esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
When considering the use of riblets on an aircraft, it is necessary to consider fabrication costs, maintainability, and sustainability through changes in the environment where they are exposed, while maintaining aircraft performance. It is also necessary to optimize riblet design according to their locations on the aircraft (e.g., fuselage, wing, etc. ), and for any changes in flight conditions and attitude. To this end, it is vital to investigate how riblets affect boundary layer flowfields and to explore useful fabrication techniques. The Japan Aerospace Exploration Agency (JAXA) has been investigating the effectiveness of riblets to reduce skin friction drag [18; 19] as part of their environmentally friendly aircraft research project: Flight Investigation of skin-friction reducing Eco-coating (FINE). The riblet study aims to develop riblets fabricated by using aviation paint and to demonstrate their drag-reducing capability by flight tests. Since many insights on the use of riblets for reducing skin friction drag have been revealed as mentioned above, this study focused on exploring a new evaluation approach for the reduction of skin friction drag. The primary objective of this study was to propose a unique method to evaluate the reduction of skin friction drag by emphasizing the turbulent length scales. To this end, the riblet designed by the JAXA's FINE project was employed as a reference riblet in wind tunnel experiments to obtain boundary layer characteristics being altered from those of a flat smooth surface. Note that the geometry optimization of riblets is not a scope of this study since this study focused on the applicability of the proposed method to evaluate the reduction of skin friction drag. Hot-wire anemometry was used for primary data acquisition since the technique offers high-resolution rapid measurements. By using the detected signals from the hot-wire probe, signal processing and statistical autocorrelation analysis were employed to evaluate the reduction of skin friction drag. The method compared the riblet surface to the smooth surface without riblets. Anotherbenefit of using hot-wire anemometry is that it may reduce uncertainty inherent in the experiment since less equipment is required than in optical diagnostics [20; 21], pitot-rake measurements, or cantilever experiments [22; 23]. The signal was temperature corrected to convert the raw output voltage [24] to quantitative freestream velocity (\(U\) velocity). Based on the \(U\) velocity data obtained by the hot-wire anemometry, the reduction of skin friction drag was evaluated by relating the skin friction drag coefficient with turbulent scales derived from statistical analysis. The turbulent boundary layer profile obtained with the riblet surface was compared to that obtained with a smooth flat plate surface.
## 2 Related Work
### Clinical Tools for Fall Risk Assessment  
Functional mobility assessments, such as the Tinetti Mobility Test [30], the Berg Balance Scale [31], and the Dynamic Gait Index [32], have been widely used to measure balance and fall risk. These tools are useful for identifying functional limitations that may predispose individuals to falls. However, they present limitations, as they do not capture critical factors such as reactive postural adjustments or changes in gait speed, which are key to assessing fall risk [33]. Additionally, many of these tools rely on the subjective evaluation of clinical staff, which can introduce variability and bias.
# Estimating the Optimal Overall Slope Angle of Open-Pit Mines with Probabilistic Analysis
## 3 Results
### Deterministic vs. Probabilistic Analysis
#### 3.1.1 Factor of Safety (FOS)  
The factor of safety (**FOS**) or critical strength reduction factor (CSRF) refers to the value by which soil/rock mass strength properties (e.g., cohesion, **C** & friction, \(\varphi\)) have to be reduced to reach failure. Figure 4 shows the factor at various slope angles. The factor of safety of 1.5 was decided as the threshold that corresponds to the safe ultimate slope angle. It can be seen that the factor of safety decreases as the slope angle increases. The deterministic analysis shows that, based on the selected threshold (e.g., **FOS** = 1.5) [57], it is not safe to design an open-pit mine in which the overall slope angle exceeds 45\({}^{\circ}\). Probabilistic analysis shows that it is unsafe to design an open-pit mine with an overall slope angle beyond 50\({}^{\circ}\). It is noteworthy to mention that the deterministic analysis gives only a single output, which does not give any information about the variability of the input variable (e.g., no distribution). However, probabilistic analysis is considered most beneficial and accurate with limited data (e.g., there is a distribution for the input variable). The probability density function (PDF) of the factor of safety after 27 simulations (e.g., using [PERSON]'s PEM), at various overall slope angles, is displayed in Figure 5.  
