esa-sceva/satcom-chunk-collection huggingface dataset:
# Finding Patterns in Signals Using Lossy Text Compression
## 5 The Reverse Engineering System  
### Example of a Protocol: The SYMA G107 Helicopter  
**Example Protocol:** The SYMA G107 helicopter supports a communication of three channels that represent the current state (level) of each button in the remote controller: throttle, pitch, and yaw of the helicopter. As in most of our RCs, the communication protocol is defined by a multi-layer language. For the special case of SYMA G107, the protocol is as follows.  
**Level I: A/B (switches).** The IR signal is essentially a stream of binary numbers that corresponds to the IR light (on or off) that can be be changed every 13 microseconds. Light on for 13 microseconds represents in our notation the letter \"A\"; otherwise, the letter is \"B\". The letters \"A\" and \"B\" are called _switches_.  
**Level II: 0/1/H/F (letters).** The _letter_ \"0\" is represented by the sequence of switches \"0=ABABABABABABBBBBBBBB\". That is, five pairs of \"AB\" followed by ten \"B\" letters. We encode the last sentence as a sequence of pairs \"0\" \(=(5,AB,10,B)\), known as run-length-encoding (RLE); see Definition 3. Similarly, we define the letters \"1\" \(=(5,AB,23,B)\), \"H\" \(=(75,AB,72,B)\) (called _header_, and \"F\" \(=S(10,AB,47,B)\) (called _footer_).  
**Level III: word.** A _word_ in the SYMA G107 protocol is defined by the followings sequence of letters:  
\[word=(1,H,1,\"0\",1,yaw,1,\"0\",1,pitch,\] \[1,\"0\",1,serial,1,throttle,1,\"0\",1,trim,1,F),\]  
where: \(H\) and \(F\) were defined in Level II above, \"serial\" is the letter \"0\" or \"1\" (Helicopter 0 or 1) that allows the support of two helicopters by a single RC. Each term yaw/pitch/throttle/trim is an integer between 0 and 255. Each integer is represented by a binary word that consists of 8 bits, where a bit is represented by the letter \"0\" or \"1\" above. In real-time, a continuous stream of such words is sent from the transmitter (RC) to the receiver of the helicopter that decodes these words.
## 3 Results and Discussions
### Shielding Effectiveness  
The electromagnetic shielding performance of the FSS unit cell is analyzed in terms of its shielding effectiveness (SE) as a function of frequency for both the TE and TM polarized waves. Thus, the SE is defined as the ratio of the transmitted electric field component to the incident electric field component on the structure and is expressed in Equation (1).  
\[SE\ (\mathrm{dB})=-20\ log_{10}\bigg{|}\frac{E_{t}}{E_{i}}\bigg{|}, \tag{1}\]  
Figure 3c reveals the SE of each element of the FSS unit cell individually and together under normal incidence.
## 4 Discussion  
### Comparison of the Spring Phenological Indicators  
Among the spring phenological indicators, CUP onset is the best indicator of annual GPP, given its consistent and high correlation at all sites. These observations confirm that phenological indicators derived from the C flux data are possible indicators of C fluxes [1,4,8,9]. Our results further suggest that this correlation is independent of plant functional type, and so has considerable potential for future applications. However, three limitations constrain the application of CUP onset as a GPP proxy. First, this metric requires the measurement of NEE, which is limited by the number, distribution, and footprint of flux towers. Therefore, an upscaling algorithm is required to interpret the results of flux towers at a regional scale. Second, CUP onset is not appropriate for recently disturbed (_i.e._, fire, harvest) ecosystems that have only few days with a positive NEP over the whole year. This could be the major limitation to the use of CUP onset, considering the large areas of disturbed forest worldwide. The final uncertainty is that previous analyses have determined that CUP onset is a good indicator of annual NEP, which was not supported at the SK-OA and SK-OJP sites in our evaluation. A possible explanation is that in addition to spring phenology, summer drought may also influence the annual NEP for boreal forests, particularly for deciduous forest [35].  
