esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Definition
### Calculation of the Satellite Position Dilution of Precision  
The pseudo-range observation equation and the carrier phase observation equation for a single GNSS tracking station can be expressed as [26; 27; 28]  
\[\left\{\begin{array}{l}P_{r,j}^{s}=\rho^{i}+c(t_{r}-t^{s})+T_{r}^{s}+I_{r,j}^ {s}+\varepsilon_{r,j}^{s}\\ \phi_{r,j}^{s}=\rho^{i}+c(t_{r}-t^{s})+\lambda_{j}^{s}N_{r,j}^{s}+T_{r}^{s}-I_ {r,j}^{s}+\widetilde{\sigma}_{r,j}^{s}\end{array}\right., \tag{6}\]  
where \(\rho^{i}\) is the geometric distance between satellite \(s\) and ground station \(r\), \(t_{r}\) and \(t^{s}\) denote the clock delay of the station (receiver) and the visible satellite, respectively. \(T_{r}^{s}\) and \(I_{r,j}^{s}\) denote the tropospheric and ionospheric delays, respectively, while \(N_{r,j}^{s}\) is the integer phase ambiguity, \(c\) is the light celerity, and \(\varepsilon_{r,j}^{s}\), \(\widetilde{\varsigma}_{r,j}^{s}\) denote the pseudo-range and phase noises, respectively.  
In observation Equation (6), the geometric distance between the \(i^{th}\) satellite \((X^{i},Y^{i},Z^{i})\) and the ground station \((x,y,z)\) is the Euclidean distance between them, which is expressed as  
\[\rho^{i}=\sqrt{\left(X^{i}-x\right)^{2}+\left(Y^{i}-y\right)^{2}+\left(Z^{i}- z\right)^{2}}. \tag{7}\]  
After linearization, it can be expressed as  
\[d\rho^{i}=\frac{(X^{i}-x)dX^{i}+(Y^{i}-y)dY^{i}+(Z^{i}-z)dZ^{i}}{\sqrt{\left(X ^{i}-x\right)^{2}+\left(Y^{i}-y\right)^{2}+\left(Z^{i}-z\right)^{2}}}. \tag{8}\]  
Similar to the DOP calculation, the coefficient matrix \(\mathbf{A}\) of the observation equation for satellite \(i\) can be expressed as [25; 26; 18; 29]  
\[\mathbf{A}=\begin{bmatrix}cx_{1}^{i}&cy_{1}^{i}&cz_{1}^{i}\\ cx_{2}^{i}&cy_{2}^{i}&cz_{3}^{i}\\ \vdots&\vdots&\vdots\\ cx_{m}^{i}&cy_{m}^{i}&cz_{m}^{i}\end{bmatrix}, \tag{9}\]  
where \(cx_{k^{\prime}}^{i}\), \(cy_{k^{\prime}}^{i}\), \(cz_{k}^{i}\) is the direction cosine from the \(i\)th satellite to the \(k\)th ground station for the total number of ground stations (\(m\)) that are visible to satellite \(i\) at a certain epoch.  
Therefore, the cofactor matrix \(\mathbf{Q}\) of observation matrix \(\mathbf{A}\) can be expressed as  
\[\mathbf{Q}=\left(\mathbf{A}^{T}\mathbf{A}\right)^{-1}=\begin{bmatrix}q_{11}&q_ {12}&q_{13}\\ q_{21}&q_{22}&q_{23}\\ q_{31}&q_{32}&q_{33}\end{bmatrix}. \tag{10}\]Finally, similar to the PDOP, the satellite position dilution of precision (SPDOP) value of satellite \(i\) at a certain epoch can be expressed as [18; 29]  
\[SPDOP=\sqrt{q_{11}+q_{22}+q_{33}}. \tag{11}\]
## 3 Results and Discussion  
### Sensitivity to Lcing Environment Parameters  
#### 3.1.1 Mvd  
The protection area of basic configuration was designed based on MVD 20 \(\upmu\)m. Comparing cases 1-3, the IPS prevented ice from forming when MVD was 30 \(\upmu\)m. When MVD increased to 40 \(\upmu\)m, ice appeared after the protection area but without rivulet characteristics, shown in Figure 7. An additional ice formation test under the same icing condition (Figure 8) showed that icing range exceeded the upper limit of the heating area (sheet B in this test). Thus, it could be inferred that the ice formation was due to the droplet impingement of MVD 40 \(\upmu\)m that surpassed the protection range rather than runback water.  
