esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Division of the Study Area  
The division of Poland in relation to the number of baseline vectors and the mean baseline vector length allowed us to characterise the following spatial relations between the points being determined and the positions of the reference ASG-EUPOS stations (Figure 4). If six baseline vectors were used, rover points in most areas were connected via mean vector lengths from 50 to 60 km (approx. 53.6%) and from 40 to 50 km (approx. 34.5%). When five baseline vectors were chosen, the percentage of areas with a mean vector length from 40 to 50 km increased to 57.8%, and the share of areas with a 50 to 60 km average length was 31.07%. Having analysed the variant with three baseline vectors, we found out that Poland is dominated by areas with a mean baseline vector length from 30 to 40 km (approx. 56.0%) and from 40 to 50 km (approx. 31.1%). Mean baseline vector lengths did not exceed 60 km in 89.9% of Poland when six baseline vectors were used, 96.9% if five baseline vectors were, and 100% if three baseline vectors were used. Therefore, five baseline vectors offer a shorter baseline vector length in APPS than the current method. It achieves better modelling of observation conditions (such as troposphere and ionosphere impact) thanks to shorter distances between the sought point and reference stations.  
We further divided the territory of Poland by baseline vector standard deviation to better characterise the geometric variability of the proposed variants (Figure 4). An analysis of changes in baseline vector length standard deviation demonstrated that the standard deviation in the length of six baseline vectors is below 20 km for 63.7% of Poland. For five baseline vectors, it is 75.3%, and, for three baseline vectors, it is 91.1% of Poland. A baseline vector deviation over 25 km affects 8.5% of Poland for the six nearest stations and 4.5% for five baseline vectors.  
If baseline vectors to the five nearest stations were used, the area with the mean vector length below 50 km increased by approx. 55% compared to the variant with six baseline vectors. A switch from six to five baseline vectors caused a 14% increase in the area with the baseline vector length standard deviation from 5 to 15 km. Therefore, it seems only reasonable to restrict the number of baseline vectors to five when considering modifying the APPS algorithm in the ASG-EUPOS. This notion is consistent with the literature [15], where it has been suggested that it is possible to identify configurations of as few as three baseline vectors among the six baseline vectors currently used in the APPS of the ASGEUPOS which yield results of positioning accuracy and solution consistency similar to those achieved with the current APPS method of six baseline vectors.
## 3 Methodology
### PdM Time Slots Estimation Using ML
Multiple sensors are used to collect data over long periods of time for PdM parameters calculations. This is attributed to the fact that the algorithms responsible for learning and prediction need large data sets to have optimal results. The processes of data transfer from the sensors to the cloud service and its preparation are shown in Figure 4.  
From the industrial point of view, several steps must be followed to determine the type of sensors used, the nature of the measured data, and the sensor locations. Most of the previous research work done on PdM monitor the machine's vibration, temperature and pressure. Several factors must be considered when determining which sensors to use such as cost and the accuracy of its measured readings. For example, if the aim of the sensor is to measure high values of temperature, one must make sure that the sensor operating range can measure such high temperatures or else the collected data will be inaccurate or the sensor itself will be damaged. To send the sensors' data to the cloud service, they must include a networking module that connects them to the Internet through a gateway. Possible connectivity options could be WiFi or Bluetooth, where both can be used by integrating the respective module onto the sensor. There are several types of commercial sensor modules that fits industrial applications such as the C series and the PXI series for temperature, pressure and vibration measurements. In our case study, the proposed sensors are small in size to ease the process of installation within the machine. The final step is determining the exact location where the sensors are going to be implemented. In this case, the sensors are going to be implemented within the body of the machine itself not within the working area of the products on the machine to obtain accurate readings. Once the data becomes available on the cloud servers, its format will be changed to be compatible with Microsoft Azure software tool. This is done over several operations to create the required format. Figure 4: Data Transfer Process and Preparation flowchart.
