esa-sceva/satcom-chunk-collection huggingface dataset:
# Key Issues on Integrating 5G into Industrial Systems
## 3 Typical Applications in Industrial Systems
### PLC Automated Production  
With the transition of industrial manufacturing to I4.0, automated production has become an inevitable trend and an important component of industrial manufacturing. As the most widely used and important control equipment in the field of automation [32], PLC can be used for many applications, whether they are simple or complicated control systems [33]. Given the importance and typicality of PLC, PLC is chosen to analyze the application of 5G in field-level scenarios.
## 4 Experiments
### Comparison with Existing pCR Prediction Methods  
To demonstrate the superiority of our method in predicting pCR after nCRT for rectal cancer, we compared it with four competing baselines: (B1) SVM classifier on radiomics features [46]; (B2) random forest classifier on radiomics features [8]; (B3) MLP classifier on radiomics features [25]; and (B4) 3D-CNN classifier on MRI images [18].  
For a fair comparison, we re-implemented all the baselines to re-train them with our large training set containing three MRI sequences, {T2, DWI/ADC, and CE}, of 592 rectal cancer patients. As input to the three baselines, (B1)-(B3), we used 3740 radiomics features extracted as shown in Figure 4. We also re-implemented the 3D-CNN classifier [18], (B4), so that it could accept three MRI sequences, instead of CT/PET images, of rectal cancer as the input.  
We compared the proposed method with four baselines by evaluating the overall pCR prediction performance using four measures: AUC, F1-score, specificity, and sensitivity. Specificity and sensitivity correspond to the true negative rate and true positive rate, respectively, and the F1-score is their harmonic mean.  
In Table 5, we observe that the proposed method outperformed all competing baselines in terms of the AUC and F1-score. (B2) performed the best among (B1)-(B3) built on radiomics features. However, while the sensitivity of the proposed method was slightly lower, the overall performance of the proposed method was better than that of (B2). This implies that MSFI embedding successfully represents novel features of tumors that radiomics features cannot capture. Compared with (B4), all four measures of the proposed method were higher, which indicates that radiomics features contribute to the improved performance of the proposed method.
## 4 Conclusions  
The comparison of freely available DEMs over plain regions of Ratlam using ICESat-2 data reveals a better performance of TanDEM-X 90 over CartoDEM 90. ICESat-2 provides a new technology and is an alternative to GNSS-based studies, which can be highly useful for difficult terrain, congested or busy urban regions and inaccessible areas. The results of the study lead to the following conclusion, that accurate DEMs such as TanDEM-X 90 can help planners to make a quick evaluation of topography for infrastructure development projects that would otherwise need time-consuming traditional surveys with theodolite or a total station.  
Conceptualization, A.B.; methodology, A.B. and U.Y.; formal analysis, U.Y. and A.B.; resources, U.Y.; writing--original draft preparation, U.Y.; writing--review and editing, A.B. and U.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Publicly available datasets were analyzed in this study. The data can be found here: [[https://bhuvan.nrsc.gov.in/home/index.php](https://bhuvan.nrsc.gov.in/home/index.php)]([https://bhuvan.nrsc.gov.in/home/index.php](https://bhuvan.nrsc.gov.in/home/index.php)) and [[https://download.geoservice.dlr.de/TDM90/](https://download.geoservice.dlr.de/TDM90/)]([https://download.geoservice.dlr.de/TDM90/](https://download.geoservice.dlr.de/TDM90/)), and NASA's [[https://openaltimetry.org/data/icesat2/](https://openaltimetry.org/data/icesat2/)]([https://openaltimetry.org/data/icesat2/](https://openaltimetry.org/data/icesat2/)) (accessed on 15 July 2021). Furthermore, the Google Earth platform was used in the study for data visualization.  
The authors would like to thank ISRO, NASA, DLR, and the Google Earth platform for their support to the researchers through openly accessible data-sharing platforms. The authors are highly indebted to IIRS for the constant support and encouragement in conducting the research activities.  
The authors declare no conflict of interest.
# Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO +
## 7 Results
### Variable Frame Length  
Using the general formulations, FRM/2, for each experiment, we found the optimal schedule according to the formulation objective, along with the corresponding frame length. We also measured the solution time for each case. We see that in terms of objective values, the optimal pilot rate increases with the number of nodes and traffic volume as would be expected (Figure 4). Note that background traffic scales smoothly with the number of slots in the frame, whereas for periodic traffic, there is a quantization effect in cases where the node's period is longer than the frame. This means that the pilot rate in high background traffic cases is more than that for high periodic traffic cases.  
