esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Adsorption Studies  
In this section, batch adsorption technique is used. Appropriate clay quantity of 200 mg was added in 250 mL conical flask to solutions of MB of different initial concentrations and stirred by magnetic hot plate stirrer with 10 positions of model MS-H-S10 (Sclilogex, Rocky Hill, NJ, USA) at the rate 300 rpm and at temperature 20 \({}^{\circ}\)C. The time required to attain equilibrium was predetermined previously based on some experiments of kinetic study and estimated to be 60 min. To ensure that the time chosen was enough to reach equilibrium, all experiments were conducted at 1.5 h. Once the dye solutions had reached equilibrium, the suspensions were separated by centrifugation within 30 min at the rate of 3000 rpm using centrifuge of type 80-2C (Xiangtian, Jiangsu, China), for further analysis of the dye concentrations. The residual concentration of the dyes was determined using a UV-visible spectrophotometer of model UV-1700 (Shimadzu corporation, Kyoto, Japan) at 665 nm. The pH of the solutions for adsorption experiments was set to 7 and controlled using a pH-Meter of type Basic 20\({}^{+}\) (Crison instruments, Barcelona, Spain). The temperature of experiments was maintained at 20 \({}^{\circ}\)C and its control was performed using a thermostatically controlled incubator.  
The amount of MB adsorbed \(q_{e}\) (mg/g) was calculated from the difference between the initial and equilibrium MB concentrations and is given by:  
\[q_{e}=\frac{(C_{0}-C_{e})\times V}{m}\]  
\(C_{e}\) (mg/L) is the concentration of adsorbate at equilibrium; \(C_{0}\) (mg/L) is the initial concentration of adsorbate; \(m\) (g) is the mass of the adsorbent; and \(V\) (L) is the volume of the solution.
# A Novel SINS/SRS/CNS Multi-Information Fusion Global Autonomous Navigation Method
## 2 Design of the Multi-Information Fusion Global Autonomous Navigation Scheme  
For the SINS/SRS/CNS multi-information fusion global autonomous navigation, SINS was selected as the major system, while SRS and CNS were selected as auxiliary systems. The velocity provided by SRS and the latitude and longitude provided by CNS are used to correct the navigation errors of SINS. Moreover, a BA (barometric altimeter) is used to compensate for the inability of CNS to measure altitude.  
A high-altitude and long-endurance UAV needs to traverse between polar and nonpolar regions during global flights. Consequently, it is necessary to consider the navigation and filter parameter conversion relationships between different navigation coordinate frames.  
In summary, the multi-information fusion global autonomous navigation scheme for UAVs is shown in Figure 1.
# Full Real-Time Positioning and Attitude System Based on GNSS-RTK Technology
## 4 Results
### Robustness of the Algorithm in Non-Orthogonal Configurations  
The algorithm for the attitude computation given the antennas' positions is also designed to work in non-orthogonal configurations, with only the constraint that the three antennas cannot be aligned. In order to validate the algorithm in these scenarios, the settings described in Table 2 were tested. The angles of each configuration were manually computed with the recorded positions of each receiver. Figure 12 shows one of the non-orthogonal configurations where the antenna vectors 1-2 and 1-3 form an angle of 60\({}^{\circ}\).  
Figure 11: Time series of the computed attitude angles for configurations (**A**–**N**).  
As in the previous tests, the results do not have any post-processing, only that the time series of each configuration were trimmed to 60 s (300 samples) so that the duration is the same for each test.  
Figure 13 shows how the standard deviation of the pitch and yaw angles remains constant between configurations, since the antennas of receivers 1 and 2 are always at the same position. In contrast, the standard deviation of the roll angle increases as the antenna of receiver 3 approaches that of receiver 2.  
Figure 12: Configuration B of the non-orthogonal static tests.  
Figure 13: Standard deviation of the attitude error for the non-orthogonal antenna configurations.  
Figure 14 shows how the average value of the three angles does not depend on the position of antenna 3 beyond the errors due to the irregularity of the surface where the tests were performed.
## 1 Introduction
### The Solution Proposed in the Smart5 Grid Project  
Smart5 Grid will support most of the above-mentioned functionalities through the implementation of four use cases (UCs), offering dedicated services not only for the energy system operators but also for DER providers and aggregators, the new emerging actors of the energy industry ecosystem. The four UCs are as follows:  
1. An innovative cross-border frequency monitoring system will be implemented to support the regional transmission system operator (TSO) to provide system stability.
