esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Satellite Platform Vibration Spectrum Model  
Inter-satellite laser links suffer from various types of noise, including vibrations from the satellite platform. These vibrations cause misalignment, degrade the received power, and impact the acquisition step during the ATP process [45]. The satellite platform vibrations are random in nature and depend on the operational requirements. The sources of vibrations can be categorized into external and internal sources [24; 48]. External sources include impacts due to micrometeorites, thermal bending of the satellite structure, solar and lunar gravity, and gravitational noise from the Earth and moon. Internal sources of vibrations are related to the mechanical structure of the platform, including antenna mechanical movement, thruster operation, fast mirror rotation in the control system, and propeller noise. External sources, such as micrometeorite impacts, are accidental and, thus, not considered as significant in most analyses, whereas internal sources are considered the dominant contributors to vibration.
# FhMYB108 Regulates the Expression of Linalool Synthase Gene in _Freesia hybrida_ and _Arabidopsis_
## 1 Introduction
The transcription factor AtMYC2 plays a pivotal role in the development of flower stamens and demonstrates specific recognition ability towards the promoters of _AtTPS11_ and _AtTPS21_ genes, thereby directly regulating their expression and facilitating the release of sesquiterpene cyanophyllene [30; 31]. However, the synthesis and transcriptional regulation of terpenoids constitute a highly intricate process that has thus far been exclusively investigated in _A. thaliana_; nevertheless, the precise underlying mechanism remains elusive. Moreover, the investigation of transcription factors derived from flowers, which play a crucial role in volatile emission, is currently lacking in the scientific literature. Therefore, it is crucial to identify a species exhibiting a robust floral fragrance in order to conduct comprehensive investigations aimed at elucidating the transcriptional regulatory mechanisms governing volatile terpenoid production in plant flowers. _Freesia hybrida_, a bulbous herbaceous flower belonging to the iris family, exhibits equivalent blooms and exquisite floral esthetics, rendering it an ideal specimen for investigating the intricate realm of botanical fragrance and floral metabolism [32; 33]. The identification and evaluation of aromatic volatile compounds in 26 cultivated and 8 hybrid _Freesia hybrida_ revealed that terpenoids, particularly monoterpenoids, constituted the predominant class of VOCs across most germplasm. Notably, linalool and d-limonene exhibited the highest levels. Additionally, hybrid varieties exhibit significant advantages in terms of flavor and VOC accumulation across cultivated species, thereby providing valuable insights for the evaluation of genetic resources, floral aroma breeding, and the advancement of commercial applications in _Freesia hybrida_[34]. In \"Shiny Gold\", a _Freesia hybrida_ variety, the main volatile components are also terpenoids such as linalool, \(\beta\)-ocimene, and d-limonene, and multiple _TPS_ genes seem to be involved in the synthesis of volatile compounds and the release of floral fragrance [35]. In this study, FhMYB108, a transcription factor involved in the regulation of terpenoid synthase gene expression, was identified through screening the _Red River(r) Freesia hybrida_ transcription database. Furthermore, the molecular mechanism underlying the regulatory role of _FhMYB108_ in volatile terpenoid synthesis was further elucidated. This study establishes a theoretical framework to elucidate the molecular mechanism underlying plant floral development, thereby providing a robust foundation for enhancing plant and flower traits.
# Alternative Design of Binary Phase Diffractive Optical Element with Non-\(\pi\) Phase Difference
###### Abstract  
It was found that binary phase diffractive optical element (DOE) with non-\(\pi\) phase difference had higher diffraction efficiency and adjustable zero-order intensity than a 0-\(\pi\) one. However, existing design methods are all based on the simulated annealing algorithm and thus computationally expensive. In this paper, a simple and efficient method using the iterative Fourier transform algorithm (IFTA) is proposed. In this method, the target pattern is first modified via reducing the zero-order intensity. Then, the IFTA is adopted to design the conventional 0-\(\pi\) DOE. Subsequently, the phase distribution remains unchanged and the phase difference is carefully adjusted to increase the zero-order intensity so that the reconstructed pattern is consistent with the target. To verify this method, several typical DOEs for beam splitting were designed and fabricated, and the result showed that the proposed method is effective.  
