esa-sceva/satcom-chunk-collection huggingface dataset:
# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation
#### 5.1.9 Segmentation
Although their scenario was to create a 3D model for 5G signal simulations in indoor environments, the segmentation method successfully generated meaningful floor plans. In a short summary, the point cloud was searched for openings and combined with the scanner trajectory for visible point cloud simulation and initial space subdivision. Additional processing, using an energy minimization function, clustered and extracted similar point clouds. Their representation as lines built a floor plan. Further processing clustered similar lines, grouped them, applied conditions, and finally generated a segmented model. In 2020, [PERSON] et al. [59] presented a segmentation method for moving well-known and tested 2D image processing techniques into three-dimensional space. Their approach processed a 3D point cloud using 3D morphological operations. Initially, the point cloud was voxelized, and its contour was extracted using a concave hull. It left for further processing only the empty voxels that were initially filling the scanned structure. Applied morphological erosion broke connections between consecutive rooms, and a 3D-connected component algorithm clustered the remaining voxels, removing noise. Finally, a morphological dilation with the same structuring element was applied, which formed a whole opening operation and restored the segmented model. A similar approach was presented in the newest article by [PERSON] et al. [66] from 2021. Aware of potential problems with memory usage, in the first step they stored the voxelized model in a structure called VDB (a variant of the B+ tree with dynamic topology). Next, Euclidean Distance Transformation (EDT) was applied, and based on its results the inner spheres were used to pack the space, resulting in the model being like a bubble-based one. Finally, the initial segmentation was achieved by analyzing the topological graph of created spheres, and the final result was obtained using the wavefront growth algorithm. [PERSON] et al. [65], in 2021, presented a novel strategy for dense 3D model reconstruction with a high control of memory consumption. Their method, which is an extension of previous research [208], minimized the used resources by constructing the model on the fly, including the spatial understanding of space segmentation, by merging sub-maps of the entire scanned environment instead of storing all captured data. As the initial processing was already prepared, the paper describes only the newer part related to the environment sub-map management. The first novelty was the trigger for a new sub-map generation. The second was the method of sub-map overlap estimation, and finally, a new confirmation method for rejecting uncertain merges of sub-maps. The reported results indicated a reduction in memory usage by 50% compared to their baseline results.
## 3 Simulation Analysis of the Theoretical Monitoring Model
### Simulation and Comparison of the Magnetic Field of a Rectangular Permanent Magnet
To address this issue, more precise magnetic field measurement equipment, such as a three-dimensional magnetometer, could be used to assess and correct the actual magnetic field distribu  
Figure 3: Distribution curve of magnetic induction density \(B_{\rm z}\) along the central axis of the rectangular permanent magnet at different lift-off heights. tion. Moreover, improving the manufacturing process of permanent magnets to minimize magnetization non-uniformity could help reduce this deviation to some extent. Similarly, the theoretical model generally assumes that the permanent magnet has achieved complete magnetization saturation. However, in practical applications, the magnet may not fully reach saturation, especially at lower magnetic field strengths, where the magnet might not be exposed to sufficient external magnetic field or current to achieve saturation. If the magnet does not reach full saturation, its magnetic field strength could be lower than the theoretical prediction, leading to a lower magnetic field strength measured by the Hall sensor and, consequently, affecting the accuracy of the measurements. As shown in Figure 4, the measured magnetic field strength is lower than the theoretical strength at different lift-off heights, which verifies this point. In practical applications, the magnet's saturation state can be improved by choosing materials with higher saturation magnetization or by boosting the magnet's magnetization with an external magnetic field, thereby improving the measurement accuracy. At the same time, neglecting the demagnetization factor during theoretical simulations is another important reason for model inaccuracies. The demagnetization factor is a parameter that describes the strength of the internal demagnetizing field (the field opposing the external magnetic field) generated due to the shape and size of the magnetic material when magnetized in an external magnetic field. This factor affects the effective magnetic field strength and stability of the magnet. Ignoring the demagnetization factor can lead to overestimation of theoretical values. In practical applications, the demagnetization factor is influenced not only by the geometry and orientation of the magnet but also by the material's magnetic permeability and operating temperature, making its precise calculation challenging. In future research, we will further investigate how to improve the measurement accuracy and reliability of the sensor by adjusting the geometry,  
Figure 4: Comparison of the theoretical and experimental distribution curves of the magnetic induction density \(B_{x}\) at different lift-off heights. magnetization direction, magnetic circuit design, manufacturing processes, and operating temperature of the magnet. Additionally, measurement error is influenced by several factors during the measurement process, such as measurement methods, system readings, and environmental conditions.
