esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Precise Point Positioning during Outages of the PPP-B2b Service
## 3 Experiments and Results
### Positioning Quality Degradation over Time
#### 3.3.2 Accuracy of the Real-Time PPP-B2b Positioning Using Extended Corrections
During the period when the PPP-B2b was not available (7:30-7:40), the 3D RMSE of the positioning results obtained with extended PPP-B2b corrections was 0.10 m, which is very close to the values achieved before and after the interruption, i.e., 0.07 m and 0.09 m, respectively. This real-time kinematic experiment proves that it is reasonable to continue the PPP process by extending the validity of PPP-B2b to 10 min when the PPP-B2b is not available. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Interruption Times/min**} & \multicolumn{5}{c}{**3D Error/m**} \\ \cline{2-6}  & **BJ01** & **JFNG** & **HKSC** & **KSKT** & **MSSA** \\ \hline No Interruption & 0.111 & 0.137 & 0.141 & 0.266 & 0.340 \\
0–10 & 0.118 & 0.138 & 0.142 & 0.289 & 0.351 \\
10–20 & 0.143 & 0.159 & 0.167 & 0.354 & 0.434 \\
20–30 & 0.179 & 0.233 & 0.210 & 0.432 & 0.479 \\
30–40 & 0.218 & 0.319 & 0.262 & 0.461 & 0.513 \\
40–50 & 0.262 & 0.405 & 0.328 & 0.505 & 0.566 \\
50–60 & 0.305 & 0.413 & 0.369 & 0.558 & 0.689 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mean RMSE of 3D positioning error of the simulated kinematic PPP-B2b solutions at different times for the five stations during DOY 355–361 in 2022. Figure 15: _Cont._
###### Abstract  
In this study, the recently launched Sentinel-2 (S2) optical satellite and the active radar Sentinel-1 (S1) satellite supported by active fire data from the MODIS sensor were used to detect and monitor forest fires in the Congo Basin. In the context of a very strong El Nino event, an unprecedented outbreak of fires was observed during the first months of 2016 in open forests formations in the north of the Republic of Congo. The anomalies of the recent fires and meteorological situation compared to historical data show the severity of the drought. Burnt areas mapped by the S1 SAR and S2 Multi Spectral Instrument (MSI) sensors highlight that the fires occurred mainly in Marantaceae forests, characterized by open tree canopy cover and an extensive tall herbaceous layer. The maps show that the origin of the fires correlates with accessibility to the forest, suggesting an anthropogenic origin. The combined use of the two independent and fundamentally different satellite systems of S2 and S1 captured an extent of 36,000 ha of burnt areas, with each sensor compensating for the weakness (cloud perturbations for S2, and sensitivity to ground moisture for S1) of the other.  
Sentinel-2; Sentinel-1; tropical rainforests; fires; burnt areas; Republic of Congo; logging roads 2016 8986 doi:10.3390/rs8120986
## 3 Materials and Methods
### Architecture of the System  
This section discusses the privacy levels of medical records and the access permissions used to develop the proposed framework. The framework has three elements or modules, as seen in Figure 1. These modules would keep the system running when coupled. The following section also explains the access rights for specific entities or modules.  
3.3.1. Levels of Privacy of Medical Records  
We take into account the following factors based on access rights' degrees of security and data privacy:  
* The citizen registry is only visible to governmental institutions, according to regulations.
* Medical records are only visible to administrative staff.
* Medical records are only visible to administrative and health personnel.
* Medical records are only visible to administrative staff, healthcare personnel, and patients or family members.  
The access structure is based on privileges and divides the users into levels. An access policy is linked to each level. A set of stated rules and the attributes that abide by those rules are specified in an access policy. Data users who meet the requirements of an access policy and have the proper qualities are members of a particular level.  
3.3.2. System Components  
Some actors and entities were considered in the system's components, such as:  
1. Patients/rightsholders: Patients who received treatment or required it out of necessity from a medical institution.
2. Health personnel: Access to the previous medical records of a visiting patient is necessary for the medical staff to provide better care. Each employee has a set of qualities (A) that can be used to divide them into categories of comparable traits and order them in a hierarchy.
