esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Optimization of Relay Communication Rates in Clustered Drones under Interference Conditions
## 2 System Model
### Channel Model  
According to Reference [17], the operational trajectory of the relay drone in the air exhibits strong Line-of-Sight (LOS) characteristics with both the cluster of drones and the ground station. Therefore, the channel model between the relay drone and both the cluster of drones and the ground station can be approximated by the free-space propagation model of electromagnetic waves, which is described as follows:  
\[H^{w}(k)=G_{t}G_{r}(\lambda^{C})^{2}/(4\pi D(k))^{2}\ \forall k \tag{1}\]  
In this model, \(G_{t}G_{r}\) represents the product of the antenna gains of the transmitter and receiver, \(\lambda^{G}\) denotes the wavelength of the electromagnetic waves at different frequencies, and \(D(k)\) signifies the distance between the relay drone and either the cluster of drones or the ground station. For example, the expression for calculating the free-space path loss between the relay drone and the ground station node can be illustrated as follows:  
\[D(k)_{R}^{S}=\left\|Q(k)\right\|_{2}=\sqrt{\left(x_{r}^{uav}(k)\right)^{2}+ \left(y_{r}^{uav}(k)\right)^{2}+\left(z_{r}^{uav}(k)\right)^{2}}\ \forall k \tag{2}\]  
Jamming devices are typically hidden in relatively concealed areas to prevent detection and subsequent damage or destruction. Therefore, according to the 3 GPP [18], the channel model between the jammer and the relay drone is usually a probabilistic loss model that  
Figure 1: Schematic diagram of swarm drone relay communication under interference conditions.  
mixes LOS and Non-Line-of-Sight (NLOS) components. The calculation expression for this model is as follows:  
\[\Lambda=P^{L}(L^{D}+\theta)+P^{NL}(L^{D}+\delta+\theta) \tag{3}\]  
In this model, \(L^{D}\) represents the spatial propagation loss of the electromagnetic waves. The calculation formula for this loss is as follows:  
\[L^{D}=32.45+20\lg(D(k)_{J}^{R})+20\lg(f_{c}^{uav}) \tag{4}\]  
In this model, \(D(k)_{J}^{R}=\sqrt{\left(x_{r}^{uav}(k)-x^{l}(k)\right)^{2}+\left(y_{r}^{uav} (k)-y^{l}(k)\right)^{2}+\left(z_{r}^{uav}(k)\right)^{2}}\) represents the distance between the relay drone and the jammer, \(f_{c}^{uav}\) denotes the communication frequency of the relay drone, \(\delta\) refers to shadow fading, which is typically modeled as a log-normal distribution, and \(\theta\) indicates the Rician distribution. The Rician distribution is used to model small-scale fading, as jammers typically have high transmission power, and there is always a line-of-sight component present in the signal, making this distribution more suitable for real-world conditions.
# Production of Fungal Pigments: Molecular Processes and Their Applications
## 2 Pigmented Fungi and Biosynthesis of Pigments
### Melanins
Melanin is widely regarded to possess radioprotective properties due to its ability to scavenge free radicals generated by radiation, and the presence of its numerous aromatic oligomers allowing the dissipation of high-energy recoil electrons [50; 52]. It is proposed that melanized fungi isolated from extreme environments, such as the leakage areas around the Chernobyl nuclear power plant and Antarctic highlands, could be used for absorption and decontamination of nuclear waste [53].
## 1 Introduction
The experimental results show that our method can achieve the fully automated and effective extraction of sea ice classification information from the Arctic region during summer, and the proposed model is better in all relevant experimental metrics. This study can provide sea ice segmentation maps and sea ice concentration data for the fast and accurate identification of sea ice in the summer polar route sea ice area and provide guidance for the planning and dynamic correction of polar routes. The rest of this paper is organized as follows. Section 2 describes the study area and related data and presents the high-resolution sea ice concentration calculation method and the proposed network structure. The third section presents the experiments and an analysis of the results. Finally, Section 4 provide discussions of the results and conclusions, respectively.
