esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Payload and Subsystem Architectures and Design
### On Board Computer  
OBC uses a high-performance and robust design, 400 MHz, power-efficient ARM9 processor-based MCU. This compatible OBC with standard CubeSat components is available from ISIS [54]. The computer was developed for nanosatellite data handling and ADCS processing. In this OBC, the FreeRTOS operating system for simple and lightweight cooperative multitasking is used.  
The chosen OBC features the following performances as standard:  
* Volatile Memory: 64 MB SDRAM;
* Code Storage: 1 MB NOR Flash;
* Critical Data Storage: 512 kb FRAM;
* Mass Data Storage: 2 \(\times\) 2 GB high-reliability SD cards for fail-safe data storage (up to 32 GB on request) or 2\(\times\) any size standard SD cards;
* A total of 2\(\times\) redundant Real Time Clock (RTC);
* I2C, SPI, and UART interfaces to another OBC;
* On-board temperature sensor;
* External onboard watchdog and power controller.  
The schematic of this OBC configuration is shown in Figure 17.  
The controlling unit of some ADCS tasks can be embedded in this low-power OBC, where it runs estimator and control algorithms, logs TLM, and manages communication to the ADCS modules.
## 2 Related Work
In this paper, various classifier techniques are proposed that involve a combination of machine learning algorithms with a feature reduction algorithm to detect the redundant features and enhance the accuracy and quality of heart disease, m- health, and diabetes disease classification. Here, we present an evaluation for various disease classifications using seven algorithms. Then, we study the performance of NB, LM, R, DT, RF, GBT, and J48 classifiers. The main goal of this research is to find the best accuracy for the prediction of different diseases using major factors based on different classifier algorithms. The use of CFS algorithm with a combination of classification algorithms provides a better accuracy value in comparison with the results of the literature works. Table 1 shows the comparison between different research studies on medical data. It presents the implemented algorithms and accuracy results, as well as the advantages and disadvantages of each work. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Year** & **Dataset** & **Algorithm** & **Accuracy** & **Advantages/Disadvantages** \\ \hline [PERSON] et al. [19] & 2016 & Adult & NB, C4.5 & 98.7\% & The accuracy changed with the increase of data. When the data increased, the accuracy of the model decreased. NB is good with a small dataset. \\ \hline [PERSON] et al. [20] & 2017 & Diabetes Pima Indian & J48, MLP, Hoeffding Tree, JRip, Bayes Net, RF & 77.6\% & They used various algorithms but no single algorithm provided a sufficient accuracy value. They need to experiment with new classification algorithms which provide high accuracy. \\ \hline [PERSON] et al. [17] & 2018 & M-health & SVM, RF & 86\% & The RF algorithm is most efficient with a high amount of data. It results in a high accuracy value. \\ \hline [PERSON] et al. [18] & 2018 & Cleveland and Hungarian. & RF, NB, R, NN, SVM & 98.136\% & While RF provides very high accuracy, the regression algorithm provides the lowest accuracy value with a high volume of data. \\ \hline [PERSON] et al. [14] & 2020 & Indian Liver Patient Dataset (ILPD) & LR, SMO, RF, NB, J48, IBk & 77.4\% & The best accuracy result was from the LR with feature selection. We suggest using the CFS algorithm for feature selection to enhance the accuracy value. \\ \hline [PERSON] et al. [23] & 2020 & Chronic Kidney Disease & J48 & 97.66\% & They developed a system that can categorize the chronic condition of kidney diseases. The J48 algorithm is suitable for the small or medium volume of data. \\ \hline [PERSON] et al. [13] & 2020 & UCI Heart Disease & LR, PCA & 86\% & LR with PCA achieved the best accuracy. \\ \hline [PERSON] et al. [16] & 2021 & Kaggle Heart Disease & MLP, RF, DT, KNN & 100\% & Three classifications based on KNN, DT, and RF algorithms have the highest accuracy value. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between different research studies on medical data.