## 5 Discussion  
### Classification Method and Effect Evaluation  
Obtaining optical images in cloudy, foggy mountain areas has great uncertainty, limiting the ability of the optical data to identify features. SAR, which is a high-resolution imaging radar with a long wavelength that can penetrate a certain cloud layer, is not affected by the climate; in addition, it is based on active remote sensing, which can function almost all day and all weather and can also penetrate certain shelters, thereby obtaining more information than that obtained via optical remote sensing. The SVM method is used to classify optical and SAR feature fusion images. Combining the respective advantages of optics and SAR can effectively expand the useful information contained in the data, enhancing the ability to identify features. The features of both optics and SAR are combined to completely use the rich structure, texture information, and information characteristics of SAR in order to improve the spectral differences among different features and enhance the separability. The Landsat OLI data with a small amount of cloud were used to extract the first-class land class with the total accuracy of 82.74% and a Kappa coefficient of 0.791 (Figure 18). The Landsat OLI data can be obtained free of charge, and the data-processing and classification operations are relatively simple. In the periods during which optical remote-sensing images with little cloud and fog can be obtained and the classification accuracy is not very high, the research area can use Landsat OLI with less than 10% cloud data categorized.  
Optical remote sensing sensors have a large revisit cycle and weather, and their ability to acquire remote sensing data is not sufficiently stable. According to the statistics of the Landsat OLI archived data in the research area for the past three years, more than 90% of the images were with cloud cover in one year. In the case in which optical remote sensing images are not available, the fully polarized ALOS-2 data show better advantages, and SAR images can be used instead of optical image classification.  
Figure 18: Improvement of the accuracy under different datasets.
# Comparative Analysis of Classifiers for Classification of Emergency Braking of Road Motor Vehicles
## 4 Methodology
### Building Classifiers  
Before proceeding with the training of classifiers, we assigned the time series data obtained from 10 sample drivers to the training set and the data of 4 other drivers to the testing set. This approach of separating the data allows good generalization for the classifier. Eventually, the number of samples in the training and testing sets are 1044 and 260 respectively: for the training set there are 447 samples of class \"1\" and 597 of class \"0\", while in the testing set, these numbers are 82 and 178, respectively.  
In order to build an eventual threshold classifier, we gradually \"move\" the threshold for both mR and aR until the classifier ceases to misclassify the samples from class \"1\" in the training set. In this way, the threshold for mR was found to be 894, and that for aR was found to be 411, as shown in Figure 10.  
Figure 7: Breakdown of the time lag between lifting the accelerator and pressing the brake pedal completely, obtained from 383 events of emergency braking.  
Despite its simplicity, the threshold classifier is able, indeed, to detect some of the emergency braking cases from the testing set (see Results). However, we presume that a combination of features  
Figure 8: Relationship between the average rate of lifting the accelerator and the position of the pressed brake pedal.  
Figure 10: Building process of the threshold classifier for aR feature.  
Figure 9: Relationship between the maximum rate of lifting the accelerator and maximum rate of pressing the brake pedal.  
(rather than single ones) pertinent to the dynamics of the accelerator pedal would be needed for successful classification of emergency braking. Therefore, we investigate the feasibility of solving the EBC problem by applying more intelligent approaches based on machine learning.  
In order to employ SVM and \(k\)-NN for the emergency brake classification, we use a powerful machine learning library for the Python programming language: scikit-learn [29]. Besides providing an enormous amount of various machine learning methods with its friendly APIs [30], scikit-learn allows us to visualize the dataset in a very representative way. To employ gradient-boosted trees in our work, we used an open source package of XGBoost [31] which supports multiple programming languages including C++, Python, R, Java, Scala, and Julia.  
It is important to note that, for using \(k\)-NN and SVM, we applied a standardization procedure to the dataset since this is a common requirement for these methods. This procedure standardizes features by removing the mean and scaling to unit variance. However, this procedure was not applied to the XGBoost classifier since the base learners of XGBoost are trees, and any monotonic function of any feature variable will have no effect on how the trees are formed. All three classifiers were trained with respect to all 3 features: mP, mR, and aR.
