esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Gas Chromatography and Mass Spectrometry (GC-MS) Analysis  
The NEO composition was analyzed by the gas chromatography technique. Here, 1 uL of oil diluted 1:1000 in hexane was injected in an HP6890 GC coupled with HP5973 MS in splitless mode. The injected sample passed through a Bruker Br-5 MS capillary column (30 m \(\times\) 0.25 mm id \(\times\) 0.25 \(\upmu\)m), with a helium gas flow rate of 1 mL/min. The column was heated in the gas chromatograph oven at a temperature range between 50 \({}^{\circ}\)C and 300 \({}^{\circ}\)C with a rate of 6 \({}^{\circ}\)C/min, the final hold was 5 min, and the solvent delay was 3 min. The mass spectrometer had the source set up at 230 \({}^{\circ}\)C, the MS Quad at 150 \({}^{\circ}\)C, and ionization energy at 70 eV. The mass of compounds ranged between 50 and 550 amu. The retention indices (RIs) were calculated based upon a linear equation obtained from serial dilutions of a C\({}_{8}\)-C\({}_{20}\) alkane standard mixture (r\({}^{2}\) > 0.9973) injected in conditions equal to the sample one. Identification of NEO components was based on comparisons of the calculated RIs with the data published in the literature [14] and their mass spectra compared with those from the NIST2.0 library (USA National Institute of Science and Technology software).
# A Novel GPU-Based Acceleration Algorithm for a Longwave Radiative Transfer Model
## 4 GPU-Enabled Acceleration Algorithm
### Parallel Strategy  
In the RRTMG_LW, the total number of \(g\) points, \(ng\), is 140. Therefore, there are iterative computations for each \(g\) point in \(inatm\), \(taumol\), and \(rrtnmc\). For example, the computation of 140 g points is executed by a do-loop in the GPU-based acceleration implementation of 1D \(rrtm\_d\), as illustrated in Algorithm 3. To achieve more fine-grained parallelism, 140 CUDA threads can be assigned to run the kernels \(inatm\_d\), \(taumol\_d\), and \(rrtm\_d\). Thus, the parallel strategy is further accelerating \(inatm\_d\), \(taumol\_d\), and \(rrtm\_d\) in the \(g\)-\(point\) dimension. Moreover, the parallelization between the kernels should also be considered in addition to that within the kernels.  
It is noteworthy that the first dimension of 3D arrays in the CUDA code represents the number of horizontal columns, the second dimension represents the number of model layers, and the third dimension represents the number of \(g\) points. If one GPU is applied, in theory, \(nx\times ny\times nz\times ng=256\times 128\times 51\times 140\) CUDA threads will be required for each kernel in the new parallel method.
## 3 Results and Discussion
### PVC Deterioration Mechanism  
In our previous studies [1, 8, 9], the dehydrochlorination of the PVC skeleton was confirmed to result in polyene formation under alkaline conditions; furthermore, the formation of a carbonyl group was indicated. In contrast, in this study, the main chain of PVC was partially cleaved and cross-linked. We considered the types of chemical reactions that resulted in these phenomena, as mentioned below.  
As shown in Figure 13, OH\({}^{-}\) first subtracted \(\beta\)-hydrogen on the PVC, thereby inducing dechlorination to form a C=C double bond [10].  
Figure 12: Effect of alkali treatment period on the tensile properties of (**a**) the AMX and (**b**) the AMX residue after Soxhlet extraction.  
This typical E2 reaction occurred readily along the polymer backbone, and the conjugation of the C=C double bonds or the polyene formation rendered the membranes darker. In the deprotonation by OH\({}^{-}\), the resulting carbanion attacked an adjacent C=C double bond to form a cross-linking point, as shown in Figure 14.  
Meanwhile, an SN2 reaction occurred in parallel with the E2 reaction, where the OH\({}^{-}\) attacked the \(\alpha\)-carbons of the PVC in a nucleophilic manner, inducing dechlorination. Subsequently, the OH\({}^{-}\) further subtracted a proton from the resulting hydroxyl group with the formation of a ketone in the subsequent reaction scheme. When another OH\({}^{-}\) reacted with a hydroxyl group immediately next to the ketone, the polymer chain was cleaved by a reverse aldol reaction [10]. These processes are shown in Figure 15.  
