esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact and Stability of mandibular Setback after Intraoral Vertical Ramus Osteotomy
## 4 Discussion
### Neurologic Disturbance  
According to the literature, the main advantage of the IVRO over the SSRO is the lower incidence of IAN injury during surgery. In IVRO, the prevalence of injuring the IAN is considered lower but might still occur while performing the osteotomy or the manipulation of the distal fragment [2; 4; 12].  
Previous studies and meta-analyses report an incidence of 0-36% for immediate IAN injury, while the prevalence of long-term damage is considered low--2.3-14% [7]. There is a wide range regarding injury incidence due to the differences in examination techniques, variation in follow-up periods, surgeon experience, and modified IVRO technique (osteotomy design), as well as performing additional mandibular surgery such as genioplasty.  
In our study, 38.46% of the patients had a sensory disturbance in the short term, and 20.51% of them remained with long-term sensory disturbance. When we classified the type of neurosensory injury, only one patient (2.56%) suffered from nerve anesthesia in the long term. Eighteen out of the thirty-nine patients were treated with genioplasty in addition to IVRO, and 27.7% of them reported long-term sensory disturbance. When genioplasty is performed, there are two more steps when an injury to IAN might occur: during exploration and tissue retraction and while performing the osteotomy [11].  
The high rate of genioplasty undertaken in our study most likely affected the incidence of IAN injury, based on the fact that in the group of patients that had only IVRO surgery, only 14.2% reported long-term sensory disturbance in comparison to 27.7% of the patients that had an additional genioplasty.  
Another interesting finding was the difference in the rate of sensory disturbance between the two osteotomy designs (vertical and oblique); even though no statistically significant connection was found when a vertical osteotomy was made, IAN neurological damage was 174% higher compared to an oblique osteotomy. The effect of the osteotomy design on the different incidence rates of IAN disturbance could be caused by the fact that vertical osteotomy requires more bone cutting in close proximity to the mandibular canal and an additional stripping of the medial pterygoid muscle compared to oblique osteotomy. We believe that in further research that includes a larger patient sample, there will be a correlation between the osteotomy design and IAN neurologic disturbance; furthermore, the osteotomy design could have an effect on other complications, such as TMD, as previously mentioned by [PERSON] et al. [3], who demonstrated that among 50 oblique osteotomy lines, no complications such condylar luxation were found as opposed to 98 vertical osteotomy line.  
The rates of long-term IAN disturbances in our study are similar to previous reports [7,12], which indicates that low neurosensory alterations can be expected following IVRO procedures, and this finding should be considered when selecting a surgical procedure to correct mandibular pragnathism.
# Performance Research and Optimization of Sound Insulation Hood of Air Compressor Unit
## 5 Conclusions  
Considering the conversational air inlet structure of the unit, a novel air inlet structure of the sound insulation hood of an air compressor unit was proposed in this paper. The sound insulation hood model established by the acoustic software LMS Virtual Lab Acoustic was expected to reflect the propagation of point sound source and simulate the influence of sound insulation hood on the radiated sound field. Acoustic finite element analysis was performed to calculate the sound pressure level of the sound insulation hood with different structures. The following conclusions were drawn through acoustic analysis and optimization:  
1. Acoustic finite element numerical simulation analysis can adequately predict the sound insulation performance of a sound insulation hood under different structures.
2. A sound insulation hood with a 90\({}^{\circ}\) or 120\({}^{\circ}\) diaphragm structure has a good absorption and blocking effect on the internal noise radiation of an air compressor unit.
3. The noise reduction effect of a sound insulation hood with a 90\({}^{\circ}\) diaphragm structure is better than that with a 120\({}^{\circ}\) diaphragm structure.  
Conceptualization, H.W. and Y.S.; methodology, Y.S.; software, M.L. and J.L.; validation, J.W., Z.L. and Y.S.; formal analysis, Y.S.; investigation, M.L.; resources, J.W. and Z.L.; data curation, M.L.; writing--original draft preparation, Y.S.; writing--review and editing, M.L.; visualization, Y.S.; supervision, H.W.; project administration, H.W.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Foundation of China (grant number 50806055) and the State Key Laboratory of Compressor Technology Open Fund Project (grant number SKL-YSJ201805).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Structure** & **Point 1** & **Point 2** & **Point 3** & **Point 4** & **Point 5** & **Overall** \\ \hline Original & 76.41 & 54.14 & 58.07 & 52.99 & 57.02 & 76.57 \\
120\({}^{\circ}\) diaphragm & 72.77 & 42.98 & 56.5 & 51.83 & 50.14 & 72.93 \\
90\({}^{\circ}\) diaphragm & 66.28 & 42.75 & 51.5 & 48.76 & 50.07 & 66.61 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Noise measurement data comparison (dB(A)).  