###### Abstract  
Spatial and temporal precipitation data acquisition is highly important for hydro-meteorological applications. Gridded precipitation products (GPPs) offer an opportunity to estimate precipitation at different time and resolution. Though, the products have numerous discrepancies that need to be evaluated against in-situ records. The present study is the first of its kind to highlight the performance evaluation of gauge based (GB) and satellite based (SB) GPPs at annual, winter, and summer monsoon scale by using multiple statistical approach during the period of 1979-2017 and 2003-2017, respectively. The result revealed that the temporal magnitude of all the GPPs was different and deviate up to 100-200 mm with overall spatial pattern of underestimation (GB product) and overestimation (SB product) from north to south gradient. The degree of accuracy of GB products with observed precipitation decreases with the increase in the magnitude of precipitation and vice versa for SB precipitation products. Furthermore, the observed precipitation revealed the positive trend with multiple turning points during the period 1979-2005. However, the gentle increase with no obvious break point has been detected during the period of 2005-2017. The large inter-annual variability and trends slope of the reference data series were well captured by Global Precipitation Climatology Centre (GPCC) and Tropical Rainfall Measuring Mission (TRMM) products and outperformed the relative GPPs in terms of higher R\({}^{2}\) values of \(\geq\) 0.90 and lower values of estimated RME \(\leq\) 25% at annual and summer monsoon season. However, Climate Research Unit (CRU) performed better during winter estimates as compared with in-situ records. In view of significant error and discrepancies, regional correction factors for each GPPs were introduced that can be useful for future concerned projects over the study region. The study highlights the importance of evaluation by the careful selection of potential GPPs for the future hydro-climate studies over the similar regions like Punjab Province.  
grafted precipitation products; abrupt changes; trends; statistical indicators; agriculture; Pakistan 202
# Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies
## 3 Evolving Computing Paradigms and Related Concepts  
### Cloud Computing  
Cloud computing pertains to extending applications via the internet as services, as well as the software and technology that underpins the data centers furnishing these services [1]. NIST formalizes cloud computing [32] as \"a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) which can be rapidly provisioned and released with minimal management effort or service provider interaction\". The essential characteristics of the cloud model include on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. A cloud infrastructure encompasses software and hardware, extending vital features of the cloud model.  
The cloud solutions are procurable through the following service models [33]:  
* Software as a service (SaaS)--the cloud provider presents consumers with applications accessible via the program interface or web browser, and the consumer has limited control over user-specific applications.
* Platform as a service (PaaS)--On the cloud infrastructure, the consumers are allowed to build and distribute applications. They can exercise control on applications deployed but not on the cloud infrastructure.
* Infrastructure as a service (IaaS)--The customer is furnished with essential computing resources vital to processing, storing, and networking. The user exercises control upon storage, applications, and operating systems, but not cloud infrastructure. The cloud service models and deployment models are depicted in Figure 4.  
The cloud solutions are deployable as [33]:  
* Private cloud--a particular organization that can also be a third party exclusively owns and controls the private cloud, which can be available on or off-premises.
* Community cloud--the specific community comprises of organizations that share common concerns may provide cloud infrastructure for exclusive usage available on or off-premises, managed by organizations within the community or by a third party.  
Figure 4: Common cloud service models and their classifications.  
* Public cloud--the public cloud available on the cloud provider's premises can be owned or managed by any enterprise and is open to the use of the general public.
* Hybrid cloud--it may be composed of two or more cloud models (private, community, or public); although they are distinct entities, they are bound by technology permitting the portability of application as well as data.
## 3 Results and Discussions
### Annual Variations in the Indoor Air Temperature and Relative Humidity
The NW room, NE room, and SE room were designated Class C and D and could easily achieve Class B. The basement could not protect collections from mold, but could achieve Class D.  