Some larger problem instances were unable to be solved before the timeout of 30 h and are therefore omitted from Figure 4. In particular, none of the problem instances in Experiment 2B (long periods, high background traffic) were able to be solved. Even in instances where the problem could be solved, solution times for this formulation were frequently unacceptably long, often taking several hours. Clearly, FPRM/2 is a much more demanding formulation than FPUM/2 and is not suitable for practical implementation.  
The results for frame length for FPRM/2 are shown in Figure 5. In most cases, the solver selected the maximum allowed frame length of 15 slots. This is understandable as a longer frame allows for greater flexibility in scheduling transmissions, such that they can more often be placed in series instead of parallel, thus using fewer pilots per slot.
## 6 Implementation and Evaluation
### Theoretical Calculation
#### 6.3.6 Impact of Caching
For example if two clients access the same entity within Max-Age, but one is requesting the average and the other is requesting the maximum value, then each entity member will be queried only once. The EM then processes the replies from the members and the cache and assembles the reply to the client as was requested. The same applies if a single member is part of two different entities (overlapping entities). The member will be queried only once within Max-Age, regardless of the entity that is requesting it. Finally, the cache can be populated via other requests/responses that pass via the gateway; it's not limited to just the requests/responses employed by the EM. Please note that caching is very interesting, but it will not work for actuators. However, for actuators one probably wants high reliability, which is again in favor of our solution. Figure 23: When requests are served from the cache the number of packets transmitted inside the LLN is reduced.
# Prediction-Based Energy Saving Mechanism in 3 GPP NB-IoT Networks
## 5 Simulation and Results
### Simulator Evaluation  
Figure 13 shows the simulation results of the non-IP UE report scenario that is used in the 3 GPP NB-IoT evaluation to confirm the simulator performance. In this scenario, the application report is piggybacked to an _RRC connection setup complete_ message, and the session is completed immediately after. The figure shows the energy consumption and active time at which the UE operates from its activation to the transmission completion according to the channel condition. As the channel quality improves and the session time reduces, the RX time is considerable in the total active time due to the acquisition of the master information block. This acquisition verifies the radio frame number after waking up from the deep-sleep state. Furthermore, as the channel quality reduces, both the active time and energy consumption increase. However, the growth rate for the energy consumption is larger than that of the active time, given the increase in the proportion of time in the TX state that consumes more energy than the other states.
## 7 Experimental Results
### Test in a River  
Figure 16 shows a testbed for river experiments. The width of the river is 500 m and the maximum depth is about 10 m. The flow velocity in murmuring state is normally 0.1 m/s. For experiments, we put the transmitting modem at the dock and the receiving modem on the ship.  
Figure 14: Output voltage with respect to distance and input voltage in a pond.  
Figure 15: Bit error rate with respect to input voltage in a pond.  
The output voltages of the receiving transducer \(V_{out}\) acquired by varying the distance \(d\) between two modems and the input voltage of the transmitting transducer \(V_{in}\) are shown in Figure 17. With the increase of the distance, experimental results match analytical ones obtained by spherical spreading in general. But the results by experiments deviate from the expected values at some distances. This is because there are too many unexpected obstacles disturbing acoustic communication beneath the natural river surface such as topographic characteristics, waterweed and aquatic organisms. According to experiments in the river, error-free communication is possible up to 70 m at 0.2 kbps if 200 \(V_{pp}\) is applied as the input voltage of the transmitting transducer \(V_{in}\).