Figure 19: Surface sound pressure cloud image of 90\({}^{\circ}\) structure.
# eJamar: A Novel Exergame Controller for Upper Limb Motor Rehabilitation
## 3 Materials and Method
### The ejamar GC
#### 3.1.2 Grasping Force Detection and Calibration  
The ejamar controller measures grip strength using a strain gauge with a range of 0-100 kgf. A spring transmits the force from the hand grip to the gauge. The original spring in the housing was replaced with one that allows a measurement range of up to 25 kgf, which increases the range of finger motion during grip and reduces the stiffness of the hand holder, making it more suitable for elderly users and patients with hand motor impairments. The hand grip opening position can be adjusted using the knob, shown in the previous Figure 2a. It enables customization based on the user's hand size and comfort preferences; however, this adaptation does not alter the spring tension. The signal from the strain gauge is processed by the HX711 24-bit ADC for weight scales, which sends the data to the microcontroller via serial communication protocol. The output 24 bits of data contain the gauge readings amplified with a gain of 128. The microcontroller processes the data and transmits them via Bluetooth to the computer.  
The spring-based mechanical model of the ejamar device is presented in Figure 4a, where the main components are the spring, the gauges, and the A/D converter. A calibration procedure was performed to identify empirically the spring elastic constant. For that purpose, several loads within the usable range of 0-25 kgf were attached to the ejamar hand grip. The output of the HX711 ADC was used to measure the spring elongation \(\Delta x\) for each case. Thus, the spring constant \(k\) can be obtained using [PERSON]'s law.  
Figure 3: Firmware flow diagram.  
According to [PERSON]'s law, the graph of the applied force \(F_{\text{s}}\) as a function of the displacement \(\Delta x\) will be a straight line passing through the origin, whose slope is \(k\). Thus, applying linear regression to the output provided by the HX7711 ADC and the force applied to the hand grip, the results shows the linear behavior of the mechanical model described in Equation (1):  
\[F_{\text{s}}=k\cdot\Delta x-\epsilon \tag{1}\]  
where \(k\) is the spring constant and \(\epsilon\) is the error term that captures all other factors which influence the measured force \(F_{\text{s}}\) other than the spring elongation \(\Delta x\). From the empirical calibration, the results were \(k=0.0197\) and \(\Delta x=0.0632\).  
Hence, the previous linear model was programmed in the microprocessor to estimate the hand grip forces, given in newtons, N. This model is independent of the user or external factors different of the spring characteristics, so calibration is only required once. The measurement resolution is 0.1 kgf. The performance of the linear model was tested using different loads applied at the hand grip, and the model response is presented in Figure 4. It can be seen that the force estimation fits a linear model (black dashed line) with 2% full-scale error.
## 4 Vehicle Storage Analysis
### Experimental Results  
Figures 4-6 illustrate the performance comparison between Model A and Model B SSDs under varying temperatures (25 \({}^{\circ}\)C, 35 \({}^{\circ}\)C, and 50 \({}^{\circ}\)C) for both sequential and random read/write operations:  
* Throughput: Model A SSD demonstrates higher throughput at lower temperatures but suffers a significant decline as temperatures increase. Conversely, Model B SSD, while initially showing lower throughput, maintains more consistent performance across the temperature range.
* IOPS: At lower temperatures, Model A SSD achieves superior IOPS performance; however, it experiences a sharp drop with increasing temperatures. In contrast, Model B SSD exhibits more stable IOPS performance across varying temperatures.
* Latency: Model A SSD shows increasing latency as temperatures rise, particularly for random read and write operations. Model B SSD, which starts with slightly higher latency at lower temperatures, exhibits a more gradual increase in latency under thermal stress, demonstrating better thermal resilience.  
In conclusion, Model A SSD excels in performance at lower temperatures, while Model B SSD provides more consistent and reliable performance across a wider temperature range.
## 4 Description of the SAVOA
### SSA Convergence Strategy  
The standard AVOA enters either the exploration or exploitation phase determined by the starvation rate \(F\). The calculation of the starvation rate in turn relies on parameters such as \(w\), \(z\), and \(h\), which are mainly fixed or randomly generated, resulting in a strong randomness in the switch between exploration and exploitation. In order to enhance the balance between exploration and exploitation, this paper introduces the SSA's discoverer mechanism.  
