esa-sceva/satcom-chunk-collection huggingface dataset:
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 4 Technical Discussion
### 4.1. Note Specifications and Applications
#### 4.1.6 Power Supply  
Most of the sensor nodes utilize lithium ion batteries and require battery voltages of up to around 3.7 V. A few sensor nodes from recent research articles such as those proposed by [25; 32] and that by [24] utilized energy harvesting as an additional source of energy. Only the sensor nodes by [25; 32] proposed the use of a supercapacitor for the storage of harvested energy. None of the popular commercial nodes reviewed in this paper considered the use of a supercapacitor for energy storage. Waspmote Plug & Sense! and Wisense are the only commercial motes reviewed in this paper offering an option for solar energy harvesting. It is also observed that, in all the articles reviewed in this paper, the authors did not introduce the use of a hybrid setup of energy harvesters for supplying additional energy to prolong the lifetime of the energy-limited wireless sensor nodes. However, it should also be noted that the size of the power supply system affects the overall physical size, cost and weight of the sensor node. Large-sized and heavy sensor nodes are not suitable for several application scenarios that require mobility and wearables such as wildlife monitoring and tracking and other health-monitoring applications.
# From Brown-Field to Future Industrial Networks, a Case Study
## 2 Network Analysis and Modeling
### Characterization and Modeling
#### 2.2.1 Characterization with Matrix and Dynamics Mixture  
The process follows a down-top approach, where communication patterns are studied with respect to network components, i.e., source and destination. The initial efforts are to either classify or model the characteristics of the traffic flows in order to create flow profiles. The dominant communication patterns of flows are modeled using various probabilistic and mathematical models, and the anomalous activities are identified where deviation happens from the tuned parameters of the initial learned model. To learn the normal communication in the network, a packet-level investigation is applied. All packets are considered, and values of various fields in the header and payload are extracted. These values are then used to profile individual communication flow based on either source or destination [7], or protocols [8; 9; 10; 14]. The created profiles are used to learn and classify each flow and further model the network traffic communication pattern to find abnormal behaviors.
## 4 Discussion
### Memory Optimization  
Running large datasets in parallel may result in the system running out of memory. The length of gamma was 201, the length of cost was 12, and the length of class.weight was 99. The total number of iterations for our hyperparameter tuning with 10 times repeated fivefold cross-validation was n(cost) \(\times\) n(gamma) \(\times\) n(class.weight) \(\times\) 5 \(\times\) 10, which was equal to 12 \(\times\) 201 \(\times\) 99 \(\times\) 5 \(\times\) 10 = 11,939,400. The size of the matrix for _foreach_ to return was 11,939,400 \(\times\) 10, which added up to 911 Mb RAM calculated by the object_size() function in the pryr package (v0.1.5). When monitoring our parallelization using the top command, the memory usage was too close to the Lonestaro's memory ceiling, which is 256 G, especially when forking 128 cores. We applied the _bigmemory_ package [25] to save memory. The _bigmemory_ package is used to implement massive matrices and support their manipulation and exploration [25]. The data structures can be allocated to shared memory, allowing separate cores on the same node to share access to a single copy of the matrix [25]. In order to avoid R crashes, we used the _describe_ and _attach.big.matrix_ functions to access the shared memory. Further memory optimization and management was undertaken for the _foreach_ expression. Retrieving values for _foreach_ to combine with the _rhind_ in a loop is known to be rather slow. The poor performance is caused by the need to repeatedly re-allocate memory for the growing data frame. Therefore we optimized the _foreach_ expression by avoiding having _rhind_ in a loop and returning a NULL value, which led to a greater performance gain (Figure 2).
## 5 Experimental Evaluation
### Experimental Setup
#### 5.1.2 Types of Tasks  
Many real-world applications can be represented by DAGs having different topologies and different numbers of subtasks. When the features of topologies and the number of subtasks are comparable, they might represent similar applications, and the offloading techniques have many similarities. In this chapter, a DAG task generator is implemented in accordance with the literature [25] to generate different DAG datasets in order to simulate a variety of application tasks.  
Four primary parameters control the DAG's topology and characteristics: the number of subtasks N, the DAG width, the DAG density, and the task computation communication share. DAG width indicates the number of concurrent subtasks; when the number of subtasks is the same, the greater the width, the greater the number of subtasks that can be executed concurrently. DAG density shows how much subtasks depend on each other. Higher values mean that there are more backward and forward links between subtasks. The ratio of computational communication is used to control the task's characteristics. The delay of task offloading is mainly composed of network communication and computational consumption time, where the larger the computational communication ratio, the larger the proportion of computational consumption time.  