2. A safety system for workers in HV power substations will also be developed, ensuring that workers (and their tools) keep the due physical distance from the live parts of the substation, as electricity still represents a danger for workers if the risks in HV power substations are not properly addressed.
3. An advanced active grid management system will be designed to provide real-time communication monitoring, preparing the ground for further implementation of edge-based computing.
4. Real-time monitoring and control of DERs will be also implemented, creating the basis for providing flexibility services to the energy system operators.
# Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories
## Appendix B  
**Comparison between DBH Cylinder and Circle Fitting**  
The mean number of points used for automatic circle fitting is significantly lower in MLS and the \(\sigma_{0}\) of the circle fit is larger than in TLS (Tables A1 and A2). The \(\sigma_{0}\) of the circle fitting of plot 1 range from \(0.21\) cm \(\leq\sigma_{0}\leq 2.59\) cm for TLS and from \(0.82\) cm \(\leq\sigma_{0}\leq 3.09\) cm for MLS data. The \(\sigma_{0}\) of the circle fitting of plot 2 range from \(0.30\) cm \(\leq\sigma_{0}\leq 2.80\) cm for TLS and from \(0.70\) cm \(\leq\sigma_{0}\leq 2.90\) cm for MLS data. The point numbers used for the automatic circle fitting are between \(57\leq n^{\mathit{TLS}}_{\mathit{circle}}\leq 336\) in the case of TLS and between \(11\leq n^{\mathit{MLS}}_{\mathit{circle}}\leq 489\) in the case of MLS for plot 1 and between \(112\leq n^{\mathit{MLS}}_{\mathit{circle}}\leq 1399\) in the case of TLS and between \(14\leq n^{\mathit{MLS}}_{\mathit{circle}}\leq 727\) in the case of MLS for plot 2. Due to the higher MLS point cloud section (0.12 m instead of 0.07 m), the maximum number of points for the circle fitting in plot 1 is higher than with the TLS. Especially the stem section of one tree (plot 1) very close to the forest road has a high number of points (489). The maximum DBH difference between the cylinder fitting and the circle fitting (cylinder-circle) is 0.9 cm for plot 1 and 3.2 cm for plot 2 for the TLS dataset and 5.4 cm for plot 1 and 15.4 cm for plot 2 for the MLS dataset (Table A1). The largest DBH deviation for one tree occurred during the circle fitting with a difference of 12.5 cm (Figure A2).  
Figure 17: DBH (cylinder and circle fit) determination of the MLS and TLS data in comparison with the manually measured diameters of plot 1.  
Figure 12: Segmented tree point clouds of a _Fagus sylvatica_ tree with the extracted QSM of the leaf-off datasets of plot 1: (**a**) TLS; (**b**) MLS.
# Situation-Aware Survivable Network Design for Tactical Environments
## 4 Performance Evaluation
### Situational Backoff Reset Algorithm
The numbers of cases in which the delay exceeds the service performance objective are 65 out of 66 and 18 out of 100 for the conventional protocol and the proposed algorithm, respectively. We also investigate the average delay CDF for video traffic for the two algorithms, as shown in Figure 8:0 In the case of the CDF graph for the conventional protocol, 68% of the results meet the required QoS for video traffic. However, if we consider 34 simulations in which none of the video packets are received, 45% from the measured average delay of video traffic is below the reference line. In 66 simulations out of the total 100 simulations, 27,142 video packets are received, and 1765 packets are received but are delayed. In the case of the proposed algorithm CDF, it meets the required QoS of video traffic in 100% of the simulations. The average video traffic of both the conventional protocol and the proposed algorithm show a relatively small delay compared to the voice traffic. This difference may be attributed to the fact that the voice traffic delay includes encoding and decoding delay. A total of 54,319 video packets are received with the proposed algorithm, and 143 packets, corresponding to 0.26%, are delayed.