Discoverers are primarily responsible for exploring areas of greater food abundance and have a larger foraging search area. To improve the global search capability of the algorithm in the early stage, the paper removes the division mechanism of AVOA that regulates the exploration and exploitation based on the starvation rate and replaces the exploration stage of AVOA with the discoverer mechanism in SSA. Its position-update equation is:  
\[X_{i,j}^{t+1}=\left\{\begin{array}{cc}X_{i,j}^{t}\cdot exp\Big{(}\frac{-i}{ \kappa\cdot iter_{Max}}\Big{)}&R_{2}<ST\\ X_{i,j}^{t}+Q\cdot L&R_{2}\geq ST\end{array}\right. \tag{50}\]  
where \(X_{i,j}^{t}\) represents the position of the \(i\) sparrow in the \(j\) dimension at the \(t\) iteration, where \(j=1,2,3,...,d\); \(\kappa\) is a random number in the range \((0,\;1]\); \(iter_{Max}\) is the maximum number of iterations; the values of \(Q\) are randomly generated and conform to a normal distribution; \(L\) is a \(1\;\times\;d\) matrix with all elements \(1\); \(R_{2}\) and \(ST\) are the warning and safety thresholds, with ranges of \([0,\;1]\) and \([0.5,\;1]\), respectively.  
Also, to improve the algorithm's ability to jump out of the local optimum, the vigilante mechanism is added at the end. The core is to activate some individuals as \"vigilantes\" to  
Figure 4: Comparison chart of population initialization by random method and Sobol sequence.  
perturb the search space directionally when the population is trapped in a local optimum, to break the current local convergence. Its position-update equation is:  
\[X_{i,j}^{t+1}=\left\{\begin{array}{ll}X_{best}^{t}+\beta\cdot\left|X_{i,j}^{t }-X_{best}^{t}\right|&f_{i}>f_{g}\\ X_{i,j}^{t}+K\cdot\frac{\left|X_{i,j}^{t}-X_{best}^{t}\right|}{(f_{i}-f_{w})+ \varepsilon}&f_{i}=f_{g}\end{array}\right. \tag{51}\]  
where \(X_{best}\) is the global optimal position; \(\beta\) is the step-size adjustment factor, which is a normally distributed random number with mean 0 and variance 1; \(K\) is a \([-1,\ 1]\) random number used to introduce directional perturbations; \(f_{i}\) is the fitness value of the current sparrow individual; \(f_{g}\) and \(f_{w}\) represent the globally optimal global worst fitness; \(\varepsilon\) is a very small constant used to avoid the molecule being zero. The proportions \(p_{d}\) of discoverers and \(p_{v}\) of vigilantes in this paper are 0.2 and 0.15.
# Safety and Security Considerations for Online Laboratory Management Systems
## 3 Literature Review
### Security in Remote Laboratories and OLMS
#### 3.4.2 Impact of Security Attacks in OLMS  
The potential damage caused by a security attack on OLMSs is proportional to the complexity of the remote laboratories. Remote laboratories with simple setups are less at risk of severe damage, though server failures could disrupt their operation. Basic security measures are enough to address these issues. On the other hand, when dealing with more advanced remote laboratories integrating complex hardware and software, vulnerabilities increase. A malicious user gaining control of the system could send harmful commands, disrupt experiments, damage equipment, or even trigger dangerous situations like flooding or fires. This risk is even higher in scientific remote labs that rely on expensive, highly specialized instruments. A security attack in these cases could lead to severe financial losses alongside the potential dangers mentioned earlier. Because of this, different remote environments require specific security measures to prevent attacks and protect the integrity of the experiments and the system's safety [67].