# Combined Navigation and Tracking with Applications to Low Earth Orbit Satellites +
## 2 Deep Aiding Architecture
### Carrier and Code Loops Aiding  
The proposed loop-aiding model is based on a classic second-order FLL and an early-minus-late FLL-aided Delay Locked Loop (DLL) (Figure 4). The predicted quantities in (2) must be propagated at the epoch of loop update \(t_{U}\) such that prediction errors can be obtained by the difference from the tracked values at the previous epoch. The state variables of the FLL are the estimated frequency \(F_{NCO}\) and frequency rate \(a\), whereas \(\tau_{R}\) is the replica code phase. For small time steps (\(|t_{U}-t_{REF}|<1\) s, typical), a constant frequency rate can be assumed, resulting in the following expressions for the prediction errors:  
\[\begin{split}\delta\dot{F}_{m}^{p}=\dot{F}_{m}^{p}(t_{REF})-a\\ \delta F_{m}^{p}=F_{m}^{p}(t_{REF})+\dot{F}_{m}^{p}(t_{REF})(t_{U }-t_{REF})-F_{NCO}\\ \delta\tau_{m}^{p}=\tau_{m}^{p}(t_{REF})-F_{m}^{p}(t_{REF})\frac {t^{p}}{f^{p}}(t_{U}-t_{REF})-\tau_{R}\end{split} \tag{3}\]  
Figure 4: Code and carrier loop deep aiding model.  
Figure 3: Typical Xona satellites profiles for (**a**) frequency rate; (**b**) frequency; (**c**) elevation.  
The errors in (3) are smoothed by coefficients \(\alpha<1\). The scaled frequency and code-phase errors are injected in the loops as \"innovations\" on top of discriminator outputs. The scaled frequency rate error is simply combined with the \(a\) state variable.  
An example of frequency and frequency rate prediction errors for a static receiver and CN0 ramp scenario are plotted and shown in Figure 5. Errors increase due to thermal noise as the CN0 is ramped (starting from 43 dBHz down to 15 dBHz) until signals are dismissed after -550 s. Note that the CN0 of SV X103 is kept constant (at 43 dB Hz) for reference.
## 2 Methods
### Measurements
#### 2.2.5 Statistical Analysis  
Statistical analysis was performed using SPSS software (version 21.0; SPSS Inc., Chicago, IL, USA). The Shapiro-Wilk test was used to assess the normality of the outcome measures. Descriptive statistics were computed for all the demographic and outcome measures. The independent variables were the gait conditions (walking, PGT, and UGT). The dependent variables were the spatiotemporal (gait velocity, stride length, normalized stride length, GT time, and GT step length), kinematic (ROM of the hip, knee, and ankle joints), and kinetic (peak GRF-y and GRF-y impulses) data. The non-parametric Friedman test was used to analyze differences in kinematic and spatiotemporal data, excluding GT time and GT step length between conditions. Bonferroni-adjusted post hoc Wilcoxon signed-rank tests were used to compare individual pairs of conditions, and a \(p\)-value of less than 0.017 was considered statistically significant [23]. The Wilcoxon signed-rank test was used to analyze the differences in kinematic, kinetic, and spatiotemporal data (GT time and GT step length) between the PGT and UGT conditions. Statistical significance was set at \(p<0.05\).
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 8 IoT Security in 5G  
### Security Requirements of IoT  
Dealing with security concerns has always been a main challenge to solve in the field of IoT. Nowadays, the importance of handling privacy and security issues can not be overstressed, as the number of IoT devices are growing rapidly not only in civil/residential sectors, but in the industrial sector, as well. As IoT is an implementation and integration of different technologies and infrastructures, all security challenges and threats of each network technologies are inherited by  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Virtual Machines** & **Containers** & **Unikernels** \\ \hline Startup time & High & Low & Low \\ Size & Large & Small & Small \\ Secure & Yes & No & Yes \\ Used for FaaS & No & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of virtual technologies.  
the IoT system that utilizes these technologies. This implies that all layers of an IoT architecture are exposed to security risks [81].  
Moreover, IoT systems have unique characteristics that raise new types of security issues that do not exist in conventional networks. Devices that operate in the sensor/physical layer of an IoT system usually have limited computational power and low storage capacity; therefore, standard security solutions such as public key encryption and spread-spectrum techniques can not be applied [82] by all endpoints. Additionally, these systems often consist of heterogeneous subsystems that have different capabilities regarding defense mechanisms. In this case, the achievable security level in the whole system is determined by the most vulnerable node. Therefore the current paper of ours focuses mainly on low-powered nodes as the \"weakest link\" in terms of IIoT network security.  