As discussed in a previous study [9], the AMX membrane contains a quaternary ammonium group and can supply OH\({}^{-}\) continuously to PVC in the AEMs. Meanwhile, CMX contains an anionic sulfonate and prevents OH\({}^{-}\) from approaching the PVC. This may have caused the deterioration of AMX to be more serious than that of CMX under alkaline conditions.
# A C-Based Framework for Low-Cost Real-Time Embedded Systems
## 7 Internet Access  
### Requirements  
As shown in Figure 1, modern embedded systems ought to be connected to the Internet to support a wide range of services, such as remote configuration, monitoring, diagnostics, and troubleshooting, also according to the industrial IoT paradigm [53]. Hence, it becomes necessary to grant the Internet-based management software (IMS) access to a portion of the embedded system state without disrupting real-time execution. In the case of RtFw, a convenient way to do so is to use a proxy, as described in Section 4.5. This approach has the advantage that access to real-time information from the Internet is always performed indirectly, through the proxy itself.  
Within the controller card, introducing a clear separation of duties between the real-time and Internet-access parts of the system alleviates security concerns and makes the system more modular. At the same time, the assignment of appropriate priorities to the proxy tasks in charge of Internet access prevents them from interfering with real-time performance and timings. In addition, all peculiarities of the Internet access method chosen for a given system--for instance, wired versus WiFi network access, or specific data encryption and authentication protocols--can easily be confined to the proxy. As an example, Figure 2 shows how a proxy can grant access to part of the system state by means of an Ethernet-based Modbus-TCP connection through path 1.  
Under this premise, the main design problem to be solved is how to do so by means of abstract, _symbolic variable names_, while keeping the access mechanism as simple and efficient as possible. When symbolic names are known at compile time, they can be represented as values of an _enumerated data type_. On the contrary, names known only at run time are usually represented as variable-length _strings of characters_. The support for symbolic names is a key point to decouple the IMS--typically not developed by means of RtFw--from the embedded system firmware. This has the twofold advantage of protecting the IMS from internal firmware changes, while enabling its reuse for different but analogous embedded systems, provided they export the same set of symbolic variables.  
Figure 9: Example of proxy access configuration.
###### Abstract  
The effects of pH (3.6 and 7.0) and irradiation UV-C dose irradiation (0, 9.81, 15.13, and 31.87 mJ/cm\({}^{2}\)) on the physicochemical properties and natural microbiota of red pickly pear juice were evaluated during processing and storage. Thermal treatments were used as the control applying high temperatures for a short time (HTST 80 \({}^{\circ}\)C/30 s) or ultra-high temperature (UHT 130 \({}^{\circ}\)C/3 s). UV-C treatments applied to juices with both pHs inactivated coliforms and mesophiles with the same efficacy as thermal treatments. Yeasts and molds were inactivated at a dose of \(>\)15.13 mJ/cm\({}^{2}\) at both pHs. The UV-C doses showed no differences in betalains, polyphenols, or antioxidant activity. However, a decrease in these compounds was observed during storage. The lowest reductions in betacyanins (11.1-16.7%) and betaxanthins (2.38-10.22%) were obtained by UV-C treatment at pH 3.6. Thermal treatments (HTST and UHT) caused a reduction greater than UV-C irradiation in betacyanins, betaxanthins, polyphenols, and antioxidant activity after treatment. However, after storage at pH 3.6, the contents of these compounds reached those of the UV-C treatments, except for polyphenols. In specific pigments, betanin retention was highest at pH 3.6 (62.26-87.24%), and its retention decreases with UV-C dose increase and storage. The indicaxanthin intentions were higher (75.85-92.27%) than those of betanin, and the reduction was mainly due to storage. The physical properties (pH, acidity, and \({}^{\circ}\)Brix) were not affected by treatments, except for the color. The results suggest that a dose of 15.13 mJ/cm\({}^{2}\) of a continuous UV-C system is a non-thermal alternative for the processing of red pickly pear juice at pH 3.6, preserving its properties.  
