and reference-cells, respectively.  
Figure 3: Discharged capacity determined by galvanostatic cycling with potential limitation using different C-rates, performed on reference- and laser-cells.  
Figure 2: Lithium-ion battery cathode samples—(**a,b**) unstructured and (**b,d**) structured—based on aluminium foil single-side coated by LiCoO\({}_{2}\) rich composite layer. (**a,b**) SEM images (top view); and, (**c,d**) light microscopy (LM) images (cross-sectional view). Ablation depth: 90–95 \(\upmu\)m.
## 3 Machine Learning Techniques Based on Linear and Regularized Linear Models
### Linear and Regularized Linear Models
#### 3.1.4 Elastic Net Regression (ENR)  
Elastic net, introduced by [PERSON] and [PERSON] [58], is a model that combines ridge regression and LASSO regression. It has the advantage of addressing the limitations of LASSO, particularly in cases where certain features are strongly correlated. The cost function of elastic net regression incorporates both the L1 regularization term, \(\sum_{j=1}^{n}\lvert\beta_{j}\rvert\), and the L2 regularization term, \(\sum_{j=1}^{n}\beta_{j}^{2}\), in addition to the cost function of linear regression. The balance between L1 and L2 regularization is controlled by the hyperparameter l1_ratio (denoted as r). When the l1_ratio = 1, the model applies pure L1 regularization and when l1_ratio = 0, it applies pure L2 regularization. The cost function of elastic net regression is presented in Equation (5) [51; 52; 54; 56; 57; 58].  
\[\begin{split} ENR_{cost}&=\sum\limits_{i=1}^{m}(y_{ i}-\hat{y_{i}})^{2}+\lambda\left(\frac{1-r}{2}\right)\sum\limits_{j=1}^{n}\beta_{j}^{2}+ \lambda r\sum\limits_{j=1}^{n}\lvert\beta_{j}\rvert\\ &=MLR_{cost}+\lambda\left(\frac{1-r}{2}\right)(RR_{cost})+\lambda r (LR_{cost})\end{split} \tag{5}\]
## 3 Fibre-Optic-Based Smart Geosynthetics
### Types of Fibre-Optic Sensors
#### 3.2.2 Interferometers or Phase Modulators as of Sensors  
Interferometry is one of the most important laboratory techniques for precisely measuring distances. Interferometers have been used to obtain phase information from intensity measurements [26]. A Mach-Zender interferometer schematic is shown in Figure 6.  
In this system, a single-frequency monochromatic laser wave is split into two light beams (with either an optical fibre coupler or a conventional optic partial mirror) propagating through routes of different lengths (approximately 380 nm for He-Ne laser red light) before being recombined, which delays one wave relative to the other and leads to oppositely summed electromagnetic fields. Therefore, the output intensity will be zero. As shown in Figure 7, when the length of one of the paths is increased or decreased by half a wavelength, the interferometer signal travels from the input power level to zero. Studies  
Figure 5: Marco- and micro-bending in OF sensors.  
Figure 6: Schematic of Mach–Zender interferometer.  
have shown that in highly accurate experiments, even 10 nm changes in the optical path length can be identified. However, because both optical paths are highly sensitive to any disturbance, utilising these OF sensors in field applications remains problematic because changes in the environmental temperature can induce many maximum and minimum drifts [49].  
Mach-Zender, Michelson, and Fabry-Perot are three other common traditional OF-based interferometer architectures.
