esa-sceva/satcom-chunk-collection huggingface dataset:
# Design Patterns and Electric Vehicle Charging Software
## 3 Design Patterns and EV Charging Software
### Cloud Design Patterns
#### 3.2.4 Retry Pattern  
The retry pattern allows an application to address passing failures while connecting to a service or to a network resource. This is accomplished by performing a transparent retrial of the operation, presuming that the malfunction is merely transitory. This translates into improved application stability. Therefore, the retry pattern is a resilience category pattern.  
An application communicating with elements that run in the cloud need to be fault tolerant for network connectivity issues, temporary service unavailability and timeout that may arise when service is busy.  
The application should handle the transient fault elegantly and transparently, minimizing the effects on applications business task.  
This pattern should be used only when the application is expected to incur temporary failures, due to that fact it has interactions with remote services or resources. These faults are most likely brief and renewing the request could be successful.  
The illustration in Figure 12 on the retry pattern works is explained below:  
1. The application calls upon an operation on hosted service and the internal server error is returned (code 500).
2. The application then waits for a short while, before trying again. The failure message is returned once more (code 500).
3. Then, the waiting time increases, before trying once again. The HTTP response is now code 200 (OK) and the request is successful.  
Applicability:  
EV charging software could benefit from this pattern when it connects the front-end app or the mobile apps to the back end applications.  
Figure 12: Invoking an operation in a hosted service using the retry pattern [46].  
The retry procedure should be adjusted to meet the business requirements and the nature of the failure:  
* Non-critical operations are preferred to fail faster rather than repeating the request several times and affecting the input of the application.
* Aggressive retry policy should have large delays and small number of retries.
* If a request has several failures, maybe it is best to prevent further requests
* Some of the retries may be idempotent (retry logic should handle errors from incomplete requests)
* All retry codes should be fully tested against a wide range of failures conditions.
* All failures should be logged, so that underlying problems can be identified.
# Seismic Responses of a Cable-Stayed Bridge with Consideration of Uniform Temperature Load
## 5 Case Study and Discussion
### Changes of Uniform Temperature Load
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Responses**} & **Earthquake** & \multirow{2}{*}{\(\Delta T=0\) (\({}^{\circ}\)C)} & **Increase** & \multirow{2}{*}{\(\Delta T=\pm 20\) (\({}^{\circ}\)C)} & **Increase** & \multirow{2}{*}{\(\Delta T=\pm 35\) (\({}^{\circ}\)C)} & **Increase** \\  & **No.** & & & & & **(\%)** \\ \hline \multirow{2}{*}{\(F_{x}\) (comp.) in deck (kN)} & 1 & 58,888 & 59,351 & 0.79 & 60,877 & 3.38 \\  & 2 & 63,686 & 64,329 & 1.01 & 66,402 & 4.26 \\  & 3 & 62,965 & 69,969 & 11.12 & 73,354 & 16.50 \\ \hline \multirow{2}{*}{\(F_{x}\) (tensile) in deck (kN)} & 1 & 26,520 & 26,617 & 0.37 & 26,982 & 1.74 \\  & 2 & 42,662 & 42,969 & 0.72 & 43,353 & 1.62 \\  & 3 & 27,409 & 27,494 & 0.31 & 30,616 & 11.70 \\ \hline \multirow{2}{*}{\begin{tabular}{c} Pounding force \\ (kN) \\ \end{tabular} } & 1 & 2501 & 3666 & 46.58 & 6155 & 146.10 \\  & 2 & 11,892 & 17,951 & 50.95 & 15,846 & 33.25 \\  & 3 & 21,196 & 26,112 & 23.19 & 27,909 & 31.67 \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Bearing disp. at \\ abutment (m) \\ \end{tabular} } & 1 & 0.11 & 0.19 & 72.73 & 0.25 & 127.27 \\  & 2 & 0.34 & 0.47 & 38.24 & 0.42 & 23.53 \\  & 3 & 0.55 & 0.68 & 23.64 & 0.76 & 38.18 \\ \hline \multirow{2}{*}{\(M_{y}\) at pylon bottom (kN-m)} & 1 & 1,176,896 & 1,224,878 & 4.08 & 1,347,783 & 14.52 \\  & 2 & 1,929,557 & 2,000,932 & 3.70 & 2,045,541 & 6.01 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Bearing disp. at \\ abutment (kN) \\ \end{tabular} } & 3 & 1,531,194 & 1,732,721 & 13.16 & 1,845,110 & 20.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Seismic responses of the bridge with varying temperature. Figure 4: Time history of the bridge components with varying temperature; **(a,b)** displacement of the bearing at abutment with changing temperatures of \(\pm\)20 \({}^{\circ}\)C and \(\pm\)35 \({}^{\circ}\)C, respectively; **(c,d)** bounding force between the girder and abutment with changing temperatures of \(\pm\)20 \({}^{\circ}\)C and \(\pm\)35 \({}^{\circ}\)C respectively; **(e,f)** Bending moment at the bottom of the pylon with changing temperatures of \(\pm\)20 \({}^{\circ}\)C and \(\pm\)35 \({}^{\circ}\)C, respectively. It also can be seen from the table that the bending moment at the pylon bottom will increase with temperature load. This is mainly because of the initial shear force of the bearings caused by temperature being transformed to the pylons before seismic excitation. Cable forces are listed in Table 4, it is found that cable forces slightly increased with temperature changes. [PERSON] and [PERSON] [PERSON] and [PERSON] (1984) studied the static and dynamic responses of stay cables with thermal effects, and found that the thermal effects influence the frequencies of stay cables. The results of cable forces here may be attributed to the OECS model's inability to take the local vibration of stay cables into consideration, and the deck and pylon are connected by moveable bearings that can relieve the thermal effects.