# Geospatial Assessment of the Territorial Road Network by Fractal Method
###### Abstract  
This paper proposes an approach to the geospatial assessment of a territorial road network based on the fractals theory. This approach allows us to obtain quantitative values of spatial complexity for any transport network and, in contrast to the classical indicators of the transport provisions of a territory (Botcher, Henkel, Engel, Goltz, Uspensky, etc.), consider only the complexity level of the network itself, regardless of the area of the territory. The degree of complexity is measured by a fractal dimension. A method for calculating the fractal dimension based on a combination of box counting and GIS analysis is proposed. We created a geoprocessing script tool for the GIS software system ESRI ArcGIS 10.7, and a study of the spatial pattern of the transport network of the Ukraine territory, and other countries of the world, was made. The results of the study will help to better understand the different aspects of the development of transport networks, their changes over time and the impact on the socioeconomic indicators of urban development.  
geoinformation technology; fractal dimension; territorial road network; box-counting framework; script Python; ArcGIS +
Footnote †: journal: Future Internet
# Numerical Test of Several Controllers for Underactuated Underwater Vehicles
## 3 Short Description of Selected Algorithms
### Selected Trajectory Tracking Methods
The surge and yaw control laws are written as:  
\[\tau_{u} =-d_{u}u-m_{2}vr+m_{1}\Big{(}\dot{u}_{d}-\alpha_{1}e_{u}-\beta_{1 }\frac{q_{1}}{p_{1}}e_{u}(\tilde{e}_{u})^{\frac{q_{1}}{p_{1}}-1}-W_{1}\text{ sgn}(S_{1})\Big{)}, \tag{30}\] \[\Gamma =-\ddot{x}_{d}\sin\psi+\ddot{y}_{d}\cos\psi-\dot{x}_{d}r\cos\psi+ \ddot{y}_{d}r\sin\psi-u_{d}r\]0 \[\Gamma =-\ddot{x}_{d}\sin\psi+\ddot{y}_{d}\cos\psi-\dot{x}_{d}r\cos\psi+ \ddot{y}_{d}r\sin\psi-u_{d}r\]1  
where \(\Gamma\) is calculated from (24), \(\text{Y}_{1}\), \(\text{Y}_{2}\) from (25), and \(u_{d}\), \(v_{d}\) from (22). The sliding surfaces are assumed in the following form:  
\[\Gamma =-\ddot{x}_{d}\sin\psi+\ddot{y}_{d}\cos\psi-\dot{x}_{d}r\cos\psi+ \ddot{y}_{d}r\sin\psi-u_{d}r\]2  
where the symbols used mean: the position errors \(x_{e}=x-x_{d}\), \(y_{e}=y-y_{d}\), the velocity errors \(e_{u}=u-u_{d}\), \(e_{v}=v-v_{d}\), and \(\tilde{e}_{u}\) is the velocity error integral. The expression \(\text{sgn}(S)\) may be replaced by \(\text{sat}(S)\) to avoid chattering. The symbols \(\alpha_{1}\), \(\alpha_{2}\) mean some positive constants. The design parameters are: \(l_{x}\), \(l_{y}\), \(k_{x}\), \(k_{y}\), \(\alpha_{1}\), \(\alpha_{2}\), \(\beta_{1}\), \(\beta_{2}\), \(q_{1}/p_{1}\), \(q_{2}/p_{2}\), \(W_{1}\), \(W_{2}\). Comment. The control algorithm is very close to TSMC. However, one component was added to each input signal. The sliding surfaces are defined slightly different, namely by adding one component multiplied by \(\alpha_{1}\) and \(\alpha_{2}\), respectively. Selection of the parameters \(\alpha_{1}\) and \(\alpha_{2}\) seems to be easy. The control scheme was tested via simulation on the same underwater vehicle as the previous algorithm. As a result of the satisfactory performance delivered in [18], this controller was selected for testing.
## 3 Results and Analysis
### Evaluation of Cooling Energy Performance
#### 3.1.2 MA Conditions  
Section 3.1.2 discusses the evaluation of the MA conditions by varying the MLSP. Figure 5 shows the range of dry-bulb temperature and absolute humidity for the MA. Table 5 presents the average dry-bulb temperature and relative humidity values for analyzing the overall variations in MA conditions by varying MLSP.  