## 4 Reliability Assurance Topology Control Strategy
Then, the reason for making the topology unconnected is from the Sub-step (1) or (2), i.e., \(G_{t}=\{p^{{}^{\prime}}_{s_{1}g},\cdots,p^{{}^{\prime}}_{s_{t}g}\}\) is unconnected and can be changed into \(G_{t}=G^{{}^{\prime}}_{0}+\cdots+G^{{}^{\prime}}_{s}\), where \(G^{{}^{\prime}}_{0},\cdots,G^{{}^{\prime}}_{s}\) have no intersections. However, all of the paths established from Sub-steps (1) and (2) have the same intersections of satellites, like GSC. Then, there exists a contradiction and the assumption is not valid. * [15]
* Topology Recursion  
Based on the topology snapshot interval \(\eta\) and the satellites initial positions \(V_{0}\), the position matrix \(V_{t}=\{V_{t}|t=t_{0}+n\eta\}\) in the next snapshot can be calculated by using the satellite dynamic model and the Runge-Kutta algorithm [36]. Then, by repeating Step 1, the strategy obtains the topologies of the satellite cluster at every time of snapshot, \(G_{t}=\{G_{t}|t=t_{0}+n\eta\}\). * Topology Distribution  
The satellite of calculation determines the MST topology \(G_{MST}\) of the satellite cluster, and sends the data of topology \(G_{t}=\{G_{t}|t=t_{0}+n\eta\}\) to all of the other satellites through \(G_{MST}\). * Strategy Execution  
From the start time \(t_{0}\) of EO missions and the next moments \(t=t_{0}+n\eta\) of topology switching, every satellite changes the adjacent satellite links according to \(G_{t}\). After applying the above four steps, this paper finally obtains a proper topology for the SCN with EO missions.
# Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks
## 4 Experimental Results
### Facial Expression Datasets  
The Real World Affective Faces Database (RAF-DB) is a real-world dataset that contains 29,672 images downloaded from the internet [65]. Around 40 annotators have labeled each image. The dataset has two types of annotation: 7 classes of basic emotions and 12 classes of compound emotions. Only the seven basic emotions (face images and labels) were used. Other metadata, such as facial landmarks, bounding box, and identity bias (e.g., age, gender, and race) are also provided, but they were not used in any step of the proposed approach. RAF-DB was used to fine-tune the pre-trained 2D CNNs.  
SEWA-DB is a large and richly annotated dataset consisting of six groups of subjects (around 30 people per group) from six different cultural backgrounds (British, German, Hungarian, Greek, Serbian, and Chinese) divided into pairs of subjects [60]. Each pair of subjects discussed their emotional state and sentiment toward four previously watched adverts. The dataset consists of 64 videos (around 1525 minutes of audiovisual data) split into three folders (34 training, 14 validation, and 16 test). Since the labels are not provided for the test set due to its use in FER challenges, the validation set was used as the test set, and the training set was split into training (28 videos) and validation (6 videos) sets. In addition, annotations are given for valence, arousal, and levels of liking. Only valence and arousal annotations were used since previous studies indicated that the liking level is not well related to facial expressions.
# Chinese Lip-Reading Research Based on ShuffleNet and CBAM
## 3 Experiment
### Recognition Results
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Model/** & **ResNet-18** & **MobileNet v2** & **ShuffleNet v2 (1\(\times\))** & **ShuffleNetv2 (0.5\(\times\))** & **ShuffleNet v2 (0.5\(\times\))** \\
