esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Tests and Results
### Circuit System Test Results
#### 3.2.2 Circuit System Principle and Output Test  
The main control unit in the circuit system is the STM32 control system, and its specific control flow chart for the output voltage is shown in Figure 11. When the system is initialized by programming the four keys on the STM32 development board to set the reset button and the set button, the reset button accounts for one (called the reset key) and the set button accounts for three (called keys 1, 2, and 3). Due to the limitations of the development environment, the settings of this project are only for this experiment, and the keys of the control system can be added or subtracted later to further meet the voltage requirements of different household DC loads.  
According to the types of DC electrical appliances in Figure 2, the initial voltage of the circuit system is set to 12 V (reset voltage), and the set voltages of 5 V, 14 V, and 24 V are controlled by setting buttons 1, 2, and 3, respectively, and the specific detection values are shown in Figure 12.  
Figure 11: STM32 control circuit system flow chart.  
Figure 10: Trajectory and frequency domain characteristics.  
The test results of the boost circuit and buck circuit in the circuit system are shown in Table 4. The physical diagram of the circuit system (including the STM32 microcontroller, boost circuit, driver circuit, and buck circuit) is shown in Figure 13.  
In order to further illustrate the advantages of the proposed DC microgrid system, the performance of the proposed system is compared with that of the existing DC microgrid system [20; 21; 22; 23]. The comparison results are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline
**Circuits** & **Set Voltage (V)** & **Measured Voltage (V)** \\ \hline  & 5 & 5.091 \\ Buck Circuit & 12 & 11.98 \\  & 14 & 14.126 \\ \hline Boost Circuit & 24 & 24.208 \\ \hline \end{tabular}
\end{table}
Table 4: Summary of measured data of boost and buck circuits.  
Figure 12: Actual measurement diagram of the circuit system. (**a**) Output voltage diagram when different keys are pressed. (**b**) Output voltage amplification diagram when key 1 is pressed. (**c**) Output voltage amplification when button 3 is pressed. (**d**) Output voltage amplification when button 2 is pressed. (The blue dashed lines in figures (**b–d**) are the average voltage).
# Design of a Binocular Pupil and Gaze Point Detection System Utilizing High Definition Images
## 2 Proposed Binocular Eye Gaze Tracking System
### Calibration and Gaze Point Estimation  
Eye tracker systems use linear or nonlinear methods when transforming eye image coordinates to user screen coordinates. In this study, we applied second-order polynomial equation to estimate user screen gaze points. In general, second-order polynomials are chosen as the mapping function shown in Equation (3).  
\[\begin{array}{l}Sx=a_{0}+a_{1}x+a_{2}y+a_{3}xy+a_{4}x^{2}+a_{5}y^{2}\\ Sy=b_{0}+b_{1}x+b_{2}y+b_{3}xy+b_{4}x^{2}+b_{5}y^{2}\end{array} \tag{3}\]  
Estimation of pupil center \(x\)-\(y\) coordinates position (\(Sx\), \(Sy\)) on the user screen is obtained by Equation (3) as follows:  
\[\left[\begin{array}{cccccc}1&x_{1}&y_{1}&x_{1}y_{1}&x_{1}{}^{2}&y_{1}{}^{2} \\ 1&x_{2}&y_{2}&x_{2}y_{2}&x_{2}{}^{2}&y_{2}{}^{2}\\.&.&.&.&.&.\\.&.&.&.&.\\ 1&x_{n}&y_{n}&x_{n}y_{n}&x_{n}{}^{2}&y_{n}{}^{2}\end{array}\right]\ \left[\begin{array}{ ccc}a_{0}&b_{0}\\ a_{1}&b_{1}\\.&.\\.&.\\ a_{5}&b_{5}\end{array}\right]\ =\ \left[\begin{array}{ ccc}S_{1x}&S_{1y}\\ S_{2x}&S_{2y}\\.&.\\ S_{nx}&S_{ny}\end{array}\right] \tag{4}\]  
Equation (4) is a transform matrix of Equation (3). This expression is represented with Equations (5) and (6).  
\[X\,A=B \tag{5}\]  
\[A=\ (X^{\rm T}X)^{-1}X^{\rm T}\,B \tag{6}\]  
In Equations (5) and (6), '\(A\)' matrix values are unknown constants, '\(B\)' matrix values are coordinates of the calibration points on the user screen and '\(X\)' matrix values are pupil center \(x\)-\(y\) coordinates in the captured images. '\(A\)' matrix values are calculated during the calibration process. In our study, the calibration process is performed using nine points on the screen as shown in Figure 8.  