As illustrated in Figure 5, the area under the curve that corresponds to **FOS** < 1.5 increases as the slope angle increases. The probability of failure, _Pf_, at different overall slope angles, based on the estimated factor of safety (**FOS**), will be presented and discussed later (e.g., Section 3.2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rock Mass Property** & \begin{tabular}{c} **Mean** \\ **(\(\mu\))** \\ \end{tabular} & \begin{tabular}{c} **Standard Deviation** \\ **(\(\sigma\))** \\ \end{tabular} & \begin{tabular}{c} **Coefficient of** \\ **Variation ( \(\delta\)=\(\frac{\sigma}{\mu}\))** \\ \end{tabular} \\ \hline \begin{tabular}{c} Cohesion (C), Mpa \\ Friction angle (\(\phi\)), deg. \\ Young’s modulus (E), GPa \\ \end{tabular} & \begin{tabular}{c} 0.757 \\ 48.96 \\ 44.70 \\ \end{tabular} & \
\begin{tabular}{c} 0.151 \\ 9.79 \\ 8.94 \\ \end{tabular} &
\begin{tabular}{c} 0.20 \\ 0.20 \\ 0.20 \\ 0.20 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Random properties for rock mass I [56].  
Figure 4: Factor of safety versus different overall slope angles (deterministic vs. probabilistic analysis).  
Figure 5: Cont.  
Figure 5: _Cont._
## 4 Discussion
### Testing of the Developed Encryption/Decryption Algorithm on ZCU102 Evaluation Board  
After the generation of the bitstream file related to the developed project including the cascade of the encryption and decryption blocks, the file has been loaded on the FPGA-ZCU102 evaluation board. To monitor the interest signals, the IP Integrated Logical Analyzer (IL) has been added to the Block Design; also, to verify the correctness of the decrypted packets, provided by the system constituted by the encryption and decryption blocks connected in cascade, during the test phase, only a single encryption key has been used, initially loaded into four 32-bit registers and subsequently automatically validated; therefore, the error_sig signal produced by the Pattern_Verificator block remains low, thus indicating the errors' absence in the comparison of the packets received by the decryption block and those contained in the Pattern_Verificator table.  
The tests carried out on the board confirmed the proper operation of both encryption and decryption algorithms, complying with the operation resulting from the post-implementation simulations reported in the previous paragraph. In Figure 24, the temporal trends related to the complete encryption/decryption system are shown, in which the plaintext data packets, provided every 9.5 clock periods, are accepted by the encryption block (red box) and thus the encrypted packets are delivered to the decryption block (white box), thereby obtaining the decrypted packets downstream (blue box in Figure 24). As expected, the error_sig signal remains low along the observation period, indicating that the processing of the packets is performed correctly, namely the packets leaving the decryption block are equal to those provided at the input to the encryption block.  
Figure 24: Temporal trends with shown the plaintext data packets entering the system (red-dashed box), the encrypted ones delivered by the encryption block to the decryption block (white-dashed box) and finally decrypted packets provided in output by the decryption block (blue-dashed box); as evident, the plaintext data packets provided in input to the system are equal to those provided by the decryption block (as indicated by the red arrow), also demonstrated by error_sig signal, which remains low along the observation period (yellow-dashed box).  
In Figure 25, the temporal trends related to the complete encryption/decryption system are shown, in which the plaintext data packets are provided in input on each rising edge of the clock signal; as can be noticed, also, in this case, the error_sig signal remains low, indicating the proper operation of the encryption/decryption system.
## 3 Results
### Results of the Second Experimental Phase  
In this phase, after the division of the path into homogeneous zones and the assignment of levels based on the accident rate and on the levels of stress, it was possible to fill the correlation matrix. This matrix, described in Section 2.5.1, takes into account all accidents selected. At this time, two important results become evident, as follows:  
* The first result concerned the testers, and it is the lack of significant differences between habitual users and users who followed the path for the first time;
* The most meaningful result was that the greater number of accidents was positioned on the diagonal of the matrix (Figure 13). This condition defines, thus, a substantial correspondence of psychophysical state-incidental level; specifically, a stressful psychophysical state corresponds to a high accident rate, and, at the same time, a relaxed state corresponds to a limited accident rate.  