The average MA temperature from Cases #2 to #7 decreased progressively compared to Case #1 by approximately 0.18, 0.33, 0.49, 0.58, 0.59, and 0.55 \({}^{\circ}\)C, respectively. This reduction was attributed to increased intakes of cooler OA compared to Case #1. Notably, Case #5 exhibited the lowest MA temperature among all cases, resulting from the utilization of maximum OA intakes under favorable conditions, unlike Cases #1 to #4. Cases #6 and #7 utilized maximum OA intakes at temperatures higher than the indoor air. The average MA absolute humidity in Cases #2 to #7 also decreased relative to Case #1, driven by the intake of OA with lower absolute humidity than the indoor air, thus reducing the indoor air absolute humidity as the OA intake increased. Consequently, the reductions in MA temperature across Cases #2 to #7 compared to Case #1 are expected to decrease cooling coil loads and subsequently reduce the cooling energy consumption needed to lower the MA temperature to the supply air set temperature.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Simulation Cases** & **Case \#1** & **Case \#2** & **Case \#3** & **Case \#4** & **Case \#5** & **Case \#6** & **Case \#7** \\ \hline Accumulated times of maximum OA intake rates [h] & 50 & 67 & 83 & 119 & 147 & 165 & 174 \\ \hline Accumulated times of partial OA intake rates [h] & 90 & 88 & 87 & 85 & 74 & 68 & 65 \\ \hline Accumulated times of minimum OA intake rates [h] & 102 & 87 & 72 & 38 & 21 & 9 & 3 \\ \hline Accumulated times of minimum OA intake rates [h] & (25\(\downarrow\)) & (30\(\downarrow\)) & (64\(\downarrow\)) & (81\(\downarrow\)) & (93\(\downarrow\)) & (99\(\downarrow\)) \\ \hline Accumulated OA intakes [m\({}^{3}\)/s] & 51 & 56 & 60 & 68 & 73 & 77 & 78 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accumulated times and OA intakes according to OA intake rates.  
Figure 4: Hourly OA intake rates by varying MLSP.
## 1 Introduction
#### Definition of irrigation  
If we are to identify and map irrigation with remote sensing, a precise definition of what is considered to be irrigated is needed. In this review, we define irrigated lands as areas that receive full or partial application of water by artificial means to offset periods of precipitation shortfalls during the growing period. Fully irrigated areas are those where more than 60 percent of crop water requirements are met artificially; partially irrigated lands (or supplementary irrigated areas) receive between 30 and 60 percent artificially. Note that both surface and groundwater deliveries are included in this definition provided human intervention to move water from one location to another is involved.  
Irrigation is practiced in virtually every country, at scales ranging from subsistence farming to national enterprise. The precise location is determined by a combination of factors that include climate, resource availability, crop patterns, and technical expertise. Climate plays an important role in the distribution of irrigation as it determines natural moisture availability (precipitation), crop demand (evaporation), and crop schedules. In humid climates, irrigation often takes the form of a supplemental water supply to meet the excess demand of crops whose growth cycle may be out of sync with natural precipitation. In arid and semi-arid climates, continual irrigation is often necessary to assure agricultural production.  
While climate is an important driver of the need for irrigation, it is water availability that primarily determines its existence and sustainability. Currently, groundwater is by far the greater source for irrigation [25]. In regions where withdrawals for agriculture exceed recharge rates, the quantity and quality of groundwater quickly deteriorates, jeopardizing its sustainability. Thus nations may be forced to decide between agricultural and domestic use in their allocation of groundwater. Surface water for irrigation purposes appears more sustainable, but this is deceptive. It requires large structures involving complex engineering such as dams, conveyers, and canals to redistribute the resources. Furthermore, most river basins span international boundaries. Diversions of water for irrigation upstream often reduce its availability downstream, leading to international conflicts as in the case in the Middle East and Central Asia.