Figure 6: Surface temperature history in case 1. (**a**) Complete history. (**b**) Time 120 s to 170 s.  
Figure 7: Test results of various MVDs. (**a**)MVD 20 \(\upmu\)m. (**b**) MVD 30 \(\upmu\)m. (**c**) MVD 40 \(\upmu\)m.  
The surface temperature decreased when MVD increased from 20 \(\upmu\)m to 40 \(\upmu\)m, as shown in Figure 9. The lower surface temperature showed a linear decrease, while the upper surface temperature exhibited multiple decreasing trends, with the lower surface experiencing the most significant reduction. A maximum temperature decrease of 1.81 \({}^{\circ}\)C (about 9.93%) was observed when MVD increased from 20 \(\upmu\)m to 30 \(\upmu\)m and 3.47 \({}^{\circ}\)C (about 19.03% compared to MVD 20 \(\upmu\)m) when MVD increased from 20 \(\upmu\)m to 40 \(\upmu\)m.  
The test utilized a symmetrical NACA0012 airfoil tested at 0\({}^{\circ}\) angle of attack. The difference in temperature between the upper and lower surfaces in this configuration resulted from their dissimilar protection areas. As such, an asymmetrical distribution of the protection areas of the upper and lower surfaces ultimately led to differences in temperature variation as MVD increased.  
According to the results of this test, MVD affects the droplet impingement limit and becomes a factor to be considered in the design of IPS. An increase in MVD leads to an increase in water collected on the surface, which necessitates a simultaneous enhancement of the protection power required, as reflected in the decrease of surface temperature under the condition of constant heating power in this test.  
In order to demonstrate the efficiency and low-energy consumption of IPS with a coating, a reference test of IPS without coatings was conducted under the same condition as case 1. The heating power densities of 5 kW/m\({}^{2}\) and 10 kW/m\({}^{2}\) were provided to prevent ice formation. As shown in Figure 10, runback ice formed after the heating area on the test model without coatings even when the heating power density was 10 kW/m\({}^{2}\) which was  
Figure 8: Ice formation test in case 3 condition.  
Figure 9: Comparison of surface temperature with different MVDs.  
twice as the power provided in case 1. IPS with a coating prevented runback ice formation and reduced power consumption by more than 50%.
## 3 Results and Discussion  
### HPLC-DAD Analysis  
The analysis of the _S. elaengifolium_ extracts revealed that they are high in bioactive molecules such as flavonoids, polyphenols, and phenolic acids; Table 2 and Figure 1 show the results of the compound identification in the two extracts studied. Fruit extract (SEFR) revealed the presence of catechin, vanillin, quercetin 3-O-\(\beta\)-D-glucose, rutin, quercetin, and kaempfer. Leaf extract (SEFE) showed an abundance of naringin, quercetin 3-O-\(\beta\)-D-glucose, rutin, cinnamic acid, p-coumaric acid, quercetin, trans-3-hydroxycinnamic acid, and flavones. The chromatogram analysis provides a difference in the chemical composition of each of the extracts, with the same molecules present in different concentrations depending on the part of the plant. Research has revealed the presence of compounds similar to those discovered in our study [15; 16; 24]. An analysis of the results revealed the presence of quercetin in the two plant extracts at a very high concentration of 429.02 \(\pm\) 0.97 mg/100 g for SEFR and 114.15 \(\pm\) 0.34 mg/100 g for SEFE. Quercitin is a bioactive molecule with a variety of pharmacological activities. Because of the inclusion of a phenolic hydroxyl group, it is considered to be a very strong antioxidant, and this compound has anti-inflammatory, antimicrobial, antitumor, and cardioprotective properties [34; 35].  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Peak**} & \multirow{2}{*}{**Compounds**} & \multicolumn{2}{c}{**SEFR**} & \multicolumn{2}{c}{**SEFE**} \\ \cline{3-6}  & & \% Area & mg/100 g & \% Area & mg/100 g \\ \hline
1 & Catechin & 0.49 & 0.49 \(\pm\) 0.21 & Nd & Nd \\
2 & Vanillin & 12.13 & 46.72 \(\pm\) 0.69 & Nd & Nd \\
3 & Naringin & Nd & Nd & 4.16 & 12.00 \(\pm\) 0.01 \\