# 5G-NR Physical Layer-Based Solutions to Support High Mobility in 6G Non-Terrestrial Networks
## 3 Overview of Non-Terrestrial Networks
### NTN in 6G Era  
A terrestrial terminal, an aerial or space station that may function similarly to a terrestrial base station, a service link between the terrestrial terminal, aerial or space station, and a gateway that joins the non-terrestrial access network to the core network via a feeder link are the main components of non-terrestrial networks in 6G technology [2]. For NTN development in the 6G era, satellite stations, HAPs, and unmanned aerial vehicles (UAVs) can be taken into consideration. Several NTN architectures are being promoted by the 3 GPP depending on how integrated the various airborne and spaceborne parts are [18]. The transparent satellite-based Radio Access Network (RAN) architecture is precisely what the 3 GPP specifically expects, in which the satellite transfers the user's signal from the feeder link to the service link and vice versa. The regenerative satellite-based RAN design, which allows for inter-satellite communication while implementing signal regeneration in the satellite payload, will be taken into consideration for 6G [4]. Moreover, 6G technology will offer multi-connectivity architecture with two transparent RANs (either GEO or LEO or a combination of both), which integrates terrestrial and non-terrestrial access.  
Non-terrestrial technology has long been thought to support operations such as home delivery, weather forecasting, video surveillance, television transmission, remote sensing, and navigation. To enable more advanced use cases, such as Communication Resilience and Service Continuity, Global Satellite Overlay, Ubiquitous Internet of Things (IoT) Broadcasting, Advanced Backhauling, and Energy-Efficient Hybrid Multiplay, the recent technological advancements in the aerial/space industry have however made it possible for integration between terrestrial and non-terrestrial technologies. The few enabling technologies to support NTN in 6G for architecture, spectrum, antenna, and higher layers advancements, respectively, include gallium nitride (GaN), cognitive spectrum, multi-beam structures, and TCP spoofing and multiplexing [2].
# Feasibility Test and Analysis for Heavy Haul Freight Using WDPS Based on MIMO-OFDM
## Abbreviations  
AP Access Point BC Brake Cylinder BP Brake Pipe CAN Controller Area Network CCU Central Control Unit DU Display Unit EB Emergency Brake ECP Electronic Controlled Pneumatic EMBR Emergency Brake contact Relay FSB Full Service Brake FSK Frequency Shift keying CDB General Distributed Board ID Identification ISI Inter Symbol Interference KORAIL Korea Rail Corporation LOS Line Of Sight MCB Main Circuit Breaker MIMO Multi-Input Multi-Output
* _Appl. Sci._**2020**, _10_, 4074
* _MVB_ _Multifunction Vehicle Bus_
* _NLOS_ _Non-Line Of Sight_
* _OFDM_ _Orthogonal Frequency Division Multiplexing_
* _RSSI_ _Received Signal Strength Indication_
* _SNR_ _Signal to Noise Ratio_
* _TCN_ _Train Communication Network_
* _TCMS_ _Train Control and Monitoring System_
* _WDPS_ _Wireless Distributed Power System_
* _WTB_ _Wire Train Bus_
# COSMIC-2 Mission Summary at Three Years in Orbit
## 3 Requirements, Products, and Performance
### Current Products
This has driven significant TGRS and ground processing software improvements and detailed investigation of potential future improvements. Figure 3: Max, mean, and median daily high-altitude SNR for neutral atmosphere profiles in 2022. For each profile, SNR is averaged between 60–80 km. Figure 2: TGRS neutral atmosphere occultations per day passing QC in 2022. As mentioned previously, the mission's data product latency requirements drive the implementation of a 10-downlink-station network. Since the addition of the 10 th station at Tahiti on 3 January 2022, the latency of both the neutral atmosphere and TEC products exceed requirements on nearly all days, as shown in Figure 5. The time series show the daily median of all computed latency values. The latency values are computed from every 10 th 1 s observation epoch in a neutral atmosphere profile or TEC track to the product creation time. This measure includes on-orbit storage time, downlink and transfer to the DPC, and processing time. COSMIC-2 data and products may be downloaded from CDAAC and CWB [29, 30]. Figure 6 below shows the monthly volume and number of files downloaded from the CDAAC public interface in 2022. Data and products are published as one daily compressed tar file per file type. On average, 9.7 TB and 23187 files were downloaded each month. Figure 4: TGRS TEC arc, occ, and arc plus occ scores in 2022. Figure 5: Data product latency in 2022. The 45 min neutral atmosphere requirement line is not shown. Unexplained outlier data on 12 April 2022 are omitted.
