esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Shap Analysis Across Reduced Features
#### 4.4.3 Results for Quadrant 2  
In Figure 12, it is clear that the 'Number of Micro-Saccade' plays a crucial role in predicting emotions in Quadrant 2 of the CMA. This quadrant is linked to low arousal and negative valence, reflecting emotions like sadness, depression, and boredom. Higher values of 'Number of Micro-Saccade' positively influence predictions, indicating that individuals feeling sadness or depression show a higher frequency of small, involuntary eye movements during visual fixation. Conversely, lower values of 'Micro-Saccade Vertical Amplitude', and  
Figure 11: Summary Plot for Quadrant 1 for 15 features.  
'Micro-Saccade Amplitude' positively impact the model, suggesting that smaller angular movements in vertical direction in micro-saccades are indicative of these intense emotional states, while higher values of 'Micro-Saccade Horizontal Amplitude' create higher SHAP values, indicating that the larger angular movements in horizontal direction are indicative of these emotions. According to the figure, 'Micro Saccade Peak Velocity' is not indicative of these emotions. Additionally, a decrease in 'Micro-Saccade Direction'--which represents the most common orientation or path of a micro-saccade--also positively impacts the model, indicating the specific orientation of micro-saccades is relevant in contexts of sadness or depression.  
Moreover, an increase in the 'Number of Fixations' has a positive effect on model predictions, while lower values of 'Fixation Duration' do not specifically affect the model, indicating that individuals experiencing sadness or depression may engage in more frequent fixations, even if not necessarily for longer periods.  
Observations from Figure 12 further indicate that higher counts of 'Number of Saccade, 'Saccade Duration', and 'Saccade Length' affect the decisions of the model positively, supporting the hypothesis that longer saccade durations are linked to decreased engagement with the environment, which is typical in states of depression, sadness, or boredom.  
Conversely, lower values for 'Blink Duration' positively influence the model, suggesting that blinking frequency and duration decrease in these emotional states. However, a decrease in the 'Number of Blink' adversely affects the model, implying that a higher blinking rate is linked to negative emotional states.
# Improving GNSS Landslide Monitoring with the Use of Low-Cost MEMS Accelerometers
## 7 Conclusions  
Low-cost sensors can be used to monitor the movements and deformation of both land and sea structures. In particular, tilt measurements can be made inexpensively with an accuracy of a few hundredths of a degree with calibratable MEMS accelerometers in the laboratory. Each mass-market accelerometer needs an individual calibration.  
This sensor can be an important part of a more complete monitoring system based on the integration of GNSS and other sensors. In this case, the inclinations serve to separate the movements of the antenna from the rotations of its support. In fact, in the prototype constructed for this work, the accelerometer was placed just below the GNSS antenna.  
However, the triaxial accelerometer can also measure inclination changes in a landslide or an infrastructure, if it is attached to it. It is difficult to measure tilt variations with other geomatic techniques. A GNSS multi-receiver system is less practical, more expensive, and requires distancing the GNSS antennas to obtain a good angular accuracy.  
The independent measurements of the inclinations using total station provided excellent and economic test results and, at the same time, verified that these biases do not vary over time.  
The residuals of these comparisons can still be filtered, obtaining even better accuracy. For this purpose, the spikes still present in Figure 6 can easily be filtered by means of a robust filter at the price of obtaining a single, more accurate result, but with greater latency, every sixty seconds.  
The theoretical part of this work was developed by [PERSON] and A.M.M. and the experimental mechanic part by I.H.B.  
This research was founded by CSI Piemonte.  
We thank the research center and the \"CSP-innovazione nelle ICT\" center in Turin for the realization of the hardware and telecommunication components of the sensors.  
The authors declare no conflict of interest.
## 4 Numerical Simulation and Discussion
### The Definition of Model Parameters  
#### 4.2.1 Spatial Discretization  
Based on the hydrogeological profiles of the reservoir area, a finite element model is developed considering the rock strata distribution and main hydraulic structures, which include the arch dam, underground powerhouses, and their grouted curtain, as shown in Figure 11. The defined model area is 5 km wide perpendicular to the river at the dam site and 4 km long along the river. The bottom of the model is set to \(-\)1 km above sea level. Due to the irregular terrain, the model geometry is discretized with 155,951 tetrahedrons and 5508 triangular prisms.