## 4 Mathematical Calculation of PSD
### Manchester PSD  
Manchester digital format uses double bandwidth in signal modulation, but there is no DC level in Manchester signal propagation. Equation (5) shows that the PSD formulation of the Manchester coding scheme has good results due to its zero power at zero frequency [27].  
\[P(f)=\frac{A^{2}T_{b}}{4}\Bigg{(}\frac{sinrfT_{\frac{b}{2}}}{\pi fT_{\frac{b}{2 }}}\Bigg{)}^{2}sin^{2}(\pi fT_{\frac{b}{2}}) \tag{5}\]
# Non Data-Aided SNR Estimation for UAV OFDM Systems
## 3 The Proposed NDA SNR Estimation Method
### Estimation Performance Comparison of Different SNR Estimation Methods  
Figure 8 describes the comparison between the improved method and the method in Reference [2] under three different multipath channels. The two methods are both effective for exponential fading channel and SUI-1 channel. For TU6 channel, the estimation bias of the method in Reference [2] becomes larger when SNR lies in 5 dB and 18 dB and this characteristic is presented in SUI-1 channel. The main reason is that Reference [2] used the minimum description length (MDL) to estimate the maximum delay in multipath to obtain the non-inter-symbol interference data interval. However, once the delay of multipath channel becomes longer, this method will obtain a large estimation bias under low SNR and thus part of the used data will have the ISI when estimating the SNR. When the SNR is low (less than 2 dB), the bias caused by the inter-symbol interference is not so obvious compared to the noise power, therefore, the SNR estimation bias is not so large. However, when having the middle SNR (between 4 dB and 18 dB), compared to the noise power, the bias caused by ISI becomes larger, thus the estimation bias becomes enlarged. When the SNR is greater than 18 dB, the MDL achieves a higher accuracy when estimating the maximum delay of multipath channel. There  
Figure 6: Estimation performance with different frequency offsets.  
Figure 7: Comparison of compensating frequency offset with no frequency offset.  
is no ISI in the data used to estimate the SNR, as a result, the estimation bias declines again. This problem does not exist in the proposed method due to it does not need to estimate the maximum delay of multipath channel.  
Besides, the main computation complexity lies in the wavelet denoising, the computation complexity of the wavelet denoising is \(O\left(N\right)\), where \(N\) is the data length of denoising. In this paper, we have \(N=320\) and the computation complexity of [2] is \(N_{c}\left(N_{c}-1\right)M/2+\left(N_{c}-1\right)^{3}\!\Big{/}6\), where \(N_{c}=64\). It can be seen clearly from this comparison, the computation complexity of this paper is lower than that of Reference [2]. In conclusion, when the delay of multipath channel is large, the method in Reference [2] are not available enough, while the proposed method is still effective and well-performed with lower complexity. As a result, the proposed method is more suitable for UAV OFDM system than method in Reference [2].
## 2 Methods and Data
### Lattice-Boltzmann Simulations  
Material characteristics have been previously obtained from fiber-based GDLs using the technique of LB simulations [26; 28; 29]. The micro-structure of a porous GDL is specified by a stack of BW images. Using a stochastic geometry model, several representations of a paper-type GDL were constructed, being analyzed by [PERSON] et al. [26; 28]. This work employs this method to obtain the permeability and tortuosity of Toray GDL given by a stack of images, both TP and IP. Because of the layer-wise construction of the fibers in the plane, the IP characteristics only needed to be determined for one direction. Another kind of GDL, represented by a stochastic model and also as a reconstruction of real data, was investigated by [PERSON] et al. [29].  