###### Abstract  
The enzyme \(\beta\)-galactosidase catalyzes the hydrolysis of lactose into glucose and galactose, although for its effective application it is necessary to establish techniques for purification, concentration, or polishing, such as membrane separation processes, in particular ultrafiltration. The present study aimed to investigate ultrafiltration and dialiltration applied as initial steps for concentration and salt removal, respectively, in the \(\beta\)-galactosidase processes. Additionally, the influence levels of the pH (6.5, 7.7, or 7.5), membrane molecular weight cut-off (30, 50, 60, or 100 kDa), operating pressure (1.5, 2.0, or 2.5 kgf/cm\({}^{2}\)), and ionic strength of the ultrafiltration using NaCl or KCl (0.01-0.1 M) were evaluated considering the enzyme recovery, purification, retention, and concentration factors in relation to the proteins, volume, activity, and protein flux and yield of the processes. The ultrafiltration of the crude enzyme extract at pH 7.5 and 1.5 kgf/cm\({}^{2}\) with a 50 kDa polyethersulfone membrane resulted in a volume concentration of the \(\beta\)-galactosidase event up to 7.1-fold greater, a purification factor 1.2-fold greater, and an enzyme recovery rate of 108.9% by eliminating metabolites during the purification process. In addition, the lowest flux variation range (16.0 to 13.1 L/m\({}^{2}\)-h) was observed under these same conditions, thereby representing a decrease of 18.0%. An increase in the operating pressure and the addition of salts results in reduced enzyme recovery (up to 38% of the process yield (734.1 to 453.7 U/h) and up to 40% of the enzyme recovery rate (108.9 to 60.6%) during the ultrafiltration using NaCl, respectively). The operation in the dialfiltration mode allowed salt removal after the purification of \(\beta\)-galactosidase (enzymatic recovery rates above 93.4%) via precipitation and ion-exchange chromatography elution and as part of an aqueous two-phase system using 6 dialfiltration cycles, thereby revealing its application potential.  
ultrafiltration; dialfiltration; permeate flux; _Kluyveromyces_; membrane 2023
# A Digitalization Algorithm Based on the Voltage Waveform of the Multifunction Vehicle Bus
## 2 The MVB Voltage Waveform Digitalization Algorithm
### MVB Voltage Waveform Digitalization Algorithm Extraction Algorithm
#### 2.2.3 Anomaly Detection  
As shown in Figure 3, data frames have a fixed format and length. The format of a data frame can be checked during the decoding process:  
Any frame that does not contain a master/slave start delimiter at the start of the data frame or does not contain an end delimiter at the end is detected as a data-frame format error.  
Before the CRC check, the length of the data frame is checked. Except for five specified lengths, data frames of other lengths are judged as data-frame format errors.  
As the data frame contains a CRC check sequence (cyclic redundancy check; Figure 3), the CRC verification algorithm can be used to check whether the decoding is correct and analyze the reliability of the data in fault detection. IEC61375-3-1 provides the CRC check code, which is represented in Equation (2).  
\[G(x)=x^{7}+x^{6}+x^{5}+x^{2}+1, \tag{2}\]  
The address and F code in the frame are the sequences to be checked. They are divided according to Equation (2) modulo 2 to obtain the remainder. After the remainder is filled up to 7 digits with \"0\", the parity check code of the sequence to be checked is added at the end, and, finally, the complement is taken as the final CRC sequence.  
When the CRC sequence is inconsistent with the original CRC check sequence of the data frame, the algorithm determines it as a CRC check error.  
The waveform feature parameters extracted in Section 2.2.2 have a specified range requirement, based on which it can be determined that there is a fault in the electrical parameters of the data frame.
## 3 Results and Discussion  
### Extraction from Z. officinal Rosc. rhizomes  
Extraction results of the main compounds from _Z. officinal_ rhizomes (Grand Cayman var.) grown on an open field and harvested in ripe state at 40 weeks in different parts of Costa Rica (_n_ = 12), were obtained through the process described in the respective Materials and Methods section. The efficiency of the extraction was evaluated through UPLC-DAD quantification in sample NR-1 of the main gingerols, namely 6-gingerol, 8-gingerol and 10-gingerol in order to obtain the Total Gingerols (TG) contents. The factorial design was performed using solvent, solvent:material ratio and stirring time as factors, considering previous reports [23] and the United States Pharmacopeia (USP) monograph on _Z. officinal_[20]. Factors levels included solvents ethanol and methanol, two different solvent volumes accounting for 25:1 and 50:1 solvent:substrate ratios, and 24 h extraction time with two different stirring periods, namely during the first 8 h or continuous during 24 h. The results are summarized in Table 1.  