Spring temperature is a potential indicator of C fluxes for evergreen sites. However, its feasibility for deciduous sites is uncertain, and requires further in-depth evaluations. We used the air temperature recorded at the flux towers to calculate spring temperature; however, this method is limited by the availability of flux towers or weather stations. While remote-sensing technology can provide systemic observations of land surface temperature, the precision of these data is dependent on various factors [36]. These limitations likely restrict the use of spring temperature in operational applications.  
We suggest that spring EVI would be better applied in future ecosystem models to enhance the simulation of the impact of climate change on C sequestration. Our results showed consistently high correlations between spring EVI and annual NEP for all sites. The most convenient aspect of EVI is that it can be acquired from time series data from satellite sensors, including MODIS and Landsat/TM [37]. In particular, assessing the feasibility of predicting a long-term EVI curve would greatly improve the data quality and facilitate ecosystem modeling [25,38]. Several studies have suggested the usefulness of satellite-based tools for phenology monitoring. This is supported by the strong correlation between spring EVI and the C flux phenology in our results, which is more appropriate for indicating net C exchange between the ecosystem and atmosphere [4,18,39]. One caveat for the application of remote-sensing data, however, is that they require the careful removal of uncertainties due to low solar illumination, the presence of ice/snow, and cloud cover in the high latitudes during the winter and early spring.
## 3 Geotechnical Characterization
### Swelling Tests: Expansion  
The aim of the expansivity test is a qualitative and quantitative characterization of the change in volume in these clayey deposits when faced with variations in humidity. The tests were performed to objectively produce the classification and quantification ranges for the volumetric variations observed, and are based on direct laboratory testing methods. To this end, and in order to determine the potential expansivity, the three most common types of tests were used:  
* Free swelling tests
* Swelling pressure tests
* Lambe's expansivity tests.  
The free swelling tests were done following the specifications of Standard UNE 1003-601. The procedure followed the lateral confinement of the remoulded and compacted sample which is subjected to a pressure of 1 \(\mathrm{kp/cm^{2}}\) and then flooded.  
The proportion of deformation in relation to the original height of the sample, expressed as a percentage, defines the free swelling. The obtained results, which are reflected in Table 2, show a wide range of values of between 0.2% and 4.45%, with very sporadic values of 10% and even 20%.  
Swelling pressure tests were performed in order to obtain the pressure caused by the expansiveness. The sample (unaltered, in this case) is subjected to a pre-established load. Later it is flooded and the load is gradually increased to the extent necessary to keep the sample at a constant volume. The pressure exerted to reach the balance is defined as swelling pressure. The obtained results are shown in Table 3.  
Finally Lambe swelling tests were performed. This test, which is performed as per Standard UNE 103600, is based on Lambe's expansion apparatus, and consists of an expansive soil identification and quantification test which results in an \"expansion index\". The advantages include simplicity and speed. The obtained results are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline
**Values** & **Free Swelling (\%)** & **Degree of Expansion** \\ \hline Maximum & 4.6 & Moderate \\ Average & 2.0 & Minor \\ Minimum & 0.3 & Unnoticeable \\ Standard deviation & 1.2 & - \\ \hline \end{tabular}
\end{table}
Table 2: Characteristic values of free swelling (%). Population: 67 tests.  
\begin{table}
\begin{tabular}{c c c} \hline
**Values** & **Swelling Pressure (KPa)** & **Degree of Expansion** \\ \hline Maximum & 120 & Very high \\ Average & 40 & Slight-moderate \\ Minimum & 10 & Unnoticeable \\ Standard deviation & 25.7 & - \\ \hline \end{tabular}
\end{table}
Table 3: Characteristic values of the swelling pressure. Population: 135 tests.
# Vision-Guided Autonomous Robot Navigation in Realistic 3D Dynamic Scenarios +
## 2 Problem Formulation
### The Vision-Based 3D Autonomous Navigation Framework  
In response to these challenges, this paper introduces a novel 3D vision-based autonomous navigation framework tailored for wheeled mobile robots equipped with RGB-D cameras. Our approach addresses the intertwined challenges of SLAM, motion planning, and obstacle avoidance in both static and dynamic environments.  