Keywords:diffractive optics; diffractive optical element; beam splitting +
Footnote †: journal: Applied Sciences
# Fracture and Deflection of Orthodontic Miniscrews--A Systematic Review
## 1 Introduction  
Orthodontic mini-implants, also known as miniscrews (MSs), are temporary orthodontic devices used for enhancing orthodontic anchorage either by supporting the teeth of the reactive unit (indirect anchorage) or by obviating the need for the reactive unit altogether (direct anchorage of orthodontic appliances with sprigs, clamps, elastomers, etc.). Temporary use of miniscrews requires subsequent removal upon completing clinical tasks [1]. Most commonly, MSs are made of grade V titanium alloy or stainless steel [2]. The introduction of miniscrews has greatly expanded the limit of clinical orthodontics by facilitating difficult teeth movements and limiting patient compliance [3]. MSs' popularity has become widespread due to the relatively low cost regarding the effects attained as well as their ease of insertion [4].  
However, the clinical application of MSs is not lacking in complications. The most common problems related to MSs are root contact upon insertion or in the course of teeth movement, the loss of MS stability, inflammation caused either by the patient's compromised hygiene or mechanical irritation caused by the MS head or auxiliaries attached to it, and plastic deformation and fracture of the MSs [5, 6].  
Introgenic root contact may occur if the MS is inserted in the narrow inter-radicular space. This may lead to potential loss of tooth vitality, osteogenesis, and dentoalveolarankylosis [7]. The proximity of the root also significantly increases the risk of miniscrew mobility and often causes MS failure. The loss of MS stability during the course of teeth movement is a common cause of screw relocation [8]. Alternatively to inter-radicular location, MSs are also inserted into extra-radicular sites such as the infrazygomatic crest in the maxilla and the buccal shelf in the mandible [9]. Considering the severity of clinical management, more troublesome than the root contact is MSs' deformation or fracture upon insertion or removal. Since both occurrences could be very demanding, they are best to be avoided. The aim of the present study is to investigate factors influencing the miniscrews' plastic deformation and fracture in accordance with the present state of scientific knowledge.
###### Abstract  
The main objective of this study was to assess the status of body schema using limb laterality discrimination tasks and pain measurement variables of patients with FMS compared to healthy subjects. The secondary aim was to analyze the relationships between laterality discrimination with respect to somatosensory variables. Thirty female patients with FMS (with a mean age of 52.43 \(\pm\) 11.82 years) and thirty healthy women (with a mean age of 47.93 \(\pm\) 5.92 years) were recruited. The main outcome measures were laterality discrimination, referral of evoked sensations, pressure pain threshold and conditioned pain modulation. The main analysis showed that patients with FMS have a longer reaction time for laterality discrimination in hands--20 images, t = 4.044, \(p\) < 0.0001, \(d\) = 1.04; hands--50 images t = 4.012, \(p\) < 0.0001, \(d\) = 1.31; feet--20 images t = 2.982, \(p\) < 0.01, \(d\) = 0.76; feet--50 images, t = 2.159, \(p\) < 0.05, \(d\) = 0.55). With regard the secondary analysis, patients with FM have higher mechanical hyperalgesia (t = \(-\)9.550; \(p\) < 0.0001, \(d\) = 2.51) and decreased response to conditioned pain modulation compared with healthy subjects (t = 15.519; \(p\) < 0.0001, \(d\) = 4.17). A positive correlation was found in patients with FMS between greater laterality discrimination ability and better function of conditioned pain modulation (hands r = 0.676, \(p\) < 0.0001; feet r = 0.485, \(p\) < 0.01). In conclusion, patients with FMS have a longer reaction time and lower accuracy for laterality discrimination, increased mechanical hyperalgesia and decreased conditioned pain modulation compared to healthy subjects. Finally, it seems that there is a positive correlation between greater laterality discrimination ability and better conditioned pain modulation function.  
