esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Information Compression for Target Tracking in Cluster-Based Wireless Sensor Networks
## 3 Data Processing Schemes
### Problem Statement  
Figure 3 shows an example of target movement with 25 time steps. In the presence of the target, to achieve load-balancing tracking, an important issue of target tracking is how to collect the desired data and to perform efficient information processing. Denote a sensor with tracking responsibility as an active sensor. Otherwise, a sensor is marked as an inactive sensor. Thus, referring to the Level 1 clustering in Figure 3, the Level 2 clustering utilizes a dynamic clustering scheme to adaptively form a local sub-cluster with active sensors for the tracking task in a distributed manner (Figure 4). Since there is a trade-off between estimation accuracy and energy consumption, this work aims to investigate the network performance on data size and energy conservation for target tracking from an information processing perspective. The main assumptions are: (1) all sensor nodes are assumed to be homogeneous with their fixed position information in a strongly-connected network; (2) the noise model is the zero-mean Gaussian noise measurement model; (3) Angle-of-Arrival (AOA) information or hybrid Time-of-Arrival (TOA)/AOA information is applied to perform target tracking; and (4) the  
Figure 2: The information processing model of Two-level Clustering Approach via Timer (TCAT) (**left**), Cooperative Tracking via Compressing Information (CTCI) (**middle**) and DCTTP (**right**).  
target broadcasts a message for measurement purposes periodically. Note that these assumptions may be applied to locate objects (e.g., patients or animals) in healthcare or habitat monitoring scenarios.  
Accordingly, as depicted in Figure 5, the CTCI scheme performs target tracking in three major phases: (1) network configuration; (2) target positioning; and (3) information processing. The following subsections describe each phase, respectively.  
Figure 4: An example of Level 2 clustering for the tracking task.  
Figure 3: The cluster-based network topology and target movement with 25 time steps.
### Purpose, Objectives, and Scientific Novelty of the Research  
In this study, the object was geopolymer concrete with the use of stone flour, and the subject of the study was the characteristics of such concrete. The aim was to reveal the component composition and prescription dosage influence on these characteristics.  
The following tasks were performed during the study:  
* A literature review, in particular over the past 5 years, on the most progressive developments in the field of geopolymer concrete, taking into account the regional factor;
* Drawing up a research program, in which it was necessary to identify the factors affecting the characteristics of concrete and to identify the degree of this influence. To this end, it was necessary to determine the materials and methods, as well as to set up a phenomenological approach;
* Conducting experimental studies to obtain data on the influence of prescription and technological factors on the properties of a new type of geopolymer fine-grained concrete;
* Analysis of the results, their interpretation, and processing to develop proposals for the practical implementation of the research results in the construction industry and determine the further prospects of the chosen scientific direction.  
The scientific novelty of this research is obtaining new knowledge about previously unknown compositions in terms of components and recipe dosage. Therefore, the quantitative and qualitative characteristics for new types of geopolymer concrete, particularly the use of stone flour obtained from raw materials of various kinds, are essential. Furthermore, considering the regional factor, this research may be helpful from the point of view of practice and implementation of this concrete composition in the construction materials industry in various regions in which the specified raw materials are contained in large quantities. This will significantly reduce the burden on the environment and significantly reduce the cost of obtaining new types of binder and concrete.  
As a scientific novelty, the development of previously known theoretical directions in the field of clinker-free binders and concrete based on them should also be noted.  
The practical application based on research results consists of obtaining information about the behavior and changes in the strength characteristics of new materials, which will be highly functional and suitable for those construction industries where the use of fine-grained concrete is developed. Furthermore, the analytical part of the article allows the scientific sector to use these studies for further research in terms of rationalizing the compositions of geopolymer concrete and helps to understand better the nature of this attractive, relevant, and promising material.