# Multi-Objective Optimisation in Multi-QoS Routing Strategy for Software-Defined Satellite Network
## 6 Experiments and Results
### Evaluation Indicators
#### 6.3.2 Total Throughput  
Figure 5 shows the comparison of total throughput for different sending rates.  
As is shown in Figure 5, with the increase of individual data transmission rate, the total throughput will increase. Comparing the three routing algorithms, the total throughput of interval type-2 fuzzy set routing algorithm is the highest, followed by ELB algorithm and the total throughput of centralized DSP routing algorithm is the least. Under the same individual data transmission rate, the smaller the packet drop rate, the higher the total throughput in the same time. With the increase of individual data transmission rate, the traffic load increases accordingly. Because the centralized DSP algorithm continues to try the same transmission path and the packet drop rate also increases, the total throughput decreases accordingly. Compared with other routing algorithms, the total throughput of centralized DSP algorithm is the lowest.
## 2 System Model
### D2D Transmission  
In this phase, the cooperation signal is transmitted from the user with a stronger channel gain to the user with a weaker gain. The cooperation signal can help user 1 to decode its data, or user 2 to perform SIC better. The cooperation signal received by user 1 \(D_{1}\) is given by  
\[z_{Di}^{PDMA}=h_{i}\sqrt{P_{\mathrm{S}}}s+f_{i}\sqrt{\varpi P_{Di}}x_{FDi}+n_ {i}, \tag{5}\]  
where \(h_{1}\) is a Rayleigh fading channel coefficient from user 1 to user 2 and vice versa. As mentioned in the channel information exchange phase, when \(\big{|}g_{1,k}\big{|}^{2}>\big{|}g_{2,k}\big{|}^{2}\), only \(z_{D2}^{PDMA}\) exists, and when \(\big{|}g_{1,k}\big{|}^{2}<\big{|}g_{2,k}\big{|}^{2}\), only \(z_{D1}^{PDMA}\) is transmitted from \(D_{2}\).  
Generally, the received SINR at user \(i\) is given by  
\[\chi_{Di}^{PDMA}=\frac{\rho|h_{i}|^{2}}{\varpi\rho\big{|}f_{i}\big{|}^{2}+1}. \tag{6}\]  
The SINR for decoding \(x_{1}\) is given by  
\[\chi=\left\{\begin{array}{l}\min\left\{\max\left\{\gamma_{SD1,k}^{PDMA}, \chi_{D1}^{PDMA}\right\},\gamma_{SD1\gets 2,k}^{PDMA}\right\},\mathrm{if} \big{|}g_{1,k}\big{|}^{2}<\big{|}g_{2,k}\big{|}^{2}\\ \min\left\{\gamma_{SD1,k}^{PDMA},\max\left\{\gamma_{SD1\gets 2,k}^{PDMA },\chi_{D2}^{PDMA}\right\}\right\},\mathrm{otherwise}\end{array}\right.. \tag{7}\]  
The antenna index can be selected to strengthen the BS to serve user \(i\) link as follows:  
\[k^{*}=\arg\underset{k=1,\ldots,K}{max}\left(\big{|}g_{i,k}\big{|}^{2}\right). \tag{8}\]  
In this case, CDF and PDF related selected channel are given respectively by  
\[F_{\big{|}g_{i,k^{*}}\big{|}^{2}}\left(x\right)=1-\sum_{k=1}^{K}\left(\begin{array} []{c}K\\ k\end{array}\right)\left(-1\right)^{k-1}\exp\left(-\frac{kx}{\lambda_{g}} \right), \tag{9}\]  
and  
\[f_{\big{|}g_{i,k^{*}}\big{|}^{2}}\left(x\right)=\sum_{k=1}^{K}\left(\begin{array} []{c}K\\ k\end{array}\right)\left(-1\right)^{k-1}\frac{k}{\lambda_{g}}\exp\left(-\frac{ kx}{\lambda_{g}}\right). \tag{10}\]  
Here, \(\lambda_{u}\) is the channel gain of \(u\).