Traditionally, security requirements within IoT and especially in IIoT applications are classified into three main categories--often cited as CIA. These are (i) confidentiality, (ii) integrity, and (iii) availability. Due to arise of new security threats, the CIA triad became insufficient to address every new threats, so other requirements such as Trustworthiness and Privacy became more important factors in this topic; however, these objectives are prioritized differently in industrial and residential applications.  
In spite of the emerging number of (Industrial) IoT applications, there is still no general standardized layered structure for IoT systems. In most cases, the common approach uses three layers: (i) Physical layer (often Sensor or Perception layer), (ii) Network layer, and (iii) Application layer. Some might even include a Data Processing Layer, especially for classifying issues that are closely related to cloud based services [83], such as in Table 7, which summarize security challenges in different layers of an IoT architecture.
# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 4 CBIR Method in the AUV Collaborative Search Mission
### Construction of Behavior Models Based on Fuzzy Inference
#### 4.1.4 Defuzzification  
The weighted average defuzzification method is used. The formula and specific process are as follows:  
\[y=\frac{\sum_{i=1}^{n}\mu(x_{i})\cdot x_{i}}{\sum_{i=1}^{n}\mu(x_{i})} \tag{3}\]  
where \(y\) is the defuzzified output, \(\mu(x_{i})\) is the membership degree of the \(i\)-th output value, \(x_{i}\) is the \(i\)-th output value, and \(n\) is the total number of output values.  
At this point, we have completed the mapping from the state space set to the action space set based on the observed data. If communication is good and the information exchange delay between AUVs is small, we can directly use this method to infer the intentions of other AUVs. However, as discussed earlier, the acoustic communication delay is quite large and variable. The same data arriving at the receiving AUV may have already lost its timeliness. Therefore, it is necessary to use data that reflects both communication quality and mission completion status but is less sensitive to time, to recognize the intentions of the AUV.
# Machine Learning Techniques for Non-Terrestrial Networks
## 5 Machine Learning General Aspects
### Machine Learning Methods Taxonomy
#### 5.1.2 Unsupervised Learning  
In unsupervised learning, the training set consists of a collection of unlabeled inputs, without knowing the related expected output. The aim in this case is to find a small set of properties, which summarizes the entire data set. Some of the most important techniques in this field are K-means clustering, and the Gaussian mixture models (GMMs) that are used to determine, for every example, to which normal distribution it belongs  
Figure 12: Types of machine learning: supervised, unsupervised and reinforcement.  
to. Unsupervised learning is also used for the dimensionality reduction purpose. In this area, principal component analysis (PCA) is often used to compress input datasets through feature extractions, thus revealing redundancies. Another recent development of unsupervised learning is the advent of _generative models_. These networks, once trained, can sample synthetic examples from the learned distribution or give a score according how similar two examples are. Unsupervised learning can be used in NTNs in a variety of scenarios, such as dimensionality reduction in order to reduce the computational costs, and also security or multicast transmissions.
# Comparative Study for Multi-Speaker Mongolian TTS with a New Corpus
## 3 MnTTS2 Dataset
### MnTTS2
#### 3.2.4 Corpus Structure and Statistics  
The file structure of the MnTTS2 corpus is shown in Figure 1. Each speaker's recording file and the corresponding text collection were saved in a folder named after the speaker. All audio was stored in WAV format files with a sampling rate of 22.05 kHz and a sampling accuracy of 16 bits. All text was saved in a TXT file encoded in UTF-8. The file name of the audio is the same as the corresponding text file name, and the name of each file consists of the speaker, document ID, and corpus ID.  
The statistical results of the MnTTS2 data are shown in Table 1 and Figure 2. As shown in Table 1, the entire corpus has a total of 23,801 sentences. For example, F1, with a total of 572,016 Mongolian characters, has an average of 79 characters per sentence, with the shortest sentence having 12 characters, and the longest sentence having 189 characters. If words are used as the statistical unit, the total number of words in this dataset for F1 is 88,209, the mean value of words in each sentence is 12, the minimum value is 3, and the maximum value is 29. As shown in Figure 2, we also counted the sentence duration to draw a histogram. Take speaker F1 for example, the word numbers of the sentences are concentrated in 12-15, and duration is concentrated in 4-5 s. In comparison, we found that the word numbers of sentences for F2 were not particularly concentrated, and the duration was relatively scattered. F3, on the other hand, is more similar to F1, with a more obvious concentration. The statistics of all three speakers are in line with the normal distribution.