For the MEC environment, the upload and download speeds were set to \(R_{u}\) and \(R_{d}\) = 8 Mbps respectively, which would have some loss as the distance between the device and the signal source increased; the edge server \(f_{vm}\) was set to 10 GHz; the clock speed of the mobile device was set to 1.5 GHz; the upload power and download power were \(p^{upload}\ =\ 0.5\) W and \(p^{download}\ =\ 0.6\) W; the charging power of WPT service was \(P^{t}\ =\ 3\) W; the battery capacity of the mobile device was 10 Wh.
# An Innovative Internal Calibration Strategy and Implementation for LT-1 Bistatic Spaceborne SAR
## 5 Experiment
### Processing Results  
Both the primary satellite and the auxiliary satellite receive signals transmitted by the horn antenna, thus simulating echo reception process of the BiSAR system. The received echoes can be used for analyzing the synchronization accuracy. Pulse compression is performed on the echoes, and the resulting synchronization performance is shown in Figure 15.  
In the experiment, the phase history of the internal calibration pulses during radar operation is shown in Figure 15a. The residual phase error before system internal calibration compensation is depicted in Figure 15b, with a standard deviation (STD) of 0.28 degrees for the residual phase. By performing system internal calibration compensation, the instrument drift can be accurately calibrated. The residual phase error after internal calibration compensation is presented in Figure 15c, with an STD of 0.16 degrees for the residual phase.  
To investigate the impact of ambient temperature variations on the synchronization accuracy of the BiSAR system, we conducted further on-ground temperature experiments. Using the same radar operation command package, the BiSAR phase synchronization performance was verified within the operating temperature range of \(-10\)\({}^{\circ}\)C to \(+45\)\({}^{\circ}\)C. The residual phase (STD)--temperature curve after data processing is shown in Figure 16. The variation range of synchronization accuracy is 0.11 degrees to 0.16 degrees, with relatively small fluctuations, indicating stable bistatic synchronization performance. And, the effectiveness of temperature compensation for system internal calibration is verified.  
Figure 14: Processing flowchart.
# Semantic Segmentation Method for Road Intersection Point Clouds Based on Lightweight LiDAR
## 3 Results
### Experimental Data and Environment  
#### 3.2.1 Dataset Annotation  
When manually labeling the datasets with correct semantic labels, the point cloud data were classified as road surface, high vegetation, low vegetation, sidewalk, guardrail, or pole. Then, the point cloud data for each category were stored separately, with feature dimensions {X, Y,Z,Intensity,R,G,B,Label} including spatial coordinates (X, Y, Z), intensity, color information (R, G, B), and category labels (Label) corresponding to 0-7. Dataset 1 contained category labels and was divided into Train Set 1 and Test Set 1. Similarly, Dataset 2 was divided into Train Set 2 and Test Set 2.  
The following describes the methodology for assigning the ground truth labels to the two datasets using CloudCompare software (v2.12.4) (Figure 9):  
* Step 1: Import the point cloud file into the software.
* Step 2: Create a scalar field associated with the point cloud and use the Edit tool to define unique class labels.
* Step 3: Manually select the points to be annotated and assign scalar values corresponding to the relevant class labels.
* Step 4: Save the annotated point clouds together with their scalar field.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experimental Data** & **Maximum RMS** & **Minimum RMS** & **Average RMS Value** \\ \hline Dataset 1 & 0.02907 & 0.00462 & 0.01364 \\ Dataset 2 & 0.02746 & 0.00306 & 0.00971 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Root mean square (RMS) of alignment errors for the intersection point cloud.  
Figure 8: Full point cloud renderings of the road intersections. (a) Set 1. (b) Set 2.
# Urbanization and Grain Production Pattern of China: Dynamic Effect and Mediating Mechanism
## 4 Results
### The Dynamic Effect of Urbanization on the GPP
The presence of path dependency implied that changes in grain production in the current period were positively affected by grain output in the previous period, and the inertia effect was evident unless in the case of a particularly large natural disaster. The presence of spatial spillover effects implied that the effects of urbanization on grain production in one region could exert a demonstration effect and transmission effect to neighboring regions, mainly due to the similar resource endowments and farming traditions among geographically adjacent regions, which strengthened the mutual influence and spatial linkage between regions. The model had more significant statistical characteristics with other socioeconomic and natural variables under control (Model 4). The estimated coefficient of urbanization was significantly negative at the 1% level, and the increase in urbanization rate would decrease the GPCI. Each unit increase in urbanization resulted in a 0.30% decrease in the regional GPCI--i.e., urbanization negatively affected the GPP. Thus, H1 is valid.