## 3 Case Study
### CFD Numerical Simulation
#### 3.1.3 Analysis of Simulation Results  
As shown in Figure 10, based on CFD numerical simulation results, the velocity along the L3 axis of each BSCN and the conventional straight cone nozzle was extracted, and then the average value was taken.  
The average velocity along the central L3 axis of each BSCN was subtracted from the average velocity on the L3 central axis of the conventional straight cone nozzle. The obtained results were divided by the average velocity along the central axis of L3 in the outlet of the conventional straight cone nozzle, and finally, the velocity increase rate of the BSCN was obtained. Then, the curves of the velocity rise rate of each BSCN were drawn, as shown in Figure 11.  
\[\eta_{0}=\left|\frac{\mathbf{V}_{\text{bn}}-\mathbf{V}_{\text{cn}}}{\mathbf{V}_{\text{cn} }}\right|\times 100\% \tag{22}\]  
In this formula: \(\eta_{0}\)--the rate of velocity increase; \(\mathbf{V}_{\text{bn}}\)--average velocity along the axis of the BSCN (m/s); \(\mathbf{V}_{\text{cn}}\)--average velocity along the central axis of the conventional straight cone nozzle (m/s).  
It can be observed from Figure 11 that the velocity increase rate of the BSCN was more significant than zero after the structural parameters were optimized. It was further noted that the resistance against the fluid flowing through the inner wall was reduced by setting circular grooves in the inlet and outlet sections of the conventional straight cone nozzle, and the drag reduction effect of the outlet section of the BSCN was better than that of the inlet section.  
In the optimized BSCN model, in the L1 inlet section, the groove width was 3.9 mm, the slot pitch was 5.2 mm, the groove depth was 5.5 mm, and the number of grooves was 2. In the L3 outlet section, the groove width was 2.25 mm, the slot pitch was 3 mm, the groove depth was 5.5 mm, and the number of grooves was 2. The maximum velocity increase rate was 13.45%, and the drag reduction effect was the best.  
Figure 10: Conceptual model: (**a–c**) BSCN and (**d**) conventional straight cone nozzle.
## 5 Discussion
### Limitations of STPC
#### 5.1.1 Inrush Current Sensitivity  
For motors controlled via inverters, inrush current is not a critical issue because the motor speed is gradually ramped up through soft-start algorithms inherent in the inverter control. However, for DOL motors without inverter control, the initial inrush current during startup can be several times higher than the rated operating current. Therefore, it is important to limit the motor capacity such that the inrush current does not exceed the rated current of the STPC, to ensure safe and reliable operation.
# Towards Digitalization of Water Supply Systems for Sustainable Smart City Development--Water 4.0
## 5 Potential Applications of Water 4.0
### Leakage Detection and Monitoring  
This is also similar to water quality monitoring applications. In this system, an integrated number of sensors such as pressure, vibration, acoustic, and flow sensors and actuators are deployed for measurement and control activities. In [143], an in-pipe system is developed for pipeline leak monitoring. Furthermore, several research studies have reported the use of machine learning (a division of AI) for leak detection purposes [144; 145; 146]. Once a leak is detected and localized, monitoring of the leaky pipe is required for active intervention and repair and to avoid future leak occurrence in such a pipe. Thus, Water 4.0 could be used to provide real-time continuous monitoring to such a system.
# Permuted Pattern Matching Algorithms on Multi-Track Strings
## 1 Introduction  
The pattern matching problem on strings is to find all occurrence positions of a pattern string in a text string. Pattern matching algorithms such as the Knuth-Morris-Pratt (KMP) algorithm [1], the Boyer-Moore algorithm [2], and the Horspool algorithm [3] perform pattern matching quickly by preprocessing the pattern. On the other hand, pattern matching can be accelerated by preprocessing the text into some indexing structure such as suffix trees [4], suffix arrays [5], or position heaps [6].  