## 4 Proposed Algorithm for Reliability Computing of Chain Routing Protocols in WSN
### Radio Energy Model  
We employ a first-order radio model to calculate energy consumption in data transmission, by sensors with the parameters shown in Table 1. Based on the distance between the transmitter and receiver (\(d\)), \(d^{2}\) attenuation is used if \(d\) is less than a specific threshold distance (\(d_{o}\)), and \(d^{4}\) attenuation is used if \(d\) is greater than (\(d_{o}\)). The dissipated power can be calculated as follows:  
\[E_{tx}(k,d)=\begin{cases}k\times E_{elc}+k\times\epsilon_{amp}\times d^{4},\ d \geq d_{o}\\ k\times E_{elc}+k\times\epsilon_{fs}\times d^{2},\ d<d_{o}\end{cases} \tag{1}\]  
\[E_{rx}(k)=E_{elc}\times k \tag{2}\]  
where, \(E_{tx}\) and \(E_{rx}\) are the powers consumed by the transmitter and receiver, respectively [44,45]. According to the first-order radio model, \(E_{elc}=50\) nJ/bit is consumed by the radio to run the transmitter or receiver circuitry, and \(E_{0}=0.5\) J, \(\epsilon_{fs}=10\) pJ/bit/m\({}^{2}\), \(\epsilon_{amp}=0.0013\) pJ/bit/m\({}^{4}\), and \(d_{o}=\sqrt{\epsilon_{fs}/\epsilon_{amp}}\) are required for the transmitter amplifier, where, \(k\) is the number of bits and \(d\) is the distance in meters. Transmitter circuitry also consumes \(E_{DA}=5\) nJ/bit, to aggregate the data received by the child nodes. Each sensor compresses the received bits by a data aggregation (\(DA\)) factor of 0.6, using distributed compressive sampling.
# Detection of Parking Slots Based on Mask R-CNN
## 5 Experimental Result
### Evaluating the Performance of Parking-Slot Detection
Corner features cannot always provide precise target positions and heading angles, besides, it is difficult for a corner feature to maintain robustness. Meanwhile, the line-based method provides an accurate direction and has fewer constraints compared with corner-based methods. Therefore, the accuracy and F1 scores of the line-based method are 7.3% and 4.3% higher than that of the corner-based method. In all training images of M2 there are obvious splice marks that affect the feature extraction of marking-points, as shown in Figure 19b. It is prone to generating false detection against the background of light and shade interlacing, which means that the number of false positives in M2 was higher than that in M1 and M3 during the test. Therefore, the recall of M2 is slightly lower than that of M1 and M3. Since M1 and M2 use only a single source of AVM images for training, the precision of M1 and M2 are 5.2% and 3.3% lower than that of M3, respectively, indicating that increasing the diversity of training samples can improve the generalization ability of Mask R-CNN. It also reflects that the proposed method can realize the detection of parking slots in different around-view parking-assist systems. AVM images with parking slots detected by Mask R-CNN are shown in Figure 21. Figure 21a,b shows slanted parking slots with different tilt angles, Figure 21c,d shows vertical parking slots; it can be found that the mask of marking-points accurately separates the guideline from the adjacent lane line. Figure 21e,f shows outdoor parking slots at night, Figure 21g-i shows indoor parking slots. From the above test samples, we conclude that the proposed method has a strong capability for accurately detecting different types of parking slots under various conditions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **SLOT** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline M1 & 256 & 89.5\% & 89.3\% & 90.4\% & 89.8\% \\ M2 & 256 & 90.2\% & 91.2\% & 88.7\% & 89.9\% \\ M3 & 256 & 93.4\% & 94.5\% & 92.7\% & 93.5\% \\ Method in [16] & 256 & 88.8\% & 86.9\% & 87.3\% & 87.0\% \\ Faster R-CNN [37] & 256 & 82.2\% & 82.6\% & 80.9\% & 81.7\% \\ Corner-based [23] & 256 & 70.5\% & 74.3\% & 72.4\% & 73.3\% \\ Line-based [26] & 256 & 77.8\% & 80.3\% & 75.2\% & 77.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Slot-detection performance among different methods.