3. Administrator.
4. A governmental body, public or private institution.
5. Family members (entitled).  
3.3.3. Functions of Each Entity
* Access instance, government, public or private institution: Health service providers and managers within the Mexican data protection laws covering all medical record data.
* Patient: The patient visited the hospital for a medical check-up. He is a data user.
* Physician: The physician is responsible for generating the patients' medical records and extracting keywords for integration into the file. In this scheme, the physician is assumed to be honest and not transacting for illegal gain.
* Data user: The data user will use the information, for example, internal departments of the institution or patients' families who can obtain the relevant records as long as their attributes comply with the corresponding access policy.
* Blockchain mechanism: The blockchain mechanism is the backbone of the architecture and performs operations based on smart contracts.  
Figure 1: Identity assignments.
###### Abstract  
It is challenging to plan paths for autonomous vehicles on half-structured roads because of the vast planning area and complex environmental constraints. This work aims to plan optimized paths with high accuracy and efficiency. A two-step path planning strategy is proposed. The classic planning problem is divided into two simpler planning problems: reduction problems for a vast planning area and solving problems for weighted directed graphs. The original planning area is first reduced using an RRT (Rapidly Exploring Random Tree) based guideline planner. Second, the path planning problem in the smaller planning region is expanded into a weighted directed graph and transformed into a discrete multi-source cost optimization problem, in which a potential energy field based discrete cost assessment function was designed considering obstacles, lanes, vehicle kinematics, and collision avoidance performances, etc. The output path is then obtained by applying a Dijkstra optimizer. Comparative simulations are conducted to assess the effectiveness of the proposed strategy. The results shows that the designed strategy balances efficiency and accuracy with enough planning flexibility and a 22% improvement in real-time performance compared to the classic Lattice planner, without significant loss of accuracy.  
autonomous vehicle; path planning; RRT-Dijkstra-based strategy +
Footnote †: journal: Applied Sciences  
1 Hubei Research Center for New Energy & Intelligent Connected Vehicle, Wuhan University of Technology, Wuhan 430070, China  
2 Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China  
3 Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China  
*Correspondence: [EMAIL_ADDRESS]
## 4 Numerical Simulations and Modeling Based on Visualization Techniques
### Idealized Aggregate Model
The pros and cons of the image-based model and the computer-generated model are summarized and shown in Table 6.  
In general, the main advantage of image-based models is the accuracy of the internal structure of the composite, while the main advantage of custom aggregate models is the cost-effectiveness and ease of implementation of the model [114]. Stochastic numerical modeling, a new technique developed in recent years, has been used to study the accumulation model and complex rock structure from a probabilistic point of view, which is of great significance in studying the complex problems existing in the accumulation. The stochastic numerical simulation technique generates the spatial distribution form of aggregates and enables the study of aggregate gradation, particle size, particle shape, and porosity. \begin{table}
\begin{tabular}{c c c} \hline
**Approach** & **Pros** & **Cons** \\ \hline \multirow{4}{*}{Image = based model} & \multirow{4}{*}{\
\begin{tabular}{c} Better accuracy of the real morphology of the aggregates and the internal structure of the composite. \\ \end{tabular} } & High time cost; \\  & & Scanning equipment requirement; \\  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \multirow{4}{*}{Computer-generated model} & Low cost; & \multirow{4}{*}{
\begin{tabular}{c} Shape and distribution for aggregate and air \\ voids may not be consistent with the \\ actual condition. \\ \end{tabular} } \\  & No equipment limitations; & & voids may not be consistent with the \\ \cline{1-1}  & Undifferentiated aggregates; & & actual condition. \\ \cline{1-1}  & Facilitates control of aggregate size. & & \\ \hline \end{tabular}
\end{table} 
Table 6: Pros and cons of image-based and computer-generated models.