_Applied_  
_sciences_  
_Article_  
**Enhancement of Skin Anti-Inflammatory Activities of**  
_Eclipta prostrata_**L. from the Ultrasonic**  
**Extraction Process**  
**[PERSON]**  
Department of Food Science and Engineering, Seowon University, Cheongju 361-742, Korea;  
[EMAIL_ADDRESS]; Tel.: +82-43-299-8471; Fax: +82-43-299-8470  
Received: 17 November 2017; Accepted: 21 November 2017; Published: 28 November 2017  
This work first showed that the skin anti-inflammatory activities of _Eclipta prostrata_ L. (_E. prostrata_ L.)n be increased through an ultrasonic extraction process at a frequency of 120 kHz with 70% ethanol at 40 \({}^{\circ}\)C for 6 h. The extract from the ultrasound extraction (UE) contained 378.6 mg/100 g of wedelolactone (a major bioactive substance in _E. prostrata_ L.), compared with the 172.9 mg/100 g from a conventional extraction process with 70% ethanol at 80 \({}^{\circ}\)C for 12 h (EE). The UE showed less than 10% of low cytotoxicity against normal fibroblast cells, and also exhibited much higher antioxidant activities than that from the EE when treating with 0.3 mg/mL of the extracts. It was proved that the high antioxidant activities of the UE were closely correlated with the enhancement of skin anti-inflammatory activities by effectively suppressing the production of tumor necrosis factor-alpha and interleukin-6 from macrophages. The production of prostaglandin E\({}_{2}\) from human skin cells was also greatly reduced, compared with the EE for all ranges of the treatments. These results strongly indicate that the various biological activities of _E. prostrata_ L. can be improved by a simple ultrasound extraction process because of the high elution of bioactive substances even at a low temperature.  
**Keywords:**_Eclipta prostrata_ L.; ultrasound extraction; antioxidant; skin anti-inflammatory activities
# A Comprehensive Analysis of Various Tokenizers for Arabic Large Language Models
## 3 Tokenization
### SentencePiece  
SentencePiece (SP) is an unsupervised subword tokenizer and detokenizer designed for neural-based text processing [12]. Because it is an unsupervised tokenizer, it does not need prelabeled data, and thus, it learns how to segment words directly from raw sentences. SentencePiece is considered to be language-Independent, since it treats the sentences just as sequences of Unicode characters (i.e., it does not extract words based on the whitespace character). It encompasses four main elements: the normalizer, trainer, encoder, and decoder. The task of the normalizer is to convert semantically equivalent Unicode (Unicode- NFKC) characters into their standard forms. The task of the trainer is to train the corpus by learning the subword segmentation strategy. SentencePiece supports two tokenization algorithms--byte pair encoding (BPE) [32] and the unigram language model [10]--to construct the appropriate vocabulary. The task of the encoder is to prepare the text for processing by converting it to a sequence of IDs, while the task of the decoder is to convert the output back to human-readable text. The model starts by creating vocabulary of a predetermined size from the basic character set. Then, a unique ID is assigned to each subword in its vocabulary. In contrast to WordPiece and BPE, SentencePiece adds spaces and nontext symbols in its vocabulary, thus allowing it to tokenize raw text directly. The SentencePiece tokenizer was used in many prominent language models such as ALBERT [42], SaudiBERT [43], XLNet [44], and T5 [45].
# An Evaluation of the Efficiency of Compartmented
## Materials and Methods
### Test Specim Compaction Procedures  
#### 2.3.1 SCB and ITS Test Specimens  
The SCB test was adopted for this study because its simple test specimen production process and test set up make it more advantageous in comparison to the standard 3 PB test, where specimens are produced from a large test slab, making it a more expensive test process in terms of the materials required for test specimen production. The cylindrical test specimens were compacted in accordance with IS EN 12697-31:2007 using a SERVOPAC gyratory compactor. The static compaction pressure was set at 0.6 MPa with an angular velocity of 30 gyrations per minute and the gyratory angle was set at 1.25\({}^{\circ}\). A set number of gyrations is used as the compaction control target; in this case, 100 gyrations. The specimens were compacted at dimensions of 100 mm in diameter and 100 mm in height. In total, five 100 \(\times\) 100 mm cylindrical specimens were produced per mix. Following compaction, the test specimens were left in the moulds to cure for 2 h, at which point the SCB test specimens were machined using a masonry cutting saw. Figure 2 shows the test specimen machining process, from which 12 SCB test specimens were produced per mix. Figure 3 illustrates the geometry of the SCB test specimen.  
