esa-sceva/satcom-chunk-collection huggingface dataset:
# EnCaps: Clothing Image Classification Based on Enhanced Capsule Network
## 3 Experiments and Results  
### Dataset  
The dataset used for experiments is one part of the DeepFashion dataset [28], and it consists of 5000 images in 10 categories: lbouse, cardigan, dress, hoodie, jeans, romper, short, skirt, tank, and tee, and each category contains 500 images of the corresponding clothing. The resolution of image in the dataset is \(224\times 224\). 4500 images of the whole dataset is used for training and another 500 images is used for testing. In addition, in order to enhance the generalization ability and robustness of the model, data enhancement operations including folding, random rotation, and random cropping are performed on the training samples. Figure 6 shows operations corresponding to data augmentation, where (b) represents flipping the original image from top to bottom or left to right, (c) represents rotating the original image at arbitrary angles, the excess area is clipped, and the missing area is filled with white pixels, and (d) represents cropping the original image randomly, and the missing area is filled with white pixels.  
\begin{table}
\begin{tabular}{c c c} \hline
**Layer** & **EnCaps-Stem** & **Shape** \\ \hline Input & / & N\(\times\)3\(\times\)224\(\times\)224 \\ Conv\_1+ReLU+BN & Conv-32(k:3,3s:2,2;p:1,1) & N\(\times\)32\(\times\)111\(\times\)111 \\ Conv\_2+ReLU+BN & Conv-32(k:3,3s:1,1;p:1,1) & N\(\times\)32\(\times\)109\(\times\)109 \\ Conv\_3+ReLU+BN & Conv-64(k:3,3s:1,1;p:1,1) & N\(\times\)64\(\times\)107\(\times\)107 \\ Conv-96(k:3,3s:2,2;p:1,1) & Max(k:3,3;s:2,2;p:1,1) & \(\backslash\) \\  & Concatenate & N\(\times\)160\(\times\)53\(\times\)53 \\ Conv-64(k:1,3s:1,1;p:0,0) & & \(\backslash\) \\  & Conv-64(k:1,3s:1,1;p:0,0) & & \\  & Conv-64(k:1,3s:1,1;p:0,0) & Conv-64(k:1,1;s:1,1;p:0,0) & \\  & Conv-64(k:1,3s:1,1;p:0,0) & Conv-96(k:3,1;s:1,1;p:1,1) & Max(k:3,3;s:1,1;p:1,1) \\ Conv-64(k:3,1s:1,1;p:0,0) & Conv-96(k:1,3;s:1,1;p:1,1) & \\  & Conv-96(k:1,3s:1,1;p:1,1) & \\ Conv-96(k:3,1s:1,1;p:1,1) & & \\ Conv\_4+ReLU+BN & & Concatenate & N\(\times\)352\(\times\)51\(\times\)51 \\ Conv-96(k:3,3;s:2,2;p:1,1) & & N\(\times\)352\(\times\)51\(\times\)51 \\ output & & Concatenate & Max(k:3,3;s:2,2;p:1,1) \\  & & N\(\times\)384\(\times\)25\(\times\)25 \\ \hline \end{tabular}
\end{table}
Table 2: The network structure of enhanced feature extraction model. ‘K’ represents ‘kernel’, ‘s’ represents ‘stride’, and ‘p’ represents ‘padding’.  
Figure 5: Overview of the Inception mechanism, which belongs to the parameter optimization. **C** denotes 2D convolution, MP denotes Maxpool, BN denotes batch normalization, and LK ReLU denotes Leaky ReLU.
# Online Track Vertex Reconstruction Method Based on an Artificial Neural Network for MPGD
## 5 Conclusions  
In this paper, the MPGD track vertex reconstruction method was investigated and a scheme for online track vertex reconstruction was proposed. The centroid method, a conventional reconstruction algorithm, was first introduced and its reconstruction results were presented. The results show that the centroid method has a larger reconstruction error when the particles are not perpendicularly incident. In this situation, we propose the use of ANN for the track vertex reconstruction. Two networks were trained using a set of simulated generated data to complete the dimensionality reduction and reconstruction of MPGD track data. The trained neural networks have good convergence and strong generalization ability. The reconstruction results of the centroid method and ANN algorithm were compared, and the results show that the neural network model reconstructs the particle incidence position more accurately than the center of gravity method, which results in a better position resolution of 0.35 mm for the detector.  
