esa-sceva/satcom-chunk-collection huggingface dataset:
# Check for special characters and punctuation
## 5 Discussion
### Security Analysis
#### 5.7.3 Security Features  
DeepStego implements the following security features:  
1. Randomized Mapping: - Binary sequences mapped to synonyms using cryptographically secure randomization - Different mapping for each message prevents pattern analysis
2. Message Segmentation: - Secret message divided into chunks using divide_chunks function - Variable bit allocation across words prevents consistent patterns  
This security approach enables DeepStego to significantly outperform existing methods in resisting detection while maintaining practical usability.
# On the Needs and Requirements Arising from Connected and Automated Driving
One objective of the project was to identify the use cases that are needed for advanced driving and their associated requirements, from the perspectives of both automotive applications and communication networks. In this paper, we summarize and extend the key findings of the 5 GCAR project in terms of use cases that form building blocks for connected automated driving. To the best of our knowledge, this is the first paper that relates the requirements on the communication network to the actual requirements from the automotive applications. The main contributions are as follows:  
* We identify a set of representative use cases that are based on an analysis of the demands arising from connected and automated driving. * We study the selected use cases in more detail to identify the corresponding challenging requirements and derive the key performance indicators (KPIs). * Based on the identified use cases and requirements, we discuss the existing V2X technologies and solutions, and point out valuable future research directions for satisfying the stringent requirements. The rest of the paper is organized, as follows. Section 2 discusses the future needs that arise from connected and automated driving and proposes a classification of the related novel V2X applications. Section 3 describes five representative use case classes that have been elaborated in 5 GCAR and will contribute to making the vision of connected and automated driving become a reality. Section 3 also translates the technical requirements from the automotive domain into requirements for the telecommunication network. Section 4 proposes a methodology for deriving the requirements on the communication systems from the automotive requirements, which is then applied to specify the requirements for the selected use cases. Section 5 draws the perspective between the use case and requirements previously identified and the one defined by the community, principally 3 GPP and the 5G Automotive Association (5 GAA) [6]. Section 6 reviews related work and future research challenges. Finally, Section 7 concludes the paper.
## 2 Materials and Methods
### Preparation of the Post Spaces  
Cytec Blanco (Hahnenkratt, Konigsbach-Stein, Germany) 1.2 mm diameter post drills were used to complete the preparation of the post space. The working length was set as 10 mm. Different irrigation protocols were applied to remove the smear layer and dentin chips formed during the post space preparation processes. The samples were randomly split into 3 groups, with 40 in each group, according to the irrigation procedure to be applied (\(n=40\)). The experimental study design of each group is given in Figure 1.  
* NaOCl group (control) (\(n=40\)  
Figure 1: Flowchart of the methodology.  
After post space preparation with post drills, post space irrigation was performed with 3 mL 2.5% NaOCl using conventional needle irrigation for the removal of the formed smear layer. The total irrigation time was set at 80 s;  
* NaOCl + EDTA group (\(n=40\))  
After post space preparation with post drills, post space irrigation was carried out with 3 mL 2.5% NaOCl for 40 s, then irrigated with 3 mL 17% EDTA for 40 s using conventional needle irrigation for the removal of the formed smear layer. The total irrigation time was set at 80 s;  
* (NaOCl + EDTA) + passive ultrasonic irrigation group (\(n=40\))  
After post space preparation with post drills, 2.5% NaOCl was applied to the prepared post cavities using conventional needle irrigation for 5 s, followed by activation with a Vdw Ultra (VDW, Munich, Germany) device using Irrisafe 20# ultrasonic tips (Acteon, Merignac, France) for 5 s, with the setting at IRRI Mode. This 10 s process (5 s NaOCl irrigation + 5 s ultrasonic irrigation) was repeated for 4 cycles. The same application was performed for 17% EDTA solution (10 s process = 5 s NaOCl irrigation + 5 s ultrasonic irrigation) and was repeated for 4 cycles. The total irrigation time was 80 s. Three mL of distilled water were used as the final irrigation solution for all three groups, and the post spaces were dried with paper cones.