**Character** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(CBAM) (\%)** \\ \hline Tai-Yang & & & & & \\ (sun) & 78.00\% & 73.00\% & 74.00\% & 65.00\% & 76.00\% \\ Gong-Zuo (work) & 73.00\% & 72.00\% & 72.00\% & 69.00\% & 76.00\% \\ [PERSON] (sorry) & 79.00\% & 71.00\% & 69.00\% & 69.00\% & 79.00\% \\ [PERSON] (sleep) & 79.00\% & 71.00\% & 68.00\% & 69.00\% & 76.00\% \\ [PERSON] & & & & & \\ (eat) & 70.00\% & 68.00\% & 62.00\% & 60.00\% & 73.00\% \\ [PERSON] (cloud) & 79.00\% & 79.00\% & 68.00\% & 66.00\% & 76.00\% \\ [PERSON] (well) & 79.00\% & 72.00\% & 69.00\% & 67.00\% & 79.00\% \\ [PERSON] (China) & 75.00\% & 70.00\% & 65.00\% & 63.00\% & 73.00\% \\ [PERSON] & & & & & \\ (sorry) & 78.00\% & 70.00\% & 68.00\% & 63.00\% & 78.00\% \\ [PERSON] (thanks) & 73.00\% & 74.00\% & 66.00\% & 65.00\% & 70.00\% \\ [PERSON] (goodbye) & 77.00\% & 70.00\% & 60.00\% & 66.00\% & 77.00\% \\ [PERSON] (school) & 75.00\% & 70.00\% & 69.00\% & 66.00\% & 73.00\% \\ [PERSON] & 79.00\% & 71.00\% & 69.00\% & 69.00\% & 78.00\% \\ (paper) & & & & & \\ [PERSON] & & & & & \\ (book) & 79.00\% & 72.00\% & 69.00\% & 63.00\% & 72.00\% \\ [PERSON] & & & & & \\ (pen) & & & & & \\ [PERSON] & 79.00\% & 69.00\% & 69.00\% & 65.00\% & 79.00\% \\ [PERSON] & & & & & \\ (computer) & & & & & \\ [PERSON] & & & & & \\ (apple) & & & & & \\ [PERSON] & & & & & \\ (banana) & & & & & \\ [PERSON] & & & & & \\ (grape) & & & & & \\ \hline \end{tabular}
\end{table} 
Table 4: Partition of the results of different models on some words. Figure 13: Comparison of loss on different models. (2) Comparison of different lengths of the input frame:  
Previously in our experiment, we would always extract 29 consequent frames from a video and the word we would pay attention to is at the center of the sequence. However, it is unrealistic to assume such deviations in a practical scenario. To highlight this problem, we design two more experiments: one is to remove N frames ranging from 0 to 4 from the sequence to test the performance of the model trained with a fixed-length sequence, and the other is to train the model with a variable-length sequence to test how robust it is. As is shown in Table 5, both experiments are conducted on ShuffleNet v2 with CBAM. If a frame is removed during testing, the model trained on a fixed-length sequence is significantly affected and the performance degrades with more frames removed. As expected, the model trained with variable-length augmentation shows more robustness to the missing frame.
## 2 The Proposed Converter
### The Proposed Converter Modeling and Design  
The proposed converter has two operating periods during a full switching cycle, \(\mathrm{T_{s}}\). The proposed converter is operated in continuous conduction mode (CCM). Therefore, the two sub-periods depend on the switch M status.  
* During \(\mathrm{T_{on}}\) of M, both \(\mathrm{D_{o1}}\) and \(\mathrm{D_{o2}}\) are in reverse bias. The inductors' currents' directions are shown in Figure 4. The inductors' currents are given by: \[I_{Lin}=\frac{V_{in}}{L_{in}}t+I_{Lin-min}\] (1) \[I_{Lo1}=\frac{V_{in-}V_{cP1}-V_{o1}}{L_{o1}}t+I_{Lo1-min}\] (2) \[I_{Lo2}=\frac{V_{in-}V_{cP2}}{L_{o2}}t+I_{Lo2-min}\] (3) where \(0<t<T_{on}\), \(I_{Lin-min}\), \(I_{L1 min}\) and \(I_{Lo2 min}\) are the lower limits of the inductor's currents.  
Figure 3: The proposed converter with three output voltages.  
During this period, the inductors' currents are given by:  
\[I_{Lin}=I_{Lin-max}-\frac{\left(V_{in}-V_{Cp1}\right)}{L_{in}}t \tag{4}\]  
\[I_{L01}=I_{L01-max}-\frac{V_{o1}}{L_{o1}}t \tag{5}\]  
\[I_{Lo2}=I_{L2-max}-\frac{V_{o2}}{L_{o2}}t \tag{6}\]  
where \(T_{on}<t<T_{s}\), \(T_{s}=T_{on}+T_{off}\). \(I_{Lin-max}\), \(I_{L1-max}\) and \(I_{L2-max}\) are the upper limits of the inductor's currents.  
