esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Packet Duplication for Industrial URLLC
###### Abstract  
The fifth-generation (5G) network is presented as one of the main options for Industry 4.0 connectivity. To comply with critical messages, 5G offers the Ultra-Reliable and Low latency Communications (URLLC) service category with a millisecond end-to-end delay and reduced probability of failure. There are several approaches to achieve these requirements; however, these come at a cost in terms of redundancy, particularly the solutions based on multi-connectivity, such as Packet Duplication (PD). Specifically, this paper proposes a Machine Learning (ML) method to predict whether PD is required at a specific data transmission to successfully send a URLLC message. This paper is focused on reducing the resource usage with respect to pure static PD. The concept was evaluated on a 5G simulator, comparing between single connection, static PD and PD with the proposed prediction model. The evaluation results show that the prediction model reduced the number of packets sent with PD by 81% while maintaining the same level of latency as a static PD technique, which derives from a more efficient usage of the network resources.  
5G; machine learning; prediction; Industry 4.0; URLLC; multi-connectivity +
Footnote †: journal: _Sensors_
## 5 Sentiment Analysis Results
### Natural Language Toolkit (NLTK)--Valence Aware Dictionary and Sentiment Reasoner (VADER)  
In Figure 3, the results of NLTK--VADER show a significant improvement over the results of the previous TextBlob. It is enough to look only at the values of the neutral categories and see significant differences in the stages of the positive and negative parts.  
How people are reacting on covid by analyzing 500 Tweets with NLTK - Vader Lexicon  
In the case of the August result, which can be seen in Figure 2(a), the neutral value decreased significantly, and now it is only 20.60 percent. Similarly, in part (b) of Figure 3, the neutral value is 19.20 percent, compared to previous results, which reached 30 percent, or it was very close to this value.  
Figure 3: Analysis of sample of 500 tweets by NLTK—VADER, using the keyword ‘COVID’. The time periods stands between 29 August 2021 and 31 August 2021, and 2 September 2021 and 4 September 2021.  
In the case of the August results, there are also significant differences within the positive parts, and there is no longer such a 'weakly positive' dominance; due to the technological changes, we can assume a more accurate result on the same datasets as we used in the case of TextBlob. Here, we can see 15 percent 'positive', 10.40 percent 'weakly positive' and 9.80 percent'strongly positive' sentiment values. In September, 17 percent 'positive', 10.40 percent 'weakly positive' and 10.80 percent'strongly positive' sentiment values were observed.  
Similar movements can be observed in the negative sections, with 9.2 percent 'weakly negative', 17.2 percent 'negative' and 17.8 percent'strongly negative' in August. In September, 9.8 percent were 'weakly negative', 17.4 percent were 'negative', and 15.4 percent were'strongly negative' sentiment values. Despite a significant decrease in the neutral section, there are still too much data in this category, although we can definitely report an improvement on previous TextBlob results. The goal is to eliminate or considerably minimize the neutral values, in order to confirm the results with subsequent analyses. A neutral value still makes the result a little bit uncertain.
## 5 Discussion
### The Effects of the Two Principal Components  
As discussed above, the first principal component describes the energy-related features of the program while the second principal component represents the rhythmic features. To  
Figure 9: Schematic representation of the work methodology.  
understand the effect of the two principal components in separating the discriminating and undiscriminating program clusters, the discriminant loadings of the two components are shown in Table 7.  
Discriminant loading measures the correlation between the independent variables and the discriminant score, which assesses the contribution of each variable to the discriminant function. According to a rule of thumb [26], the variables with loadings above \(\pm\)0.4 are thought to be substantively discriminating. Additionally, by comparing the discriminant loadings of the two components, the relative importance of the components in differentiating between the groups can be determined.  
As shown in Table 7, both components have a strong effect, although Component 1 is more important. The result indicates that both the energy-related features and the rhythmic features have notable effects in explaining the program classification, and the energy-related features have more influence than the rhythmic features.
## 1 Introduction  
Mechanical thrombectomy (MT) is the current standard of care in patients with acute stroke and large vessel occlusion (LVO) [1, 2]. The presence or absence of an LVO is a prerequisite in patient selection for MT. For this purpose, computed tomography angiography (CTA) is the most frequently used modality. It demonstrates the site of cerebral artery occlusion and yields noninvasive information on the extra- and intracranial vasculature before endovascular treatment.  