4 & Quercetin 3-O-\(\beta\)-D-glucose & 4.36 & 16.72 \(\pm\) 0.55 & 1.59 & 1.84 \(\pm\) 0.05 \\
5 & Rutin & 3.22 & 98.17 \(\pm\) 0.651 & 1.34 & 3.83 \(\pm\) 0.23 \\
6 & Cinnamic acid & Nd & Nd & 0.81 & 2.17 \(\pm\) 0.44 \\
7 & p-coumaric acid & Nd & Nd & 6.56 & 75.85 \(\pm\) 0.13 \\
8 & Quercetin & 75.84 & 429.02 \(\pm\) 0.97 & 26.29 & 114.15 \(\pm\) 0.34 \\
9 & Trans-3-hydroxycinnamic acid & Nd & Nd & 55.95 & 243.51 \(\pm\) 0.46 \\
10 & Kaempfer & 3.96 & 12.09 \(\pm\) 0.08 & Nd & Nd \\
11 & Flavone & Nd & Nd & 3.30 & 9.28 \(\pm\) 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: HPLC chromatographic examination of the chemicals found in _S. elaengifolium_ fruit and leaf extracts.
# Implementation of Explosion Safety Regulations in Design of a Mobile Robot for Coal Mines
## 2 State of Art
### Requirements for the Robot  
Required functionality and parameters of the robot TeleRescuer were specified based on the analysis of existing robots and a survey made in the Central Mine Rescue Station (Bytom, Poland).  
The proposed unmanned vehicle should have a compact structure, small size, and high stability and mobility. Its dimensions cannot exclude the possibility of transport through a fire-dam tube (\(\O\) 800 mm) in an anti-explosion dam. The device should also have as low weight as possible in order to enable manual handling (additional transport handles would be useful). Uncomplicated control shall be performed remotely--from the rescue base. Instrumentation (sensors, cameras) should be protected from possible damage.  
The main obstacles and hindrances which the robot can encounter during the inspection and which should be dealt with include:  
* significant reduction or total lack of visibility,
* high temperature (up to 60 degrees Celsius) and humidity (up to 100%),
* difficult terrain, i.e., significant excavation slope, uneven ground, water spills of different depths,
* reduced cross-sectional area of mining working,
* numerous obstacles specific to cave-ins and related to stored improperly or scattered material, and
* technological obstacles: structures of conveyors, conveyor drives, excavation protection structures and their intersections, hydraulic or wood racks, railroad tracks, turnouts, loading ramps, winches, transformers, switchgear or single switches, pumps, hoses, drainage, sheet, elements of concrete, machine constructions and their fixing--beam, struts, chains, wire ropes, tubes, pipes, cables, ventilation fans, and lutes.  
As far as the sensory system is concerned, the device should be able to measure temperature, relative humidity and the four major gases (O\({}_{2}\), methane--CH\({}_{4}\), CO, CO\({}_{2}\)). Beneficial could also be the ability to measure the air velocity and temperature of selected elements of the robot body. The exact scope and frequency of measurement should always be programmed after consultation with the head of the rescue operation. There also must be equipment for recording and transferring images to the operator (colour cameras operating in the visible light spectrum supported with additional lighting and infrared cameras), together with a 3D mapping functionality (not critical).  
The respondents considered that the optimum working time for the robot would be:  
* about 3-4 h of work, and
* from several hours to several days in idle mode.
## 3 Hardware Design  
### Sensors and Wheatstone Bridges  
Although there are many types of temperature sensors available, there are not many that can achieve a resolution of 0.5 mK. After comparison, a 10 K NTC (negative temperature coefficient) thermistor was chosen as the temperature sensor. It was installed in a pre-drilled hole in the cesium oven, near the heating element. This was because the cesium oven was in a vacuum environment and was insulated from the installation base by a heat-insulating ceramic, which provides good thermal insulation. As long as the temperature of the heating element is kept constant, the temperature of the cesium oven can be constantly maintained.  
Figure 1: The system structure diagram.  