Leveraging RGB-D data, our framework ensures seamless navigation through multi-level environments while adapting to dynamic obstacles. Key contributions include the following:  
1. The development of a robust 3D SLAM system that integrates RGB-D data for accurate mapping and localization.
2. The implementation of efficient 3D pathfinding algorithms that reduce computational complexity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Description** & **Advantages** & **Disadvantages** \\ \hline A* [9] & Utilizes a heuristic function to guide the search towards the goal. & Guarantees the shortest path; optimal; efficient in grid-based environments. & Computationally expensive; struggles in highly dynamic environments. \\ \hline Dijkstra’s Algorithm [14] & Explores all possible paths by considering the cumulative cost of each edge. & Guarantees the shortest path; effective for weighted graphs. & Computationally intensive for large graphs; inefficient in dynamic environments. \\ \hline Rapidly exploring Random Trees (RRT) [15] & Constructs a tree by randomly sampling the configuration space. & Efficient in high-dimensional and complex environments; handles non-holonomic constraints. & Does not guarantee optimality; paths may be suboptimal and jagged. \\ \hline Probabilistic Roadmap (PRM) [16] & Builds a roadmap by randomly sampling configurations and connecting feasible paths. & Well suited for complex, high-dimensional spaces; precomputed roadmap speeds up queries. & Requires significant preprocessing; roadmap may not be optimal for all scenarios. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different pathfinding algorithms.  
The design of collision avoidance mechanisms optimized for real-time operation in 3D dynamic spaces.  
By integrating SLAM, pathfinding, and motion control components, our framework provides a comprehensive solution for navigating complex environments, ensuring safe and efficient robot operation.
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 2 Routing on Wireless Sensor Networks
### Location Based Routing  
The position of sensor node is required in applications like military tracking, ecology monitoring, or health care. Contrary to data centric based routing where the position of a node can be unknown, the location-based protocols are very interesting since they can significantly decrease the complexity of finding best routes through the network. The distance between two neighbor nodes can be therefore estimated by the Received Signal Strength (RSS) [12]. When the study area is well known in advance, using the location of sensors will eliminate the number of transmissions significantly because the queries would be assigned only to a particular region at a particular time [12]. However, information about a position can be done through the use of a GPS (Global Positioning System) module on the sensor. Since the one goal when designing a sensor network is low cost and energy management, the use of GPS by sensors on a large scale network is quite expensive and energy consuming [16]. An example of location-based protocol is the Minimum Energy Communication Network (MECN), it reduces the energy consumption into the network by using a low power GPS module on each sensor node. Meanwhile, it is best applicable to sensor networks, which are not mobile [12]. Another well-known location-based algorithm is the Geographic Adaptive Fidelity (GAF) designed initially for mobile ad hoc networks. Presentations of location-based protocols are conducted in [3; 17].
## 3 Data Description  
This study utilizes a large-scale trajectory dataset made available through a national regulatory mandate requiring all HDTs in China with a payload capacity exceeding 12 tons to be equipped with satellite-based vehicle positioning devices. Introduced in 2014 under the Road Transport Vehicle Dynamic Supervision and Management Measures, this policy aims to enhance road safety, improve logistics efficiency, and ensure environmental compliance by enabling the real-time tracking of HDT operations. As a result, vast volumes of trajectory data have been generated, offering a rich resource for both researchers and transportation authorities to conduct high-resolution analyses of freight travel behaviors.  
The dataset employed in this research was obtained from the Shanghai Environmental Monitoring Center (SEMC), which collects, anonymizes, and curates HDT trajectory data primarily for environmental assessment purposes. The dataset covers a continuous six-day period from 12 to 17 September 2022 and includes trajectory records from 158,723 unique HDTs recently operating within Shanghai's administrative boundaries. In total, the dataset comprises approximately 8.3 billion timestamped observations. Each record includes a unique truck identifier, geographic coordinates (latitude and longitude), and a vehicle type classification. The data are characterized by high temporal granularity, with an average sampling interval of 30 s, enabling detailed insights into trip origins and destinations, route choices, stop locations, and operational rhythms.  