\(x\) and \(y\) coordinates (pixels) of these nine calibration points from 1 to 9 are (80, 80), (640, 80), (1200, 80), (80, 512), (640, 512), (1200, 512), (80, 944), (640, 944) and (1200, 944), respectively. '\(X\)' matrix values can be taken as a difference vector of glint (\(gx,gy\)) and pupil (\(px,py\)) center coordinates. Therefore, Equation (3) is reorganized as Equation (8) utilizing Equation (7).  
Figure 8: User calibration points of the eye gaze tracker.  
\[dx = g_{x}-p_{x} \tag{7}\] \[dy = g_{y}-p_{y}\] \[Sx = a_{0}+a_{1}dx+a_{2}dy+a_{3}dxdy+a_{4}dx^{2}+a_{5}dy^{2}\] \[Sy = b_{0}+b_{1}dx+b_{2}dy+b_{3}dxdy+b_{4}dx^{2}+b_{5}dy^{2} \tag{8}\]  
After a calibration period, found constants (\(A\)) are used for mapping the pupil center coordinates in the test image to the user screen positions in pixels as follows:  
\[\left[\begin{array}{cccc}1&dx_{t}&dy_{t}&dx_{t}dy_{t}&dx_{t}{}^{2}&dy_{t}{}^{ 2}\end{array}\right]\ \left[\begin{array}{cccc}a_{0}&b_{0}\\ a_{1}&b_{1}\\.&.\\ a_{5}&b_{5}\end{array}\right]\ =\ \left[S_{tx}\,S_{ty}\right] \tag{9}\]  
\[S_{t}=X_{t}\,A \tag{10}\]
# Prediction Method for Mine Earthquake in Time Sequence Based on Clustering Analysis
## 4 Engineering Experiments
### Prediction of Mine Earthquake  
The microseism clusters can be regarded as mine earthquake sequences. The location and frequency corresponding to different energy levels of events in the cluster have a time-series law, that is, the location and frequency of subsequent microseism events are related to historical microseism information. The largest microseism cluster containing 1077 events was selected, and the SVR was used to establish a model to conduct prediction experiment in sequence for the microseism events in the cluster. The prediction includes two aspects: occurrence location, and daily frequency of high-energy event. Generally, when the energy released by mine earthquake reaches above \(10^{4}\) J, the rock burst may be induced, causing serious injury to underground roadway and personnel [34]. Therefore, the high-energy event refers to the mine earthquake event that releases energy greater than \(10^{4}\) J. In data preprocessing, considering the strong correlation of microseism events within the cluster and the limited number of events, the sampling step of position prediction is 10 times, the sampling step of frequency prediction is 3 days, and the moving step is 1. In order to evaluate the prediction effect of model, MSE and R\({}^{2}\) are selected as assessment indexes [35], and their calculation formulas are as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cluster Number** & **Amount** & **Cluster Number** & **Amount** \\ \hline
1 & 60 & 11 & 1077 \\
2 & 527 & 12 & 95 \\
3 & 128 & 13 & 71 \\
4 & 749 & 14 & 948 \\
5 & 149 & 15 & 151 \\
6 & 807 & 16 & 914 \\
7 & 74 & 17 & 193 \\
8 & 135 & 18 & 286 \\
9 & 98 & 19 & 59 \\
10 & 104 & 20 & 52 \\... &... &... &... \\ \hline \hline \end{tabular}
\end{table}
Table 4: Clustering results.  
Figure 11: Distribution of microseism clusters on working face.  
\[MSE=\frac{1}{n}{\sum\limits_{i=1}^{n}}{(y_{i}-\hat{y_{i}})}^{2} \tag{19}\]  
\[R^{2}=1-\frac{{\sum\
olimits_{i=1}^{n}}{(y_{i}-\hat{y_{i}})}^{2}}{{\sum \
olimits_{i=1}^{n}}{(y_{i}-\overline{y})}^{2}} \tag{20}\]  
where \(n\) is the total number of samples, \(\hat{y_{i}}\) is the predicted value, \(y_{i}\) is the true value of the sample, \(\overline{y}\) is the mean of the true value. The smaller the value of MSE is, the more accurate the model prediction is. R\({}^{2}\) is between (\(-\infty\), 1), and the larger the value is, the better the effect of model fitting ability is.