Statistical analysis of the results did not show a significant effect (\(p<0.05\)) for any variable, 2-way or 3-way interactions. However, by excluding the stirring factor showing the least effect, the FD findings indicated a significant difference (\(p<0.05\)) for the solvent and the volume factors, with methanol and the 50:1 solvent:substrate ratio yielding better results. In addition, the analysis indicated the highest fitted mean associated with the combination of these two conditions and 8 h stirring time. These results indicating methanol as an adequate solvent align with previous reports [23] while the solvents:sample ratio of 50:1 and the 8 h stirring time during the 24 h total extraction time represent USP settings [20]. Therefore, considering the results, these conditions were chosen to perform the extraction of _Z. officinal_ Roscoe samples (_n_ = 17).
# Influence of Random Modulated Power Converter on G3 Power Line Communication
## 4 Experimental Test Setup  
A principle drawing of the test bench is shown in Figure 3. Two G3-PLC modems are used as transmitter and receiver, in the frequency band 35-91 kHz (see spectra in Figure 4), and are connected by a 42-m-long 230 V AC cable to transmit the communication signal between the two AC line terminals. An isolation transformer is used to separate the AC line from the grid. The AC line is coupled to the random modulated DC-DC converter through a coupling capacitor of 10 nF. In this work, a three-phase inverter is used, and one of its legs is used as a buck DC-DC converter (step-down 50 V DC source to 25 V with a duty cycle of 0.5). The converter has a maximum DC link voltage capacity of 450 V with a 2200 \(\upmu\)PC DC link capacitor. Six IXGH40N60C2D1 IGBTs are deployed in this specific converter circuit. The output of the DC-DC converter is connected to a variable resistor load fixed at 8 \(\Omega\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Region** & **Organization** & **Frequency Band (kHz)** \\ \hline \multirow{4}{*}{EU} & CENELEC A & 3-95 \\  & CENELEC B & 95-125 \\  & CENELEC C & 125-140 \\  & CENELEC D & 140-148.5 \\ \multirow{4}{*}{USA} & FCC & 10–490 \\  & EPFI & 3–500 \\ \cline{1-1}  & JAPAN & ARIB & 10–450 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of available NB-PLC frequency bands in different regions of the world.  
Figure 3: Schematics of the experimental test bench with the G3-PLC system coupled to the DC-DC converter through a coupling capacitor.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Feature** & \\ \hline \multirow{4}{*}{PHY} & Modulation & OFDM \\  & No. of sub carriers & 36 \\  & Frequency Band & CENELEC A (35–91 kHz) \\ \cline{1-1}  & Sub carrier modulation & DBRSK \\ \cline{1-1}  & Max data rate & up to 34 kbps \\ \cline{1-1}  & Sent frames & 3000 \\ \cline{1-1}  & Time between frames & 100 ms \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of the G3-PLC considered in this work.  
PWM/RPPM can be easily implemented using a microcontroller and SIMULINK. Hence, the code for a specific modulation technique is generated using SIMULINK and deployed to the F28379D launchpad for real time operation (see Appendix A). A picture of the experimental test setup is shown in Figure 5.
## 2 Materials and Methods  
### Model Description and Experiment Design  
Version 4.5 of the International Center for Theoretical Physics (ICTP) regional climate model (i.e., RegCM4) was used in this study. In addition, the community land model version 4.5 (CLM45) was used because it offers substantial improvements such as reducing the excessive tropical GPP, a revised canopy radiation scheme, and the canopy scaling of leaf processes [7]. The CLM45-CN land model adapts the formula of [8] to simulate LAI using knowledge of the leaf carbon content (C\({}_{\mathrm{L}}\); in gC m\({}^{-2}\)) and the specific leaf area at the top of the vegetation canopy (SLA\({}_{\mathrm{o}}\); in m\({}^{2}\) gC\({}^{-1}\)).  