Figure 1: CNN architecture of the proposed ML model.  
Transport simulations of gas flow through the porous micro-structure were calculated in a domain defined by a series of BW images. A small free space buffer was added upstream and downstream of the porous media to allow for physically meaningful boundary conditions. As was described in more detail in [PERSON] et al. [26], a velocity boundary was applied at the input and a constant pressure condition at the output. Wall boundary conditions were specified at the other four sides of the simulation domain. As before, the permeability was calculated from the resulting flow field using Darcy's law.  
\[\kappa=-\frac{q\cdot\mu}{\
abla P} \tag{1}\]  
In Equation (1), the permeability \(\kappa\) is a function of the flux \(q\), the dynamic viscosity \(\mu\), and the pressure drop \(\
abla P\). The tortuosity \(\tau\) was calculated by Equation (2).  
\[\tau=\frac{\langle|v|\rangle}{\langle v_{x}\rangle} \tag{2}\]  
This is the simplest variant of a set of equations presented by [PERSON] et al. [30] for the estimation of the flow-based tortuosity in porous structures. The tortuosity was calculated as the ratio between the average values of the absolute velocity and of the x component. The transport simulations were run on JURECA [31].
# A Stock Market Decision-Making Framework Based on CMR-DQN
## 3 Models and Methods
### GRU-LSTM-Attention  
In Rainbow DQN, we use the GRU-LSTM-Attention model instead of the original DNN as the \(Q\)-value estimator. This model combines GRU, LSTM, and attention mechanisms to better process sequence data. It can effectively capture long-term dependencies in sequence data and improve the model's ability to understand sequence data. Within the model architecture, the outputs of the GRU layer serve as inputs to the LSTM layer, and the outputs and final state of the LSTM layer are passed to the attention mechanism. Specifically, the output \(h_{t}\) from the GRU layer, representing the hidden state at the current time step, is used as input to the LSTM layer. Conversely, the outputs and final state \(h_{n}\) from the LSTM layer are utilized as inputs to the attention mechanism, where \(h_{n}\) denotes the hidden state at the last time step.  
For GRU, the inputs \(x_{t}\) and the previous hidden state \(h_{t-1}\) undergo a series of operations to produce the current hidden state \(h_{t}\), as shown in Equation (11):  
\[\begin{split} r_{t}&=\sigma(W_{r}\cdot[h_{t-1},x_{t }]+b_{r})\\ z_{t}&=\sigma(W_{z}\cdot[h_{t-1},x_{t}]+b_{z})\\ \tilde{h}_{t}&=\tanh(W_{h}\cdot[r_{t}\cdot h_{t-1}, x_{t}]+b_{h})\\ h_{t}&=(1-z_{t})\cdot h_{t-1}+z_{t}\cdot\tilde{h} _{t}.\end{split} \tag{11}\]  
For LSTM, the inputs \(x_{t}\) and the previous hidden state \(h_{t-1}\) are processed to produce the current hidden state \(h_{t}\) and cell state \(C_{t}\), as shown in Equation (12):  
\[\begin{split} f_{t}&=\sigma(W_{f}\cdot[h_{t-1},x_{t }]+b_{f})\\ i_{t}&=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i})\\ \tilde{C}_{t}&=\tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{C} )\\ C_{t}&=f_{t}\cdot C_{t-1}+i_{t}\cdot\tilde{C}_{t} \\ o_{t}&=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o})\\ h_{t}&=o_{t}\cdot\tanh(C_{t}).\end{split} \tag{12}\]  
The LSTM outputs and final state \(h_{n}\) are passed to the attention mechanism. The attention mechanism calculates attention scores \(e_{ti}\) between each LSTM output \(h_{i}\) and \(h_{n}\). These scores are normalized to obtain attention weights \(a_{ti}\), representing the importance of each LSTM output. Finally, the context vector \(c_{t}\) at time step \(t\) is computed as a weighted sum of the LSTM outputs, using the attention weights \(a_{ti}\), as show in Equation (13):  
\[\begin{split} e_{ti}&=\text{score}(h_{i},h_{n})\\ \alpha_{ti}&=\frac{\exp(e_{ti})}{\sum_{j=1}^{T}\exp (e_{tij})}\\ c_{t}&=\sum_{i=1}^{T}\alpha_{ti}\cdot h_{i}.\end{split} \tag{13}\]  
By embedding the GRU-LSTM-Attention model into Rainbow DQN, we leverage its ability to capture long-term dependencies and dynamically attend to important parts of the sequence, thereby enhancing the performance and effectiveness of Rainbow DQN.