## 2 Literature Review
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline \multicolumn{1}{c}{**Parameters**} & \multicolumn{5}{c}{**Sentiments**} \\ \multicolumn{1}{c}{**Paper**} & **Language** & **Cg \({}^{*}\)** & **Fg \({}^{**}\)** & **Number of Sentiments** & **Methods** & **Data Source** & **Data size** & **Performance** \\ \hline \hline \multirow{3}{*}{[PERSON] et al. [19]} & \multirow{3}{*}{English} & \multirow{3}{*}{\(\checkmark\)} & & \multirow{3}{*}{3} & Naive Bayes, & & & \\  & & & & SAM, & AdaBoost, & & & \\  & & & & LinearSVC, & & Logistic & & \\  & & & & & Regression & & & \\ \hline \multirow{3}{*}{[PERSON] et al. [35]} & \multirow{3}{*}{English} & \multirow{3}{*}{\(\checkmark\)} & & \multirow{3}{*}{6} & DNN, LSTM, & & & \\  & & & & & LSTM+ & & & \\  & & & & FastText, & & & \\  & & & & LSTM+Glove & & & \\ \hline \multirow{3}{*}{[PERSON] and [PERSON] [40]} & \multirow{3}{*}{English} & \multirow{3}{*}{\(\checkmark\)} & & \multirow{3}{*}{2} & Gradient & & \\  & & & & & boosting, & & \\  & & & & & Random Forest, & & \\ \cline{1-1} \cline{5-7}  & & & & Simple embedding+ & & & \\ \cline{1-1} \cline{5-7}  & & & & & & average pooling, & & \\ \cline{1-1} \cline{5-7}  & & & & & BLSTM & & \\ \hline \multirow{3}{*}{[PERSON] et al. [45]} & \multirow{3}{*}{English} & \multirow{3}{*}{\(\checkmark\)} & \multirow{3}{*}{11} & BERT, & & & \\  & & & & RoBERTa & & & \\ \cline{1-1} \cline{5-7}  & & & & 3 & CNN, LSTM & & & \\ \hline \multirow{3}{*}{[PERSON] et al. [38]} & \multirow{3}{*}{English} & \multirow{3}{*}{\(\checkmark\)} & & \multirow{3}{*}{2} & [PERSON], & & & \\  & & & & Logistic & & & \\ \cline{1-1} \cline{5-7}  & & & & Regression & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [39]} & \multirow{2}{*}{Chinese} & \multirow{2}{*}{\(\checkmark\)} & & \multirow{2}{*}{3} & BERT & Weibo & 120,000 & 0.75 \\ \cline{1-1} \cline{5-7}  & & & & 2 & [PERSON] & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [21]} & \multirow{2}{*}{English} & \multirow{2}{*}{\(\checkmark\)} & & \multirow{2}{*}{2} & [PERSON] & Twitter & 523,000 & - \\ \cline{1-1} \cline{5-7}  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the surveyed sentiment analysis systems in COVID-19 context (Performance refers to F1-score unless indicated otherwise).