The bridge circuit is shown in Figure 2[23]. To ensure accuracy, the bridge power supply uses a high-precision, low-temperature-drift reference voltage source instead of the power supply voltage. The other resistors on the bridge use 0.1% precision, low-temperature-drift metal film resistors. These measures can improve the accuracy of the bridge's output voltage. Changes in temperature cause changes in the resistance value of the thermistor, which in turn causes changes in the output voltage of the bridge.  
According to the specific configuration of the bridge and the relationship between the thermistor resistance value and temperature, it can be expressed as follows [24]:  
\[R_{T}=R_{T_{0}}e^{B(\frac{1}{T}-\frac{1}{T_{0}})} \tag{2}\]  
Here, \(R_{T}\) is the resistance value of the thermistor at temperature \(T\), \(R_{T_{0}}\) is the resistance value of the thermistor at temperature \(T_{0}\), and \(B\) is a constant related to the manufacturing process.  
When the temperature varies between 20.77 \({}^{\circ}\)C and 141.16 \({}^{\circ}\)C, the thermistor resistance changes between 12 k\(\Omega\) and 0.39 k\(\Omega\), and the bridge output varies between \(-\)585 mV and +585 mV. The reason for processing the bridge voltage into a differential output is that the subsequent analog-to-digital conversion chip ADS1232 requires a differential signal input to maximize the measurement range. Additionally, since the ADS1232 has an internal Programmable Gain Amplifier, the bridge output is directly filtered and then input into the ADS1232 for the conversion to a digital quantity.
## 9 Conclusions  
The proposed approach in this study focuses on the key aspects of offloading and load balancing, recognizing their potential to enhance the performance of any computing network. To achieve this, fuzzy logic systems were employed to aid in processing, offloading, and workload balancing within IoT networks. The proposed MFHS systems initiate operations at the Mist level, enabling early decision making and aiming to improve both energy efficiency and processing time. The evaluation of MFHS involved the use of Eclipse and MATLAB, and the results demonstrated successful reductions in processing time and power consumption. Notably, the MFHS approach outperformed the feedback-based optimized fuzzy scheduling approach (FOFSA) algorithm, as well as the adaptive task allocation technique (ATAT) and osmosis load balancing algorithm (OLB), in terms of energy efficiency and processing time.  
In conclusion, the proliferation of the Internet of Things (IoT) has ushered in a new era of connectivity and innovation, fostering seamless communication between devices and systems across diverse sectors. In healthcare, IoT networks hold the promise to reshape patient care, expedite critical interventions, and streamline administrative operations. As the demand for IoT networks continues to surge, the introduction of novel solutions like the Mist-based fuzzy healthcare system (MFHS) underscores the commitment to enhancing the efficiency, stability, and adaptability of IoT technology. By strategically allocating IoT data resources through the Mist layer, MFHS contributes to faster processing times, increased energy efficiency, and overall improved performance within IoT networks, thereby advancing the capabilities of healthcare systems and various other industries alike.  
**Author Contributions:** Conceptualization, Z.A. and B.S.; methodology, Z.A. and B.S.; software, Z.A.; validation, Z.A.; formal analysis, Z.A.; investigation, Z.A., B.S. and A.L.; writing--original draft, Z.A.; writing--review and editing, B.S. and A.L.; supervision, B.S. and A.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Multi-Source Cooperative Data Collection with a Mobile Sink for the Wireless Sensor Network
## 2 Multi-Source Sparse Cooperation Models with a Mobile Sink  
We consider a wireless sensor network consisting of a large quantity of static sensor nodes and a mobile sink node connecting to the remote base station, as shown in Figure 1. The sink node moves around the region where the sensor nodes are located to collect the data in the wheel-moving or jumping mode [26; 27]. We devise the multi-source coded cooperation schemes to improve the power efficiency for wireless sensor networks.  
The network model is defined as follows. In a wireless sensor network, multiple sensor nodes \(s_{1},s_{2},\ldots,s_{N}\) need to transmit their data to a common mobile sink node \(d\) moving along the distribution region of source nodes. Assume that each source node \(s_{i},1\leq i\leq N\) is constrained to the half-duplex mode and transmits signals on the orthogonal channel such as time division multiple access (TDMA) or code division multiple access (CDMA). We assume TDMA is used for different source nodes in this paper. Each inter-source channel denoted by \(s_{i}-s_{j},i\
eq j\) and each source-sink channel represented by \(s_{i}-d\) are modeled as independent quasi-static Rayleigh fading channels. For these channels, the fading factors stay constant during a frame and vary from one frame to another.  