To avoid physical problems in museum buildings, it is recommended to meet the ASHRAE Class B standard, that requires an indoor temperature between 15 \({}^{\circ}\)C and 25 \({}^{\circ}\)C, an indoor air relative humidity between 40% and 60%, with maximum hourly and daily temperature fluctuations no more than 5 \({}^{\circ}\)C, and maximum hourly and daily relative humidity fluctuations not exceeding 10%. Table 10 shows that, due to the large seasonal fluctuation of relative humidity and temperature, the Class AA criteria had a low value. Class B criteria, in which seasonal and short fluctuations may vary more (Table 3), a similarity of approximately 80.1 and 98.6% of the time was observed. For classes C and D, with the highest values, RH remained below 75% for NV, NE, SE rooms. The risk for objects displayed in these rooms is best described by classes C and D, as the criteria for class C and D have been met 100% of the time. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Statistical Data**} & \multicolumn{4}{c}{**Temperature**} & \multicolumn{4}{c}{**Relative Humidity**} \\ \cline{2-10}  & **Mean** & **Drop** & **Rise** & **Tmin** & **Tmax** & **Mean** & **Drop** & **Rise** & **RHmin** & **RHmax** \\ \hline NW room & 20.0 & 3.3 & 5.1 & 12.5 & 27.0 & 51.1 & 11.0 & 7.5 & 32.4 & 69.3 \\ NE room & 19.7 & 3.6 & 5.3 & 11.7 & 27.1 & 52.1 & 12.0 & 7.2 & 34.0 & 71.0 \\ SE room & 19.6 & 3.4 & 5.6 & 11.4 & 27.4 & 52.2 & 12.0 & 6.8 & 34.4 & 72.7 \\ Basement & 17.3 & 3.8 & 4.8 & 12.7 & 24.0 & 59.5 & 13.0 & 15.0 & 24.4 & 90.4 \\ \hline
**ASHRAE Classification** & **AA (\%)** & **As (\%)** & **A (\%)** & **B (\%)** & **C (\%)** & **D (\%)** \\ \hline NW room & 37.4 & 65.7 & 61.1 & 98.6 & 100.0 & 100.0 \\ NE room & 35.1 & 60.7 & 56.2 & 97.7 & 100.0 & 100.0 \\ SE room & 35.5 & 58.7 & 56.3 & 97.0 & 100.0 & 100.0 \\ Basement & 27.2 & 49.9 & 51.4 & 80.1 & 89.3 & 89.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: ASHRAE climate classification values specific to the Poni-Cernátescu Museum, temperature (T \({}^{\circ}\)C), relative humidity (RH%), and gradient (K) fluctuations. The similarity between ASHRAE climate classification and the indoor climate of the Poni-Cernátescu Museum. Figure 11: Daily mean humidity for the entire period. in temperature and relative humidity. These charts can be used to understand indoor climate performance by following the blue lines. As a total distribution, the indoor climate conditions for Class B in the NE room, SE room, and NW room were 51% (performance), 53%, and 56%, respectively, while in the basement, the climate condition value was very low, at 24%.
# Heatwave Damage Prediction Using Random Forest Model in Korea
## 2 Methodology
### Variable Selection  
Relevant variables were selected to predict heat-related damage. Heat-related diseases mainly occur in the form of cardiovascular and respiratory diseases and heatstroke [17]; consequently, various epidemiological studies of their occurrence have been conducted worldwide [17; 18; 19]. Among the most important characteristics of the damage caused by heatwaves and the corresponding vulnerabilities are the damage patterns of disasters, which cannot be obtained from temperature variables alone [20; 21]. Studies have shown that the damage caused by disasters is related to geographic features [22; 23], surface relative humidity [24; 25; 26], wind speed [27; 28], population density [29], economic status [30], and vulnerable occupational groups (laborers, construction, and agricultural workers) [31; 32; 33; 34]. On the basis of important characteristics determined in previous studies, we selected the following variables: temperature, humidity, wind speed, number of vulnerable occupational groups, insurance premiums per person, personal income per person, floating population, and registered population of residents (the number of people counted by the administration). The vulnerable population can be inferred from data on insurance premiums, income, and vulnerable occupational groups; further, as the values of these indicators increased, the number of patients with thermal diseases increased. However, both the aggregate and floating populations were used as population variables, and it was expected that the floating population, which reflects real-time information, would be a more useful variable for predictions than the aggregate population.
# Routing Topologies of Wireless Sensor Networks for Health Monitoring of a Cultural Heritage Site
## 2 WSN
### Routing Algorithms
#### 2.2.2 Collection Tree Protocol (CTP)  
The TinyOS-2.x operating system provides a well-tested, tree-based routing protocol called the collection tree protocol (CTP). A number of nodes in a network advertise themselves as tree roots. The remaining nodes form a set of routing trees to these roots. CTP is address-free in that a node does not send a packet to a particular root; instead, it implicitly chooses a root by choosing the next hop. Nodes generate routes to roots using a routing gradient [30]. CTP uses expected transmissions (ETX) as its routing gradient, with roots having an ETX of 0. The ETX of a node is the ETX of its parent plus the ETX of its link to its parent. This additive measure assumes that nodes use link-level retransmissions. Given a choice of valid routes, CTP should choose the one with the lowest ETX value.  
The main problems that emerge in a CTP network are routing loops and packet duplication. Routing loops generally occur when a node chooses a new route that has a significantly higher ETX than the older one, perhaps in response to losing connectivity with a candidate parent. If the new route includes a node that was a descendant, then a loop occurs. Packet duplication is an additional problem that can occur in CTP. This occurs when a node receives a data frame successfully and transmits an acknowledgment (ACK), but the ACK is not received. The sender retransmits the packet, and the receiver receives it a second time. This can have disastrous effects over multiple hops, as the duplication is exponential. For example, if each hop, on average, produces one duplicate, then on the first hop there will be two packets, on the second hop there will be four, on the third hop there will be eight, and so on.