## 5 Analysis and Results
### Capacities of Self-optimization and Self-configuration Applied into AODV protocol
That is explained due to the fact that the protocol AODV originally proposed to use technique for demand, in atmospheres that the scale of the network is small, in other words, the amount of devices is small in the network, the protocol needs to locate their destiny nodes through the demand technique, that increases the number of requests of routes also increasing in consequence the  
Figure 13: Comparison of total number of replicas sent to destination nodes of protocol AODV: Self-organized e AODV:Original versus Comparison of total replicas of routing sent between protocol AODV: Self-Organized and AODV:Original. Figure 14: Comparison of total requests of routes sent by AODV: Self-Organized e AODV: Original versus Comparison of total of routes errors between AODV: Self-Organized and AODV: Original. Figure 15: Comparison of the delay of the AODV: Self-organized protocol and AODV: Original versus Comparison of the access delay to the middle of the AODV: Self-organized Protocol and AODV: Original. load of the network. The time of route discovery is visualized, in which the AODV: Self-organized protocol obtained better time of route discovery in all the scales of the WMN. Figure 17: symbolizes the relationship of the throughput of network, among OLSR: Self-organized compared to OLSR: Original. It can be visualized in this Figure that the way the nodes enter in the network, together with the variation of the small scale for medium scale until the big scale, it is noticed a significant improvement of the throughput of the OLSR: Self-organized protocol. The comparison of the throughput can be observed in Figure 17. In this case the AODV: Self-organized obtained a significant throughput improvement when the scale of the network is growing. While in the small scale a minor difference is observed. After the OLSR and AODV results we argue that this specific analysis on protocols is better for sceneries where the position of the nodes is practically known, due to techniques of finding the routes for the general knowledge of the network, this process demand a very big load of control messages and maintenance of routes. It was observed that the self-adjustment of these intervals can guarantee a good acting of protocols when the number of nodes growing to medium for large scales.
# Cluster-Based Transmission Diversity Optimization in Ultra Reliable Low Latency Communication
## 4 Simulation Results
### Path Loss  
We estimate the received power for different positions inside the vehicle. As all the general sensors and specialized audio and video sensors are placed randomly, we consider a transmit power range. To calculate received power in different distances, we consider \(-5\) dBm, \(-10\) dBm, \(-15\) dBm, and \(-20\) dBm transmit power, respectively. A fixed path loss exponent is used for the free-space medium, and the referred distance is the distance between the receiver antenna and the closest cluster. From Figure 12, it can be said that as the distance between sensors and receiver antenna increases, the received power decreases. With the increase in transmit power, received power is also increasing. For example, with \(-5\) dBm transmit power, the received power is about \(-54.54\) dBm, having a distance of \(1.2\) m. At the highest level of \(3\) m distance, received power decreases to \(-63.36\) dBm. For \(-10\) dBm and \(-15\) dBm received power at \(3\) m distance, and we have received \(-68\) dBm and \(-72.8\) dBm, respectively.
# Spaceborne Sparse SAR Imaging Mode Design: From Theory to Implementation
## 4 Experiments and Discussion
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Sliding Spotlight**} & \multicolumn{2}{c}{**TAR**} & \multicolumn{2}{c}{**TBR of T2**} \\ \cline{2-5}  & **T1** & **T3** & **Traditional** & **Sparse** \\ \hline MF & 5.53 & 2.35 & 10.22 & 10.22 \\ \(L_{q}\)-norm & 17.97 & 9.77 & 22.69 & 26.85 \\ \(L_{2,4}\)-norm & 23.67 & 12.69 & 41.88 & 46.42 \\ \hline \multirow{2}{*}{**Scan**} & \multicolumn{2}{c}{**TAR**} & \multicolumn{2}{c}{**TBR of T5**} \\ \cline{2-5}  & **T4** & **T6** & **Traditional** & **Sparse** \\ \hline MF & 4.94 & 5.35 & 7.45 & 6.94 \\ \(L_{q}\)-norm & 10.88 & 16.90 & 20.57 & 23.07 \\ \(L_{2,4}\)-norm & 20.88 & 24.83 & 30.51 & 34.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: TAR and TBR values of the images recovered by different methods in the designed sparse modes \([\text{dB}]\). Figure 12: Reconstructed images of islands by different methods from the data collected by the sparse Scan mode. (**a**) MF (traditional parameters). (**b**) MF. (**c**) \(L_{q}\)-norm regularization-based sparse imaging method. (**d**) \(L_{2,q}\)-norm regularization-based sparse imaging method.