The cutting was carried out in three steps (as illustrated in Figure 2):  
1. The samples are cut over the height in three equal parts of 30 \(\pm\) 3 mm, using a 3 mm sawing blade.
2. These parts are cut across the diameter to obtain semi-circular specimens. The thickness of the specimen was adjusted to 20 \(\pm\) 2 mm for crack propagation purposes (it creates a better crack visibility and speeds up the healing process). A 2 mm thick sawing blade was used for more precise cutting and excess material was discarded.
3. A triangular shaped notch of 10 \(\pm\) 2 mm in length and 10 \(\pm\) 2 mm in height was created at the base of the specimen. The same sawing blade was used as described in step 2. Figure 3 shows a schematic diagram of an SCB test specimen.  
Figure 3: Schematic diagram of SCB test specimens (dimensions are given in mm).  
Figure 2: SCB test specimen cutting and marking process.  
For the production of the ITS test specimen, the same compacted asphalt cylinders were used as for production of the SCB test specimens, as described above. Following the curing process, a thin cylinder test was machined from each compaction cylinder, producing a total of six ITS test specimens. Figure 4 shows a schematic illustration of the ITS test specimen.
## 4 Summary and Conclusions
Nomenclature AM Active medium APD Avalanche photodiode CAD Computer-aided design CIV Current-voltage connector CW Continuous wave DG DEE-Glenair connector DKC Dark current EPHC Effective photocurrent FWHM Full width at half measure HR Highly reflective InGaAs Indium Gallium Arsenide LD Laser diode LiDAR Light Detection and Ranging MHS Main heat sink MLI Multi-layer insulation OC Output coupler TEC Thermoelectric cooler TIA Transimpedance amplifier TOF Time-of-flight PCB Printed circuit board PETG Polyethylene Terephthalate Glycol PHC Photocurrent PQC Passive quenching circuit QCCW Quasi-continuous wave SA Saturable absorber SHP Sensor holder plate SWaP Size, weight and power WG Broadband precision window
# High-Efficiency Navigation of Nonholonomic Mobile Robots Based on Improved Hybrid A* Algorithm
## 3 Simulation and Experiment
### Navigation Experiment  
The practical navigation experiment was conducted at a 10 m \(\times\) 10 m testing site, and cardboard boxes and chairs were allocated within the site to imitate the obstacles (Figure 10). In the experiment, the obstacle ratio of the whole map is 0.15, so \(\beta\) is 0.15, \(\alpha\) is 5, and the Chebyshev distance \(d_{q}\) from the starting point to the end point is 7 m.  
We used SLAMTEC A2 radar to build the map (Table 2). The accuracy of the radar is 1 cm to 5 cm, the scanning radius is 12 m, and the scanning frequency is 5 Hz to 15 Hz. The global map was established with the data collected via LiDAR and processed by the mapping algorithm. It can be seen that the constructed map is roughly the same as the practical environment, with a relative error of about 1%. The Rviz visualization interface was launched to verify the normality of map information, robot model, LiDAR, and other relevant data. Moreover, the planned path, such as global and local paths, can be visualized in the Rviz interface to monitor the results of the tests. After the generation of the path according to the global map, various speed instructions and other relevant data such as steering angle and rotating speed of the front wheel were generated in real-time. The autonomous navigation system in the upper computer constantly received and released topics (i.e., data and instructions) in the ROS communication mode. The lower computer received relevant instructions, performed the necessary tasks, and ultimately completed the navigation task.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Number** & **Accuracy** & **Scanning Radius** & **Scanning Frequency** & **Sampling Frequency** & **Angular Resolution** \\ \hline SLAMTEC A2 & 1–5 cm & 12 m & 5–15 Hz & 16 k & 0.9\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specific parameters of the radar.  
Figure 10: The practical environment. (**a–c**) are for navigation experiment and the constructed map. (**d**) practical environment.