# A Self-Diagnosis Method for Detecting UAV Cyber Attacks Based on Analysis of Parameter Changes
## 1 Introduction
Finally, their proposed method can ensure that the receiver keeps the authenticated signal blocked when the correlation peak of the fake signal overlaps or moves away from the correlation peak of the authenticated signal and can further ensure that the receiver is positioned correctly in the presence of spoofing. However, many authors say these previous methods are prone to false positives. Instead we present a method for detecting anomalies and attacks on UAVs based on the analysis of data collected and analyzed from the UAV's own sensors only. The analysis compares normalized readings of the sensors at different points in time and identifies the degree of difference between them. This offers a new method for detecting anomalies and attacks on UAVs based on the analysis of changes in the UAV's state. The main difference of our method from the methods discussed above is that, to detect an attack, we do not need reference values or need to correlate the collected data with additional GPS receivers or other external systems. Our \"self-diagnosis\" method is based on the UAV's ability to detect critical changes in its own behavior over time. The analysis is based on the collection and processing of data received from various parts of the UAV's sensor system. Any sensors that produce numerical readings can be used. At the same time, as experimentally confirmed, our method makes it possible to detect accurately the presence of anomalous behavior and identify an attack. The main purpose of this article is to present our method for detecting UAV sensor anomalies under the influence of a GPS attack. Therefore, the following tasks should be solved:  
* To develop and implement a stand for carrying out an attack on a UAV;
* To carry out flight tests with a UAV in normal mode and during an attack;
* To collect the results in the form of UAV logs;
* To analyze the logs and identify the parameters that are susceptible to attack;
* To get raw data for testing the method;
* To develop a method for detecting anomalies for a UAV and to prove its effectiveness.
# The Computability of the Channel Reliability Function and Related Bounds
## 4 Computability of the Channel Reliability Function and the Sequence of Expurgation Bound Functions
This implies that the sequence \(\{R^{ex}_{k}\}_{k\in\mathbb{N}}\) is computable, since we have an algorithm that can generate each function in the sequence. Notably, we can express \(f_{k}(\cdot)\) as \(R^{ex}_{k}(\cdot)\). Given an input \(k\), the Turing machine \(TM_{*}\) produces the description of \(E_{ex}(\cdot,\cdot,k)\), from which \(R^{ex}_{k}\) can be directly obtained via projection (in the sense of primitive recursive functions). According to [PERSON], [PERSON], and [PERSON] [13], the following limit holds:  
\[\lim_{k\to\infty}R^{ex}_{k}(W)=C_{0}(W)\]  
for all \(W\in\mathcal{C}H(\mathcal{X},\mathcal{Y})\). Furthermore, the sequence \(\{R^{ex}_{k}(W)\}_{k\in\mathbb{N}}\) is monotonically increasing, i.e.,  
\[R^{ex}_{k}(W)\leq R^{ex}_{k+1}(W)\quad\text{for all }k\in\mathbb{N}\text{ and }W\in\mathcal{C}H(\mathcal{X},\mathcal{Y}).\]  
Let us consider the set  
\[\{W\in\mathcal{C}H_{c}(\mathcal{X},\mathcal{Y}):C_{0}(W)>\lambda\}\]  
for \(\lambda\in\mathbb{R}_{c}\) with \(0<\lambda<\log_{2}(\min\{|\mathcal{X}|,|\mathcal{Y}|\})\). We are now constructing a Turing machine \(TM_{*}\) with only one holding state, \"stop\", which means that it either stops or computes forever. \(TM_{*}\) should stop for input \(W\in\mathcal{C}H_{c}(\mathcal{X},\mathcal{Y})\) if and only if \(C_{0}(W)\) applies, that is, \(TM_{*}\) stops if \(W\) is in the above set. According to the assumption, \(\{R^{ex}_{k}(\cdot)\}_{k\in\mathbb{N}}\) is a computable sequence of Turing computable channel functions. For the input \(W\), we can generate the computable sequence \(\{R^{ex}_{k}(W)\}_{k\in\mathbb{N}}\) of computable numbers. We now use the Turing machine \(TM^{1}_{\lambda}\), which receives an arbitrary computable number \(x\) as input and stops if and only if \(x>\lambda\), i.e., \(TM^{1}_{\lambda}\) has only one hold state and accepts exactly the computable numbers \(x\) as input for which \(x>\lambda\) holds. We now use this program for the following algorithm. 1. We start with \(l=1\) and let \(TM^{1}_{\lambda}\) compute one step for input \(R^{ex}_{1}(W)\). If \(TM^{1}_{\lambda}(R^{ex}_{1}(W))\) stops; then, we stop the algorithm. 2. If \(TM^{1}_{\lambda}(R^{ex}_{1}(W))\) does not stop, we set \(l=l+1\) and compute \(l+1\) steps \(TM^{1}_{\lambda}(R^{ex}_{r}(W))\) for \(1\leq r\leq l+1\). If one of these Turing machines stops, then the algorithm stops; if not, we set \(l=l+1\) and repeat the second computation. The above algorithm stops if and only if there is a \(k\in\mathbb{N}\) such that \(R^{k}_{ex}(W)>\lambda\). But this is the case (because of the monotony of the sequence \(\{R^{ex}_{k}(W)\}_{k\in\mathbb{N}}\)) if and only if \(C_{0}(W)>\lambda\). But with this, the set  
\[\{W\in\mathcal{C}H_{c}(\mathcal{X},\mathcal{Y}):C_{0}(W)>\lambda\}\]  
is semi-decidable. So, we have shown that this is not the case. We have thus created a contradiction.