Keywords:fibromyalgia; limb laterality recognition; conditioned pain modulation; mechanical hyperalgesia
## 3 Physical Simulation Experiments for E-CSS of Horizontal Well
### Experimental Equipment and Parameters  
Figure 2 shows the experimental equipment of E-CSS, which mainly consists of four parts:  
1. **Injection system**. Including two water tanks, an oil tank, two ISCO pumps and a steam generator. The ISCO pump is used to saturate the model with formation water and crude oil during the preparation process, and inject steam into the model during the experiment. Steam generator is used to produce water vapor for the experimental process;
2. **Model system**. Including a CSS experimental model, a cylindrical horizontal well, a thermal compensation insulation sleeve, a resistance heater and 6 thermocouples. The model size is 17.8 cm \(\times\) 60 cm; the horizontal well pipe is deployed in the center of the model and resistance heater is deployed on the outside of the horizontal well pipe. The thermocouple is a kind of multi-point measuring thermocouple, which five temperature sensors evenly distribute on. Moreover, 6 thermocouples with 30 sensors are evenly deployed from the center to the inner wall inside the model;
3. **Electric heating control and data acquisition system**. Including intelligent electric control unit for resistance heater, thermocouple temperature acquisition unit, monitoring computer, electric heating and injection data acquisition software, which is used to control the temperature of resistance heater, collect the injection data, collect model temperature and output the model temperature profile;
4. **Production system**. Including high-temperature back-pressure valve, output vapor-liquid automatic collector, electronic balance. High temperature back pressure valve is used to provide back pressure. Output vapor-liquid automatic collector is to collect produced fluid. In addition, electronic balance is used to measure produced fluid in real time.
# WAMs Based Eigenvalue Space Model for High Impedance Fault Detection
## 4 Case Study  
The \(IEEE\) 5-Bus system modeled in Matlab [30] is used to evaluate the performance of the framework presented in this work. Table 1 presents system data. Two scenarios were examined: First a single phase to ground fault is simulated on C-Phase of the line between Bus-4 and Bus-5. The results are presented in Fault Scenario I. Then, a single phase to ground fault is simulated on A-Phase of the line between Bus-1 and Bus-2. These results are presented in Fault Scenario II. Each scenario is set up as follows:  
* All Loads follow the trend shown in Figure 7.
* The system is in steady state per the powerflow results shown in Table 2.
* Each scenario consists of three fault iterations with fault resistances of 10 \(\Omega\), 100 \(\Omega\), and 1000 \(\Omega\).
* During each iteration, the line is faulted twenty four (24) times to evaluate all the cluster zones and to account for dynamic loading conditions.
* Clusters are built using historical loading data.
* One standard deviation of Gaussian white noise is added to all the current measurements.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Line** & \multicolumn{2}{c}{**Line Impedance**} & **Line Charging** \\ \hline  & R (PU) & X (PU) & Y/2 (PU) \\
1–2 & 0.02 & 0.06 & 0.0 + j0.030 \\
1–3 & 0.08 & 0.24 & 0.0 + j0.025 \\
2–3 & 0.06 & 0.25 & 0.0 + j0.020 \\
2–4 & 0.06 & 0.18 & 0.0 + j0.020 \\
2–5 & 0.04 & 0.12 & 0.0 + j0.015 \\
3–4 & 0.01 & 0.03 & 0.0 + j0.010 \\
4–5 & 0.08 & 0.24 & 0.0 + j0.025 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Impedance parameters of the 5-Bus system.  
Figure 12: Flowchart illustrating the eigenvalue space analysis model.
## 3 Results
### Diel pCO\({}_{2}\) Measurements  
Mean \(p\)CO\({}_{2}\) during the study period was 1514 \(\upmu\)atm (\(\pm\)652), with all samples measuring greater than atmospheric CO\({}_{2}\) pressure (400 \(\upmu\)atm). Spring \(p\)CO\({}_{2}\) measurements were significantly (\(p\) < 0.0001) higher than summer measurements, averaging 2140 (\(\pm\)179) and 888 \(\upmu\)atm (\(\pm\)124), respectively. There was no difference of means between summer measurements; however, mean \(p\)CO\({}_{2}\) was significantly (\(p\) = 0.0105) higher on 17 May than on 11 May (2246 \(\pm\) 75 and 2033 \(\pm\) 192 \(\upmu\)atm, respectively). During all sample events, \(p\)CO\({}_{2}\) measurements were highest at 6:00 and lowest at 15:00 or 18:00 (Figure 3a,b). A diel trend was clear during all sample trips, with \(p\)CO\({}_{2}\) decreasing from 6:00 to 15:00 and gradually increasing from 15:00 to 6:00 the following day (Table 1).  