# Adaptive Network Routing Technology for Near-Moon Space Cross-Domain Transmission
## 3 Routing Scheduling Algorithm  
### Sub-Network Clustering  
After filtering out available communication nodes according to Formula (3), there are still many remaining nodes, and direct routing processing may cause calculation delay. Meanwhile, due to the large number of nodes, the search strategy can only focus on local optimization, and the resulting routing results may not necessarily be the optimal choice. Sub-network clustering mainly divides effective nodes into sub-networks based on predetermined clustering rules. In this design, it is mainly divided into two types of sub-network clustering: the fixed topology sub-network set \(T_{F}\) and time-varying topology sub-network set \(T_{V}\). In the clustering process, \(T_{V}\) only needs to consider limiting the distance so that the closer nodes are networked according to the principle of proximity, while \(T_{F}\) needs to consider factors such as the occlusion of the Earth and Moon, as well as the rate attenuation caused by distance.
# GNSS Timing Performance Assessment and Results Analysis
## 1 Introduction
In order to promote GNSS compatibility and interoperability, the International Committee on GNSS (ICG) established the Interoperability and Service Standards Subgroup and it has held regular workshops and seminars to discuss open service performance standards. At present, ICG has published the latest version of _Guidelines for Developing Global and Regional Navigation Satellite Systems Performance Standards_ to provide GNSS users with unified GNSS timing performance standards [15; 16; 17]. For multi-GNSS users, it is necessary to clarify the timing performance indicators and assessment methods, which ultimately better guide the use of time services and the manufacture of timing equipment. In this paper, the assessment of GNSS timing performance is investigated. The principle of GNSS timing is studied (Section 2.1). On this basis, the definition and connotation of the key indicators of GNSS timing performance are discussed. Additionally, an assessment method of GNSS timing performance is proposed on the basis of UTC(NTSC) and the offset between Rapid UTC (UTCr) and UTC(k) provided by BIPM (Section 2.2), and the various components of uncertainty are analyzed. Lastly, the assessment results of GNSS timing performance are compared and analyzed (Section 3).
## 3 Experiments and Analysis
### Experimental Results and Analysis
#### 3.4.4 Effectiveness of Multi-Scale Integration  
This experiment is used to verify the effectiveness of multi-scale integration. The integration of three identical network is used for comparing with different scales of integration. In order to ensure the fairness of the experiment, each group of experiments uses three VGG-16-7 networks.  
In the DRIVE dataset, we used three identical scales with a size of 23 \(\times\) 23. In another group of experiments, we used three different scales integration, as shown in Table 8. In the CHASE_DB1 dataset, the same scale size is 31 \(\times\) 31, and the different scales are shown in Table 9. The testing results on the two datasets are shown in Tables 10 and 11.
###### Abstract  
Recent studies have focused on achieving sound osseoeintegration applying methods of surface reprocessing of dental implants using ultraviolet (UV) irradiation. However, there have been few reports on the effect of UV irradiation on osseoeintegration in both the short and long term, and experimental studies using the canine model do not exist. Therefore, the aim of this study was to determine the short- and long-term degree of osseoeintegration of dental implants according to UV irradiation using a histometric analysis in canine models. Four beagles were used for this study and 24 dental implants were placed. Six implants were inserted with flawless protocols in each mandible. Half of the implants were UV-irradiated prior to implantation. UV-treated and untreated implants were placed one after the other; implant stability was recorded using the implant stability test (IST) immediately after implantation and every seven days thereafter. Two of the beagles were sacrificed after four weeks and the other two after 12 weeks. The implants were removed in block sections and were evaluated histomorphologically and statistically. As a result, we found no statistically significant differences in the mean IST values and in bone-to-implant contact (BIC) in all groups at 4 and 12 weeks.  