## 2 Materials and Methods
### Data Analysis  
Preliminary subtraction of the background spectrum acquired in a free-cell zone of the slide was performed for all the spectra. Mie scattering and dispersion artifacts due to scattering between mid-infrared radiation (wavelengths 3-10 \(\upmu\)m; \(\sim\)1000-3000 cm\({}^{-1}\)) and cell nuclei and organelles (size between 1-10 \(\upmu\)m), were corrected using a Resonant Mie Scattering--Extended Multiplicative Signal Correction (RMieS-EMSC) [30; 31]. Afterwards, a piecewise baseline correction was performed, and the spectra were normalized using Standard Normal Variate method [32]. Average spectra with standard deviation were obtained for all sample types. Additionally, second derivative spectra were calculated, using a Savitsky-Golay smoothing algorithm (17 smoothing points) and a 3 rd order polynomial approximation, as this approach permits the better resolving of absorption bands [33]. Pre-processing procedures were performed using algorithms written by the authors using MATLAB (ver. 2020, MathWorks Inc., Natick, MA, USA) as interpreter. The spectra were also analyzed by means of convoluted Gauss-cross-Lorentzian shaped vibrational modes. The starting conditions of the procedure were manually selected and a best-fit routine from the GRAMS-AI software (Thermo Fischer Scientific, Waltham, MA, USA) was adopted to evaluate the optimized intensity, position, and width of the peaks. The inflection points were localized in the second-derivative spectra and the \(\chi^{2}\) parameter was employed for assessing the convolution procedure performance. The deconvolution procedure was principally employed for inspecting the changes in the peak position due to proton exposure.  
The Amide I band was further analyzed since changes in protein configuration can cause substantial variations in its characteristics [34; 35; 36]. To study protein secondary structure, Gauss-cross-Lorentzian shaped components, obtained as before, were used for deconvoluting the Amide I band (1740-1580 cm\({}^{-1}\)). The area of each absorption band was assumed to be proportional to the relative amount of the structure in infrared spectra [37; 38; 36]. Amide III (1350-1200 cm\({}^{-1}\)) band was also analyzed since it can offer interesting information about secondary protein structures [39] without interference due to residual water content in examined samples.  
Further quantitative details on the changes, which are present in the infrared spectra related to different experimental conditions, were acquired by estimating the ratios between the absorbance of selected peaks. The list of the ratios associated to protein content, rearrangement, and phosphorylation, DNA content and modification, lipid content and saturation is shown in Table 1. The ratios values evaluated from the spectra obtained from different samples were compared using a one-way ANOVA test with a 0.05% significance level.
# An Improved Fuzzy Vector Signature with Reusability
## 5 Performance Analysis
### Implementation  
We implemented our fuzzy vector signature as a C program to measure actual time consumption. All implementations are performed on an Intel Core i7-8700k with 8 GB RAM running Ubuntu 18.04 LTS, and GNU GCC version 7.5.0 is used for the compilation. For implementation, we selected the BLS12381 curve that offer around a 128-bit security level and a SHA-256 for a hash function. In a BLS12381 curve, the size of a element of \(\mathbb{G}_{1}\) and \(\mathbb{G}_{2}\) are 192 bit and 384 bit, respectively. Our implementation codes are available to [[https://github.com/Ilhwan123/FVS](https://github.com/Ilhwan123/FVS)]([https://github.com/Ilhwan123/FVS](https://github.com/Ilhwan123/FVS)).  
For each error rate 5% and 10%, we measured the time consumption required for running our scheme several times. Table 4 presents parameter setting, storage size, and time required for each algorithm, which is the results of our implementation. Compared with Table 2, the size of the \(\mathbb{G}_{1}\) group element is 192 bits, which is less than 256 bits. Therefore, the size of a signing parameter is smaller. However, this depends on the curve type, so it can be different depending on which curve is selected.  
Note that, if signature verification fails in the parameter setting in Table 4, the total verification time takes about 56 s.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(n\)} & **Signing** & **Verification** & \multirow{2}{*}{**Signature**} & **Setup** & **Sign** & **Verify** & **Error Rate** \\  & **Parameter** & & **Key** & & **(s)** & **(ms)** & **(s)** & **\(bio^{\prime}\)** \\ \hline
512 & 24.04 KB & 1.95 MB & 12.10 KB & 23.97 & 133.86 & \
\begin{tabular}{c} 0.302 \\ 17.954 \\ \end{tabular} &
\begin{tabular}{c} 5\% \\ 10\% \\ \end{tabular} \\ \hline
1024 & 48.04 KB & 1.95 MB & 24.11 KB & 24.47 & 264.88 & \
\begin{tabular}{c} 0.254 \\ 21.830 \\ \end{tabular} &
\begin{tabular}{c} 5\% \\ 10\% \\ \end{tabular} \\ \hline
2048 & 96.04 KB & 1.95 MB & 48.10 KB & 25.92 & 532.01 & \
\begin{tabular}{c} 0.377 \\ 21.364 \\ \end{tabular} &
\begin{tabular}{c} 5\% \\ 10\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Implementation results of FVS using BLS12381 curve in case of error tolerance rate \((t/n):12.5%,\) the number of components in each subset \((\ell):80\), and the number of subsets \((d):15\),268 with \(\delta=1/2\).  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Operation**} \\ \hline
[17] & \(d\cdot(|H|+|X|)\) + **Sign** \\ \hline
[18] & \(\frac{d}{\pi}((\frac{n}{\tau})\cdot\tau m(m-1))|X|+d\cdot(|H|+|X|)\) + **Sign** \\ \hline
[20] & \((2n+2)|E|\) \\ \hline Ours & \((n+4)|E|\) \\ \hline \(\circ\)\(|H|\): Hash, \(|X|\): XOR, \(|E|\): Exponentiation. \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of computational costs necessary for signature generation.