## 3 Results
The limitations in performing fine parameter tuning confirmed that an automated, pixel-by-pixel adjustment of the search window can produce more effective results than a manual  
method that involves human error to adjust the search window consistently. Despite being computationally intensive, the proposed method is able to achieve optimal results for resolution and noise, which makes it viable in real-world radiological images. Figure 5a shows an ESF graph obtained by applying a low-dose CT image and each filtering method. The normalized LSF graph calculated from the obtained ESF is shown in Figure 5b. In the graph in Figure 5b, we confirmed that the shapes of the low-dose CT image without the applied filter and the profile using the adaptive NL-TV filter were the most similar. In contrast with the profile displayed for unprocessed low-dose CT images, the profile displayed by applying TV and NL-TV to the low-dose CT image contained an error in the shape of the graph in the edge area. A graph of the sigma values calculated from the obtained LSF is shown in Figure 6. The calculated sigma values were derived as 1.82, 1.52, 1.50, and 1.50 for the low-dose CT images processed using TV, NL-TV, and adaptive NL-TV filters and unprocessed low-dose CT images, respectively. The best sigma value was confirmed in the unprocessed low-dose CT image, but the adaptive NL-TV filter produced comparable results. Figure 3: (**a**) CNR and (**b**) COV graphs measured from the resulting image after the application of each filtering method to the acquired low-dose CT image. In both the CNR and COV result graphs, the excellent value was derived from the adaptive NL-TV filter regardless of the hole diameter. Figure 4: Images obtained by applying low-dose CT and filters to evaluate spatial resolution. In the acquired low-dose CT image (**a**), the boundaries of the holes are clearly observed, but the overall noise level appears to have increased (the AB line indicates the position for measuring the spread function.). As a result of applying (**b**) TV, (**c**) NL-TV, and (**d**) adaptive NL-TV filters to (**a**) image, it was confirmed that the overall noise level was reduced in the images. As a result of applying TV and NL-TV filters in the area indicated by the red arrow in (**d**), we were able to observe that the boundary surface was more clearly distinguished than in the image. Figure 5: (**a**) ESF and (**b**) normalized LSF graph results measured at the AB line marked in red in Figure 4a. ESF and LSF graphs of almost similar shapes were observed in the unprocessed low-dose CT image and the image processed using the NL-TV filter. Figure 6: Sigma value results graph according to each filtering method. The sigma value calculated from LSF was derived from the best value in the unprocessed low-dose CT image and the image processed using the adaptive NL-TV filter method.