## 5 Conclusions  
The universal approach proposed in the paper for learning an intelligent beamsteering algorithm based on vehicle positioning data without relying on GNSS increases the generalizing ability of the applied ML algorithm by simulating a variety of scenarios within the V2I network. The developed digital traffic model acted as a priori information for the ML algorithm, reducing the impact of the received signal's power parameter on the system performance. It also increased the system's robustness to multipath propagation. Moreover, the algorithm operation was demonstrated with the accurate PAA model to provide a more detailed simulation of beamsteering. The simulation results revealed that the percentage of correctly identified classes from the total number of examples was 94%, which is 19% more than the previous version. The probability of an incorrect choice of the vehicle's location sub-sector (that is, the probability of power loss at the receiver by more than 3 dB) was also estimated. We identified two distortion factors that future work will aim to eliminate: low service areas at sub-sector boundaries and discrete phase-shifting applied in the simulation models.  
Conceptualization, E.L., E.G. and G.V.; methodology, E.L.; software, E.L. and A.A.; validation, E.L.; formal analysis, E.L., E.G. and G.V.; investigation, E.L. and A.A.; resources, E.L., E.G. and G.V.; data curation, E.L.; writing--original draft preparation, E.L.; writing--review and editing, E.G. and G.V.; visualization, E.L. and G.V.; supervision, E.G. and R.K.; project administration, E.G.; funding acquisition, E.G. All authors have read and agreed to the published version of the manuscript.  
This work was funded under the grant of the Russian Science Foundation (Project \(N\)21-79-10407).  
Not applicable.  
Data Availability Statement: A dataset for training the ML algorithm is available on request to the corresponding author's e-mail.
# Review of Indoor Positioning: Radio Wave Technology
## 4 Positioning Algorithms
### Triangulation Algorithm  
Triangulation is a range-based localization method, which includes angular (triangulation) and alteration (triletration). Lateration is a distance-based method and angulation is an angle-based method. In the lateration method, TOA, TDOA, and RSS are involved. Angulation methods include, such as AOA, and ADOA. Triangulation estimation techniques are used to compute the relative location of a user by determining distances, using a geometrical property of triangles, and is called a fine-gained technique. This technique uses the point of overlap shaped by three circles of reference points to define the position. Basically, it provides a range of localization based on known distances. The distances are estimated by using different signal measurement procedures such as RSS, time-based  
Figure 8: Description of the overall indoor positioning algorithms.  
Figure 9: Proximity-based positioning method.  
technology (such as TOA, RTT, and TDOA), and angle-based AOA [1]. The triangulation method is more adjustable, such as the system estimates' location in the actual environment and the system is capable of contending with the distinct environmental variations [160]. The trilateration method is a different fingerprinting method so it does not need an offline phase. Conversely, this method needs the relevant coordinate position of reference points (RPs) and its MAC address collected in a central database [161]. Although trilateration is able to produce accurate locations, it is actually sensitive to the precision and accuracy of the distance estimations [162]. Moreover, the triangulation technique needs an adjustment feature to decrease the signal attenuation produced by the barriers and human body intrusions that can affect the localization precision [160].
# To What Extent Are the Effects of UV Radiation on Grapes Conserved in the Resulting Wines?
## 3 Discussion
### Comparing the Effects of UV on Grapes and the Resulting Wines  
The effects of UV on both grapes and the resulting wines were compared by measuring a total of 51 variables related to phenolic compounds and VOCs (Table 3). Among the 51 variables, only five showed similar significant effects on grapes and wines. This low number of coincidences may not be strange because, at first sight, the transformations occurring during winemaking would weaken the relationship between grape and wine compounds. Nevertheless, some of the most conspicuous UV-induced changes in grapes were indeed reflected in the resulting wines, which showed increased concentrations of total flavonoids, kaempfer-glucosidase, and three quercetin-glycos in PAB and/or PAB\(\uparrow\) samples in comparison with the remaining samples (Table 3). In addition, a fatty acid (nonanoic acid) also showed similar responses in grapes and wines. It is difficult to compare these results with previous findings because of the scarcity of studies considering the effects of UV radiation on both grapes and the resulting wines. Moreover, in two of the existing studies, the variables measured in grapes were not measured in wines and vice versa [11; 22], and thus no comparison between grapes and wines is possible. More interesting results can be derived from the two remaining studies, carried out on Tempranillo variety, because (1) ambient UV increased 12 phenolic variables in both grapes and wines, including the five UV-reactive variables that also increased in our study [23],and (2) high UV-B increased total flavonoids in both grapes and wines [16]. These results confirm that the increase in flavonoids is the most reliable response of both grapes and the resulting wines to different UV conditions, including a sufficient dose of a combination of UV-A and UV-B radiation [23], or an irradiance peak of only UV-B [16].