UV-irradiation; dental implant; bone-to-implant contact; implant stability test 2020
## 4 Discussion
NIRv is a relatively new index that is considered to be more correlated with SIF and GPP at specific temporal scales [9], but still, its interpretation is debated due to several facts, such as the consideration of spectrum band related to FAPAR estimation, its linear correlation with NIR, etc. [39]. Interestingly EVI and NIRv have shown a very similar pattern in this study with a correlation of 1. For the manipulation sites, the variation in VIs was observed, but the differences between the treatments were non-significant for most of the cases, indicating the need for more data collection, including the vegetation composition data. Whereas the differences in VIs and LAI between CL and CR during the summer were observed to be significant, indicating the importance of hydrological regimes and fluctuations of water table depth for peatland vegetation (Supplementary Table S2) [18]. All the studied VIs showed a slow increase and decrease in their value, whereas the SIF was observed to increase slowly but showed a sharp decrease in its value in October. SIF is dependent on three factors: light absorption, SIF reabsorption and scattering, and the relation between photosynthesis and Non-Photochemical Quenching (NPQ), which is nonlinear [15]. The temperature is decreasing rapidly in October, which significantly impacts the photosynthetic activity. Lower light intensity results in a lower amount of  
Figure 8: A representative picture of the control plot showing the seasonal dynamic of peatland vegetation at CL and CR sites. absorbed energy (in comparison to previous months), most of which goes to photosynthesis and NPQ in colder conditions [40], whereas higher biomass increases the scattering of SIF in the far-red region, which may result in a significant decrease in SIF 760 value as observed at the top of the canopy. The SIF signal is a relatively new signal that needs high-resolution hyperspectral sensors, complex processing, and several other logistic and financial limitations like other new sensors, limiting the available data. The retrieval of SIF is still in its developing stage, and the number of experts to understand and retrieve these signals is limited although increasing. Like all new sensors in their initial phase, the SIF measuring instruments are comparatively expansive in respect to several remote-sensing devices used for reflectance collection and vegetation characterization through VIs. Thus, people are trying several ways to estimate SIF through the existing remote-sensing methods [10; 11; 12]. RTM is one of the methods widely used to estimate vegetation properties in the remote-sensing field. SCOPE is a widely accepted canopy model for the purpose of SIF retrieval [22; 23; 24]. Our observation indicated the SCOPE retrieved SIF, i.e., modeled SIF (even if slightly underestimated), correlated well with the measured SIF for a complex ecosystem like peatland, indicating the robust nature of SCOPE for the retrieval of SIF signals.
## 3 Results
### Influence of Check Dam Heights and Spacings on Infiltration Rate of Grassed Channel
Figure 10: Water level along the channel sections for different times for different check dam design cases: (**a**) case 1; (**b**) case 2; (**c**) case 3; (**d**) case 4; (**e**) case 5. Figure 11: _Cont._  
Table 4 shows that the infiltrated water volume increased from 34.3 for \(h_{w}\) = 10 cm and spacing = 10 m (case 1) to 45.5 m\({}^{3}\) for \(h_{w}\) = 15 cm and spacing = 10 m (case 2), and 59.6 m\({}^{3}\) for \(h_{w}\) = 20 cm and spacing = 10 m (case 3). Therefore, the amount of infiltration was increased from 12% to 20% by increasing the height of the check dam from 10 to 20 cm at channel downstream, whereas increasing the spacing of 20 cm height check dams from 10 to 30 m caused a slight decrease in total infiltration from 59.6 to 56 m\({}^{3}\) (20% to 19%). Figure 11: The values of cumulative infiltration \(F\) for different times for the five check dam design cases: (**a**) case 1; (**b**) case 2; (**c**) case 3; (**d**) case 4; (**e**) case 5. Figure 12: The percentage of the total volume of conveyed and infiltrated water. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Total Inflow Volume (m\({}^{3}\))** & **Total Outflow Volume (m\({}^{3}\))** & **Total Infiltrated Volume (m\({}^{3}\))** \\ \hline
1 & 297 & 262.7 & 34.3 \\
2 & 297 & 251.5 & 45.5 \\
3 & 297 & 237.4 & 59.6 \\
4 & 297 & 239 & 58 \\
5 & 297 & 241 & 56 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The total inflow, outflow, and infiltration volumes after 90 min.
## 6 Blade Loads
### Structural Blade Regions  
The modern blade can be divided into three main areas classified by aerodynamic and structural function (Figure 14):  
* _The blade root_. The transition between the circular mount and the first aerofoil profile--this section carries the highest loads. Its low relative wind velocity is due to the relatively small rotor radius. The low wind velocity leads to reduced aerodynamic lift leading to large chord lengths. Therefore the blade profile becomes excessively large at the rotor hub. The problem of low lift is compounded by the need to use excessively thick aerofoil sections to improve structural integrity at this load intensive region. Therefore the root region of the blade will typically consist of thick aerofoil profiles with low aerodynamic efficiency.
* _The mid span_. Aerodynamically significant--the lift to drag ratio will be maximised. Therefore utilising the thinnest possible aerofoil section that structural considerations will allow.
* _The tip_. Aerodynamically critical--the lift to drag ratio will be maximised. Therefore using slender aerofoils and specially designed tip geometries to reduce noise and losses. Such tip geometries are as yet unproven in the field [1], in any case they are still used by some manufacturers.  
Figure 14: The three blade regions.  
Figure 13: Edgewise bending about \(yy\).
# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 2 Systematic Review Approaches  
A structured and reproducible methodology was employed to conduct this systematic review, adhering to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [49]. Literature searches were conducted across three major databases: Web of Science, Scopus, and PubMed. These were selected due to their comprehensive coverage of research in aquaculture, artificial intelligence (AI), and the Internet of Things (IoT).  