## 4 Conclusions  
Biomass exploitation for covering the thermal loads of the buildings, both for heating and cooling, consists of a sustainable solution with multiple benefits. The present study suggested a novel system based on an absorption heat pump operating with the LiBr/water working pair. As a reference, a case study of a building with a 400 m\({}^{2}\) floor area is examined in the location of Athens, Greece. The examined building presents specific heating and cooling loads at 22.84 kWh/m\({}^{2}\) and 20.42 kWh/m\({}^{2}\), respectively. The maximum heating loads were found in January and the maximum cooling load in July. According to the simulation results, the total biomass energy demand was found to be a total of 17859 kWh and it is separated at 10870 kWh for cooling (61% of the total demand) and 6989 kWh for heating (39% of the total demand). Moreover, the yearly energy performance for heating is found to be 1.307, while the yearly energy performance for cooling is calculated at 0.751. The exergy efficiency of the heating period was found to be 14.48% and for the cooling period at 6.31%. Additionally, it is interesting to state that the cooling period of the system is 2627 h and the heating period is 3041 h. The heating period is separated into two parts; 63% with heat pump operation and 37% with the only boiler operation. It is important to highlight that the present system was found to be more efficient compared to another conventional system which operates with an oil boiler and electric heat pump. More specifically, a 10.3% decrease in the yearly operating cost was calculated, as well as 4.8% savings in primary energy were found. The CO\({}_{2}\) avoidance was estimated at 4258 kgCO\({}_{2}\) per year. In the future, the examined system can be tested in different climate conditions, and also it can be evaluated financially in detail. Additionally, different working pairs can be tested and for example, the use of water/NH\({}_{3}\) is a choice that has to be investigated. Lastly, the hybridization of the system with solar thermal systems, as well as the design of the system for domestic hot water production are extra ideas that have the potential for sustainable designs. Figure 15: Cumulative operating cost with the absorption heat pump and the conventional system during the year period. **Author Contributions:** E.B. : conceptualization, methodology, software, validation, formal analysis, investigation, writing--original draft preparation, writing--review and editing: P.L. : software, writing--original draft preparation; C.T. : writing--original draft preparation, supervision. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Data Availability Statement:** Data available after request. **Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
Grassland monitoring can be challenging because it is time-consuming and expensive to measure grass condition at large spatial scales. Remote sensing offers a time- and cost-effective method for mapping and monitoring grassland condition at both large spatial extents and fine temporal resolutions. Combinations of remotely sensed optical and radar imagery are particularly promising because together they can measure differences in moisture, structure, and reflectance among land cover types. We combined multi-date radar (PASAR-2 and Sentinel-1) and optical (Sentinel-2) imagery with field data and visual interpretation of aerial imagery to classify land cover in the Masai Mara National Reserve, Kenya using machine learning (Random Forests). This study area comprises a diverse array of land cover types and changes over time due to seasonal changes in precipitation, seasonal movements of large herds of resident and migratory ungulates, fires, and livestock grazing. We classified twelve land cover types with user's and producer's accuracies ranging from 66%-100% and an overall accuracy of 86%. These methods were able to distinguish among short, medium, and tall grass cover at user's accuracies of 83%, 82%, and 85%, respectively. By yielding a highly accurate, fine-resolution map that distinguishes among grasses of different heights, this work not only outlines a viable method for future grassland mapping efforts but also will help inform local management decisions and research in the Masai Mara National Reserve.  
remote sensing; synthetic aperture radar; satellite imagery; grasslands; grass height; land cover +
Footnote †: journal: Remote Sensing, 2020, 12, 1086; doi:10.3390/rs12071086
# Teenagers and Automated Vehicles: Are They Ready to Use Them?
## 4 Results and Discussion
### Focus Group Results
#### 4.1.8 What Information about AVs Would You like to Know before You Use Them?  
For this question, the participants mainly emphasised the need for information about the safety of AVs. Some would like to know the details about the prioritisation algorithms that the vehicle will be set to follow in critical situations or when interacting with vulnerable road users. The teenagers would like to know in advance the emergency plan and where the emergency button that manually deactivates the car in case of malfunction is. Moreover, they would be interested in knowing more details about the functions and the equipment inside the vehicle. Safety certificates and performance testing results were listed among the things they would wish to know before using AVs. During their responses, the teenagers expressed their fear that these tests might not be rigorous enough or would be farmed out to non-trustworthy companies because governments usually lack the relevant technical knowhow.  
Moreover, some of the teenagers were not keen to be among the first users of AVs. They preferred to wait to take advantage of the information and the experiences shared by early adopters. In this way, possible malfunctions of the systems could be improved and ways to deal with them would be known to the public. On this occasion, the teenagers highly recommended the need for training before the actual use of the vehicles so that the users would become familiar with AVs and only a successful completion would give them permission to use and avoid awkward situations.  
Additionally, the teenagers (considering that AVs will be electric) mentioned that, before each ride, they would like to be informed about the state of charge and whether it was adequate to reach their destination without intermediate stops for charging, which would increase the travel time. Some others, less concerned about the charging stops, mentioned that all the charging stations would need to be mapped and kept in an updated file so that they could be easily reached without wasting time. It is important to remark that although electrification of transport was not among the main areas of interest of the current research, several teenagers identified AVs as being electric.  
Lastly, some of the teenagers would like to know beforehand which personal data would be kept, for how long, and how they would be treated if they declared their unwillingness to share their location data with strangers.  
The safety aspects, the AV's reaction in an emergency, and the trustworthiness certifications remained some of the important issues reiterated by teenagers in this section. This confirms once again the findings in the literature [92] that the perceived level of safety and solving the ethical dilemma and challenges related to the programming of the vehicle, as well as the liability and regulations, remain some of the most important aspects influencing people's acceptance of AVs.