## 3 Results  
Figure 1 shows the study identification and selection process. A total of 167 studies were identified in an initial search of PubMed (n = 38), SCOPUS (n = 64), and Web of Science (n = 65) databases. It was found that there were 45 duplicate (or triplicate) records. Thus, the search decreased to 90 results. After reading abstracts, 71 records were excluded because they were articles in which footballers with CP did not participate, did not analyze match-play responses (physical or physiological), or did not fit the topic. Afterwards, a detailed reading of the 19 resulting studies was made, with 10 of them excluded because they were articles written in other languages (Portuguese and Korean). They did not analyze physical or physiological responses during match-play, or they analyzed these variables in training sessions or assessed functional tests. Therefore, a total of 81 records were excluded, and finally, 6 of these studies were included in the systematic review. The results of methodological quality and risk of bias in the studies included in the review are shown in Table 1; the mean score is 73.5 \(\pm\) 3.7%. The highest value, 77.8%, was reported in two studies [30; 31], while 66.6% of the studies reached a methodological quality of 72.2% (n = 4) [27; 28; 29; 32]. All the studies fit the inclusion and exclusion criterion analyzing physical responses [27; 28; 29; 30; 31; 32], but only one the physiological responses [27]. Three studies attended to the new classification FT1 to FT3 [28; 29; 30] and three to the old classifications C5/6, C7 to C8 [27], and FT5/6 to FT8 [29; 31]. The number of participants in the studies analyzed was 488, of whom 4 belonged to C5/6, 29 to C7, 7 to C8, 9 to FT5/6, 32 to FT7, 7 to FT8, 64 to FT1, 238 to FT2, and 56 to FT3; of the remaining 42, it is not known how many correspond to each FT, since the authors determine and quantify the sample (8 from FT5, 3 from FT6, 42 from FT7, and 9 from FT8) by the total number of individual observations, \(n=62\)[32]. Four studies determined the sex of the sample (all male) and only two did not determine it [27; 28; 30; 31], but they were men observing the city and the year of the competitions. Tables 3 and 4 shows the total and distance covered at different speed ranges by players with PC during match-play, expressed in absolute and relative terms, respectively. The speed ranges to classify the distance covered were diverse: between 17.64-23.04 km\(\cdot\)h\({}^{-1}\)  
Figure 1: Diagram flow of the review. above 23.04 km\(\cdot\)h\({}^{-1}\)[27], between 0.6-6 km\(\cdot\)h\({}^{-1}\), 6-12 km\(\cdot\)h\({}^{-1}\), 12-18 km\(\cdot\)h\({}^{-1}\), 18-21 km\(\cdot\)h\({}^{-1}\), 21-24 km\(\cdot\)h\({}^{-1}\), above 24 km\(\cdot\)h\({}^{-1}\)[28], below 0.4 km/h, between 0.4-3 km\(\cdot\)h\({}^{-1}\), 3-9 km\(\cdot\)h\({}^{-1}\), 9-13 km\(\cdot\)h\({}^{-1}\)[29; 30; 32], 13-18 km\(\cdot\)h\({}^{-1}\), and above 18 km\(\cdot\)h\({}^{-1}\)[29; 30; 32].
## 2 System Description
### Operation of the Absorption Cooling System  
The heating water, coming from the solar system, is circulated through the desorber to separate the refrigerant from the concentrated solution. At the exit of the generator, there is a two-phase flow. The vapor-phase in this flow consists of the ammonia vapor, the liquid-phase is an ammonia-lithium nitrate mixture with a low concentration of ammonia. The two-phase flow exits the generator and enters to one of the small capacity tanks, there, the phase separation is carried out.  
The ammonia vapor flows to the condenser where it is liquefied by means of the cooling water supplied by the auxiliary cooling system. Once the ammonia has been condensed, it flows through the expansion valve. As a consequence of the expansion process, the ammonia pressure and temperature are reduced in such a way that the corresponding thermodynamic state for the ammonia is a saturated liquid-vapor mixture. In this point, the ammonia is at the lowest temperature of the cycle, having the maximum cooling potential. At these conditions, the ammonia enters the evaporator to produce the desired cooling effect, taking a heat load from the auxiliary fluid (water) circulating in the opposite side of the evaporator. As a result of the heat transfer, the ammonia thermodynamic state is changed from a saturated liquid-vapor mixture to a superheated vapor. The ammonia vapor leaves the evaporator and is mixed with the diluted solution coming from the generator, the mixture process takes place before the diluted solution enters the absorber. The refrigerant-absorbent mixture provokes an exothermic reaction, so the heat released in this process is transferred from the solution to the stream flowing in the adjacent channels of the plate heat exchanger acting as the absorber. As a result of the absorption process, a mass flow rate of concentrated solution exits the absorber and is pumped to the generator, in order to start the cycle again.  