In spring, \(p\)CO\({}_{2}\) levels dropped 9-18% from 6:00 to 15:00 and proceeded to rise 9-35% from 15:00 to 6:00 the following morning, demonstrating a diel range of 206-607 \(\upmu\)atm. The 17 May sample did not demonstrate as linear of a rise in \(p\)CO\({}_{2}\) in the evening hours as 11 May. The diel pattern was much more drastic in summer, dropping 33% from early morning to late afternoon and rising 35-55% until the following morning, resulting in a diel range between 344-377 \(\upmu\)atm. On the morning of 13 August, \(p\)CO\({}_{2}\) dropped until 12:00, rather than 15:00, and remained at a fairly consistent level until the 21:00 sample, resulting in not as strong of fit in our model (Table 1). This diel pattern resulted in summer \(p\)CO\({}_{2}\) measurements 25% (std. \(\pm\) 10%) greater during darkness hours than daylight hours (Figure 4a). Spring darkness \(p\)CO\({}_{2}\) measurements were on average 7% (\(\pm\)8%) greater than daylight measurements.
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
## 2 D* Lite
### Disadvantages of D* Lite  
As we know, D* Lite plans a path faster than other path planning algorithms such as A*, LPA* and D* in dynamic environments. However, there are some disadvantages that limit its application to USVs.  
* The path planned by D* Lite is close to obstacles, which is unrealistic and risky for USVs.  
* D* Lite usually searches eight adjacent nodes for expansion. This means that the steering angle of USVs should be integral multiples of 45 degrees, which may be too much for a real USV to turn each time due to its limited steering maneuverability. We can increase the number of adjacent nodes to decrease the steering angle. However, this will significantly increase the search time for path planning. Thus, we should comprehensively consider the balance between the steering maneuverability of USVs and the path planning efficiency.
* D* Lite searches reversely from the goal node to the start node for path planning. This strategy is beneficial for replanning in dynamic environments. However, the planned path may have sharp turns [28], which increases the path length and results in a suboptimal path.
* D* Lite plans path between two nodes (the start and goal nodes). USVs may need to reach several specific positions during autonomous navigation. This means that it is necessary to achieve multi-goal path planning for USVs.
* D* Lite spends much computing time on the comparison of the key values of nodes in the priority queue \(U\) as well as push in and out operations on the priority queue. Each time, when we select a node from the priority queue \(U\) to expand, the key values of all nodes in the priority queue should be traversed and sorted, which is a time-consuming operation, and its time complexity is \(O(n)\). When we plan a path within a large environment map with thousands of grids, the number of nodes in the priority queue for expanding is also increased, which can significantly decrease the search efficiency.
## 2 Literature Review
Two types of speaker recognition (SR) approaches, speaker identification (SI) and speaker verification (SV), were used. According to the experiment results, the best accuracy was achieved by the neural network based on ResNet-34 in both SI and SV. [PERSON] [12] used CNN to develop a speaker recognition model. The system experimented with a novel combination of low-level and high-level features using MFCCs. They used two classifiers to enhance the system's performance: support vector machine (SVM) and k-nearest neighbors (KNN). To sum up, the results in Table 1 illustrate the different experiment accuracy based on different SNR levels. [PERSON] et al. [13] developed voice source waveforms for utterance-level speaker identification using SVM: each speech signal of the desired YOHO dataset is processed for the feature extraction phase. The feature extraction process goes through many steps to determine the pitch period for each frame. The source-frame feature is then used as input for the SVM model. For the experiment, they expressed multi-class SVM and single-class SVM regression. The experiment results were 85.30% and 72.50% for multi-class and single-class, respectively. [PERSON] et al. [14] proposed a two-branch network to extract features from face and voice signals in a multimodal system and tested by SVM. The results indicate an overlap between face and voice. Furthermore, they demonstrated that facial data enhances speaker recognition performance. [PERSON] et al. [15] evaluated an end-to-end framework for speaker identification under challenging circumstances, including emotion and interference, using a learned voice segregation and speech VGG. Using the Ryerson audio-visual dataset (RAVDESS), the presented model outperformed recent literature on emotional speech data in English and Arabic, reporting an average speaker identification rate of 85.2%, 87.0%, and 86.6% using the RAVDESS, speech under simulated and actual stress (SUSAS) dataset, and Emirati-accented speech dataset (ESD), respectively. Despite that, since speech carries a wealth of information, obtaining salient features that can identify speakers is a difficult problem in speech recognition systems [16; 17]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Speaker Recognition**} & \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Technique**} & \multirow{2}{*}{**Accuracy (\%)**} \\ \cline{5-6}  & & & **SI** & **SV** & & & Multi-class: 85.30 \\ \hline
[13] & 2013 & YOHO & ✓ & Pitch & SVM & Single-class: \\  & & & & & & 72.50 \\ \hline
[6] & 2017 & - & ✓ & Spectrogram & Correlation & 91.00 \\ \hline
[8] & 2019 & VoxCeleb & ✓ & MFCCs & VGG ResNet & 88.20 \\ \hline
[9] & 2020 & \
\begin{tabular}{c} KSU- \\ Emotions, \\ EPST \\ \end{tabular} & ✓ & Spectrogram & CRNN &
\begin{tabular}{c} Arabic: 97.46 \\ English: 97.18 \\ \end{tabular} \\ \hline
[10] & 2020 & LibriSpeech & ✓ & MFCCT & FFNN & 92.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related works comparison with results.