* To calculate the duty cycle of the proposed converter, each converter has its own duty cycle, which is given by: \[D_{zeta}=\frac{V_{o1}}{\left(V_{o1}+V_{in}\right)}\] (7)  
Figure 4: The proposed converter during T\({}_{on}\).  
Figure 5: The proposed converter during T\({}_{off}\).  
\[D_{Mahfzah}=\frac{V_{o2}}{(V_{o2}+V_{in})} \tag{8}\]  
The proposed converter has a duty cycle that can be calculated when connecting the common output voltage (\(V_{o3}\)) as follows:  
\[V_{o3}=V_{o1}+V_{o2} \tag{9}\]  
Substituting (7) and (8) in (9) yields:  
\[V_{o3}=V_{in}\frac{D_{M}}{1-D_{M}}+V_{in}\frac{D_{M}}{1-D_{M}} \tag{10}\]  
Solving for \(D_{M}\) gives:  
\[D_{M}=\frac{V_{o1}+V_{o2}}{2V_{in}+V_{o1}+V_{o2}} \tag{11}\]  
And the voltage gain of the proposed converter is given by:  
\[VG=\frac{V_{o3}}{V_{in}}=\frac{2D_{M}}{1-D_{M}} \tag{12}\]  
From (12) and Figure 6, it is clearly seen that the proposed converter has a boosting capability at lower duty cycles. Moreover, it has a higher voltage gain over the duty cycle range. Therefore, it is able to decrease the conduction losses of the selected switch.
## 3 Power Allocation Optimization and Problem Formulations
### MMSE Channel Estimation in Downlink FDD Systems  
To obtain a CSI estimate in the downlink, in the training phase, the BS is required to transmit the training sequence with length \(\tau_{\text{tr}}\) to the users. Accordingly, the received signal, which is denoted here by \(\mathbf{z}_{k}\in\mathbb{C}^{\tau_{\text{tr}}}\), at the \(k\)th user side when the training phase is performed can be expressed as  
\[\mathbf{z}_{k}=\sqrt{\rho_{\text{tr}}}\mathbf{S}_{\text{tr}}^{\text{H}} \mathbf{h}_{k}+\mathbf{n}_{k}, \tag{10}\]  
where \(\mathbf{n}_{k}\) is the noise introduced at the training stage, which is modeled here as \(\mathcal{CN}(\mathbf{0},\mathbf{I}_{\text{tr}})\), and \(\mathbf{S}_{\text{tr}}\) denotes the training sequence matrix and satisfies the power constraint with \(\text{tr}\big{(}\mathbf{S}_{\text{tr}}^{\text{H}}\mathbf{S}_{\text{tr}}\big{)}= \rho_{\text{tr}}\tau_{\text{tr}}\). The training matrix depends on essential parameters, which are the training sequence structure, training sequence power \(\rho_{\text{tr}}\) and training sequence length \(\tau_{\text{tr}}\). By taking advantage of traditional linear processing, the MMSE CSI estimate can be used as provided in [63]. Performing Bayesian channel estimation, such as minimum mean square error (MMSE) filters based on channel and noise statistics [63], can lead to a considerable improvement in the quality of channel estimation. To this end, the MMSE estimation can be expressed as  
\[\mathbf{G}_{k}=\sqrt{\rho_{\text{tr}}}\mathbf{H}\mathbf{S}_{\text{tr}}\big{(} \rho_{\text{tr}}\mathbf{S}_{\text{tr}}^{\text{H}}\mathbf{H}\mathbf{S}_{\text{ tr}}+\mathbf{I}_{\text{tr}}\big{)}^{-1}. \tag{11}\]  
Upon applying the MMSE CSI estimate, the downlink CSI estimate is expressed as  
\[\mathbf{\hat{h}}_{k}=\mathbf{G}_{k}\,\mathbf{z}_{k}, \tag{12}\]  
where the received training signal is \(\mathbf{z}_{k}\in\mathbb{C}^{\tau_{\text{tr}}}\) and given according to Equation (10). The error vector of the channel estimation can be represented as  
\[\mathbf{\hat{h}}_{k}=\mathbf{h}_{k}-\mathbf{\hat{h}}_{k}, \tag{13}\]  
where the CSI estimation of the MMSE covariance matrix can be given as \(\mathbf{\Theta}=\mathbb{E}\left\{\,\mathbf{\hat{h}}_{k}\mathbf{\hat{h}}_{k}^ {\text{H}}\right\}\), which is as expressed as  
\[\mathbf{\Theta}=\mathbf{G}_{k}\sqrt{\rho_{\text{tr}}}\mathbf{S}_{\text{tr}}^{ \text{H}}\mathbf{H}. \tag{14}\]  