The outputs highlighted that this second experiment allowed the identification, in a more defined way, of a correlation between stress and accidents. The result obtained made it possible to define a scoring method useful to support the identification of the sections that may require interventions to reduce the risk of accidents.  
Relying on the scoring method, it was possible to make the two following observations:  
Figure 12: Example of a route—accidents identified in red, and stress points in blue. Circles indicate the buffer around stress points (QGIS elaboration of Google Maps).  
Figure 13: Correlation matrix of psychophysical status and the level of accident. Data are expressed in percentages.  
* Considering the total number of stressful zones (28 zones were classified as stressful), representing 45% of the total zones of the path, it can be observed that, among these, 14 are located in areas with high accident rates. It follows that half of the zones characterized by stress have a direct correspondence with high-accident areas;
* By sorting the 62 zones by their respective stress scores, and by selecting the 15 sections with a higher stress score (more stressful), 12 of these areas (80%) were represented by high-accident areas, as reported in Figure 14.  
This represents an appreciable and promising result that shows how this method, referring to the test developed, is able to provide an effective tool to support preventive road safety analysis.
## 3 Methods
### Word Embeddings  
Word embedding (WE) is a representation technique of a text where the words with the same meaning have a similar representation. Recently, there have been several word embeddings widely used in ML and DL models. In the literature, there are many pre-trained WEs that can be categorized into two groups [10]: static representation models and contextual models. Word2 vec, GloVe, and FastText are types of static WEs that can convert a text into vectors of meaningful representation.  
* _Word2 vec_ works as a language model [35], which is widely used for many NLP tasks. In general, the word2 vec embeddings can be obtained using either skip gram or common bag of words (CBOW) [36]. The skip-gram model computes the conditional probability of a word by predicting the surrounding context words given the central target word. The CBOW does the opposite of skip-gram, by computing the conditional probability of a target word give the context words surrounding it across a window of size \(k\)[23]. Mathematically, both CBOW (Equation (1)) and skip-gram (Equation (2)) models are trained as follows: \[J=\frac{1}{V}\sum_{t=1}^{V}\log p(w_{t}|w_{t-c},\ldots,\ w_{t-1},\ w_{t+1}, \ldots,\ w_{t+c})\] (1) \[J=\frac{1}{V}\sum_{t=1}^{V}\sum_{i=t-c,\ i\
eq j}^{t+c}\log p(w_{i}|w_{t})\] (2) where \(J\) is the loss function, \([-c,\ c]\) is the word context of the target word \(w_{t}\), and \(V\)-vocabulary size. In this work, we used both models and the results of their influence on the proposed model was examined. The pre-trained word2 vec word embeddings have 300 features which trained on 100 billion words.
* _GloVe_ is an unsupervised training \"count-based\" model [23]. Opposite to word2 vec, the _GloVe_ word embedding generates the embedding vector using word occurrences. Formally, the space vector is computed using a weighted least-squares method ([PERSON] et al., 2020) as follows: \[J=\sum_{i=1}^{V}\sum_{j=1}^{V}f(X_{ij})\Big{(}w_{i}^{T}\ w_{j}+\ b_{i}+\ b_{j}-\ log (X_{ij})\Big{)}^{2}\] (3) where \(V\) is the vocabulary size and \(f(X_{ij})\) is a weighting function. The smallest package of the embedding is 822 Mb, called \"glove.6B.zip\". The GloVe model is trained on a dataset having one billion words with a dictionary of 400 thousand words. There are different embedding vector sizes, with 50, 100, 200, and 300 dimensions for processing. In this paper, we used the 100 dimensional version.
* _Fast Text_ is an extension of the word2 vec approach where the word embedding is represented using n-gram [37]. Once the word has been represented using n-grams, a skip-gram model or CBOW is trained to learn the embeddings. Today, the pre-trained FastText word vector supports 157 languages. The main parameters that need to be adjusted before using the FastText word embedding are the dimension and the range of subwords size. By default, the size of 100 dimensions is used. However, it is allowed to have a value in the 100-300 range. In this paper, we set the dimensionality of word embeddings to 300.