red prickly pear juice; betalains; betanin; indicaxanthin; UV-C irradiation Vol. 2022, 12, 3488. [[https://doi.org/10.3390/app12073488](https://doi.org/10.3390/app12073488)]([https://doi.org/10.3390/app12073488](https://doi.org/10.3390/app12073488))
# Investigation on the Tip Positioning Accuracy of Cable-Driven Serpentine Manipulators
## 3 Simulation Analysis and Experimental Validation
### Simulation Analysis
When we added \(\pm\)1 mm error to the fifth cable/+0.35 mm error to the third disk's holes, the maximum deviation of the end pose was at the positive \(Y\)-axis boundary;
4. The influence of the first and third cable errors and the first and second cable location hole errors was larger than that of the fifth cable error and the third cable location hole error; that is, the closer to the end, the smaller the influence of cable length and cable location hole errors on the end accuracy;
5. The effect of the cable hole location error on the tip positioning accuracy was smaller than that of the cable length error. To better explain \"jump points\", we pick the blue picture from Figure 5a; the points in the green circle are defined as jump points because the variation trend of the deviation before and after the points in the green circle changed. At the same time, the deviation suddenly changed greatly from the points in the green circle to the points in the red or yellow circles. Finally, we analyzed the influence of combining the cable length error with the cable hole location error. We added random cable length errors within the range from \(-\)1 to \(+\)1 mm to each upper cable. At the same time, we added random cable hole location errors within the range from \(-\)0.35 to +0.35 mm to each disk. A total of 1000 groups of random composite errors were used in the simulation. The average deviation value of 1000 groups of random composite errors were calculated and plotted as seen in Figure 7.  
Figure 7: _Cont._  
In Table 2, the blue line represents the initial state of the manipulator. The red line represents the state after adding errors. As can be seen from the configuration diagram, reducing the error values or avoiding the jump points and some boundary points could improve the tip positioning accuracy effectively.
# Review of Artificial Intelligence Adversarial Attack and Defense Technologies
## 2 Adversarial Samples and Adversarial Attack Strategies
### Adversarial Example (AE)
#### 2.1.1 Causes of Adversarial Examples  
Researchers have proposed some explanations for the existence of adversarial samples. Some [58] believe that the reason is the over-fitting or under-regularization of the model led to the insufficient generalization ability that learning models predict unknown data, while others [21] consider that the adversarial samples are caused by the extreme nonlinearity of the deep neural network. However, [PERSON] et al. [15] added perturbation to the input of a regularization model and a linear model which has enough dimensions, and demonstrated that both models' effectiveness of defending adversarial attacks were not significantly improved.  
[PERSON] et al. [15] believe that the cause of adversarial samples is the linear behavior in high dimensional space. In a high dimensional linear classifier, each input feature is normalized, so that slight perturbation for one dimension of each input will not change the overall prediction of the classifier, while small perturbations to all dimensions of the inputs will lead to effective change. As is shown in Figure 2, the score of class \"1\" is increased from 5% to 88% by adding or subtracting 0.5 from  
Figure 1: The model considers the original image as a “panda” (57.7%). Indeed, the image added a tiny perturbation is classified as a “gibbon” by the same model (99.3%), while the human eyes cannot identify the difference (Image Credit: [PERSON] et al. [15]).  
each dimension in a specific direction of the original sample \(x\), which proves that the linear models are vulnerable to the adversarial samples.
# Development of an Energy-Efficient Smart Socket Based on STM32F103
## 3 System Development
### Voltage Monitoring Unit  
The voltage and current detection module in this system are constructed with a voltage monitoring unit and a current monitoring unit. As shown in Figure 2, the voltage monitoring unit is built up with an AC voltage transformer supported by an operational amplifier circuit.  
Figure 1: System framework of the intelligent power-saving socket. AC-L: Live wire of Alternating Current AC-N: Neutral wire of Alternating Current.  