## 3 Data Preparation and Description
### Data Description  
As shown in Figure 5, when visualizing all data in the form of histograms and violin plots, it is evident that the hydraulic fracturing segments of the reservoir are predominantly concentrated from 1600 to 1800 m and from 2000 to 2300 m. Acoustic slowness (DT)  
Figure 4: Schematic diagram of the sample collection and preparation process. The red box represents the gas production dissected at each logging depth.  
predominantly falls within the range of 65 to 70 ft/us. The density distribution (ZDEN) is centered around 2.5 g/cm3. The GR values are primarily below 100, and the CNCF distribution is mainly between 5 and 15%, with a substantial proportion of the data at 0. For M2 RX, the values mostly lie below 100 \(\Omega\)/m, with the vast majority ranging from 0 to 200 \(\Omega\)/m. Therefore, the petrophysical properties of this block strongly indicate that it is a hydrocarbon-bearing sandstone reservoir. The porosity generally follows a normal distribution centered around 8%. Notably, about 350 data points display zero porosity, indicating that certain reservoir fracturing segments are suboptimal. These \"suboptimal\" data points were incorporated into our test set, since our goal is to predict gas and oil production capabilities in other regions. To make the database as comprehensive as possible, it is essential to include these points. The water saturation (SW) was predominantly around 50%, though nearly 350 formations registered as water-bearing layers with a saturation of 100%. After production segmentation (PROD), the overall changes in output lie between 0 and 1000 m3/d. We utilized two methods for displaying data, namely, histograms and violin plots, in order to assess the characteristics of data distribution and facilitate the selection of standardization methods. The histogram enables us to examine the distribution shape of each variable and determine whether it follows a normal distribution; for data conforming to normal distribution, StandardScaler becomes an optimal standardization tool, derived from the standard normal distribution of the data. Violin plots offer a visual representation of data distribution and outliers. In cases where variables contain outliers, RobustScaler is particularly suitable, as it can effectively mitigate the impact of outliers.  
Figure 5: _Cont._
# Analysis of a Novel Automatic Control Approach for the Free Forging Hammer
## 6 Conclusions  
A fuzzy-PID-based nonlinear servo control system has been proposed in this paper for the free forging hammer. The proposed method can address the harsh production environment of the free forging process, backward control methods, low processing accuracy, and inability to adapt to modern production. Changing the traditional control system of free forging hammers has achieved a reduction in personnel and increased the efficiency, which has brought new vitality to the forging hammer and its industry. The effectiveness of the presented method has been verified through serval sets of comparative experiments. Based on the theoretical analysis, simulations, and experiments presented in this paper, the following conclusions can be drawn.  
(1) Automatic control scheme  
A closed-loop control system composed of PLC, an electrohydraulic servo valve, a servo cylinder, and feedback sensors was used to drive the follow-up control valve that controls the motion of the forging hammerhead. Since the reference instruction and the position feedback were employed, the striking energy of each stroke is controlled by the designed controller according to the forging size of the selected pass. After the strike is completed, the hammerhead automatically returns to its original position, and the forging process is completed automatically.  
(2) High machining accuracy  
Based on fuzzy logic rules and PID control methods, high precision control of the striking energy is realized, and the maximum machining error was reduced to 22 mm, which was at least 50 mm less than the traditional free forging hammer.  
(3) Configuration and structure optimization  
Accurate measurement of the real-time position of the hammerhead and the servo cylinder is a key factor for ensuring successful automatic control. As detecting sensors cannot be installed successfully and the measurement data contain a lot of noise due to the vibration and shock generated during the striking process, this paper proposes a non-contact measurement system to address this problem. In addition, optimization of the hammer structure has been achieved for this detecting system and the servo driving system.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/24/9127/s1](http://www.mdpi.com/2076-3417/10/24/9127/s1)]([http://www.mdpi.com/2076-3417/10/24/9127/s1](http://www.mdpi.com/2076-3417/10/24/9127/s1)), Video SI: Automatic Free forging hammers, Video S2: Traditional Free Forging Hammers.  