Most traditional methods of track vertex reconstruction are serial algorithms on a computer, which result in a reduction in the speed of data analysis. In this paper, we proposed that the ANN reconstruction algorithm be implemented on FPGA. An FPGA design based on the principle of the ANN algorithm was proposed to improve the data analysis efficiency by using the high operating speed of FPGA. The computer receives only valid data from the analysis of the raw trail data, thus reducing the amount of data storage.  
Conceptualization, H.L., Y.Z.; methodology, H.L.; writing--original draft preparation, H.L.; writing--review and editing, Y.Z.; visualization, H.L.; supervision, Y.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
## 2 Materials and Methods
### Data Collection  
The following data regarding freight transportation orders were collected for the study from the historical activity of the locomotives: activity start and end dates, start railway station, end railway station, registration number, the assigned driver for that transportation order (the label), location (latitude and longitude) of all the drivers before each transportation order start date, number of working hours of all drivers in the week prior the transportation order start date, and whether at the moment of activity start date the engineers were on medical or annual leave.  
The labeling process consisted of several steps. Firstly, all the necessary information about the freight transportation orders (including departure time, departure place, destination) and driver availability (work hours, days off, location with distance to the transportation orders departure place) was collected. Secondly, scheduling started with a blank timetable that represents the available time slots for transportation orders for the next week. The adding of the assignments followed a systematized approach by exhaustive search in the space of all possible assignments that respect legal regulations. The exhaustive search was needed to ensure the optimization of several parameters for the current week: minimization of the number of assigned drivers, maximization of the time spent driving the locomotives (less time spent by the drivers commuting to the place of departure), and fair distribution of work time to all drivers involved. The exact technical details of the manual implementation of driver assignment are confidential data belonging to the railway company and cannot be disclosed in full. Consistency in the labeling process was warranted by weekly quality checks made by appointed control officers and performance evaluators that had to ensure the dispatchers were correctly following the company's protocol for driver assignments. Although this optimization strategy reduces driving personnel expenses, it is also very time expensive and overloads dispatchers. This prompted the need for an automatic solution that can learn from all the labels manually assigned thus far.
# A Durability Prediction Method for Historical Square Rebar Reinforced Concrete Buildings
## 2 The Durability Prediction Method and Research Method
### Research Method  
The research method mainly consists of two parts. The first part is the component importance analysis method to determine the critical components with the calculations based on the finite element model. The second part is to provide an adaptive calculation method for the durability life of the square rebar RC component. However, it should be noted that the durability of an RC component or building scale encompasses so many factors that extensive investigation is unattainable. As mentioned above, there is a China standard for the durability assessment for the concrete structure, which is based on many long-term studies and a massive amount of data. Therefore, considering the contained long-term studies and the massive data, the China standard GB/T 51,355 for the durability assessment for the concrete structure can be taken as a basis for predicting the square rebar RC component durability especially for those equations which are not related to the rebar type. Regarding durability analysis, the key differences between current and historical RC components are concrete carbonization resistant ability and steel rebar type [22; 26]. The ability of carbonization resistance of concrete is related to the overall quality of the concrete and the working condition, which can determine the time before rebar corrosion. Different shapes of rebar sections can present different mechanical models for the rebar corrosion-induced cover cracking, which can determine the time of corrosion-inducing concrete cracking. In general, the qualities and properties of historical concrete were relatively weak,  
Figure 1: The procedures of the durability prediction method for historical RC buildings.  
due to the low quality of cement, mix proportion, construction technology, and curing conditions [22]. The square rebars were widely used in early history for decades, while the round rebars are used for every RC building around the world at present. Thus, these two different aspects are the main research objects in this study. The current limitations of the study should also be stated. The main limitation is the amount of the experimental square rebars. The majority of the historical RC buildings with square rebars are protected buildings in China, and they are forbidden to be demolished and replaced by the current round rebars in principle. Thus, it is very difficult to collect the experimental square rebars. Our team group have taken ten years to collect dozens of historical square rebars from the abandoned historical concrete components.  
To introduce the detailed application of the proposed method, two typical historical RC buildings in China were analyzed, and the two application cases can also help to evaluate the reliability and the applicability of the proposed durability prediction method.