For the electrical isolation between the high voltage and low voltage circuits, the fluctuation of detected voltage values is transformed into current variations in the primary layer of the AC voltage transformer by integrating resistors in this circuit. After that, the current signals are transformed back into voltage values within a predefined range by employing a sensing resistor in the second layer for voltage output, which is easier for measurement. The characteristic values of the selected AC transformer, primary resistor and secondary resistor, as well as associated amplification coefficient and reference voltage are listed Table 1.  
Given the proportion of input and output currents is 2 mA:2 mA for the AC voltage transformer, and the value of the primary resistor is 200 K\(\Omega\) in Table 1. The current value in the primary layer is then obtained as 1.55 mA. The voltage applied on the secondary resistor \(R_{10}\) is 155 mV. Therefore, the voltage output of the amplifier can be expressed as Equation (1) according to its physical characteristic.  
\[V_{o}=(1+R_{15}/R_{16})V_{i}-(R_{15}/R_{16})V_{ref}, \tag{1}\]  
where \(V_{o}\), \(V_{i}\), and \(V_{ref}\) are the output, input and reference voltages respectively, while \(R_{15}\) and \(R_{16}\) denote the resistances of resistors \(R_{15}\) and \(R_{16}\). As \(V_{i}\) is the resultant of voltages applied at the resistor \(R_{10}\) and \(V_{ref}\), the formulae then can be adopted as Equation (2):  
\[V_{o}=V_{R10}(1+R_{15}/R_{16})+V_{ref}. \tag{2}\]  
Given the AC voltage of 220 V and the reference voltage of 1.25 V, the voltage output is regulated within 0.49-2.0 V, which falls in the acceptable detecting range of STM32F103 microchip. The voltage output VOL is connected to the input pin PA0 of the microchip STM32F103 in this system. A related algorithm was designed to read the voltage values of the smart socket, and then the power management unit provides overvoltage protection by cutting off the power supply if the detected voltage value is out of the predefined range.
## 4 Experimental Research
### Performance Evaluation of the MSPDDE Algorithmic  
In order to verify the optimization effect of the MSPDDE algorithm, this subsection sets the MSPDDE algorithm parameters as described above and runs the algorithm 10 times for each case. At the same time, each case is rendered 10 times using Cesium's default rendering parameter configuration and progressive loading on demand (PLOD) rendering parameter configuration. The rendering time results of the three case models under the optimal rendering parameter configurations found by the MSPDDE algorithm, Cesium's default parameter configuration and progressive loading on demand (PLOD) parameter configuration are shown in Tables 4 and 5 and Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Size** & **Features Involved** \\ \hline Huxi Village & 2.54 GB & Vegetation predominates, with a few buildings, terrain, etc. \\ Qishan Park & 5.39 GB & Buildings predominantly, minor vegetation, roads, etc. \\ Huludao City & 54.8 GB & Buildings predominantly, minor vegetation, roads, etc. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental case data information.  
Figure 2: Experimental case rendering display.  
In all three cases, the rendering time of the MSPDDE algorithm is smaller than that of the progressive loading on demand and Cesium default parameter configurations, and the \(p\)-value of the Mann-Whitney U test for the rendering time results for MSPDDE and the other two parameter configurations is less than 0.05. This can be seen that the MSPDDE algorithm's optimization results are significantly better than progressive loading on demand and Cesium's default parameter configuration.
## 5 Conclusions  
In this study, the mechanical properties and constitutive relationship of Cretaceous water-rich sandstone at different freezing temperatures and confining pressures were examined under the effect of surrounding rocks. The main results of the study are summarized as follows:  
1. Triaxial compression tests were conducted on the frozen samples of weakly cemented red sandstone in the Luohe Formation of Lower Cretaceous in Lijiagou air-return shaft of Wenjiapo Mine under different confining pressures. The stress-strain curves of rock subjected to triaxial compression exhibited four phases: crack compaction, elastic deformation, yielding phase, and failure phase. Different from the deformation characteristics under confining pressure, there was no strain-softening stage behind the peak of rock under uniaxial compression, which manifested as a sudden stress drop and rock failure.