## 4 Room Acoustic Parameter Analysis
### Clarity  
Clarity (\(C_{80}\)) characterizes the transparency of the sound and is computed from the RIR \(h(t)\) as follows [1; 31]:  
\[C_{80}=10\log(\frac{(\int_{0}^{80\,\mathrm{ms}}h^{2}(t)\;dt)}{(\int_{80\, \mathrm{ms}}^{\infty}h^{2}(t)\;dt)})\quad\mathrm{dB}\]  
Figure 7a,b present the \(C_{80}\) values across frequency in octave bands, derived from RIRs measured by the Rode NT-SF1 microphone and em32 Eigenmike, respectively. \(C_{80}\) has an inverse relation to \(T_{20}\)[35] and we can observe this between Figures 5 and 7. Similar to the \(T_{20}\) plots in Figure 5, the distinction between the felt and wood panels becomes apparent after 500 Hz, where the felt panel environment exhibits higher clarity. This aligns with expectations, as felt panels absorb more late reflections, resulting in enhanced clarity.  
Figure 8a,b illustrate 2D heatmaps of \(C_{80}\) values across receiver locations for both room paneling environments and all three source locations, calculated from the RIRs measured by the NT-SF1 microphone and em32 Eigenmike, respectively. Higher \(C_{80}\) values are concentrated directly in front of the source location, with lower values for receivers situated further away. This underscores the influence of loudspeaker directivity and interference of reflections from the walls. Source S3 comparatively has the lowest levels of clarity. This  
Figure 7: Clarity (\(C_{80}\)) across frequency in octave bands for the felt and wood environments calculated from the RIRs measured by (**a**) Rode NT-SF1 microphone, (**b**) em32 Eigenmike.  
may be potentially due to its proximity to glass doors and windows along the control panel wall, leading to increased interference from reflections. In both felt and wood environments, the highest C\({}_{80}\) values were observed for source S1. These observations are crucial for recording singers and speech sources, guiding the selection of optimal source-receiver placements.  
Figure 8: Clarity (C\({}_{80}\)) in dB at different receiver locations for each of the three source locations and the two wall paneling types calculated from the RIRs measured by (**a**) Rode NT-SF1 microphone, (**b**) em32 Eigenmike. The black arrowhead indicates the source location and orientation. The white areas represent spaces where no receivers were positioned.  
The spatial spread of high clarity around the sources is more restricted in Figure 8b compared to Figure 8a. This again corroborates the differences in spatial capturing capabilities of the two microphones, necessitating further analysis as mentioned in Section 4.1.
## 3 Interactions of Antitoxins and Toxins with Cellular Components
### Interactions Involving Antitoxins
#### 3.2.1 Direct Interactions with TA Promoters  
Antioxins and/or antitoxin-toxin complexes have the capacity to mediate transcriptional autoregulation. Antitoxins and/or antitoxin-toxin complexes bind in general to palindromic sequences located in the promoter region. These repressors sense the level of antitoxin and toxin proteins. An excess of toxin destabilizes the repressor complex and leads to derepression to adjust a steady-state level of both proteins. This property is called conditional cooperativity [26; 27; 58]. Mathematical models show that conditional cooperativity and stochastic increase of toxin levels can lead to a switch between the growing and non-growing state and can induce persister cell formation [107; 108]. Further layers of regulation can be involved, as in the case of the plasmid Axe-Txe system of _Enterococcus faecium_. This system contains two promoters. The major one, located upstream of the antitoxin gene, is cooperatively regulated by the antitoxin-toxin complex. A second promoter, which is embedded in the antitoxin CDS, could be involved in antitoxin:toxin ratio regulation in cooperation with the modulation of toxin transcript stability [109]. Furthermore, conditional cooperativity is not universal. Indeed, in the case of the _mqsRA_ system, the toxin destabilizes the MqsA-DNA complex. This is due to a partial overlap (Arg61) of the MqsA DBD and the toxin binding site [110].  
Despite antitoxin diversity, a limited number of DNA-binding domains are found in antioxins, _i.e._, ribbon-helix-helix fold (CcdA, ParD), helix-turn-helix (MqsA, RelB) and SpoVT/AbrB-type DNA-binding domains (VapB, MazE) [25; 111; 112; 113; 114; 115]. Antitoxins bind specific palindromes that can be inverted repeats, alternating palindromes or long and short adjacent palindromes [25; 115; 116; 117].