The _permuted pattern matching problem_, proposed by [PERSON] et al. [7, 8], is a generalization of the pattern matching problem, where we compare tuples of strings. We call a tuple of strings of the same length a _multi-track string_. The permuted pattern matching problem is as follows: given two multi-track strings \(\mathbb{T}=(T_{1},T_{2},\ldots,T_{k})\) and \(\mathbb{P}=(P_{1},P_{2},\ldots,P_{k})\) where \(|T_{1}|=\cdots=|T_{k}|=n\geq|P_{1}|=\cdots=|P_{k}|=m\), to find all positions \(i\) such that \((T_{1}[i:i+m-1],\ldots,T_{k}[i:i+m-1])\) is a permutation of \(\mathbb{P}\). The problem can be solved by constructing indexing structures from the text such as multi-track suffix trees [7], multi-track position heaps [8], and filtering multi-set trees [9], or by preprocessing the pattern such as the AC-automaton-based algorithm [7].  
Multi-track strings can model various types of real data when sequences in the data have the same meaning. For example, data from a three-dimensional accelerometer in a smartphone can be considered a multi-track string. Consider two smartphones with the same position, and rotate oneof them several times where each rotation is 90 degrees on the \(x\), \(y\), or \(z\) axis. If we perform the same motion on both smartphones, the generated data from three-dimensional accelerometer of one phone is a permutation of the other one. By using permuted pattern matching, we can find occurrences of a pattern on both smartphones without permuting the pattern.  
In this paper, we propose several algorithms that solve permuted pattern matching quickly. These algorithms can be classified into three groups. The first group consists of algorithms that are based on the KMP algorithm [1]. This group includes the _multi-track KMP_, _multi-track AC-automaton_, and _multi-track permuted matching automaton_ algorithms. The second group consists of algorithms that are based on the Boyer-Moore algorithm [2] and the Horspool algorithm [3]. This group includes the _multi-track Boyer-Moore_ and _multi-track Horspool_ algorithms. The third group consists of filtering algorithms. Moreover, we conduct experiments and show that our algorithms perform permuted pattern matching faster than existing algorithms. The worst case running times of the proposed algorithms and existing algorithms are summarized in Table 1, where \(d\) is the total length of the patterns and \(\sigma\) is the size of the alphabet.  
Preliminary versions of this work appeared in [10; 11].
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 4 Discussion
This can be useful for predicting yield in areas where data for other predictors such as soil and climate variables are not readily available [99]. Other datasets such as land surface temperature (LST) and solar-induced chlorophyll fluorescence (SIF) can also be used for crop yield prediction. Recent research has shown that replacing air temperature with MODIS LST can improve corn yield prediction [15]. Furthermore, the VI and GPP data used in this study have low spatial resolutions. Moderate-resolution multi-sensor data such as Sentinel-2 and Landsat-8 can also be used for yield prediction at the county level.
# Automatic Detection of Linear Thermal Bridges from Infrared Thermal Images Using Neural Network
## 4 Experimental Results
### Evaluation Metrics  
To validate the methodology proposed in this study, the results were compared with the ground truth. To create the ground truth, the author checked each thermal image and designated the areas corresponding to thermal bridges. The comparison between the thermal bridge detection results and the ground truth was measured through precision, recall, and F-score. These three metrics have been used to validate methodologies for detecting thermal anomalies in thermal images, including thermal bridges [12] and moisture [35]. All three metrics are calculated as true positive, false positive, and false negative. True positive refers to the number of pixels predicted as thermal bridges that are actual thermal bridges. False positive refers to the number of pixels predicted as thermal bridges that are not actual thermal bridges. False negative refers to the number of pixels predicted not to be thermal bridges that are actual thermal bridges. Precision, recall, and F-score are calculated as follows:  
\[\text{Precision}=\ \frac{\mathit{TP}}{\mathit{TP}+\mathit{FP}} \tag{4}\]  
\[\text{Recall}=\ \frac{\mathit{TP}}{\mathit{TP}+\mathit{FN}} \tag{5}\]  
\[\text{F}-\text{score}=\ 2\times\frac{\mathit{Precision}\times\mathit{Recall}}{ \mathit{Precision}+\mathit{Recall}} \tag{6}\]  
where \(\mathit{TP}\) is true positive, \(\mathit{FP}\) is false positive, and \(\mathit{FN}\) is false negative.