# On-Board Parameter Optimization for Space-Based Infrared Air Vehicle Detection Based on ADS-B Data
## 2 Methodology
### Electro-Optical Performance Model of a Space-Based Infrared System
#### 2.2.1 Target Signal Model  
The orbit height of satellites is generally hundreds of kilometers in low orbit and up to 36,000 km in high orbit, while the flight altitude of aircraft is generally around 10 km. Since the size of an air vehicle is generally smaller than the ground sample distance (\(GSD\)) of the AVD system, it can be considered as a point target [17]. Due to the diffraction effect of the electro-optical system, the point target was represented as a Gaussian distribution, in which the central pixel obtains most of the energy of the target, and generally forms a 3 \(\times\) 3 point target by mixing with the background. The AVD system collected the infrared radiation, converted it into electrons and stored them in the integrating capacitor. The number of electrons received by the target pixel can be expressed as  
\[N_{s}=QE\cdot\frac{\lambda_{0}}{hc}\cdot\frac{J\pi D^{2}\tau_{0}\tau_{a}EE}{4 l^{2}}\cdot T_{int}, \tag{4}\]  
where \(QE\) is the quantum efficiency of the detector, \(h\) is [PERSON]'s constant (\(6.6\times 10^{-34}\) J\(\cdot\)s\({}^{-1}\)), \(\lambda_{0}\) is the center wavelength, \(c\) is the speed of light, \(\tau_{a}\) is the atmospheric transmittance at the height of the target, \(\tau_{0}\) is the transmittance of the optical system, \(J\) is the target radiant intensity, \(l\) is the detection range and \(D\) is the effective optical aperture diameter. \(EE\) is the encircled energy of the optical system and \(T_{int}\) is the integration time.
# The Development of Key Technologies in Applications of Vessels Connected to the Internet
## 2 Internet of Vessels Architecture
### The Technical Architecture of the Networks Layer
#### 2.3.3 Base Station Networks  
The mobile communication network is an important foundation to build the networks of the IoV system which can be used in the coastal base stations, island base stations and river base stations. The mobile communication network which is a nice complement to the marine communication facilities has a variety of access networking technologies such as the general packet radio service (GPRS), wideband code division multiple access (WCDMA), and third and fourth generation telecommunication technologies (3G and 4G) [27]. The GPRS network is developed based on the GSM network [28]. The user datagram protocol (UDP) and transmission control protocol (TCP) are the main communication protocol for the GPRS networks. There are many advantages to using the GPRS technology in the base station networks. The connection cost of GPRS is very cheap and the resource utilization is very high. The GPRS could provide 115 kbps transmission speed. Now a kind network called Wideband Code Division Multiple Access (WCDMA) is widely used in the construction of base station networks [29]. The WCDMA could adapt various transmission rates of the information, ranging from 8 Kbps to 2 Mkbps. The development of WCDMA technology realizes global roaming which has great impact on the communication of the IoV system. Currently, the WCDMA technology occupies more than 77% of the communication market. Apart from the WCDMA technology, 4G uses the orthogonal frequency division multiplexer (OFDM) technology [30]. It could run at the uplink rate of 20 Mbps. At the same time, combining with satellite and optical fiber technology, 4G could provide the information services and information collection to realize fast communication between the base station and the ships. Based on the actual needs such as the cost, stability, efficiency and so on, it can choose different technologies to construct the base station networks.  
Now a new communication technology, the fifth generation telecommunication technology (5G), is the extension of 4G. Although there are no standards for 5G, the key technologies including the architectures, wireless networking, wireless transmission, radio frequency (RF) and the development of a new spectrum will be quite useful to downscale the base station, remove the traditional nodes and the flat network architecture. With the development of mobile communication technology, the networks of the IoV system will depend on 5G in the future [31].