Ref. [9] reported that LAI is overestimated concerning MODIS. To overcome such overestimation, ref [6] proposed a new LAI formula based on the BIOME-BGC model. Because of the empirical nature of the BIOME-BGC model, it was calibrated with respect to the MODIS (as the observational based dataset), ensuring reasonable bias compared to a study conducted by [9]. Additional details of the new BIOME-BGC over Africa can be found in [6]. While [6] investigated the role of LAI parameterization on the surface energy balance and climate, this study examines the potential role of LAI parameterizations in simulating the terrestrial carbon fluxes of Africa. Figure 1 shows the topography (in meters) over the domain, including three focus areas [10].  
The simulations shared the same initial and lateral boundary condition from the National Centre for Environmental Prediction/National Centre for Atmospheric Reanalysis version 2 (NCEP/NCAR2, [11]), which was used as the lateral boundary conditions, and ERA-Interim data were used as the sea surface temperature (SST; [12]). The RegCM4 model simulations adopted the physical configuration of [10]. Initializing the coupled RegCM4-CLM4-CN-LAIorg/RegCM4-CLM4-CN-LAImod model requires a long-term spinning up  
Figure 1: The figure shows the model domain and topography elevation (unit: meters).  
of the CN (which is provided by the NCAR) to ensure that the vegetation carbon and NEE are in equilibrium, as recommended by [3; 4].
# Point Cloud Upsampling Algorithm: A Systematic Review
## 2 Benchmark
### Evaluation Metrics
Unlike CD, HD, and other indicators that can be calculated under the XYZ format, P2F requires raw data in mesh format. In addition to evaluating the deviation between point clouds, the uniformity of point clouds is also an important evaluation indicator. **Normalized Uniformity Coefficient (NUC) [5]**. PU-Net defines NUC, which randomly places D disks of equal size on the surface of the generated point cloud, calculates the standard deviation of the points in the disk, and then normalizes each object's density and calculates the overall uniformity of the set of points across all objects in the test dataset. Define NUC using the percentage of disk area \(p\):  
\[avg=\frac{1}{K*D}\sum_{k=1}^{K}\sum_{i=1}^{D}\frac{n_{i}^{k}}{N^{k}*p} \tag{4}\]  
\[NUC=\frac{1}{K*D}\sum_{k=1}^{K}\sum_{i=1}^{D}\left(\frac{n_{i}^{k}}{N^{k}*p}- avg\right)^{2} \tag{5}\]  
where \(n_{i}^{k}\) is the number of points within the \(i\)-th disk of the \(k\)-th object. \(N^{k}\) is the total number of points on the \(k\)-th object. \(K\) is the total number of test objects. \(p\) is the percentage of the disk area over the total object surface area. Uniform metric in PU-GAN [13]. NUC ignores the local clutter of points and cannot distinguish between different disks containing the same number of points. Another evaluation metric for evaluating the uniformity of point clouds is proposed in PU-GAN to avoid this problem, and is defined as follows:  
\[Uniform\big{(}S_{j}\big{)}=\sum_{j=1}^{M}\lfloor\frac{\big{(}|S_{j}-\hat{n}| \big{)}^{2}}{\hat{n}}\times\sum_{j=1}^{|S_{j}|}\frac{\big{(}d_{j,k}-\hat{d} \big{)}^{2}}{\hat{d}}\rfloor \tag{6}\]  
where \(M\) is obtained by farthest sampling of the generated point cloud \(Q\). \(S_{j}\) is the point set obtained using a ball query for each point in \(M\) with radius \(r_{d}\). \(\hat{n}=\hat{Q}\times r_{d}^{2}\) is the expected number of points in \(S_{j}\). \(d_{j,k}\) is the distance from each point in \(S_{j}\) to its \(k\) nearest neighbors. \(\hat{d}=\sqrt{\frac{2r_{d}^{2}}{|S_{j}|\sqrt{3}}}\) is the expected distance of the point in the uniform point cloud to its \(k\) nearest neighbors. The deviation of \(S_{j}\) from \(\hat{n}\), \(d_{j,k}\) from \(\hat{d}\) is evaluated using a chi-squared model. **F-Score**[19]. The above two mainstream methods are susceptible to the influence of outliers. AR-GCN [20] uses the F-score to evaluate the quality of generated point clouds by manipulating the upsampling of point clouds as a classification problem. It evaluates the precision by checking the percentage of points in the generated point cloud or ground truth that can find a neighbor from the other dataset within a certain threshold \(\tau\). Then, it calculates the F-score as the harmonic mean of precision and recall. For this metric, larger is better.