# A Road Behavior Pattern-Detection Model in Queretaro City Streets by the Use of Shape Descriptors
## 1 Introduction  
The rapid growth of urban areas has led to the development of equipment that measures and analyzes information to make day-to-day decisions. This equipment and infrastructure are known as smart cities and require reliable communication and interoperability between technologies. The infrastructure measures urban variables and analyzes data to develop efficient, reliable, adaptable, and robust algorithms capable of identifying events and atypical behaviors of interest. This information is stored in a database using camera technologies, and historical trends can be analyzed over time. These data are useful for decision-making on events and generating video analytics on the activity of an area.  
The increase in population density and the growth in vehicle use have led to a decrease in the standard of living for inhabitants due to the saturation of communication, transportation, and telecommunication routes. This has resulted in road congestion, making road monitoring systems more in demand. However, existing monitoring systems have general drawbacks that need to be addressed:  
* Human operators are required to monitor day-to-day activities.
* Human intervention is required to locate the same object if multiple cameras are used.  
The physical infrastructure of roads is sufficient for the collection of information on traffic flow and speed. However, since this method is invasive, it requires frequent maintenance to ensure proper functionality. Additionally, the presence of monitoring equipment may cause drivers to alter their driving behaviors [1]. To address this issue, a model based on Granger causal patterns can be used to efficiently monitor roadways and detect abnormal behaviors. A distributed monitoring system that utilizes conventional camera technology is chosen to identify the behavior of moving objects and situations of interest. This model can help identify periodic behaviors in traffic patterns and analyze the relationships between them. Currently, a monitoring system consists of cameras thatcapture and store data in a database. These data include information such as the number of objects, their trajectories, size, color, and flow.  
This article is divided into several sections. In Section 1.1, we provide information about the related work. In Section 2, we describe the materials and methods used in characterizing the behavior of the study scenario. In Section 3, we present the results. Section 4 concludes the article by discussing the model. In Section 5, we present the conclusion. Finally, we list the references that support this work.
# Experimental and Numerical Studies on the Impact Energy Absorption of Cutting Shear Rings
## 4 Numerical Simulation
### Comparison of Simulation Results  
To verify the accuracy of the finite element model, the axial collision simulation analysis was carried out using the model of test case 3 as an example. The numerical simulation result showed that the peak force during the collision process is 118.47 kN, and the suction energy of the whole collision process is 1889.12 J. Taking the first ring closest to the base as an example, the shear energy absorption process of a single ring was analyzed, and the specific process is shown in Figure 7. When t = 0.0030 s, the impactor impinges on the energy absorption device and the tendency of relative displacement between energy absorption rods and pedestal. When t = 0.0035 s, the contact force between the energy absorption rods and pedestal is greater than the stress limit at the shear rings, and the shear energy absorption process starts, accompanied by the peak force. When t = 0.0040 s, the shear rings closest to the pedestal are sheared off. This process will be repeated for each subsequent shear ring.  
Figure 8 shows the F-T curve of the numerical simulation process. From comparing it with the F-T curve of case 3, the peak force of the load curve, the waveform trend, and the collision energy absorption, the peak force error of the collision process is 11%, and  
Figure 6: Finite element model of the CSR system.  
the energy absorption error is 12%. It shows that the numerical simulation results are in good agreement with the experimental results, and the effectiveness and correctness of the established finite element model are verified.
## 6 Therapeutic Approaches of Dentin Hypersensitivity
### At-Home Therapeutic Treatments of Dentin Hypersensitivity
#### 6.1.2 Sodium Fluoride  
The most common fluorine compound in toothpastes is sodium fluoride (NaF). Fluoride helps to deposit minerals, such as calcium and phosphate, onto the dentin surface, which can help strengthen and harden it. Toothpastes incorporating NaF and calcium phosphate [Ca\({}_{3}\)(PO\({}_{4}\))\({}_{2}\)], aimed at dentin remineralization, also notably mitigate DH. When fluoride ions come into contact with the tooth's surface, they can block the exposed dentin tubules [72].