An additional heat exchanger called \"solution heat exchanger\" or \"economizer\" is included in the absorption cycle to exchange heat between the streams flowing in and out of the generator, the use of the economizer augments the coefficient of performance (COP) by reducing the heat released in the absorber and the heat supplied to the generator. Figure 3 shows the absorption cooling system.  
Figure 2: Schematic diagram showing the main components of an absorption system with rectifier and heat exchanger for the solution streams.
# Modelling and Experimental Testing of Passive Magnetic Bearings for Power Loss Reduction
## 4 Results and Discussion
### Static Tests  
The static tests involve adjusting the PMB configuration by varying the axial offset between the rotor and stator rings through the rotation of the stator supports. The contact reaction force \(N\), equal to the downward force measured by the scale, and the axial displacement \(\Delta z\) are recorded at each support rotation step, thus allowing for the evaluation of the corresponding magnetic force \(F_{z}\). Centring bushes are used at both the lower and upper supports to keep the rotor radial offset null during the tests. The lower bearing axial force is evaluated at different ring axial offsets. Each test is performed according to the following procedure:  
* The starting position of the tests corresponds to axial position of the stator ring at which \(F_{z}\) is null; \(\Delta z\) is set equal to zero and the scale measures a force equal to the rotor weight force;  
Figure 5: Axial position variation of the PMB stator rings.  
Figure 6: (**a**) Rotor test rig; (**b**) test rig CAD model.  
* The stator support is progressively rotated to vary its vertical position, moving downward by steps of \(\Delta z\), up to the maximum upward value, corresponding to the condition where a further rotation of the stator support causes a decrease in \(F_{z}\).  
The experimental axial force of the lower PMB is compared to the force estimated with Equation (8) in Figure 7. The relative error between the experimental measurements and the model force at the corresponding axial offsets \(\Delta z\) are listed in Table 1. The maximum relative error is equal to 5%, according to the experimental characterisation of the \(B_{r}\).
# Aquasafe: A Remote Sensing, Web-Based Platform for the Support of Precision Fish Farming
## 2 Materials and Methods
### Parameters
#### 2.3.2 Fish Growth Parameters  
The parameters relating to the growth of the fish in the fish farm are directly obtained from a bioenergetic model that simulates their metabolism under the prevailing environmental conditions. The model is based on the Dynamic Energy Budget (DEB) theory, which is a quantitative and qualitative framework for describing the metabolism throughout the life cycle of an organism, in this case fish, under dynamically changing environments [31] and has been widely applied in fish research [32; 33; 34]. In this study, models were developed for three fish species, namely the European sea bass, the gilthead seabram, and the meagre (_Argysromus regins_) using experimental and literature data. The models have been presented in detail in previous work and have been validated against production data from commercial farms, showing, overall, good performance and adequate capacity to capture the seasonal growth patterns of the fish under various rearing conditions. For further details on model development and validation, we refer to [PERSON] et al. [35; 36] and the AquaExcel2020 deliverable D5.6 ([[https://aquaexcel2020.eu/results](https://aquaexcel2020.eu/results)]([https://aquaexcel2020.eu/results](https://aquaexcel2020.eu/results)) (accessed on 20 April 2023)).  
In the Aquasafe platform, the fish models are used to provide information to the system user about anticipated changes in growth parameters. Specifically, the models receive as input the water quality parameters at the farm area as well as the user specification of the initial conditions at each cage (species, number of fish, initial size) and simulate growth for the following days. The generated predictions include the weight of the fish, the feed consumption rate, the total fish biomass, and the oxygen consumption rate. All parameters are reported as average values along with the standard deviation for the fish population at each cage.