To ensure analytical reliability, the dataset underwent extensive preprocessing. This included the removal of duplicate entries and exclusion of records falling outside national boundaries. The resulting dataset presents a high level of spatial and temporal fidelity, well-suited for capturing the nuanced operational characteristics of HDTs across different functional categories. Figure 1 presents the spatial distribution of the processed data, constructed using ArcMap 10.8 (and all subsequent figures in this study were generated with the same software to maintain analytical and visual consistency). A key strength of this dataset lies in its comprehensive coverage of Shanghai's freight vehicle ecosystem. It encompasses a wide range of vehicle types, including urban delivery trucks, intercity tractors, and construction-related fleets, thereby facilitating direct comparisons of activity patterns across sectors.
# Reinventing Mobility Paradigms: Flying Car Scenarios and Challenges for Urban Mobility
## 3 The Model
The minimum cost criterion corresponds to a minimum travel time, where the travel time on the generic ground link has been assumed as \(t(f)=l/v_{g}=l/(a-bf_{g})\), where \(f_{g}\) is the link ground traffic volume, which here corresponds to the number of agents that are on that link at a given time, \(v_{g}\) is the ground speed empirically computed for urban roads as \((a-b\cdot f_{g})\), a and b - empirically computed for averaged road features (such as width, obstacles,secondary interferences, slope) - are respectively equal to 37. 5 and 8.5\(\cdot\)10\({}^{-6}\) for \(v_{g}\) measured in _Km/l/l_; for the aerial link, travel time is computed just depending on link length and allowed speed \(v\), \(t=\) l/\(v\). The automated/autonomous features of flying vehicles may imply ground speed harmonization--then reduced speed variance--and better traffic conditions [51]. The previous travel time functions are coherent with the vehicle automation features, which in any case are expected to suit road features; 4. The hypothesis here is that flying cars are highly automated/autonomous; however, given that (_i_) it can be assumed that pilots can take control if needed, particularly on ground mode, (_ii_) human perception of speed and distance is not reliable at high speeds, mainly on flying mode, and (_iii_) short reaction times are required of a driver, to this aim, it is assumed that agents communicate also with a central monitoring UAM Agency and are autonomous in their choices to some extent. To minimize the risk of collision during a free-drive conditions, in addition, agents will communicate with the UAM Agency sending/receiving information about their position, those of other agents and obstacles as well as the state of the transportation network in terms of link travel cost at that time (see also below); all the \(A_{i}\) adopt the same take-off and landing procedures and, to avoid collisions with other agents, variations of speed and altitude are controlled by the Agency, which provides suitable information to the agents; 5. For the combined path, i.e., ground and aerial sections, \(A_{i}\) will start the trip only when it receives information by the Agency, in order to avoid congestion effects at the transition areas; 6. During the flying section, \(A_{i}\) will move only between a couple of transition areas by following the Euclidean route. To compare some case C scenarios, the ratio between the total \"flying ground\" travel time and the total ground travel time for \"only ground\" mode to move between an _O/D_ pair, over all the agents and _O/D_ pairs, has been considered: \[\mu=\frac{\sum_{i}T_{O/D,i}^{8+f}}{\sum_{i}T_{O/D,i}^{8}}\] (1) where: * \(\sum\limits_{i}T_{O/D,i}^{8+f}\) is the travel time to move between the _O/D_ pair in \"ground + flying\" mode on the path chosen by \(A_{i}\); * \(\sum\limits_{i}T_{O/D,i}^{8}\) is the travel time to move between the _O/D_ pair in \"only ground\" mode on the path chosen by \(A_{i}\).
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 3 Methodology
### Soil Resistivity Analysis
#### 3.2.2 Seasonal Variations in Soil Resistivity  
Soil resistivity varies with seasonal changes, particularly with moisture content. During wet seasons, soil resistivity decreases, which may improve the grounding system's performance. In contrast, dry conditions can significantly increase resistivity, leading to a decrease in the system's fault current dissipation capacity. These variations are taken into account during the design and testing phases.  