## 4 Discussion  
In this study area, landslides mainly occurred along the Yangtze River, with an elevation from 145 to 300 m. When the altitude was higher than 750 m, there were no landslides. The distance to rivers (\(<\)300 m) and lithology (T\({}_{2}\)b\({}^{3}\), T\({}_{2}\)b\({}^{4}\)) had a positive effect on landslides in this area, and their average merit values were 0.061 and 0.029, respectively (Figure 3). A total of 62% of the landslides were within 300 m from the Yangtze River, and nearly 60% of the landslides were with the stratigraphic lithology of T\({}_{2}\)b\({}^{3}\) and T\({}_{2}\)b\({}^{4}\), which were regarded as the main stratum of landslide in the TGRA [37].  
The landslide development laws vary in different landslide-prone areas, hence the susceptibility models often perform in varied ways in different regions. In this study, we wanted to find an effective model in TGRA, and thus three machine learning models (SVM, ANN, and CART) and one multivariate statistical model (LR) were utilized. The results showed that the SVM model performed the best (Table 8). At the same time, the SVM performance behavior for susceptibility modelling in other regions were collected. As shown in the literature (Table 9), the accuracy of SVM was always larger than 0.8. We could see that SVM performed acceptably in different regions, and thus it can be used as a recommended model in TGRA and other landslide-prone regions.  
In this study, 14 causal factors were preliminarily selected for susceptibility modelling. On the basis of the analysis of the IGR model, the factors could be grouped into the noise factors and the crucial factors. When the noise factors (TWI, curvature, plan curvature, and profile curvature) were removed, the accuracy of the model was gradually improved, but when the crucial factor was eliminated, the accuracy of the model was greatly reduced (Table 5). In this study area, distance to rivers was the most important factor, and the impoundment of the TGRA impacted the landslide development in three aspects: (1) the long-term immersion of reservoir water gradually reducing the strength of rock (soil) at the saturated zone (mostly near the Yangtze river), reducing the resistance force of landslide; (2) the strong dynamic action of water enhancing the lateral erosion on the bank slope, changing the slope shape, and thus reducing the slope stability; (3) the periodic fluctuation of the reservoir water making the self-weight, static, and dynamic water pressure of the landslide change, which could increase the resistance force or reduce the sliding force of the landslide and even cause overall instability and damage [41; 42; 43; 44]. Hence, in order to reduce the losses caused by landslides in TGRA, we should pay more attention to the early warning of reservoir bank landslides.
## 3 Results
### Structural and Electronic Properties
#### 3.1.1 Cr Doping  
In the case of Cr\({}_{i}\), the chromium atom resides at 2.25 A from the nearest titanium atom and 2.25 A from the nearest oxygen atom (Figure 1a). Furthermore, we have also examined the Ti simple substitution with Cr and present the relaxed structure in Figure 1b. In this case, the Cr atom resides 1.91 A from the nearest oxygen atom. For the substitutional case, the lattice parameters are reduced. Our results agree with other available theoretical and experimental data [48; 49], which predict a decrease in the cell volume due to the Cr incorporation in a substitutional position.  
Figure 1: (**a**) Chromium (Cr) as an interstitial dopant, (**b**) Cr as a substitutional dopant, (**c**) molybdenum (Mo) as an interstitial dopant, (**d**) Mo as a substitutional dopant, (**e**) tungsten (\(W\)) as an interstitial dopant, (**f**) W as a substitutional dopant.  
Focusing on the DOS graph, the bandgap is estimated at 3.04 eV for the Cr\({}_{\text{i}}\) and 3.09 eV for the Cr\({}_{\text{Ti}}\) cases (Figure 2a,b). The bandgap of perfect anatase is calculated at 3.12 eV (shown in Figure 2g as a reference), which agrees with other theoretical studies and already published reports that show experimental values for the bandgap at 3.2 eV [27; 33; 34; 50].  
As shown in Figure 2a, the Cr interstitial gives rise to three available energy states inside the bandgap, near the valence band edge, in the middle of the bandgap, and near 2.5 eV. However, when a Ti atom is replaced with Cr, it is observed that the gap states are shifted towards the conduction band (Figure 2b). To fully explain the hybridizations that create these gap states, we used the PDOS for the Cr\({}_{\text{Ti}}\):TiO\({}_{2}\) and Cr\({}_{\text{Ti}}\):TiO\({}_{2}\). Figure 3a shows that O-2p and Ti-3d contribute mainly to the valence and the conduction band, respectively. Furthermore, the interstitial Cr doping mainly contributes to gap states with a minor contribution also as the conduction band.  