# Research on Fracture Mechanism and Stability of Slope with Tensile Cracks
###### Abstract
Among them, the major advantages of limit equilibrium method are simplicity of concept and convenience of calculation, so that it is widely used all over the world. For the past several decades, several researchers have implemented the limit equilibrium method to analyze the stability of slope [18; 19; 20]. After that, [PERSON], [PERSON], [PERSON] and [PERSON], [PERSON] and [PERSON], [PERSON] et al. also developed this traditional method to investigate the effects of tensile cracks on the stability of slope [11; 21; 22; 23; 24]. Unfortunately, the crack depth was only considered as a constant, which was not consistent with actual landslides. On the other hand, in recent years, under the framework of limit analysis method, associating pre-existing cracks of slope stability was also analyzed by different scholars, such as [PERSON] et al., [PERSON] et al., [PERSON] et al., [PERSON] et al., [PERSON] et al., [PERSON] et al., who have been developing 2D and 3D calculated models and analyzing seismic effects, surcharge, and water pressure on the slope with tensile cracks [25; 26; 27; 28; 29; 30]. In the above research works, a prominent problem was the crack depth, and location in the slope was still assumed. Therefore, the accuracy of these methods was limited. In fact, crack depth is uncertain before the slope analysis. Although some scholars estimated the depth of tension cracks with corresponding methods, and achieved gratifying results, most of the research about the crack depth had a lack of support of practice verification and in-depth study. The problem of what is a more reasonable crack depth, whether in theory or in practice, has not reached a unified consensus. Therefore, the critical problem to be solved in this study is to give a reasonable value of crack depth. This study aims to determine the optimal crack depth and analyze stability of slope with cracks by using the limit equilibrium approach. In this paper, in the classical limit equilibrium framework, a static model was firstly proposed to consider the influences of tensile cracks at a slope crest on the stability of clay slope, and the crack depth was defined as a variable. Then, tensile crack was introduced into the slope failure modes with three types of failure surfaces, i.e., toe circle slope, base circle slope, and face circle slope, which are considered in the analysis. Finally, the paper determines the optimal crack depth and minimum safety factor and verifies the rationality of the proposed method by two cases and discusses various geotechnical parameters effects on the crack depth and stability of slope. In addition, an engineering example was introduced to test the practicability of the present method.
# Monitoring Rainfed Alfafa Growth in Semiarid Agrosystems Using Sentinel-2 Imagery
## 3 Results and Discussion  
### Fvc-Ndvi Correlation  
The examination of the correlations between the FVC and the different spectral indices used in this study indicated that polynomial fitting reported the best correlations followed by linear fitting correlation values, while exponential fitting correlations showed lower values for all indexes. Furthermore, NDVI resulted in the highest correlation values among all the indices, both for polynomial and for lineal fitting (Table 5). Similar results have been reported in previous studies [11; 14; 15; 57].  
Figure 3A,B show the correlation between the in situ FVC values and NDVI values derived from Sentinel-2 images at the parcel and pixel levels, respectively. At the parcel level, \(R^{2}\) = 0.727 (Figure 3A; Table 5), while at the pixel level, the correlation was higher in 2018 (\(R^{2}\) = 0.586) than in 2017 (\(R^{2}\) = 0.196) (Figure 3B).  
Plots where alfalfa was not sufficiently developed may influence the correlations between in situ FVC measurements and NDVI values from Sentinel-2A images, as Figure 3A shows a higher dispersion in these areas, where FVC seemed underestimated. High spatial resolution images are essential when working with yield or FVC values. Thus, the 10 m spatial resolution Sentinel-2 images in red, green, blue, and NIR wavelengths, defined in bands 2, 3, 4, and 8, respectively, are interesting when working with small plots as in the present study.  
Another key advantage of the Sentinel-2 satellite is the 5 day revisit period, which is especially relevant for temporal monitoring studies in cloudy areas [58]. In addition, ESA free-access imagery represents an advantage in economic terms when working with remote sensing satellite data in contrast to SPOT or Quickbird-2 on-demand remote sensing data.
## 2 Proposed Approach
### Multi-Node Thevenin Equivalent Circuit Model  
A general multi-node network power system model is shown in Figure 3, where \(n_{\rm G}\) generators and \(n_{\rm L}\) loads are outside the transmission network. Such complex network can be converted into a multi-note TE network model. Let us begin with the relationship between the injected node currents and the node voltages given by  
\[\mathbf{I}=Y_{\rm bus}\mathbf{V} \tag{1}\]  
where \(\mathbf{I}\) is the vector of injected node currents; \(\mathbf{V}\) is the vector of node voltages; \(Y_{\rm bus}\) is the bus admittance matrix.  
Due to three types of buses in the system, Equation (1) can be expressed as  
\[\left[\begin{array}{c}\mathbf{I}_{\rm G}\\ -\mathbf{I}_{\rm L}\\ \mathbf{I}_{\rm T}\end{array}\right]=\left[\begin{array}{ccc}Y_{\rm GG}&Y_ {\rm GL}&Y_{\rm GT}\\ Y_{\rm LG}&Y_{\rm LL}&Y_{\rm LT}\\ Y_{\rm TG}&Y_{\rm TL}&Y_{\rm TT}\end{array}\right]\left[\begin{array}{c} \mathbf{V}_{\rm G}\\ \mathbf{V}_{\rm L}\\ \mathbf{V}_{\rm T}\end{array}\right] \tag{2}\]  
in which the subscripts G, L, and T are utilized to denote generator bus, load bus, and tie bus, respectively. From Equation (2), \(\mathbf{V}_{\rm L}\) can be expressed as  
\[\mathbf{V}_{\rm L}=K\mathbf{V}_{\rm G}-Z\mathbf{I}_{\rm L} \tag{3}\]  
where  
\[Z=\left(Y_{\rm LL}-Y_{\rm LT}Y_{\rm TT}^{-1}Y_{\rm TL}\right)^{-1} \tag{4}\]  
\[K=-Z\left(Y_{\rm LG}-Y_{\rm LT}Y_{\rm TT}^{-1}Y_{\rm TG}\right) \tag{5}\]  
In Equation (4), \(Z\) denotes the impedance matrix, and can be computed from \(Y_{\rm bus}\).  