For the wireless sensor network above, we investigate cooperative models, where \(N\) sensor nodes consist a cooperative set \(S=\{s_{1},s_{2},\ldots,s_{N}\}\) to jointly transmit data to the mobile sink node \(d\). In this paper, we design two sparse cooperative models according to different moving trajectories of the sink node. In the first model, the moving sink node \(d\) moves along the distribution region of the active source nodes one time only. For the other model, the mobile sink \(d\) moves twice in the distribution region of the nodes along different routes. Indeed, the two moving trajectories interlace with one another. How to form the cooperative models are explained in the following.
# Research on Interference Mechanism of 25 Hz Phase Sensitive Track Equipment from Unbalanced Current
## 5 Conclusions  
In this paper, through the analysis, simulation and field test of the mechanism of misoperation of 25 Hz phase-sensitive track circuit equipment under an unbalanced current, the following conclusions are drawn:  
1. It is explained that the main causes of the track unbalanced current are the unbalanced impedance of the traction current loop, the traction unbalanced current caused by the magnetic field coupling of the adjacent rail traction current and the unbalanced current caused by the grounding wire.
2. Through the simulation of the excitation current, the flux and core loss of the choke transformer and the flux and core loss of the relay coil under the unbalanced impulse current, the analysis of the influence of the unbalanced current on the choke transformer and the relay are verified. At the same time, it is found that the unbalanced impulse current will lead to the increase of the core loss of the choke transformer and the relay, affecting its life and causing interference to the equipment.
3. Through the unbalanced current simulation test, an unbalanced current test during driving and the grounding wire test, it is further verified that the magnetic saturation of the choke transformer under unbalanced current interference affects the relay coil voltage, resulting in misoperation. It is found that the unbalanced impulse current will be generated during driving, resulting in the decrease of receiving voltage. At the same time, it is found that the unbalanced current is also affected by the distance between the grounding wire and the measuring point. When the grounding wire is close to the measuring point, the unbalanced current is larger, and vice versa.  
Methodology, B.Z.; Software, M.Z.; Formal analysis, Z.L.; Resources, C.X.; Writing--original draft, L.W.; Writing--review & editing, J.L.; Funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the research and application of the influence of the electrified railway traction power supply system on the track circuit under grant E19L00700.  
The data presented in this study are available in 'Research on Interference Mechanism of 25 Hz Phase Sensitive Track Equipment from Unbalanced Current'.  
The authors declare no conflict of interest.
# Pointer Meter Reading Recognition Based on YOLOv11-OBB Rotated Object Detection
## 3 Materials and Methods
### Angle Analysis Algorithm to Realize Reading Recognition  
The meter image mapping creates a direct correlation between the pointer's deflection angle and the meter reading. To mathematically link the related pointer orientation to the meter reading, a mapping function is required. For a pointer meter with a uniform scale, a linear mapping can be established by identifying the pointer direction at both the maximum and minimum scale values. In contrast, instruments with non-uniform scales require more sophisticated fitting techniques to define the mapping relationship accurately. Concretely, the two scales between which the pointer is positioned are determined, and subsequently, the fraction of the pointer's alignment within the division grid is computed.  
Figure 8: Identify the potential pointer by the pointer area.  
Figure 7: Feature matching between inspection image and registration image.  
This study employs the angle method to calculate the central angle formed at the intersection of the pointer and the scale circle, alongside two adjacent scale points. These points may represent the minimum, maximum, or any two neighboring scales on the circle. This angle calculation facilitates the determination of the proportion within the division grid.  
The algorithmic concept is illustrated in Figure 9a, and the corresponding formula is as follows:  
\[r=v_{1}+\frac{\theta_{0}}{\theta_{1}}(v_{2}-v_{1}) \tag{1}\]  
In the formula, \(\theta_{0}\) represents the angle from the pointer's straight line to the initial scale (or adjacent scale 1) in the plane coordinate system, while \(\theta_{1}\) denotes the angle from the initial scale to the maximum scale (or adjacent scale 2) in the plane coordinate system. Additionally, \(v_{1}\) and \(v_{2}\) correspond to the initial and maximum scale readings (or two adjacent scales) respectively, and \(r\) indicates the pointer meter reading result.