## 2 Prototype
### Architecture
To implement this architecture, it is necessary to take into account various aspects that could hinder the development of the prototype that demonstrates this concept. Table 1 shows the challenges identified along with the hypotheses considered for developing the prototype. In the following sections, the proposed solutions for each of these challenges will be discussed and explained, considering the different hypotheses analyzed. \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Challenges**} & \multicolumn{1}{c}{**Hypotheses**} \\ \hline  & Create an attribute (i.e., threshold) to control the number of bridge nodes that carry the users count information. \\ (A1) When can data be deleted from the sensor node? & After a period of time. \\  & After the storage capacity is above a certain threshold. \\  & Specific event. \\  & A request from the infrastructure manager. \\ \hline (A2) How can data be transmitted securely between the sensor node and the bridge node? & Data encryption. \\  & Mutual authentication between nodes. \\ \hline (A3) Are all of the different types of bridge nodes able to receive the data transmitted by the node? & Will the speed of the bikes make it impossible to establish a connection and exchange data between the sensor and bridge nodes? \\  & Compatibility of the user’s smartphone (operating system, operating system version, Bluetooth version). \\ \hline (A4) How can data transmission be ensured transparently, without the user having to interact with the application? & Automatic device detection. \\ \hline (A5) What happens if the sensor and bridge nodes disconnect the middle of the transmission process? & Try reconnecting the devices and sending all of the information again. \\  & Attempt to reconnect the devices and resend the information that was not successfully sent. \\ \hline \end{tabular}
\end{table} 
Table 1: Aspects that could hinder the implementation of the prototype.
## 3 Results and Discussion
### Equilibrium and Kinetics Studies
#### 3.3.2 Kinetic Studies  
Four kinetics models, namely, pseudo-first order (PFO), pseudo-second order (PSO), Elovich, and Weber-Morris, were applied to simulate the kinetics of the adsorption of TY onto ADAV. Figure 10A,B show the plots of ln(\(q_{e}-q_{l}\)) and the time/\(q_{l}\)_vs_ time for the PFO and the PSO kinetic models, respectively. The calculated parameters of the two models are listed in Table 7. By comparing the coefficient of determination values (R\({}^{2}\)) of the two models, it could be concluded that the experimental data were in good agreement with the PSO model (R\({}^{2}\) = 0.993) [49; 50]. Therefore, the reaction is represented as follows:  
\[TY+ADAV\;(\xrightarrow{k})\;[TY-ADAV] \tag{9}\]  
Figure 10: (**A**) First order, (**B**) second order, (**C**) Elovich, and (**D**) intra-particle diffusion (Weber) curves of adsorption of TY on ADAV.  
Consequently, the rate of the reaction equaled k[TY][ADAV], indicating that the adsorption rate depended on both the adsorbate and the adsorbent.  
According to the Elovich model shown in Figure 10C, the high R\({}^{2}\) values (0.987) show that the desorption rate was higher than the initial sorption. From the Weber-Morris intraparticle diffusion model (Figure 10D), it is clear that there were two regions; the first region had almost no boundary layer, the diffusion rate was very fast, and the negative sign indicated the penetration of TY to the surface of ADAV. After a while, a TY layer formed around the particles, preventing further penetration of TY with a boundary layer equal to 52.13 mg/g, which was close to the \(q_{max}\) calculated by the Langmuir isotherm. Therefore, the reaction in the beginning was controlled by diffusion but later on by adsorption.
# Influence of Selected Model Parameters on the Electromagnetic Levitation Melting Efficiency
## 3 Results
### Selected Variant
\[E_{R}(t)=t\cdot\epsilon\cdot C_{b}\cdot(T_{B}^{4}-T_{s}^{4})\cdot S, \tag{15}\]  
where:  
* \(\epsilon\) is the emissivity of chosen material type,
* \(C_{b}\) is the Stefan-Boltzmann constant,
* \(T_{B}\) is the current temperature of the batch,
* \(T_{s}\) is the current temperature of surroundings,
* \(S\) is the surface of the batch. If the heat losses caused by convection are taken into account, due to, e.g., lack of vacuum, those can be estimated according to:  
\[E_{B}(t)=E_{I}(t)-E_{R}(t)-E_{C}(t), \tag{16}\]  
where:  
* \(E_{C}(t)\) is the energy lost by convection in the time domain. The energy lost at convection is calculated based on (18). \[E_{C}(t)=t\cdot\beta\cdot(T_{B}-T_{s})\cdot S, \tag{17}\]  
where:  
* \(\beta\) is the heat transfer coefficient. The final equation describing the energy requirement for melting the charge is given by the equation:  
\[E_{B}(t)>=E_{M} \tag{18}\]  
Based on equations from this subsection, the calculation was conducted for \(\beta\) = 10 \([\frac{W}{m^{2}C}]\), \(\epsilon\) = 0.01, and at the start, \(T_{B}\) is equal to \(T_{s}\) = 20 \({}^{\circ}\)C. The time step for the calculation was \(t\) = 1 s. According to Figure 8, the \(P_{B}\) for the selected variant is 0.1197 W. The result is:  
* if only radiation is considered, the temperature stabilized at 452 \({}^{\circ}\)C, which unfortunately was insufficient to achieve melting. * if convection is also taken into account, it resulted in even lower temperature of 157 \({}^{\circ}\)C.  
