esa-sceva/satcom-chunk-collection huggingface dataset:
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 5 Performance Results and Evaluation
### Trade-Offs and Inefficiencies
#### 5.5.2 Challenges with YOLOv5 in Resource-Constrained Environments  
YOLOv5's strength lies in its speed and accuracy; however, deploying it in resource-constrained environments, such as industrial facilities, presents specific challenges:  
* Memory Usage: YOLOv5 requires significant memory resources for model execution. In environments where edge devices like ESP32-CAM are used, the system must offload processing to devices like Raspberry Pi, which adds latency.
* False Positives and Negatives: In cluttered industrial settings, where visual noise, such as smoke, glare, or overlapping objects, is common, the model may generate false positives or miss detection. This can compromise the system's reliability, especially in critical scenarios [23].
* Model Optimization: While YOLOv5 is lightweight compared to other deep learning models, further optimization (e.g., pruning or quantization) may be required to enhance performance on edge devices, potentially affecting accuracy.
# Optimization of the 3D Point Cloud Registration Algorithm Based on FPFH Features
###### Abstract
Different binary feature descriptors are generated by binarizing the unit length and standard deviation number in the model, and the optimal solution is determined by experiments. Compared with the traditional SHOT descriptors, this method greatly reduces the matching time of feature points, shortens the complexity of calculation, improves comprehensive performance, and has a wider application. [PERSON] et al. [17] proposed a 4-point congruent sets (4 PCS) algorithm based on key points, which enhanced the robustness of the algorithm. [PERSON] et al. [18] put forward a benchmark that can be strictly compared among different methods, which makes the point cloud registration algorithm more accurate. At present, most point cloud registration algorithms in use first perform rough registration to obtain a good initial position and then perform accurate registration. Coarse registration can vaguely register point clouds of unknown attitudes, which provides an initial position of point clouds, and it is convenient to narrow the difference of spatial positions of point clouds. Therefore, this paper improves the rough registration algorithm, which can quickly complete the rough registration process using point cloud feature point extraction, feature matching, and point cloud filtering. The improved rough registration process and ICP algorithm based on curvature and FPFH features can achieve better-matching results.
## 4 Problem Formulation and Brief System Architecture
### Operating Layer
#### 4.2.1 Hadoop MapReduce Framework  
In the data-collection phase, HPC collects healthcare data in a continuous time domain. Hadoop provides the \"Hadoop Distributed File System\" (HDFS) to store those data in a structured database.  
The MapReduce algorithm has two primary class--the mapper class and the reducer class--which enables making a structured distributed database from raw unstructured and semi-structured data. The data record is kept as input in the mapper class with an assigned key to the corresponding value of the data record. Then, the Map function shuffles those records, sorts them, and sends them to the reducer class. The reducer class merges the same keyed data as per the algorithm instruction and provides the structured database, which is stored in HDFS.  
To apply the MapReduce framework, let us consider that HPC provides \(\theta\) number of active servers shown in a set \(S=\{S_{1},S_{2}......S_{\theta}\}\). The server set performs parallel computation in the operating layer for the MapReduce framework. From the (4), we need to analyze \(\beta\) amount of data. Each server receives the input dataset and makes multiple partitions for parallel distribution and computation. Suppose the input dataset is divided into k number of partitions or chunks. Thus, each partition size will be C, which is represented in Equation (5).  
\[C=\left\lfloor\frac{\beta}{k}\right\rfloor \tag{5}\]  
Therefore, C amount of data quantity is distributed in each partition block of each server for analysis and sent to the set of Mapper class M where \(M=\{M_{1},M_{2}.......M_{m}\}\) and m are the number of map functions. The map() then tokenizes all the input records with the key and value, and sends on to reducer class. Let r be the number of the reducer function in a set of reducer class R where \(R=\{R_{1},R_{2}......R_{r}\}\) and \(R\leq M\). reducer() receives the mapped data and shuffles and sorts them and provides a structured database that sends to the data warehouse with all metadata and log files.
## 3 Results on ML/DL-Based Channel Estimation
### Key Characteristics of Channel Estimation Techniques  
Tables 6 and 7 elaborate key characteristics for SC-UWA and MC-UWA systems. The discussed parameters include bandwidth, transmission-reception (Tx-Rx) distance, modulation schemes, system types, subcarriers, and CP. The tables reveal clear differences in system design and operation between SC-UWA and MC-UWA approaches. SC-UWA systems operate with bandwidths ranging from 4 kHz to 25 kHz, with transmission distances between 0.2 km and 3 km. The primary modulation schemes used are BPSK and QPSK, with some studies employing frequency hopping spread spectrum (FH-SS). In contrast, MC-UWA systems utilize higher bandwidths, ranging from 4 kHz to 1000 kHz, with transmission distances between 0.5 km and 5 km. OFDM is the dominant system, with some studies incorporating OFDMA (Orthogonal Frequency-Division Multiple Access), MIMO-OFDM (Multiple-Input Multiple-Output-Orthogonal Frequency-Division Multiplexing), AFDM, and OTFS (Orthogonal Time-Frequency Space). Modulation schemes include BPSK, QPSK, 8-QAM, 16-QAM, and 8 PSK, with cyclic prefixes varying from 0 to 256. The number of subcarriers ranges from 32 to 1024, reflecting the diversity in system configurations. To summarize, SC-UWA systems offer simpler, low-bandwidth designs for short-range links, while MC-UWA systems adopt complex, high-bandwidth configurations to support robust and scalable underwater communication.