In Figure 3a, the gap states are shown in more detail. The bandgap states are formed at 0.32 eV, which is attributed to the hybridization of O-2p, Ti-3d, and Cr-3d orbitals, with O-2p contributing more to this state. The gap state at 1.25 eV has a contribution of T-3d, O-2p, and Cr-4s orbitals. The energy states at 2.4 eV are created from the hybridization of Cr-3d with Ti-3d orbitals. The results are in good agreement with other theoretical works [51]. In Figure 3b, the Cr\({}_{\text{Ti}}\):TiO\({}_{2}\) case is examined. The substitutional doping of Cr creates three gap states inside the bandgap and one state at the conduction band edge and also reduces the bandgap to 3.08 eV.  
Regarding the origin of gap states (Figure 3b), our calculations predict that they are mainly created due to the hybridization of Cr-3d with Ti-3d and O-2p orbitals. Similar theoretical and experimental works predict a bandgap close to the present value for the same doping percentage [48; 52].
# Analysis of Injected Electron Beam Propagation in a Planar Crossed-Field Gap
## 5 Discussion  
We have developed a 3-D simulation model of a basic crossed-field device for comparison with 1-D space charge limited theories for a PCF geometry [5], and examined the effect of magnetic field, current density, and magnetic field misalignment [11]. The results show that, for a fixed gap distance, the crossed-field beam has a stable band of propagation for a fixed magnetic field. The results from the 3-D simulations for the PCF agree reasonably  
Figure 12: CST simulation for a 5\({}^{\circ}\) tilt with \(B=0.05\) T, \(D=20\) mm, and an input beam current of (**a**) 50 mA, (**b**) 70 mA and (**c**) 90 mA (left: center cross section, right: top view). The current density at circuit middle is (**a**) \(\approx\)26 mA/cm\({}^{2}\), (**b**) \(\approx\)36 mA/cm\({}^{2}\) and (**c**) \(\approx\)43 mA/cm\({}^{2}\). The beam is nominally stable. The color intensity of the trajectories indicates the kinetic energy of the electrons.  
well with the 1-D PCF theory for maximum stable current density. Tilting the magnetic field by 1\({}^{\circ}\) destabilizes the beam for a current density much lower than the critical value for the PCF [5] and in agreement with the simple 1-D model accounting for magnetic field misalignment [11]. We also observed the presence of instability and stability bands for various magnetic field misalignments and current densities, as predicted by the previous 1-D study [11].
# The Effect of Brewing Time on the Antioxidant Activity of Tea Infusions
## 4 Discussion
As demonstrated, next to tea variety, growing environment and manufacturing conditions, tea brewing time and its form (loose or bags) are important determinants of the antioxidant activity of polyphenols in black tea infusions [43]. The above study showed that about ten minutes of brewing leaf tea and two minutes of brewing tea bags were sufficient for extracting approximately half of water-soluble phenolic compounds.
## 5 Common Systems
### Timing and Synchronization
#### 5.1.6 Continuous Wave Fiber-Optic Links for RF Reference Distribution
Based on the de-tuned S-band (2.9988 GHz), the de-tuned X-band (11.9952 GHz) and the C-band (5.712 GHz) frequency, a reasonably high base frequency could still be found (142.8 MHz), which needs to be a common subharmonic of those frequencies and was chosen as the repetition rate of the OMO. All frequencies throughout the SwissFEL accelerator, which need to be synchronous in some way, are derived from this source of the timing reference distribution. Concerning the transmission of the C-band frequency, it was decided to use the Libera Sync 3 links to transmit half of the actual C-band frequency (2.856 GHz), for which the S-band links could be used with only minor modifications. At the output of those links, an ultra-stable RF frequency doubler with amplitude/phase stabilized power amplifier will be used to provide the necessary frequency and amplitude level. The frequency doubler was developed at PSI and a prototype has been successfully tested. For the X-band transmission, a similar philosophy is adapted: an S-band link together with an ultra-stable quadrupler unit built in-house is installed and in operation.