Our main objective in this paper is not to minimize the estimation error in the equation above but rather to maximize the DASR of FDD MMIMO systems. In particular, this paper investigates the maximization of the DASR of the MMIMO system through the optimization of the power allocation with respect to \(\rho_{\text{tr}}\) and \(\rho_{\text{d}}\).
# A Seismic Inversion Method Based on Multi-Scale Super-Asymmetric Cycle-JNet Network
## 4 Conclusions  
In this paper, a seismic intelligent inversion method based on the multi-scale super-asymmetric Cycle-JNet network is proposed to predict seismic inversion. The network includes many technologies such as CNN, RNN, and wavelet transform. Through about 100 rounds of machine learning, the coincidence rate of well vibration curve reached 92.1%, which proves that the neural network is sensitive to changes in well vibration data and has strong learning ability. Compared with traditional seismic inversion prediction, it can easily identify and predict the thin sand layer. During the pumping test, the profile of sand bodies was uniform and continuous, and the coincidence rate of sand prediction in the test well reached 81.2%, which proves the accuracy of the proposed method in predicting sand bodies and also proves that the Cycl-JNet network is a new and reliable seismic inversion  
Figure 7: Seismic inversion profiles of sand bodies by each method: (**a**) random modeling inversion; (**b**) SMI; (**c**) AI seismic inversion.  
prediction model. The prediction model can realize the identification and prediction of sand bodies of different thickness, which greatly improves the accuracy and resolution of seismic inversion.  
In practical applications, since the method completely relies on the network of machine learning to generate the inversion body, it will not be able to make accurate predictions in the face of the types of seismic data that are not learned, and the accuracy will be greatly reduced. However, in the face of problematic data, the network cannot judge its correctness and will still output seismic inversion bodies based on wrong data. It is necessary to continuously carry out a large amount of seismic data training to improve the network and train its ability to identify the wrong data.  
Conceptualization, M.T. and B.H.; methodology, M.T. and B.H.; validation, M.T., B.H., Z.C. and R.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation (grant numbers. 42072163, 41972250), and the Foundation of Shandong Province (grant number ZR2019 MD006), and the Foundation of CNPC (grant number 2021 DJ3302), and the National Natural Science Foundation of China (62305196), and the National Postdoctoral Researcher Program (GZC20231498).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 4 Data Processing and Analysis
### Eye-Tracking Matrix (ET)  
To assess participants' near-miss recognition performance, eye-tracking matrices were extracted for each of the four pre-defined fatal AOIs. Visual sensing data were processed using Tobii Pro Lab version 1.142.1, which offers a comprehensive platform for analyzing eye gaze data with visual and analytical tools. Fixation matrices for each participant were obtained from near-miss recognition activity recordings and mapped onto predefined AOIs using an I-VT fixation filter, with a gaze data classification threshold of 30\({}^{\circ}\)/s. This process was applied to all participants across the three experimental conditions. For analysis, eye-tracking matrices, heat maps, and gaze plots were used. The selection of eye-tracking matrices was based on the identification process and cognitive demands being investigated. This study chose two fixation-related matrices to evaluate recognition performance: fixation duration (FD) to measure visual attention and time to first fixation (TFF) to assess adaptation. Figure 9 provides an overview of the experiment's data collection.  