## 3 Results and Discussion
### Schmidt Rebound Values  
The Schmidt rebound values are shown in Figure 7. It is seen that outliers, more clear in this case, are present only for marble and quartzite. It is also observed that the descending order of materials with respect to the median \(R_{L}\) value is quartz, quartzite, marble and metasandstone. The statistical results of \(R_{L}\) for the materials tested also show that the mean \(R_{L}\) values follow the same descending order as observed for the median values. Finally, it is also indicated that the values obtained in this study are well within the range of values reported in other studies for quartz and quartzite [54], marble [55] and metasandstone [56].  
Figure 6: P-wave velocity of all materials used.  
Figure 7: Schmidt rebound values (\(R_{L}\)) of all materials used.
### The Advantages and Development of Remote Sensing Methods  
The application of satellite-sensed data for earth observation purposes might hold biases and inaccuracies arising from the distance and atmosphere between the platform and the objective. For this reason, due to its modularity and quick deployment, currently unmanned aerials provide a cost-effective solution for the validation of satellite data at a detailed scale. In the context of land cover and fire severity analysis, UAVs equipped with specific sensors can provide a useful mean to gain insights in areas were satellite mapping proved to be less accurate, such as the burned area perimeters and uneven burned patches [56; 57].  
The present study focuses on multitemporal burned area perimeter mapping by analyzing the soil-vegetation recovery phenomena for fire affected areas via Sentinel-2 and PRISMA remotely sensed data. To assess the potentialities of the spaceborne data, the obtained results were compared with UAV acquired data to determine the benefit resulting in the higher resolution of the platform and provide validation for the satellite-sensed indices. Two study sites, Portugal and Italy, were chosen because of their different aspects and severity. The very different aerial extent of the two events allowed for site-based technique development.
# A Comprehensive Review on Clay Soil Stabilization Using Rice Husk Ash and Lime Sludge
## 3 Laboratory Tests
### Unconfined Compressive Strength (UCS)  
The unconfined compressive strength test is a laboratory test to determine improved soil strength and load-bearing capacity. The UCS test is commonly performed on cylindrical soil specimens under axial compression without lateral confinement. Different references were reviewed to present the relationships between the UCS values of different mixtures  
Figure 7: Effect of RHA and LS in MDD [98, 111].  
demonstrates that the UCS value of the soil samples shows an increase with the addition of RHA up to 10%. However, beyond this threshold, it is seen that the UCS of the clay soil starts to decline due to the reduction of cohesion and internal friction of the soil, consequently leading to a decrease in the soil's ability to resist deformation and load. When the amount of RHA exceeds the optimal level, there might be too much binder relative to the amount of soil available for the reaction. The extra RHA particles can start to act as a filler rather than a reactive component, leading to a reduction in the overall strength of the mixture. Additionally, excess RHA can increase the porosity of the mixture due to its lightweight and relatively high specific surface area. Higher porosity can reduce the density and, consequently, the strength of the soil. Figure 9 shows the variation of the UCS value with respect to the LS content. It indicates that an increase in the LS content increases the UCS value of the soil samples due to better bonding of soil and lime. Adding LS content up to 4% can increase the UCS value. Figure 10 shows the variation of the UCS value with respect to the combined RHA and LS content. The combined use of RHA and LS can have a significant influence, leading to a notable increase in the UCS value of the soil. However, it is indicated that above 6% RHA content, the strength decreases independently of LS content, which seems to be an upper threshold for RHA. Also, it can be seen that the higher the LS, the higher the strength, which conforms with the Figure 9 analysis. Figure 8 shows an optimized value of RHA of 10%, but when mixed with LS, this threshold is clearly lower.
## 2 Challenging Open Issues  
Generally, a routing protocol can be defined as a set of rules to successfully deliver data from a source to a destination node. Designing a routing protocol for a specific environment is not trivial work, as it is influenced by many challenging issues and factors that must be overcome to achieve a particular design objective. As mentioned earlier, WBSNs inherit the typical challenges and issues of WSNs. However, WBSNs have more specific requirements, due to their placement in the human body. They  
Figure 1: A general example of Wireless Body Sensor Networks (WBSNs).  
must put human safety as the top priority in the design over other requirements, such as packet delay or packet drop rate. In this section, we discuss the challenging open issues that need to be considered when designing a routing protocol in WBSNs, with emphasis on bioeffects as the determining factor that distinguishes itself from WSNs.