# Performance Limits of GNSS Code-based Precise Positioning: GPS, Galileo & Meta-Signals
## 7 Results: Time-Delay Estimation Accuracy Limit for Some Representative GNSS Signals
### Theoretical Performance Limits of AltBOC-Type GNSS Signals  
A remaining point is to assess the theoretical performance limits of the promising AltBOC-type signals, that is, which is the ultimate achievable time-delay estimation performance for higher SNR at the output of the matched filter and which are the conditions which allow to achieve such results. From the previous results, we know that the MLE for these types of signals is efficient if the SNR\({}_{\text{out}}>16\) dB for the E5, SNR\({}_{\text{out}}>19\) dB for the AltBOC(35,10,5), and SNR\({}_{\text{out}}>21\) dB for the AltBOC(50,10,5); thus, in the following, we do not plot the MLE. Notice that, to be able to increase the SNR at the output of the MLE, we must increase the coherent integration time (refer to Equation (25)), but the E5 is limited to the 4 ms data bits, and the meta-signals are limited to the 1 ms bits of the E6. Then, let us assume that we can obtain the navigation message by other means, perform a data wipe-off, and therefore use longer coherent integration time periods. In this case, for a \(C/N_{0}=45\) dB-Hz, we can obtain a SNR\({}_{\text{out}}=30,35,40,45\), and 50 dB by roughly integrating 30 ms, 100 ms, 300 ms, 1 s, and 3 s, respectively. For Galileo signals, the nominal \(C/N_{0}\) is considered around 50 dB-Hz, which translates to an integration time roughly equal to 10 ms, 35 ms, 100 ms, 350 ms, and 1 s, respectively. Notice that these values are the typical ones considered in the context of HS-GNSS receivers [38] to be able to operate at very low \(C/N_{0}\) such as indoors or in space exploration. The standard deviation results obtained with such long integration times are summarized in Table 2. This is an important result, which shows that we can go below the time-delay cm accuracy if we adopt long coherent integration times.
## 3 Discussion  
This study provided valuable insights into the use of finite element modelling (FEM) and power spectral density (PSD) simplifications to predict fatigue life in two distinct mechanical systems under random vibration conditions. The key contribution of this research lies in the demonstration that PSD simplifications can significantly streamline fatigue life predictions without sacrificing accuracy, as well as in the validation of FEM models for complex dynamic systems.
###### Abstract  
This paper investigates the performance of dual-hop unmanned aerial vehicle (UAV)-assisted communication channels, employing a decode-and-forward (DF) relay architecture. The system leverages terahertz (THz) communication for the primary hop and visible light communication (VLC) for the secondary hop. We conduct an in-depth analysis by deriving closed-form expressions for the end-to-end (E2E) bit error rate (BER). Additionally, we use a Monte Carlo simulation approach to generate best-fitting curves, validating our analytical expressions. A performance evaluation through BER and outage probability metrics demonstrates the effectiveness of the proposed system. Specifically, our results indicate that the proposed system outperforms Free-Space Optics (FSO)-VLC and Radio-Frequency (RF)-VLC at a higher signal-to-noise ratio (SNR). The results of this study provide valuable insights into the feasibility and limitations of UAV-assisted THz-VLC communication systems.  
BER; outage probability; THz communications; VLC; UAV Article
# _Applied_
## 3 Results
### Isoorienttin and Caffe Acid of SB Extracts Were Identified by HPLC Analysis  
To identify the components responsible for the neuroprotective activity of the SB extracts, we used HPLC. A major component of SBW and SBE was isoorienttin (Figure 3, peak 10). As shown in Table 2, the amounts of isoorienttin (6.75 \(\pm\) 0.17 mg/g extract, d.b.) and caffe acid (0.73 \(\pm\) 0.01 mg/g extract, d.b.) in SBE were higher than in SBW (2.70 \(\pm\) 0.03 and 0.72 \(\pm\) 0.00 mg/g extract, d.b., respectively). Therefore, isoorienttin is linked to the neuroprotective activity of the SB extracts.  