The search was performed using a combination of keywords and Boolean operators to capture all relevant literature. The main search terms included:  
* 'Artificial intelligence' OR 'AI' OR'machine learning' OR 'deep learning' OR 'Internet of Things' OR 'IoT' AND 'aquaculture'.
* 'Aquaculture' OR 'fish farming' OR'shrimp farming' OR 'aquatic farming' OR'smart aquaculture' AND'monitoring' OR 'automation' OR 'disease detection' OR 'water quality' OR 'feeding optimization'.  
These searches retrieved both general and specific research articles related to AIoT in aquaculture. The literature was limited to English-language publications from 2015 to 2024. As shown in Figure 1, the screening process involved the following steps.  
1. Identification: An initial search across three databases--PubMed (320), Scopus (192), and Web of Science (129)--yielded a total of 641 records after duplicate removal.  
Figure 1: PRISMA flow diagram depicting the study selection process for the systematic review.  
* Screening: Titles and abstracts of all 641 articles were reviewed for relevance. At this stage, 346 articles were excluded based on predefined exclusion criteria such as domain mismatch, language restrictions, or lack of AI/IoT relevance in aquaculture.
* Eligibility Assessment: The remaining 295 articles were reviewed in full to assess their alignment with the study's objectives and inclusion criteria. During this phase, 135 full-text articles were excluded due to methodological limitations, lack of practical implementation, or insufficient detail.
* Inclusion: Finally, 150+ full-text articles were selected for detailed analysis in the systematic review, representing the most relevant and high-quality studies addressing AIoT applications in aquaculture.
# Impacts of GPS Spoofing on Path Planning of Unmanned Surface Ships
## 3 GPS Attack Designs and Spoofing Analysis
### GPS Attacks
#### 3.2.1 GPS Jamming  
The GPS signals of satellites, measured at the surface of the earth, are very weak. According to IDC-GPS-200 [33], the transmitting power of the L1 signal only guarantees the minimum signal power level of \(-160\) dBW on the earth surface and the minimum signal level of \(-166\) dBW when the L2 signal strength reaches the earth surface. The experiment result of Lincoln Laboratory [34] shows that a jammer with a power of 1 W can make the receiver of C/A code within 85 km out of work. If the jamming power is increased by 6 dB, the jamming distance is doubled. Therefore, the GPS is vulnerable to jamming, resulting in difficulties in acquiring navigation data. GSP jamming technologies based on bandwidth (\(B_{j}\)) is classified into three types, including Continuous Wave (\(B_{cw}\)), Narrowband (\(B_{nb}\)), and Wideband (\(B_{wb}\)), where \(B_{cw}\) < 100 kHz, \(1<B_{nb}\leq 1.023\) kHz, \(B_{wb}\approx 10.23\) MHz. The GPS signal acquisition is a search process to determine whether the satellite signals exist or not. Based on [35], the detection probability of GPS signal \(P_{d}\) is proportional to the carrier to noise ratio \(C/N_{0}\). A jammer broadcasts on the same frequency to reduce \(C/N_{0}\), i.e., increasing noise (\(N_{0}\)). Therefore, the victim receiver cannot acquire GPS signals normally. A simple GPS jamming system, including a power amplifier, an antenna of a GPS simulator, and an RF signal transmitter, can jam GPS receivers. Many papers have detailed introduction, such as [36; 37; 38].
# Optimizing Non-Glare Zone Width of Adaptive Driving Beam (ADB) Using Fuzzy Logic Control
## 2 Modeling: Night Drive Simulator
### Night Driving Model Development
For example, if a deer is standing by the roadside, the ADB system with a narrower non-glare zone will illuminate the deer sooner, giving the driver more reaction time, thus enhancing driving safety. This comparison indicates the importance of defining the best non-glare zone width that balances glare mitigation and driver visibility. Figure 4: Effect on glare when altering non-glare zone width, showing that widening the non-glare zone could effectively eliminate glare: (**a**) 100% non-glare zone width (glare); (**b**) 150% non-glare zone width (no glare). Figure 5: Effect on road illumination when altering the non-glare zone width, showing that a widened non-glare zone sacrifices road illumination. For example, the traffic signs circled on (**a**) are well illuminated;  
however, when the non-glare zone increases on (**b**), the signs are included in the non-glare zone and, thus, are not illuminated. This indicates the driver will have better and wider visibility with the narrower non-glare zone, which enhances driving safety.