# The Development of an Optimized Impact Pad for a Six-Strand Tundish Using CFD Simulations
## 6 Discussion  
Developing new flow modifiers using mathematical simulations before deployment can improve the efficiency of steel casting.  
The flow of liquid steel in a six-strand tundish was studied using mathematical simulations with different impact pad shapes while keeping the casting velocity constant. Three types of impact pads were evaluated and compared based on the results of the simulations. The analysis involved using contours, velocity vectors, and a tracer to determine the flow characteristics and steel temperature distribution throughout the tundish. The minimum and maximum residence times were identified using C-curves, and the volumes of interconnected flow zones such as the dead volume, mixed volume, and plug volume were calculated for each configuration.  
The results of the simulations showed that by directing the steel flow to the rear of the section in the whole volume of the tundish and gradually directing the flow to the slag area at the steel surface, the erosion of this area was eliminated. By observing the flow characteristics based on vectors, it was possible to identify the optimal flow direction.  
The characteristics of the flow in the tundish space have changed, resulting in more consistent residence times at each tundish outlet according to the RTD curves. When evaluating the arrangement of flow zones as a criterion for assessing thermal and chemical homogeneity and conditions for the flotation of non-metallic inclusions, the K configuration resulted in better outcomes compared to the SP-K2 configuration. However, with the SP-K4 configuration, the values for the criterion of thermal and chemical homogeneity were higher than those observed with the reference K configuration. For the evaluation of a more even distribution of temperatures of molten steel based on visual observation across the entire tundish space, it was found that the temperature field in the inflow area was more extended in the configuration with the \"SPHERIC-K2\" and \"SPHERIC-K4\" impact pads.
# Real-Time Monitoring of Passenger's Psychological Stress
## 1 Introduction
### General Background and Motivations
Using participant's reports on a small number of events that happened during the first trip (the _learning phase_), a personalized stress model was inferred and integrated into a home-made application, designed to assess online participant's stress levels over time. This software has been used for the second trip (the _test phase_) to generate real-time stress data.
# An Adaptive Neuro-Fuzzy Inference Model to Predict Punching Shear Strength of Flat Concrete Slabs
## 7 Parametric Studies  
After building and testing the ANFIS, and based on the comparison between the results obtained from the ANFIS model and the ACI 318-14 code, Model Code 2010, BS-8110, and Euro-Code 2, it could be concluded that the ANFIS was a suitable model in the prediction of the punching shear strength of two-way flat concrete slabs. The effect of each input parameter used to build the model was further investigated. The methodology of the parametric study was to vary one input parameter at a time, and the other input parameter were kept constant. Figures 19-23 show the predicted punching shear strength of a two-way flap slab as a function of each input variable. They show that the punching shear strength increased with an increase in the slab effective depth, concrete strength, and width of square loaded area. In general, the parametric tendencies of ANFIS agreed with the results from the ACI318-14 code, Model Code 2010, BS-8110, and Euro-Code 2, as shown in Figures 19-21. The punching shear strength increased with an increase in the reinforcement ratio: This result agreed with the other models, except for the ACI code, as shown in Figure 22. Finally, the sensitivity of the punching shear strength to the yield strength of reinforcement is presented in Figure 23, where it can be seen that all models except ANFIS showed no effect on the punching shear strength. Interestingly, ANFIS predicted a slight increase in shear strength with increasing yield strength, which was in agreement with some of the experimental results used to build the ANFIS model.  
Figure 21: Effect of width of square loaded area on the punching shear strength.  
Figure 22: Effect of the reinforcement ratio on the punching shear strength.