Figure 2: (a) SBW and SBE (12.5 to 200 \(\upmu\)g/mL) reduces H\({}_{2}\)O\({}_{2}\)-induced ROS generation in PC12 cells. (b) SB extracts (25 to 200 \(\upmu\)g/mL) reduced ROS generation in non-treated cells in a concentration-dependent manner. Negative control (-) means untreated group. SBW, _Sasa borealis_ water extract; SBE, _Sasa borealis_ ethanol extract. Data are expressed as mean \(\pm\) SD (n = 3). Lowercase letters represent statistical differences at \(p<\) 0.05. (c) Cells were cultured with SBE (50–200 \(\upmu\)g/mL) and H\({}_{2}\)O\({}_{2}\) (50 \(\upmu\)M) and stained with DCF-DA. H\({}_{2}\)O\({}_{2}\)-induced ROS production in PC12 cells was visualized by fluorescence microscopy. Scale bar, 5 \(\upmu\)m.  
Next, we examined the correlations among antioxidant activity, isoorientin, caffeic acid content, and the intracellular ROS generation (Table 3). DPPH and ABTS+ radical-scavenging activities were positively correlated with isoorientin (0.995 and 0.959, respectively) and caffeic acid (0.889 and 0.902, respectively) contents, whereas antioxidative activities (DPPH and ABTS+) were inversely correlated with cellular ROS generation (\(-\)0.932 and \(-\)0.887, respectively). The isoorientin content was positively correlated with ROS generation in the PC12 cells (\(-\)0.925). Therefore, a major constituent of SB extracts, isoorientin, is associated with their antioxidant and neuroprotective activities.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Phenolic Acids**} & \multicolumn{2}{c}{**Content**} \\  & & **(mg/g Extract, Dried Basis)** \\ \cline{3-3}  & & **SBW 1** & **SBE 2** \\ \hline
6 & Caffeic acid & 0.72 \(\pm\) 0.00 2  \\
10 & Isoorientin & 2.70 \(\pm\) 0.03  \\ \hline \hline \end{tabular}
\end{table}
Table 2: Phenolic acid (caffeic acid) and flavonoid (isoorientin) contents of SB extracts by HPLC.  
Figure 3: HPLC of phenolic compounds in SB extracts. (**a**) The peaks represent the following phenolics (0.5 mg/mL): 1, gallic acid; 2, homogenistic acid; 3, protocatechic acid; 4, chlorogenic acid; 5, vanillic acid; 6, caffeic acid; 7, \(p\)-cumaric acid; 8, ferulic acid; 9, veratric acid; 10, isoorientin; 11, ellagic acid; 12, naringin; 13, rosmarinic acid; 14, cinnamic acid; 15, quercetin; 16, luteolin; 17, naringinin; 18, apigenin; 19, kampferol; 20, tricin. (**b**) _Sasa borealis_ water extract (SBW), (**c**) _Sasa borealis_ ethanol extract (SBE).
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 3 Opportunities Given by System-Level Testing
### Opportunities Given by SEE Tests
#### 3.2.1 Margins Given by the System Are Incorporated in Test Results  
Some kind of margins might be also expected in the SEE system-level testing: masking of soft SEEs (such as single event upsets (SEUs) or single event transients (SETs)) or mitigation of some hard SEEs (such as single event latchup (SEL)) by protection mechanisms applied at system-level. Therefore, system-level SEE testing might also give more realistic results than tests at the component level because these masking and mitigation effects will be included in test results.  
As an example of soft SEE masking, several test campaigns (e.g., heavy ion tests at GANIL, CHARM mixed-field, CHARM heavy ions, heavy ion tests at UCL, laser tests using the IES PRESERVE platform of the University of Montpellier) enabled to observe SETs at the voltage reference chip output, but most of these transients were not propagating to the general output of the device. These SETs were filtered out by the internal filter in the PoL circuit. An example of such a non-propagating transient (one of the several thousand similar events recorded during the CHARM UHE experiment) is presented in Figure 4 (left curve).  
Such results might be used for verification of the mitigation techniques efficiency in the system.