Figure 2: Proposed framework for determination of VSM. LID: the load increase direction; \(\lambda\): the load increase parameter; \(Q_{\rm G}\): generator \(Q\) limits.  
Figure 4 illustrates a multi-node TE network model for the representation of Equation (3). For any load bus \(i\in\{1,\cdots,n_{\rm L}\}\), we have  
\[\mathbf{V}_{i}^{\rm L}=\mathbf{E}_{i}^{\rm th}-Z_{i}^{\rm th}\mathbf{I}_{i}^{\rm L} \tag{6}\]  
where  
\[\mathbf{E}_{i}^{\rm th}=\left[K\mathbf{V}_{\rm G}\right]_{i}-\sum_{j=1,j\
eq i}^ {n}Z_{ij}\mathbf{I}_{j}^{\rm L} \tag{7}\]  
\[Z_{i}^{\rm th}=Z_{ii} \tag{8}\]  
\(\mathbf{E}_{i}^{\rm th}\) and \(Z_{i}^{\rm th}\) correspond to TE voltage and TE impedance at load bus \(i\), respectively. \(Z_{ij}\) is the \(i\)-\(j\) element of \(Z\) and \(Z_{ii}\) is the diagonal element of \(Z\). In addition, the load impedance of the complex load power \(S_{i}^{\rm L}\) is given by  
\[Z_{i}^{\rm L}=\frac{\left|\mathbf{V}_{i}^{\rm L}\right|^{2}}{\left(S_{i}^{\rm L }\right)^{*}} \tag{9}\]  
where \(\left(S_{i}^{\rm L}\right)^{*}\) denotes the conjugate of \(S_{i}^{\rm L}\).  
Figure 4: Multi-node Thevenin equivalent network model. \(\mathbf{E}^{\rm th}\), Thevenin equivalent voltage; \(Z^{\rm th}\), Thevenin equivalent impedance; \(\mathbf{V}^{\rm L}\), load bus voltage; red dashed box, Thevenin equivalent; arrows, load demand.  
Figure 3: Multi-node network power system model. \(\mathbf{V}^{\rm G}\), generator bus voltage; \(S^{\rm L}\), complex load power; \(n_{\rm G}\), number of generators; \(n_{\rm L}\), number of loads.
# Research on Energy Harvesting Mechanism and Low Power Technology in Wireless Sensor Networks
## 4 Simulations and Results  
### Energy Harvesting System  
#### 4.1.1 Performance of the Solar Energy Harvester  
The test data of the energy harvesting system that we designed are shown in Table 1, and the table records the test data of this system on a sunny day in Yiyang, Hunan Province. The elements of the test mainly include voltage regulator circuit input voltage, node power supply voltage, and node power supply current. The test results show that the energy harvesting system is stable in power supply to the node, maintains about 3.3 V, has less than 1.3% in voltage deviation, and it has high accuracy, strong robustness, and can provide reliable energy guarantee for WSNs.
## 2 Measurement Approach
### Actual Flow Transition
#### 2.1.1 Method A: No Image Rotation and 2D Gradient Evaluation  
For each image pixel, the temperature gradient components along the \(x\)- and \(y\)-axis in the image plane are calculated using a gradient image filter. Next, the gradient components are converted to polar coordinates so that the magnitude and direction of each gradient is obtained. A selection is then made of those gradients whose direction aligns perpendicular to one of the three orientations of the transition line segments. The selected gradients are sorted out again according to their magnitude by applying a minimum threshold. Using a small threshold when selecting the local gradients representing the premature flow transitions allows the detection of weak, low contrast turbulence wedges. A high threshold for the selection of gradients representing the natural flow transition on the other hand allows a robust detection of the natural flow transition in image columns without premature flow transitions. The result is a gradient image containing only those gradients that represent the flow transition line, which is denoted as the actual flow transition.