# Dynamic Rating of Three-Core XLPE Submarine Cables for Offshore Wind Farms
## 2 Dynamic Cable Rating
### Thermal-Electrical Analogy for Cables  
To model a ladder network consisting of thermal parameters, analogical thermal and electrical parameters must be determined. This section walks through the modelling of a thermal ladder network representing a three-core XLPE submarine cable and defines the thermal parameters. Insulation materials other than XLPE can be used if the cable structure is equivalent to the cable illustrated in Figure 2. The IEC 60287 series [4] and IEC 60853-2 [8] describe the electrical and thermal properties of the most common insulation materials. The electrical properties of a cable are often represented by infinite small sections of lumped parameters containing resistance and reactance in series \(R+j\cdot X\) and two capacitances \(C\) divided into two equal parts in parallel [9]. To represent the properties thermally, the electrical reactance \(X\) is neglected since it is the resistance \(R\) of the cable that determines the voltage drop and the capacitance \(C\) that defines the time constant of the voltage. Comparing the electrical model shown in Figure 5 and the thermal analogy of Figure 6, we can conclude proportionality between voltage \(V\) and temperature \(\theta\); current \(I\) and heat flow \(w\); electrical resistance \(R\) and thermal resistance \(T\); electrical capacitance \(C\) and thermal capacitance \(Q\).  
The IEC 60853-2 [8] considerations of thermal analogies were written at the time when oil-paper cables were used; this means that the computation of a thermal ladder network does not represent a modern submarine cable with high accuracy. To represent the thermal properties of a three-core XLPE submarine cable with asymmetry between the internal and external parts of the cable and a lot of non-conductive layers, it is necessary to split the physical objects of the cable into small parts and represent each of them with thermal resistance and thermal capacitance. The thermal resistance represents the ability of the material to impede heat flow, and the thermal capacitance is defined as the ability to store heat [8]. This method is only applicable in cases where thermal parameters do not change as a function of temperature variations, because it solves heat flow equations using the superposition principle, which is used exclusively for linear systems.  
Figure 4: Flowchart of the iterative dynamic cable rating method.
# The Modeling of Time Series Based on Least Square Fuzzy Cognitive Map
###### Abstract
In order to improve the effectiveness of long-term prediction, an improved evolutionary approach for learning of the FCM model was proposed [32]. [PERSON] [7] proposed fuzzy grey cognitive maps (FGCMs) as a nonlinear predictive model to predict the multivariate interval-valued time series, in which evolutionary algorithm was also applied for learning FGCMs. [PERSON] [33] developed a methodology that joins a fuzzy cognitive map and moving window approach for time series prediction, in which the FCMs were optimized using a particle swarm optimization technique. In this paper we investigate the FCM model and propose a novel method to construct the time series model based on FCM. The aim of this research is to improve the efficiency and accuracy of the time series model with FCM. Following are the major steps of the proposed model processing flow. First, fuzzy c-means clustering is applied to fuzzify the time series and form concepts from the given time series, i.e., each clustering center serves as a concept. Then, the weights of the FCM are learned based on historical data with the least square method. After that, in order to improve the model accuracy, the concepts are further refinement. The concepts obtained by fuzzy c-means clustering may not be the best representatives; therefore, we use a population-based optimization algorithm to further adjust the concepts. Finally, the developed FCM model makes the numerical prediction. The exposure of the material is structured as follows. Section 2 gives a brief overview of the fuzzy c-means clustering and fuzzy cognitive map. Section 3 presents a learning of LSFCM. Subsequently, in Section 4, the outline of the proposed method along with its essential functional modules is thoroughly explained, in particular the refinement of concepts. Modeling and forecasting of time series are presented in detail in this section. In Section 5, we exploit six publicly available datasets to verify the validity and feasibility of the proposed method and the effect of parameters in the proposed model is discussed,based on accuracy of the constructed FCM prediction model. Finally, Section 6 provides some conclusions.