2. The peak strength and the elastic modulus of rock increased with the decrease in the temperature when the confining pressure was constant and showed a linear growth trend under negative temperature conditions, but the rate of growth gradually decreased. When the test temperature was constant, as the confining pressure increased, the peak strength and the elastic modulus increased, while the Poisson's ratio decreased.
3. The TPHM was able to effectively describe the constitutive relationship of rock before the yield point. On the basis of the Lemaitre strain equivalence hypothesis, the micro-elements in the sandstone were assumed to obey Weibull distribution. According to the D-P criterion, the damage constitutive relationship of rock was established to describe the stress-strain relationship after the yield point.
4. The results based on the constitutive relationship were compared with the test results. The absolute error between the two was not more than 1 MPa, and the relative error was less than 5%. This indicated that the established constitutive model was not only better able to describe the rock fracture compression and elastic stages before the yield point, but also revealed the post-peak strain softening section, verifying the applicability and rationality of the model.  
Conceptualization, Z.Y. and S.S.; methodology, S.S. and Z.Y.; software, S.S., Y.X., C.W. and K.H.; validation, S.S., Y.X., and K.H.; formal analysis, Y.X.; investigation, Y.X., C.W. and S.S.; resources, Z.Y.; data curation, S.S.; writing--original draft preparation, S.S.; writing--review and editing, Y.X., C.W. and Z.Y.; visualization, K.H. and C.W.; supervision, Z.Y. and C.W.; project administration, Z.Y. and C.W.; funding acquisition, Z.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Anhui Provincial Fund: Temperature Crack Control in Winter Prefabrication Construction of Drilling Shaft Lining in Alpine Region (YJS20210410).  
Not applicable.  
The authors declare no conflict of interest.
## 4 The Missing Data Completion Algorithm Based on Residual Value Tensor Decomposition
### Features Extraction and Residual Value Tensor Construction
According to the experimental data distribution, this paper adopts univariate spline to predict the \"hour-minute trend\" features that cannot be calculated, which is as following:  
\[\overline{x}_{\varnothing_{m}}^{d,n}=Univariate\ Spline(\overline{x}_{m}^{d,n}) \tag{12}\]  
where \(\overline{x}_{\varnothing_{m}}^{d,n}\) represents the \"hour-minute trend\" features that cannot be calculated, while \(\overline{x}_{m}^{d,n}\) represents the \"hour-minute trend\" features that can be calculated by Equation (10). Set \(\overline{x}_{m}^{d,n}=\overline{x}_{m}^{d,n}\cup\overline{x}_{\varnothing_{m}} ^{d,n}\) when we have predicted \(\overline{x}_{\varnothing_{m}}^{d,n}\). Figure 3: Sequence number of time slices. In this paper, we extract the \"day-hour trend\" features \(\overline{x}_{h}^{d,n}\) from the original traffic flow tensor \(\chi_{t,d,n}\). And we get an intermediate tensor \(\chi^{\prime}{}_{t,d,n}\) by eliminating the \"day-hour trend\" features from the original traffic flow tensor. Then, we extract the \"hour-minute trend\" features \(\overline{x}_{m}^{d,n}\) from intermediate tensor. Moreover, we obtain a residual value tensor \(E\) by eliminating the \"hour-minute trend\" features from intermediate tensor. The temporal characteristics have been approximately determined by \"day-hour trend\" and \"hour-minute trend\" features, while the remaining residual value means fluctuation caused by random factors. The residual value tensor is decomposed by CP-ALS, and the potential structure of the residual tensor is obtained. Since the two main features have been eliminated, the difference of the data elements of the residual value tensor is small. Therefore, the missing values could be recovered more accurately by CP decomposition. Then, we add \"day-hour trend\" features and the \"hour-minute trend\" features to the residual value tensor after reconstruction \(\hat{E}\). We can get the completed traffic flow tensor as follows:  
\[x_{t,d,n}=x_{t,d,n}*\omega+(\hat{E}+\overline{x}_{h}^{d,n}+\overline{x}_{m}^{d,n})*(1-\omega) \tag{13}\]  
which means the non-missing data remains unchanged, and the missing data is set to \(\hat{E}+\overline{x}_{h}^{d,n}+\overline{x}_{m}^{d,n}\).