Recently, several artificial intelligence (AI) based automated analysis platforms for detecting LVO on CTA images have been introduced and are increasingly used. They are supposed to allow faster review of CTA data for therapy decisions. One of the vendors of automated analysis platforms claims a very high accuracy of their software for detecting LVOs, reaching a sensitivity of 97% and a specificity of 96% [3]. However, the results of several previous studies, all within the last two years, are significantly discrepant, providing inconclusive evidence for the real diagnostic value of automated analysis platforms for the detection of LVOs [4, 5, 6, 7, 8].  
A systematic review published by [PERSON] et al. in 2020 cites only three conference abstracts on using AI algorithms to detect LVOs from CTA images [9].  
Therefore, there is an ongoing discussion within neuroradiiological and neurological communities regarding the application of AI-based automated analysis platforms for the detection of LVOs in making therapeutic decisions in stroke patients. The role of this software in the diagnostic work-up of stroke patients has not been established yet.  
Considering this, we designed the study aiming to test the diagnostic accuracy of an AI-based automated analysis platform for detection of LVO on CTA images in acute ischemic stroke patients using an experienced neuroradiologist's evaluation as the reference.
## 2 Materials and Methods
### Impedance Measurements
Continuously recorded results provide precise determination of changes occurring at the cellular level in real time. The process of proliferation causes changes in electrode impedance values due to morphological modifications. In this case, the morphological changes are closely related to the life cycle of the cells and cause synchronous growth until a confluent culture is obtained. Under normal conditions, cells do not proliferate at the same rapid rate. Acceleration of this process is associated with the use of a substance that accelerates their growth. The ECISsystem has other functions in addition to its ability to detect changes in cell impedance values. One of them is the elevated electric field mode, by means of which foreign genetic material is introduced into the culture. The advantage of performing this type of testing is that the electric field only affects the cells on the electrode. Thus, it is possible to compare their condition with cells deposited outside the electrode area. The non-invasive way of conducting experiments using impedance measurement has dramatically reduced the number of toxicity studies and animal tests for new cosmetics and drugs. With the modern and relatively inexpensive method of observing cellular functions which has been made possible by the ECIS\({}^{\otimes}\) apparatus, substances can be evaluated for cytotoxicity and its effects on living cells [18; 19; 20].
# The Holistic Perspective of the INCISIVE Project--Artificial Intelligence in Screening Mammography
## 3 Data Preparation and Integration
### Data Collection and Integration Experiences
Despite this, the looping process was time-consuming, and multiple feedback ensured thorough software testing, which supported the upgrade and final version of the software for the studies to follow within the project. DPs identified some flaws in the design of the commercially available ITK-Snap annotation tool, which caused a more time-consuming annotation process. The time leaking during the annotation task was caused by a need to set the loading directory for each DICOM image, as the previous image settings were not preserved. The storage directory for the corresponding annotation may not be set semi-automatically, e.g., the same folder as the loading one, but the last one was preserved and had to be changed on an image basis. The annotation of volumetric images, such as MR or CT scans, requires annotation of multiple images, for which the software offers an automatic interpolation of annotations based on the annotation of several non-consecutive images. However, this option does not work when there are multiple ROIs to be annotated in each image. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & **Ulrasound** & & **Mammography** & & **MRI** & **CT Scan** & **PET/CT Scan** \\ \hline \multirow{4}{*}{Data requirements} & Healthy and & \(\bullet\) & Healthy and & \(\bullet\) & MST: 3 mm & & & \\  & non-healthy & \(\bullet\) & CC and MLO & & Sequences: T2W & & \\  & images & & projections & & & & \\  & & \(\bullet\) & Tomo-synthesis & & & & \\ \hline Annotation & \multirow{2}{*}{Bounding box} & \multirow{2}{*}{Contouring} & \multirow{2}{*}{Contouring} & \multirow{2}{*}{Bounding box} & \multirow{2}{*}{Bounding box} & \multirow{2}{*}{Bounding box} & \multirow{2}{*}{Bounding box} \\  & & & & & & & \\ \multirow{4}{*}{Labels} & \(\bullet\) & Benign & \(\bullet\) & Suspicious or & & & \\  & Suspicious/ & & indeterminate & \(\bullet\) & Suspicious or & & \\  & indeterminate & \(\bullet\) & Malignant & & indeterminate & & \\  & \(\bullet\) & Calcification & \(\bullet\) & Malignant & & \(\bullet\) & Malignant & \\  & & \(\bullet\) & Surgical clip & \(\bullet\) & Axial lymph node & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The modality annotation convention in the INCISIVE project.