During a survey of possible variants, we considered the wide range of frequencies, so the current in simulations was set to a value that is achievable for all of them. Fortunately, the selected variant uses \(f\) = 10 kHz, which allowed us to increase the current value. For the higher current value, \(P_{B}\) increased, and melting occurred. The calculations of energy for the follwing selected variantsleads to the results summarized in Table 2  
* \(E_{H}\) = 101.835 J,
* \(E_{L}\) = 69.979 J,It must be mentioned that at this stage of the research, the parameters of the process are not yet fully optimized, so the melting is possible but the time is not relatively high.
## 5 Conclusions  
This paper addresses the challenges in detecting weak targets for PBR and the high computational demands of traditional sparse representation approaches. It proposes an PBR target detection model based on batch sparse representation and recovery. The model processes CAF into blocks, filters out blocks that require sparse representation and recovery based on whether targets exist in them, and improves the construction and utilization of the measurement matrix. Through the above processes, both weak target detection and computational efficiency are achieved. Compared with traditional approaches that rely on the sparsity of the delay-Doppler domain to detect weak targets, this approach requires fewer memory resources and computational complexity, while achieving an excellent weak target detection performance. In the sparse recovery process of CAF, the use of an iterative sparse recovery algorithm based on the \(l_{0}\) pseudonorm avoids the challenge of verifying the RIP conditions, providing a more stable theoretical basis for the sparse recovery results. Experimental results demonstrate that, compared with the traditional approach, the proposed optimization approach achieves fast and stable detection of weak targets. As the number of blocks increases four-fold, the number of real multiplications decreases by approximately one order of magnitude. Therefore, the proposed optimization approach effectively realizes the fast and stable detection of weak targets. Furthermore, the sparse recovery for all blocks can be executed in parallel, laying the foundation for improving the algorithm's real-time performance.  
Figure 10: The number of real multiplications for the sparse representation and recovery approaches. (**a**) \(L=M\). (**b**) \(L=4M\). (**c**) \(4L=M\).  
**Author Contributions:** conceptualization, C.W.; methodology, K.C.; software, K.C.; formal analysis, K.C.; data curation, W.F.; writing--original draft preparation, K.C.; visualization, K.C. and C.W.; writing--review and editing, K.C. and C.W.; project administration, F.Z., C.L. and Y.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, Grant Nos. 61631019, 61201418 and 62001350.  
**Data Availability Statement:** The data that support this study are available from author [PERSON], upon reasonable request.  
**Acknowledgments:** The authors thank the editors and anonymous reviewers for their help comments and suggestions.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 LiDAR-Inertial odometry
### LiDAR Odometry
With the IMU preintegration factor (Section 3.2), frame-by-frame matching is not required, and the method of matching features from frame to local map is directly used, which saves processing time for global map and improves matching efficiency. \(\mathrm{P}_{i,u}^{e}\) and \(\mathrm{P}_{i,v}^{e}\) are the two points closest to the edge feature \({}^{\prime}\mathrm{P}_{i+1,k}^{e}\) of the previous frame, which form the edge line. \(\mathrm{P}_{i,u}^{p}\), \(\mathrm{P}_{i,v}^{p}\), and \(\mathrm{P}_{i,w}^{p}\) are the three points closest to the previous frame plane feature \({}^{\prime}\mathrm{F}_{i+1,k}^{p}\), which together form a plane block in M\({}_{i}^{p}\). Following the principle that edge features correspond to edge lines and plane features correspond to plane blocks, the equation for minimizing the positional transformation is:  
\[\min_{\mathrm{T}_{i+1}}\left\{\sum_{\mathrm{P}_{i+1,k}^{e}\in\mathrm{F}_{i+1} ^{e}}d_{e_{k}}+\sum_{\mathrm{P}_{i+1,k}^{e}\in\mathrm{F}_{i+1}^{p}}d_{p_{k}}\right\} \tag{7}\]  
Using the Gauss-Newton method to solve for the optimal pose transformation relationship of the robot at moments \(i\) and \(i+1\), \(\Delta\mathrm{T}_{i,i+1}=\mathrm{T}_{i}^{\mathrm{T}}\mathrm{T}_{i+1}\), and added to the factor graph as a LiDAR odometry factor.