## 4 Privacy-Preserving Smart Metering with Authentication Capability
### Scheme Construction
#### 4.2.3 Signing  
For \(1\leq i\leq n\), \(s_{i}\) generates the aggregate signature by running the \(Sign\) on the input of \(sk_{ID_{i}}\), \(c_{i}\), and \(\sigma\), which is an aggregate-so-far signature (for a first smart meter \(s_{1}\), \(\sigma\) is defined as \((1_{\mathbb{G}},1_{\mathbb{G}},1_{\mathbb{G}})\)), and \(L_{i-1}\). \(s_{i}\) first parses \(\sigma\) as \((\sigma_{1},\sigma_{2},\sigma_{3})\) and computes  
1. \(r_{i},x_{i}\stackrel{{\$}}{{\leftarrow}}\mathbb{Z}_{p}\),
2. \(\sigma_{3}^{\prime}\leftarrow\sigma_{3}\cdot g^{x_{i}}\),3. \(\sigma_{2}^{\prime}\leftarrow\sigma_{2}\cdot g^{r_{i}}\),
4. \(\sigma_{1}^{\prime}\leftarrow\sigma_{1}\cdot(\sigma_{3})^{r_{i}}\cdot(\sigma_{2 }^{\prime})^{x_{i}}\cdot H_{2}(ID_{i})^{\alpha_{2}}\cdot H_{1}(ID_{i})^{\alpha_ {1}\cdot H_{3}(ID_{i}||c_{i})}\).  
It then sets \(\sigma=(\sigma_{1}^{\prime},\sigma_{2}^{\prime},\sigma_{3}^{\prime})\) and forwards it to the next smart meter \(s_{i+1}\) together with \(L_{i}\).
## 2 Basic Theory
### Pre-Tightening Torque Dependent Parameters  
The identification method of pre-tightening torque dependent parameters for empirical modeling of bolted joints in this paper is mainly divided into the following steps  
1. Modelling. The interface of the bolted joint is characterized by the isotropic thin layer element, ignoring the influence of the bolt.
2. Experiment. The torque wrench is used to get modal data under different pre-tightening torque within a range.
3. Identification. Under each pre-tightening force, different parameters of the thin layer unit can be obtained through parameter identification, and the precise thin layer element under this pre-tightening force can be obtained.
4. Prediction. All parameter values of thin-layer elements within a certain range of pre-tightening can be predicted by taking appropriate pre-tightening step and curve fitting.
5. Verification. New experimental data are used to verify the predictions.
6. The flowchart of this article is shown in Figure 4.
## 4 Results
### Learnings
On the other hand, there was no significant association between sex and enjoyment in the non-gamified condition (\(\chi^{2}\) (1, N = 30) = 0.03, \(p\) = 0.873), or when allowing for both conditions together (\(\chi^{2}\) (1, N = 62) = 3.72, \(p\) = 0.054). In addition, in the analysis of the condition (gamified/non-gamified) by gender, we observed no significant differences in males (\(\chi^{2}\) (1, N = 31) = 1.389, \(p\) = 0.239), females (\(\chi^{2}\) (1, N = 31) = 1.55, \(p\) = 0.213), and overall (\(\chi^{2}\) (1, N = 62) = 0.01, \(p\) = 0.915). There were no significant differences by sex in the perception of results satisfaction, either gamified (\(\chi^{2}\) (1, N = 32) = 1.25, \(p\) = 0.265), non-gamified (\(\chi^{2}\) (1, N = 30) = 1.18, \(p\) = 0.277), or overall (\(\chi^{2}\) (1, N = 62) = 0.08, \(p\) = 0.783). However, when focusing on the effects of the condition by gender, we observed that the non-gamified condition presented a significantly higher proportion of subjects reporting as satisfied when compared to the gamified version, for males (\(\chi^{2}\) (1, N = 31) = 5.98, \(p\) = 0.015, \(\varphi_{\rm c}\) = 0.44), females (\(\chi^{2}\) (1, N = 31) = 15.75, \(p\) < 0.001, \(\varphi_{\rm c}\) = 0.71), and overall (\(\chi^{2}\) (1, N = 62) = 24.40, \(p\) < 0.001, \(\varphi_{\rm c}\) = 0.57).