The authors declare that they do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
# Evaluation and Characterization of Alginate Extracted from Brown Seaweed Collected in the Red Sea
## 2 Materials and Methods
### Sodium Alginate FTIR Spectroscopy Analysis  
The FTIR spectra of sodium alginate isolated from five brown macroalgae species are given in Figure 4 and Table 3. Broad bands at 3217 cm\({}^{-1}\), 3283 cm\({}^{-1}\), 3235 cm\({}^{-1}\), 3243 cm\({}^{-1}\), and 3261 cm\({}^{-1}\) were assigned to hydrogen bonded (OH) for _Padina bbergeseni_, _Turbinaria triquetra_, _Homophysa cuneiforms_, _Dictyota ciliolate_, and _Sargassum aquifolium_, respectively, as shown in Table 3. The occurrence of two strong peaks around 1600 and 1400 cm\({}^{-1}\) were assigned to asymmetric and symmetric stretching vibrations of carboxyl groups (COOH) of alginate, respectively. The band around 1400 cm\({}^{-1}\) represents the methylene C-H stretch.  
The anomeric region of carbohydrate was between 947 and 811 cm\({}^{-1}\). The alginate FTIR spectra also showed the typical alginic acid peaks, which indicate the ratio of manuronic and galvanonic acid (ratio m; g, 870/900) and demonstrated an M/G ratio above 1.0, revealing a high content in the manuronic acid units [(23; 42; 51)].  
Moreover, the FTIR analysis demonstrated the absence of a peak in 1230-1280 cm\({}^{-1}\) related with sulphated polysaccharides (such as fucoidan or laminarin), demonstrating a highly purified sodium alginate.
## 3 Results and Discussion
### Electrochemical Performance Measurement of LTO in LTO/LFP Battery
It proves that the crystallinity of the LTO material can improve the rate ability performance compared with the previous studies [17; 18]. Figure 2: The 2000\(\times\) magnification SEM images of LTO with NaCl addition of: (a) 0 wt %, (b) 2 wt %, (c) 4 wt %, (d) 6 wt %. However, both samples have a good reversibility while returning to the first rate at 0.3 C (99.9% for LTO-4 wt % NaCl and 99.7% for LTO-0 wt % NaCl). Then, in order to study the performance stability of the LTO material, a cycle performance test at 10 C was conducted. Figure 5a shows the cycling test of LTO-0 wt % NaCl and LTO-4 wt % NaCl at the 10 C-rate. In the 1 st cycle, the specific capacities of LTO-0 wt % NaCl and LTO-4 wt % NaCl were 86.3 and 118.6 mAh/g, respectively. After the 1065 th cycle, the specific capacities of LTO-0 wt % NaCl and LTO-4 wt % NaCl decreased to 74.2 and 114.5 mAh/g, respectively. The cycling test shows that the drop capacity percentages of LTO-0 wt % NaCl and LTO-4 wt % NaCl after 1065 cycles are 13.9% and 3.4%, respectively. It exhibits that the LTO-4 wt % NaCl provides superior cyclability, which has only a 0.003% drop capacity per cycle. The result confirm that highly crystalline LTO shows not only good performance in rate ability but also cycling stability as reported by [PERSON] et al. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**LTO**} & \multicolumn{8}{c}{**Capacity Retention (\%)**} \\ \cline{2-7}  & **0.3 C** & **1 C** & **3 C** & **5 C** & **7 C** & **10 C** & **0.3 C** \\ \hline LTO-0 wt \% NaCl & 100 & 97.1 & 90.3 & 80.3 & 75.0 & 69.2 & 99.7 \\ LTO-4 wt \% NaCl & 100 & 97.4 & 91.8 & 86.6 & 84.9 & 81.4 & 99.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Capacity retention of LTO/LiFePO\({}_{4}\) batteries at various C-rates. Figure 4: Rate ability of LTO-0 wt % NaCl and LTO-4 wt % NaCl. Figure 3: Specific initial discharge capacities of LTO with various NaCl additions at 0.3 C-rate. and [PERSON] et al. [17; 18]. On comparing with previous studies on LTO synthesis, it was found that employing a simple modification of NaCl addition during the synthesis resulted in high capacity, good rate ability, and good cyclability. Different from other studies listed in Table 3, the electrochemical performance of LTO samples was tested in an LFP/LTO 18650 cylindrical cell that is often found in the market; thus, the result provides better proof for large-scale application. Figure 5: (**a**) Cycle perfomance of LTO material at 10 C-rate; (**b**) Further cycle analysis of LTO-4 wt % NaCl material at 10 C-rate.