Figure 9: Experiment data collection flow—near-miss stimuli AOIs, participant performing baseline experiment, and sensing data collected (Left to Right).
## 3 Results and Discussion
### Trace Elements in Sediments  
The mean concentrations (\(\pm\)SD) of trace elements and the organic matter content in the sediment of sampling sites 1-7 over time are summarized in Table 3. The content of Fe and Mn in sediment was within the range of percentages described in the literature for AMD and mining-impacted areas [46]. Therefore, their content was not included in the results and discussion of trace elements in sediments.  
Comparatively higher concentrations for most trace elements were measured at sites 1 and 4. The concentration order of trace elements in sediment for site 1 was Ti \(>\) Zn \(>\) Ni \(>\) Cr \(>\) As \(>\) Sr \(>\) V \(>\) Cu \(>\) Pb \(>\) U \(>\) Rb \(>\) Au \(>\) Hg \(>\) Cd \(>\) Ag, which is similar to site 4 but nonetheless showed some significant differences. Site 1 showed a significantly higher mean content for Zn, Ni, As and U, whereas significantly higher contents of Cr and Cu, were measured at site 4. The only element experiencing a higher content at another sampling site besides 1 and 4 was Ti at site 7.  
The mean OM content of sediments was significantly higher at site 1 than at any other sampling site. Additionally, a significantly higher content (compared to sites 2, 3, 5, 6, and 7) was measured at site 4.  
Hg and Cd concentrations in sediments of all sampling sites were not of concern and are not furtherly discussed.  
Elements that either appeared in concentrations of concern or displayed an interesting behavior are discussed in the following:
# XR Technology Deployment in Value Creation
## 5 Discussion  
### Interactions of the Proposed Taxonomy  
The five dimensions are formed from the existing basis of research reviews and implementation reports as the application purposes data acquisition, assistance, visualization, optimization, and collaboration. It can be seen that both VR and AR are included in each deployment purpose through the XR technologies. Figure 3 summarizes the dimensions and characteristics of the XR technology deployment purposes.  
While data acquisition in AR is more likely to be achieved through the input units, it is the user experience in VR that generates data about user behavior. Assistance is more represented by AR use cases, especially in industrial AR applications [53]. Since every deployment purpose depicts data in a certain way to the users, visualization means visual fidelity and a high level of detail. While AR primarily represents a non-real product in reality that offers visually added value, the interactability with the three-dimensional model in VR adds value to value creation. The optimization through XR can be user- or activity-oriented. Thus, the possibilities of XR technologies can optimize the user through knowledge transfer, i.e., training. On the other hand, XR technologies can improve the process side by virtualizing process steps and simplifying them through virtualization and simulation of future states. While data acquisition, assistance, and visualization focus on the performance of a value creation activity, the deployment purposes of optimization and collaboration focus on the organization and administration of supporting or main activities as an integral part of value creation.  
In principle, a taxonomy can be hierarchical [88]. The derived deployment purposes are not mutually exclusive, and some are linked to each other from a technical point of view. Each deployment purpose requires the capability of data acquisition to a certain extent. Visualization is also basically included in each purpose, as any XR technology setup requires a visualization unit. However, it is only in the foreground in the visualization itself through detailing. Overall, it can be seen that, from a technical point of view, the purposes of deployment build on each other in an interrelation of the dimensions and characteristics given. From the value creation perspective, however, it is necessary to examine which purpose is fulfilled by the technology deployment. From this economic perspective, there is no hierarchical relationship between the purposes.