## 3 The Proposed CNN-SNN Conversion Method
### Construction of Converted SNN
```
Input: (\(T_{infer}\)): desired inference latency Output:\(V_{th,k}^{l}\): firing threshold for neurons for the \(k\)-th channel at the \(l\)-th layer Notations: (\(n_{layer}\)): number of layers; (l): layer index; (\(n_{channel}^{l}\)): number of channels at the (l)-th layer; (k): channel index in a layer; (\(Z_{k}^{l}\)): maximum input current over time steps at the (k)-th channel in the (l)-th layer; \(w_{j}\): the connection weight from the \(j\)-th connection from the preceding layer; \(S_{j}[t]\): the input spike at the \(j\)-th presynaptic neuron from the preceding layer begin # Initialize the firing threshold list \((V_{th}=[V_{th}^{1},V_{th}^{2},...,V_{th}^{l},V_{th}^{n_{layer}}])\) # where each (\(V_{th}^{l}=[0.0]*n_{channel}^{l}\)) for(\(l\)) \(\leftarrow\) (\(l\)) to (\(n_{layer}\))do  Initialize (\(Z_{k}^{l}=0\)), \(\forall\) (k=1,... \(n_{channel}^{l}\)) if(\(l\)=\(l\))then # Determine the maximum input current across time steps from the whole training set for(\(k\)) \(\leftarrow\) (\(l\)) to (\(n_{channel}^{l}\))do (\(Z_{k}^{l}=\max(Z_{k}^{l},\max\left(\sum_{j}w_{j}X_{j}\right))\))  end for # Set firing threshold for neurons at every (\(k\)th) channel in the (\(l\)th) layer for(\(k\)) \(\leftarrow\) (\(l\)) to (\(n_{channel}^{l}\))do (\(V_{th,k}^{l}=Z_{k}^{l}\))  end for elseif(\(l=n_{layer}\))then (\(V_{th}^{l}=+\infty\)) else # Determine the maximum input current across time steps from the whole training set fortime step (\(t\)) \(\leftarrow\) (\(l\)) to (\(T_{inf\sigma}\))do for(\(k\)) \(\leftarrow\) (\(l\)) to (\(n_{channel}^{l}\))do (\(Z_{k}^{l}=\max(Z_{k}^{l},\max\left(\sum_{j}w_{j}S_{j}[t]\right))\)  end for # Set firing threshold for neurons at every channel at the (\(l\)th) layer for(\(k\)) \(\leftarrow\) (\(l\)) to (\(n_{channel}^{l}\))do (\(V_{th,k}^{l}=Z_{k}^{l}\))  end for  end for  end for
```  
**Algorithm 2**The proposed threshold balancing method. Given desired inference latency \(T_{infer}\) for the SNN, the technique records the maximum accumulated activation, also known as input current, \(Z_{k}^{l}\), at each \(k\)-th output channel across time steps at each \(l\)-th layer by passing the entire training dataset through the SNN. Note that \(Z_{k}^{l}\) is computed by Equation (2). Then, the technique sets the threshold of the neurons at each \(k\)-th channel equal to \(Z_{k}^{l}\). After the assignment of the firing threshold for a layer, it freezes the thresholds of the layer and repeats the threshold determination procedure for the next layer. As mentioned earlier, the thresholds are determined for each channel of layers sequentially in a layerwise manner.