## 2 Exosome Features
### Mechanisms of Exosome-Target Tissue Interaction and Exosome Transport
#### 2.3.5 Tunneling Nanotubes  
Recent studies suggest that exosomes may also transfer their cargo through tunneling nanotubes, which are long, filamentous structures connecting cells. This mechanism facilitates targeted delivery of exosomal content to distant cells [11; 39]. Understanding these mechanisms is crucial for designing therapeutic strategies. Exosomes hold promise as delivery vehicles for drugs, nucleic acids, and other biomolecules in various diseases, including cancer, metabolic disorders, and neurodegenerative conditions.
## 2 Spoilage Characteristics and Shelf Life of Fresh Fruit and Vegetables
### Transpiration  
Transpiration describes the physiological process in which water vapor evaporates from the product's surface to the surrounding air. It continues after harvest and causes progressing water loss in the product, resulting in turgor loss, wilting, shriveling and shrinking [24]. This leads to a deterioration of quality parameters such as taste, turgidity or texture, until the product is unacceptable to the consumer [5; 9; 25]. The degree of water loss that is still accepted is product-specific [5; 16]. Next to visible quality parameters, the nutritional value can also decrease from water loss [16]. Quality decline and spoilage due to moisture loss concerns primarily leafy vegetables, vegetables with leaves on top, citrus fruit and products with a high surface area to-fresh weight ratio, such as bell pepppers [16; 24]. The ambient RH and temperature play a crucial role in reducing postharvest transpiration [5; 9]. High RH (95-100%) for optimum shelf life and quality retention during storage and distribution is recommended for many products [21]. Only few products, such as onions and garlic, require a lower RH [18].
# A Convenient Way to Determine the Optimum Angle of Incidence of Fizeau Interferometer
## 5 Conclusions  
In this paper, the modulation effect of the parallel plate on spatial interference intensity distribution in the Fizeau interferometer system is analyzed theoretically. It is found that if the focal plane array is behind the parallel plate, the optimum incidence angle is the same as that when the parallel plate does not exist. Moreover, based on previous studies, this paper proposes an improved calculation method of the optimal incidence angle and obtains a more accurate estimation formula of the optimal incidence angle. Therefore, we no longer need to use the cumbersome graphing method to determine the optimum incidence angle, which makes it fast and straightforward to decide on the optimum incidence angle.  
Conceptualization, B.D. and Y.Z.; writing--review and editing, B.D.; funding acquisition, Y.Z., C.L. and H.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Research and Development Program of China, grant number 2018 YFB0504603, the National Key Research and Development Program of China, grant number 2016 YFB0500300 and the Natural Science Foundation of Jilin Province, China, grant number 20200201203 JC. The authors declare no conflict of interest.
# A Survey on Data Storage and Information Discovery in the WSANs-Based Edge Computing Systems
## 3 Analysis of the Data-Storage and Information-Discovery Schemes Proposed for WSANs
### Schemes Based on the Event-Driven Model
proposed a dynamic-interest-based lightweight routing protocol named LRP-QS (Lightweight Routing Protocol with QoS Support) [47]. In LRP-QS, the sensor nodes can evaluate the importance rankings of the event reports corresponding to the interests, which are actually the types of the event data and disseminated from the Sink to the actuators and the sensor nodes, locally according to the variable quality of the event data of each interest. Specifically, the interest with higher value fluctuation has higher importance ranking than those with lower value fluctuation for a given time period, and the ones with higher importance rankings will be allocated more resources to ensure their delivery quality. The simulation results in [47] show that LRP-QS can achieve a higher packet delivery ratio and a lower memory consumption than the existing state-of-the-art protocols. To a certain degree, Ref. [47] shows that differentiating data according to their importance ranking can directly affect the QoS performance of the routing protocols, and consequently affect the performances of data storage and information discovery of WSANs in terms of time and energy efficiency. In 2016, to enable sensing data to reach actuators reliably and efficiently, [PERSON] et al. proposed a Kautz-based REal-time, Fault-tolerant and EneRgy-efficient WSAN system(REFER) [48]. REFER divides a WSAN field into cells, and embeds Kautz graphs into the physical topology of a WSAN in each cell. Then, it connects the Kautz graphs in every cell using Distributed Hash Table (DHT) for high scalability. The architecture of the REFER system is shown in Figure 6. In this system, communications  
Figure 5: MLS: mobility strategy sharing location service [55]. can be classified into two types: intra-cell communications and inter-cell communications. A sensor node that detects an event firstly transmits the event report to one of the actuators in its own cell using inter-cell communications, and then the event report is transmitted by the actuators using intra-cell communications. After studying the routing paths in the Kautz graph-theoretically, an efficient fault-tolerant routing protocol, an example of which is shown in Figure 6, was also proposed in [48] based on the Kautz graph. The simulation results in [48] show that REFER can outperform many other existing WSA systems in terms of energy efficiency, fault-tolerance, real-time communication, and scalability. However, the cell division and the maintaining of the Kautz-graph-based topology in each cell all depend on the locations of the actuators. If the actuators move randomly and frequently, it would cost much energy to maintain the Kautz-graph-based topology. Thus, REFER is much more suitable for the scenarios where actuators are static.
# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
## 2 Problem Formulation
### User Authentication in Frequency Hopping Spread Spectrum Networks  
In an FHSS network, the core process for user authentication can be performed in two steps: (1) determining whether or not the appropriate hopping frequency is measured, and (2) determining whether or not the header information of the MAC frame is correct.  
Because we assume that the attacker can reproduce the predefined hopping pattern \(f^{k}=\left[f_{1}^{k},f_{2}^{k},...,f_{H}^{k}\right]\), the imitated FH signal will display the same hopping frequency pattern. The imitated FH signal will be demodulated and passed through the MAC layer, that is, Step 1 is disabled. The process of inspecting the address field in the MAC header remains. However, because this address information has been digitized, the attacker can possess and imitate this address field. If an attacker sends an address field similar to an authenticated emitter, there is no way to detect and prevent it. Therefore, the emitter identification process based only on header information of the MAC frame is not sufficient to reject the imitated FH signal.
# Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Celephu, Bhutan
## 3 Results and Discussions
### Discussions
#### 3.7.2 NDVI Change Dynamics  
Analysis of NDVI change detection data reveals complicated shifts in vegetation cover, enabling a more nuanced understanding of observed land cover changes. The notable 9.56% increase in the category of \"No vegetation\" underscores areas facing degradation, while the expansive 41.24% growth in \"Low vegetation\" signals potential ecological disturbances.  
The staggering 75.11% decrease in \"Very healthy vegetation\" emphasizes the severity of vegetation ecology. These NDVI results also complement the land cover observations and provide additional insights into the varying degrees of vegetation health and environmental dynamics within the study area. The NDVI indices also represent one of the approaches to monitoring urban built-up areas, which are generally used to optimize land use patterns [52].  
The observed dynamics of NDVI change from 2016 to 2023 have implications for the impacts of climate change in the study area. These NDVI change dynamics are consistent with broader discussions about the influence of climate change on vegetation patterns. The findings serve as a call for continuous monitoring and adaptation strategies to manage the evolving environmental landscape and mitigate the potential consequences of climate change impacts in the region. This particularly highlights the need to further investigate the impacts on land surface temperature, which is directly related to NDVI change dynamics [53; 54], through the utilization of other suitable satellite products. In summary, the NDVI change statistics essentially provide useful information and quantification on vegetation health and density. The statistics are valuable for monitoring changes in plant growth, stress, and environmental conditions, and provide insights into the health of ecosystems, potentially helping to conserve biodiversity [55] in the context of the impacts of climate change.
# Managing Emergency Situations in VANET Through Heterogeneous Technologies Cooperation
## 3 Architecture and Communication Protocol
### Protocol Communications
#### 3.2.5 Neighbor Vehicles for Short Range Advertisements  
In order to avoid an uncontrolled flooding along the network it is important to create a neighbors list for each vehicle to inform them in case of recognized dangers along their journey. Thus, we can define a \(l\)-th vehicular node equipped with an E-OBU by calling it \(n_{l}\). In the same way, the global set of vehicles that are traveling across the ITS managed area is called \(N\). In this way we can define \(n_{l}\in N\). Each lane is identified in the system as an edge of the ITS graph and it is marked as \(e_{i,j}\) where \(i\) and \(j\) are the vertexes of the edge ant it belongs to the set of roads called \(E\), therefore \(e_{i,j}\in E\). A vehicle node \(n_{m}\) is a neighbor of \(n_{l}\) if their distance falls below a given threshold called \(T_{cov}\). In VANETs, the average coverage radius of a generic vehicle belongs to the interval \([200,400]\) m, and it depends of several parameters, as referred in [43; 44]. In this paper, we are not analyzing the effects of the coverage range of IEEE802.11p and DSRC, so we simply consider that \(n_{l}\) and \(n_{m}\) are neighbors if their Euclidean distance is less than \(T_{cov}\). We are also considering a 2D environment (assuming that the considered geographical region belongs to a plane distribution). Each node manages its own neighbor list by receiving the periodical POSUP message sent by each vehicle once the position and current speed are achieved by GPS.