## 5 Frequency Hopping Techniques
### Channel-Aware, Reduced-Hop-Set Techniques
#### 5.2.2 Matched Frequency Hopping  
Channels with high gains tend to be adjacent to each other, and so, it might be easier for a jammer to jam adjacent frequency channels. The Matched Frequency Hopping (MFH) not only selects channels with high gains, but also tries to select channels with dispersed frequencies [28]. In this technique, the main parameter used to select the \(M\) channels is the set of channel gains, \(H_{k}\), from which the power metrics, \(Q_{k}\), are calculated:  
\[Q_{k}=|H_{k}|^{2}\]  
Figure 6: Classification of frequency hopping techniques.  
To select channels with high gains and reasonably well-spaced frequencies, a normalized power metric, \(B_{k}\), and a cumulative metric, \(C_{k}\), are defined:  
\[B_{k}=\frac{Q_{k}}{\sum_{i=1}^{K}Q_{i}}\quad k\in\{1,2,\ldots,K\} \tag{6}\]  
\[C_{k}=\sum_{i=1}^{k}B_{i} \tag{7}\]  
The cumulative metric will always be a monotonically increasing function of \(k\). This metric is used with the \(M\) equally spaced values over \([0,1)\) given by  
\[y_{m}=\frac{1}{M}\left(m-\frac{1}{2}\right)\ m\in\{1,2,\ldots,M\} \tag{8}\]  
to determine the indices of the selected channels. The index of the \(m\)th channel is given by the value of \(k\) such that  
\[C_{k-1}\leq y_{m}<C_{k} \tag{9}\]  
Figure 7 illustrates how \(M=10\) channels are selected among \(K=100\) available channels using this method. For the considered metric, using the highest gain technique would result in selecting the first five and last five channels, while only attending to channel separation would result in equally spaced channels. The MFH algorithm tends to select channels with high gains while avoiding selecting clusters of adjacent channels.
## 5 Conclusions  
In this paper, two-dimensional numerical studies were conducted to investigate the pressure gain characteristics of premixed CRD combustors using a methane-air mixture. The main conclusions are as follows:  
1. From the perspective of potential power, pressure gain characteristics were associated with a change in Gibbs free energy. For the constant inlet parameters, the CRD combustor had a higher pressure ratio and lower Gibbs free energy loss than the approximate constant pressure-based combustor, which is usually used in the gas turbine studied. However, due to the complex effects of detonation waves, oblique shock waves and other factors, the performance of the CRD combustor was inferior to that of the ideal constant volume-based combustor.
2. With the CRD combustor, the detonation wave was the main factor that significantly increased pressure, temperature, entropy generation, and Gibbs free energy loss. Gibbs free energy loss  
Figure 17: Variation in \(w_{\Delta x}\) with different axial lengths.  
Figure 18: Variations in \(\Delta S\) and \(\Delta G\) with different axial lengths.  
in the detonation region was similar along different streamlines and the oblique shock led to an extra Gibbs free energy loss of 20.9% along the second streamline.
3. As the CRD combustor axial length decreased, there was no noticeable change in both the detonation wave propagating characteristics and combustion thermal efficiency. However, due to the effects of oblique shock wave height, the parameters of pressure ratio and Gibbs free energy loss, were affected by combustor axial length. When the axial length of the combustor varied from 200 mm to 100 mm, the above two parameters changed from 2.0664 to 2.3922 and 4668.8 kJ/kg to 4548.2 kJ/kg, respectively.
4. According to the present numerical results, it was found that the oblique shock waves did not directly disappear at the back of the CRD combustor when the axial length was decreased only. Therefore, how to effectively weaken and even eliminate oblique shock waves is the key to designing a connected structure between a CRD combustor and turbine. This is an important problem that should be urgently remedied if we are to realize the application of CRD combustion in gas turbines.  