# Estimation of Hand Motion from Piezoelectric Soft Sensor Using Deep Recurrent Network
## 3 Application of Recurrent Network
### Data Acquisition  
For network training, we need the dataset including the input data and the ground-truth target data which need to be predicted. In this study, we extracted the ground-truth moving state from the finger joint angle measured by leap motion controller. Different from other vision sensors, the leap motion controller facilitates the direct acquisition of the finger joint angle without additional processing. Although the measured angle from the leap motion controller exhibits differences in the real joint angle, as compared to [PERSON] et al. [23], we chose the leap motion controller as a ground-truth sensor due to its characteristic and because it can facilitate large dataset generation.  
To obtain a dataset for learning, we conducted an experiment using the interface mentioned in Section 2. For data acquisition, we set up the experiment as follows:  
* We used the soft sensor located on the glove, and its piezoelectric voltage was measured with the interface board.
* Joint angle for acquiring moving state was simultaneously measured with leap motion controller.
* Single movement was composed with flexion and extension.
* The range of flexion angle was determined randomly between 30 degrees and 90 degrees.
* Movements for training set were conducted 1080 times, and those for the test set were conducted 270 times.  
The decision of the moving state \(m_{gt}\) is given by:  
\[m_{gt}(t_{n})=\begin{cases}0,&\frac{|\sum_{k=n}^{n}\theta(t_{k})}{10}-\theta( t_{n})|<\Delta\theta_{th}\\ 1,&else,\end{cases} \tag{14}\]  
where \(\Delta\theta_{th}\) is the threshold angle variation. We assumed that the finger is moving when the difference between the average of 10 recent values and the new value is larger than \(V_{th}\). We selected \(\Delta\theta_{th}\) as 2 degrees based on preliminary tests.  
Figure 5 displays the sample of the dataset acquired from the experiment. The network was trained using the input data in Figure 5a to estimate the moving state in Figure 5c.  
Figure 5: Sample of the dataset used to train network: (**a**) piezoelectric voltage from the sensor; (**b**) joint angle from leap motion controller; and (**c**) moving state calculated from joint angle.  
Since our goal was to extract real-time moving state with using piezoelectric sensor outputs, we preprocessed the piezoelectric voltage into sequential input data \(X(t_{n})=[x(t_{n-9}),...,x(t_{n})]\) using the sliding window method, where  
\[x(t_{n})=[v(t_{n}),\dot{v}(t_{n})], \tag{15}\]  
\[\dot{v}(t_{n})=\frac{v(t_{n})-v(t_{n-1})}{t_{n}-t_{n-1}}. \tag{16}\]  
In addition, we defines target data of the network as \(y(t_{n})=m_{gt}(t_{n})\). Summaries of the processed training and test sets are shown in Table 1.
## 3 Security and Privacy Issues
### Security Threats
As a result, it disturb network communication, and network resources exhaustion [66, 67]. DoS attacks may damage the wireless healthcare application network, and can lead to the loss of the patient's life. Thus these (DoS) attacks are always harmful to the mission-critical applications, such as location tracking, ambulatory, home care monitoring, _etc._ For more detailed studies of denial-of-service attacks, readers may refer to [60, 66, 67].