Seasonal resistivity \(\rho_{season}\) can be approximated using a temperature and moisture content-dependent formula:  
\[\rho_{season}=\rho_{dry}(1+k\cdot T+m\cdot M) \tag{10}\]  
where \(\rho_{dry}\) is the resistivity of dry soil, \(T\) is the temperature, \(M\) is the moisture content, and \(k\) and \(m\) are constants.
## 2 Distributed Cooperative Dispatch Framework for DN-DHN-BESS  
The structure of the DN-DHN-BESS system studied in this paper is shown in Figure 1. The DN includes distributed photovoltaic (PV), distributed wind turbine (WT), and load and is dispatched by the distribution network operator. The DHN includes CHP units, gas boilers (GB), electric heaters (EH), and loads and is dispatched by the district heat network operator. The charging and discharging of the BESS are dispatched by the energy storage aggregator. The difference in boundary power is to distinguish the boundary power information exchanged by different subjects; for example, \(P^{\text{D-E}}\) is used to represent the boundary power information exchanged by the distribution network, and \(P^{\text{E-D}}\) represents the boundary power information exchanged by the energy storage.  
In order to follow the autonomous operation characteristics and the information privacy of different operators, a distributed cooperative dispatch framework for DN-DHN-BESS is constructed in this paper, as shown in Figure 2. The coupling variable between the DN, DHN, and BESS is the boundary power, so under the premise of satisfying the boundary power consistency constraint in (1), the system can be decoupled into three subsystems, and thus the cooperative dispatch problem can be transformed into a distributed optimization problem. By independently optimizing the three subsystems and exchanging the information of boundary power for coordination, accurate results can be obtained.  
\[\left\{\begin{array}{ll}P^{\rm{D-E}}=P^{\rm{E-D}}\\ P^{\rm{D-H}}=P^{\rm{H-D}}\end{array}\right. \tag{1}\]  
Figure 1: Structure of the DN-DHN-BESS system.
## 2 System Model
### Channel Model  
Considering realistic satellite-to-ground propagation conditions, the path loss of the \(i\)-th user when served by the \(b\)-th beam on the \(f\)-th sub-band is composed of three main components: free-space attenuation, atmospheric losses, and Rician fading. The overall path loss is expressed as follows [27]:  
\[PL_{b,f}^{i}=\left(\frac{c}{4\pi df}\right)^{2}A^{-1}(d)\rho_{1} \tag{5}\]  
where \(\rho_{1}\) is the Rician fading factor, \(d\) denotes the distance between the \(b\)-th beam and the \(i\)-th user, and \(A(d)\) represents a atmospheric attenuation, which can be further expressed as follows: \(A(d)=10^{\frac{d(44\text{M}\rho_{1}+\rho_{2})}{10\text{M}_{\text{S}}}}\), where \(\rho_{2}\) and \(\rho_{3}\) are the cloud and rain attenuation coefficients, respectively. \(H_{S}\) is the orbital altitude of the satellite.
###### Abstract
One of the main contributions is that we provide here a different perspective to gather network parameters of CPPW structures by using the equivalent transmission line model with their scattering matrices. Graphical illustrations of EE and AFGSM methods of single and glide-symmetric double CPPW structures were compared for the analysis and design stages. Other contributions can be explained from the analysis and design results, demonstrating that the presented method can be efficiently utilized for observing the electromagnetic bandgap of CPPWs. The proposed design method that obtains the unit cell's S-parameters using an equivalent circuit model is more fruitful than those in the literature, which utilize full-wave electromagnetic solvers for gathering S-parameters [44; 45]. Another important contribution in this study is providing the ability to minimize the glide-symmetric DCPPW unit cell design for compactness via the AFGSM method. Additionally, the auxiliary functions provide a fast design advantage in determining the passband/stopband regions in the structures under examination. The next sections present important information about how to easily use single and glide-symmetric double CPPW periodic structures to find the electromagnetic bandgap and bandwidth using the AFGSM method. The sections also discuss the use of these structures to create a periodic filter using the proposed method as an example application.