The authors would like to acknowledge the National Nature Science Foundation of China (Grant No. 51709059) and the Province Nature Science Foundation of Heilongjiang (Grant No. QC2017045) for supporting this work. We acknowledge and give thanks for the financial support from the China Scholarship Council (CSC No. 201706685040) to visit Princeton University.  
[PERSON] was responsible for the numerical approach and results analysis. [PERSON] performed the numerical simulation of the CRD combustors. [PERSON] discussed the evolution characteristics of CRD waves and wrote the main parts of paper. [PERSON] provided the investigation into the thermodynamic parameters of CRD combustors. [PERSON] did the model validation.  
The authors declare no conflict of interest.
## 2 CFD Model  
### Turbulence Model  
The IDDES method employed in this study amalgamates the strengths of Reynolds time-averaged Navier-Stokes (RANS) and large eddy simulation (LES) methods. The RANS model is harnessed for simulating the region proximal to the train and falling debris. Simultaneously, the LES model is applied to replicate the flow field at a distance from the train body, ensuring the comprehensive development of turbulence. In contrast to detached eddy simulation (DES), IDDES incorporates more LES characteristics, facilitating a deeper infusion of large eddy information into the RANS region. This enhancement contributes to an improved accuracy of simulation in the RANS-LES transition zone. The shear stress transport \(k\)-\(\omega\) two-equation turbulence model (SST \(k\)-\(\omega\)) is employed to simulate the flow field near the train and falling debris. By using the appropriate length scale parameters, the method can be adjusted according to the specific flow field characteristics and can better simulate the turbulent separation and turbulence structure. The energy equation including temperature transport is directly solved in the fundamental governing equations of the IDDES turbulence model, with density dynamically calculated via the ideal gas equation of state. The calculation formula of the length scale parameters is calculated according to the following formula [31; 32]:  
\[l_{hyb}=f_{hyb}(1+f_{e})\frac{\sqrt{k}}{C_{p}\omega}+(1-f_{hyb})C_{DES}\Delta \tag{1}\]  
\[f_{hyb}=\max\{(1-f_{dt}),f_{step}\} \tag{2}\]  
\[f_{dt}=1-\tanh\left[\left(20\frac{\upsilon_{t}}{\kappa^{2}d_{w}^{2}\sqrt{0.5 \left(\mathbf{S}^{2}+\Omega^{2}\right)}}\right)^{3}\right] \tag{3}\]  
where \(f_{e}\) and \(f_{hyb}\) represent the lifting function and mixing function; \(C_{p}\) is an SST model constant, typically taken as 0.09; \(\omega\) represents the specific dissipation rate; \(k\) represents the turbulent kinetic energy; \(\Delta\) represents the sublattice length scale; \(f_{dt}\) represents the delay function of DDES; \(f_{step}\) represents a function that provides a fast switching from RANS to LES at the depth of the boundary layer; \(\upsilon_{t}\) represents the vortex viscosity; \(\kappa\) represents the Von Karmen constant; \(d_{w}\) represents the distance from the wall; \(\mathbf{S}\) and \(\Omega\) represent the strain rate and rotation rate tensor normalized by the turbulence time scale, respectively. IDDES provides a definition for the subgrid length scale as follows:  
\[\Delta=\min\{C_{w}\text{max}[d_{w},h_{\text{max}}],h_{\text{max}}\} \tag{4}\]  
where \(C_{w}\) is an empirical constant taken as 0.15 and \(h_{\text{max}}\) represents the maximum local mesh spacing.
# IoT-Chain and Monitoring-Chain Using Multilevel Blockchain for IoT Security
## 6 Experiment
### Block Propagation Delay  
The time for the block to propagate from \(Node_{leader}\) to \(Node_{ic}\) was measured according to the data size to check whether the data size affected the propagation to nodes after the block was created in \(Network_{ic}\). To measure the block propagation delay time according to various data sizes in \(Network_{ic}\), the data were divided into sizes of 8, 128, 1K, and 10K bytes; and blocks were created in the leader node for five IoT devices, \(Node_{ic}\).  
Figure 11: Latency comparison according to IoT-Chain consensus algorithm.  