## 2 System Model and Problem Formulation
### Problem Formulation  
Our objective is to maximize the SE shown in (3) by jointly optimizing the transmit power, the resource reuse indicator, and the IRS reflection coefficients. Therefore, the optimization problem (OP) can be formulated as follows:  
\[\text{(OP)}:\max_{\mathbf{x},\mathbf{P},\mathbf{\Phi}}R(\mathbf{x},\mathbf{P},\mathbf{ \Phi})\] (4) subject to: \[\text{C1}:\ \gamma_{k}^{c}\geq\gamma_{\text{min}}^{c};\ \ \forall k\in\mathcal{K},\] \[\text{C2}:\ \gamma_{m}^{d}\geq\gamma_{\text{min}}^{d};\ \ \forall m\in\mathcal{M},\] \[\text{C3}:\ \sum\limits_{k=1}^{K}x_{k,m}\leq 1,\] \[\text{C4}:\ \sum\limits_{m=1}^{M}x_{k,m}\leq 1,\] \[\text{C5}:\ 0\leq P_{k}^{c}\leq P_{\text{max}}^{c};\ \ \forall k\in \mathcal{K},\] \[\text{C6}:\ 0\leq P_{m}^{d}\leq P_{\text{max}}^{d};\ \ \forall m\in \mathcal{M},\] \[\text{C7}:\ |\Phi_{n}|=1,0<\theta_{n}\leq 2\pi;\ \ \forall n\in\mathcal{N}.\]  
Here, constraints C1 and C2 denote the required minimum SINRs to ensure the QoS for the cellular communication links and the D2D communication links, respectively. Constraints C3 and C4 indicate that a D2D pair shares at most one CU's spectrum and the spectrum of a CU can be shared by at most one D2D pair, respectively. Constraints C5 and C6 represent the maximum transmission power constraints for the CUs and the D2D pairs, respectively, and constraint C7 denotes the practical reflecting coefficients of the IRS.  
Note that the formulated OP in (4) is a mixed integer non-linear optimization problem and thus is NP-hard [23]. The variables \(\mathbf{x},\mathbf{P},\mathbf{\Phi}\) in the objective function are all coupled,which makes their joint optimization computationally intractable and thus difficult to solve. In addition, in practical network scenarios, all the parameters associated with the network will change dynamically. Therefore, utilization of traditional optimization solutions to solve the formulated OP generally leads to an infeasible solution. Hence, an RL-based model-free architecture is used for learning the optimization policy to obtain the feasible \(\boldsymbol{x},\boldsymbol{P},\boldsymbol{\Phi}\).
## 2 Artificial Neural Network
### Dataset  
A deep and careful survey of the literature was conducted to establish a model based on the machine learning approach to predict the compressive strength of the concrete containing PET fibers. The dataset includes 85 samples with 12 distinguishable features. The collected dataset contains information about water content (W), binder (B), coarse aggregate (G), fine aggregate (S), PET fibers (P), tensile strength of PET fibers (F\({}_{\mathrm{f}}\)), PET fiber modulus of elasticity (E), PET fiber length (I), PET fiber width (d), unit weight of PET fiber (U), and PET shapes (F). Four different fiber shapes are considered based on the available experimental studies in the literature, namely string shape, irregular shape, deformed shape, and embossed shape (Figure 1). The mold of specimens was also considered, so the compressive strength was converted to a 150 mm cubic standard mold. The PFRC's 28-day compressive strength is considered as the network's output. The statistical indices for PFRC are summarized in Table 1. The distribution of the features are shown in Figure 2.  
Figure 1: Four different types of PET shape considered in the dataset.  
Figure 2: _Cont_.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Attribute** & **Unit** & **Min** & **Max** & **Average** & **Standard Deviation** \\ \hline Water & kg/m\({}^{3}\) & 79.7 & 225 & 183.3 & 38.5 \\ Binder & kg/m\({}^{3}\) & 290 & 558 & 408.3 & 63 \\ Coarse aggregate & kg/m\({}^{3}\) & 600 & 1223 & 882.6 & 175.8 \\ Fine aggregate & kg/m\({}^{3}\) & 677.5 & 1000 & 838.3 & 111.1 \\ PET fiber & kg/m\({}^{3}\) & 0.64 & 27 & 10.1 & 5.8 \\ Fiber tensile strength & MPa & 100 & 550 & 188.5 & 139.5 \\ Fiber modulus of elasticity & MPa & 190 & 12,000 & 3821 & 3720.5 \\ Fiber length & mm & 10 & 64 & 32.2 & 15.6 \\ Fiber width & mm & 0.2 & 12 & 1.9 & 2.1 \\ Fiber unit weight & kg/m\({}^{3}\) & 1100 & 1800 & 1331 & 215.7 \\ Fiber shape & - & - & - & - & - \\
28-day compressive strength & MPa & 20.9 & 84.7 & 45.7 & 16.3 \\ \hline \end{tabular}
\end{table}
Table 1: Statistical parameters for the concrete containing PET fiber dataset.