# A Novel Coding Based Dimming Scheme with Constant Transmission Efficiency in VLC Systems
## 2 System Model  
In this section, we first introduce the system model of the proposed scheme. For simplification, intensity modulation and direct detection (IM/DD) can be applied in the VLC system, where the message is carried by the light intensity emitted from the LED. In general, LEDs can be modeled as Lambertian emitters, and the line of sight (LOS) path is considered for the indoor VLC system where reflected light is much weaker then direct light [13]. At the receiver side, we assume perfect symbol synchronization. After the optical channel, the signal detected by the receiver can be expressed as:  
\[\textbf{y}=H\textbf{x}+\textbf{n}, \tag{1}\]  
where **x** denotes the transmitted optical signal and **n** can be considered as additive white Gaussian noise (AWGN) with variance \(\sigma^{2}\). \(H\) represents the optical channel gain in VLC links, which is given as [1]:  
\[H=\left\{\begin{array}{cc}\frac{\rho A_{s}(m+1)}{2\pi D^{2}}cos^{m}(\phi)T_{ s}(\psi)g(\psi)cos(\psi),&0\leq\psi\leq\Psi_{C}\\ 0,&\psi>\Psi_{C}\end{array}\right. \tag{2}\]  
As shown in Figure 1, \(m\) denotes the order of the Lambertian emission, defined as \(m=\frac{-\ln 2}{\ln(cos\Phi_{1/2})}\). \(\rho\) is the receiver responsivity. \(A_{r}\) denotes the effective receiver area of the receiver, and \(D\) is the distance between the transceivers. \(\phi\) and \(\psi\) are the angle of irradiance and incidence from the LED to the Photodetector (PD), respectively. \(T_{s}(\psi)\) is the gain of the optical filter, and \(g(\psi)\) is the gain of the optical concentrator. \(\Psi_{C}\) denotes the field of view (FOV) of the receiver.  
Figure 1: Optical channel model.  
Unlike RF systems, the transmitted optical signal should meet the practical lighting constraints, which should be restricted to be nonnegative and less than the maximum limit of light intensity. Without loss of generality, we make the peak light intensity \(P=1\). Meanwhile, considering the dimming function, the average light intensity of the transmitted optical signal is defined as \(\mathbb{E}(\mathbf{x})=\gamma P\), with dimming level \(\gamma\in(0,1)\). For reference, the whole process of the proposed dimming scheme is shown in Figure 2.  
Dimming controller: According to the dimming level \(\gamma\), the \"dimming controller\" puts parameter \(K\) into the \"message generation\" part and the \"dimming code\" part, generating block code \(\mathbf{b}\) and dimming code \(\mathbf{g}\), respectively.  
Dimming encoding: With the input block code and dimming code, the \"dimming encoding\" makes the two parts a bitwise AND operation to generate the transmitted codewords \(\mathbf{c}\) satisfying the dimming requirement.
# Wireless Home Assistive System for Severely Disabled People
## 4 Conclusions  
In this study, a WHAS including assistive input accessories, MCT, and human-machine interfaces has been successfully developed to help the severely disabled communicate with humans and machines. Many different types of assistive input accessories such as mechanical switches, sensing switches, and bio-signal switches can be effectively used to meet the special physical requirements or limitations of the severely disabled. MCT has been developed to ease the severely disabled in inputting commands and messages. Moreover, human-machine interfaces including PC-based AAC and home appliance control interface are developed to ease the severely disabled in communicating with human or devices, and then the quality of life can be greatly improved. The experimental results showed that the accuracy of the Morse codes typing test with MCT is about 92% and the errors include insertion errors (1.71%), deletion errors (2.99%), and substitution errors (5.13%). Therefore, the efficiency of MCT is also proved to be practical and feasible for the severely disabled. Besides, subjectively evaluating the performance of the human-machine interface showed that the severely disabled are very satisfied with the proposed PC-based AAC and home automation applications. Once the severely disabled can manipulate the computer, Internet, PC-based AAC, and home automation appliances, they will be getting more confident and independent; thus, less help from caregivers or family will be needed. In the future, a multi-language version of the novel assistive communication can be continuously further developed to help more severely disabled in the world.