Figure 12: CPU usage comparison according to IoT-Chain consensus algorithm.  
For the experiment, after the block was created, the block propagation delay time from \(Node_{leader}\) to all \(Node_{ic}\) was measured, and the maximum, minimum, and average Boxplot graphs are displayed.  
By operating and configuring \(Network_{ic}\), we measured the propagation time after agreeing to \(Block_{ic}\), as depicted in Figure 13. It was confirmed that the propagation time of \(Block_{ic}\) increased depending on the data size, but the increase was not large, and the maximum average delay time was only 36 ms.
## 2 Materials and Methods
### Multi-Gap Ionization Detector
#### 2.1.2 Detector Readout and Data Acquisition System  
The detector front-end readout is based on the TERA08 chip [12], which is designed in the CMOS AMS 0.35 \(\upmu\)m technology. It integrates 64 identical channels and, in each channel, a conversion from the instantaneous current to a digital pulse frequency is performed where each digital pulse corresponds to a fixed input charge quantum. The converter accepts charges of both polarities and is followed by a 32-bit counter with up/down counting capability to measure both negative and positive currents.  
The maximum conversion frequency is 20 MHz, and the charge quantum is set to 200 fC, leading to saturation at the maximum input current of \(\pm\)4 \(\upmu\)A per channel. In order to overcome this limit, the anode current of each chamber is split evenly into the 64 channels of the chip, and all the counts of the channels are added to extend up to \(\pm\)256 \(\upmu\)A the input current range while preserving a linearity within \(\pm\)1% [13].  
The data acquisition system is based on the National Instruments PXle platform equipped with a system controller (NI PXIe-8115) and the FlexRIO FPGA board coupled with a high-speed digital I/O module (NI 6581). A LabVIEW program was developed to read out the number of counts of the three ICs through the FPGA board interfaced with three TERA08 chips. Hereafter, the collected charge is referred to as the number of counts, and each count corresponds to a charge quantum of 200 fC.  
Figure 1: (**a**) Exploded view of the detector assembly, where anodes, cathodes and empty frames are labeled as A, C, and F, respectively. (**b**) Picture of the detector box where the cover was removed.
# A Systematic Review of the Effects of Nutrient Intake in Handball Players on Exercise Performance
## 4 Discussion
### Vitamins  
Much of the literature collected in this review pays special attention to vitamin D intake and status in handball athletes. The effects of insufficient 25-OH-D3 levels on sports performance and the risk of injury are now widely understood [112; 113; 114]. In handball athletes it has been observed that although athletes have an adequate dietary intake of vitamin D [51; 52], a large proportion of them have insufficient (<30 ng/mL) or deficient (<20 ng/mL) serum levels of 25-OH-D3 [50; 54; 55; 59]. [PERSON] et al. [59] even showed how serum vitamin D levels failed to reverse with the use of supplementation, something that did happen with outdoor athletes, which may call for greater attention to vitamin D in court handball players compared to those in outdoor disciplines such as beach handball. These data highlight the importance of monitoring this micronutrient in indoor sports such as court handball, as well as taking into account several additional factors that may influence the vitamin D status of handball athletes, such as the race of the athlete, teams or players from higher latitudes, and times of lower sun exposure such as winter or early spring [115]. In relation to other vitamins, only two studies showed insufficient intakes of B1, B2, B12 and folic acid [29; 33; 53]. These data should be interpreted correctly, as reference values for micronutrient intakes vary depending on the study and the country of the selected sample. However, it could be speculated that handball athletes may have an older adult requirement for riboflavin and vitamin B6 because of their involvement in the main energy pathways during exercise [33; 116; 117], while others such as vitamin B12 or folic acid involved in cell synthesis and repair do not seem to show special differences with handball practice, although a low intake of plant-based foods or special situations such as following vegetarian diets or periods of pregnancy could compromise the status of some of these micronutrients in handball athletes [118; 53; 116; 119]. In addition, handball athletes should pay greater attention to the specific needs of different vitamins and micronutrients related to antioxidant capacity due to the increase in biomarkers of oxidative stress produced, especially during periods of higher intensity training and competition [120; 121]. A greater adequacy of intake of these antioxidant capacity vitamins in handball athletes is required [22; 30; 36].