## 3 Proposed System Model
### Energy-Efficient Communication Flow  
The DAWPC-MAC protocol communication process starts as the terrestrial-based station sends the signal to the buoy through satellite communication. This underwater communication network (UWCN) utilizes a satellite link to establish long-range connectivity. The base station sends necessary commands, data requests, or synchronization signals to the buoy. Subsequently, the signal is relayed vertically downwards to the sink node positioned directly beneath the buoy. The sink node functions as the central communication hub, receiving data from the buoy and collecting information from relay nodes dispersed throughout the network. Data is then gathered from regular sensor nodes distributed across the underwater environment to measure various environmental parameters like temperature, pressure, and salinity. After the network is synchronized with a time-synchronized signal from the base station to the buoy, sink, and relay nodes, the data collection process is initiated. The communication follows a layered structure: from the buoy to relay nodes and then to ordinary sensor nodes. This hierarchical setup ensures efficient data flow and coordination within the underwater network.  
Energy efficiency is enhanced by the MAC protocol through the control of nodes' wake-up and sleep cycles. A receiver-initiated wake-up mechanism is used, in which a low-power wake-up signal (WS) is broadcasted by the courier node as the receiver node, notifying neighboring transmitter nodes (Tx) about the upcoming communication window. Only Tx nodes with data to send respond, minimizing energy consumption. The transmission power is adjusted by the adaptive power control mechanism based on distance, ensuring energy conservation while maintaining reliable communication. Communication slots are allocated by the adaptive scheduling mechanism based on node energy levels, positions, and traffic priorities.  
Time-sensitive information is a typical illustration of high-priority data that is given preference, on the other hand, nodes with more residual energy are assigned higher transmission duties. Finally, resource allocation and scheduling are dynamically optimized based on real-time conditions such as node positions, traffic load, and energy levels. In summary, the communication process from the satellite to the buoy, then to the sink node, and finally to the transmitter nodes is designed to ensure energy efficiency, reliable communication, and optimized network performance in the underwater network. Figure 7 represents flow chart of proposed receiver initiate DQN based MAC protocol.
# Performance Evaluation of Reinforced Recycled Aggregate Concrete Columns under Cyclic Loadings
## 3 Parametric Investigation
### Mathematical Model of GRA  
The maximum load (\(P_{\text{u}}\)) and the displacement ductility coefficient (\(\mu\)) reported in [PERSON] et al. [42] and [PERSON] [43] were determined as the reference matrix, \(X_{0}(j)\), where, \(j=1\), \(2\), \(\ldots\), \(n\). The key experimental parametric variables, including \(r,f_{y}\), \(L/h\), \(\rho_{\text{s}}\) and \(n\), were selected as the comparative matrix, \(X_{i}(j)\), where, \(i=1,2,\ldots,m\). The following equation is a mathematical model for the construction of the reference matrix and the comparative matrix.  
\[\begin{array}{l}\mathbf{X}_{0}=\mathbf{X}_{0}(1),\;\mathbf{X}_{0}(2),\;\ldots\;\mathbf{X}_{ 0}(n)\\ \mathbf{X}_{1}=\mathbf{X}_{1}(1),\;\mathbf{X}_{1}(2),\;\ldots\;\mathbf{X}_{1}(n)\\ \ldots\\ \mathbf{X}_{m}=\mathbf{X}_{m}(1),\;\mathbf{X}_{m}(2),\;\ldots\;\mathbf{X}_{m}(n)\end{array} \tag{8}\]  
Normalization of variables in the matrix is processed in order to eliminate their numerical fluctuation.  
\[x_{i}(j)=\frac{X_{i}(j)}{\frac{1}{n}\sum_{i=1}^{n}X_{i}(j)}. \tag{9}\]  
The grey relational coefficient \(\xi_{i}\) is calculated:  
\[\xi_{i}[x_{0}(j),x_{i}(j)]=\Bigg{|}\frac{\min_{i=1,n}\min_{j=1,m}\Delta_{i}(j)+ \rho\max_{i=1,n}\max_{j=1,m}\Delta_{i}(j)}{\Delta_{i}+\rho\max_{i=1,n}\max_{j=1,m}\Delta_{i}(j)}\Bigg{|}, \tag{10}\]  
\[\Delta_{i}(j)=|x_{0}(j)-x_{i}(j)|, \tag{11}\]  
\[\min_{i=1,n}\min_{j=1,m}\Delta_{i}(j)=\max_{i}\biggl{(}\max_{j}|x_{0}(j)-x_{i} (j)|\biggr{)}, \tag{12}\]  
\[max_{i=1,n}\max_{j=1,m}\Delta_{i}(j)=\min_{i}\biggl{(}\min_{j}|x_{0}(j)-x_{i} (j)|\biggr{)}, \tag{13}\]  
where \(0\leq\rho\leq 1\), and its frequently-used value is equal to 0.5 [81].  
In GRA, grey relational entropy density (\(\gamma\)) can be used to measure the degree of correlation between the reference matrix and the comparative matrix:  
\[\gamma=\frac{1}{n}\sum_{i=1}^{n}\xi_{i}[x_{0}(j),x_{i}(j)]\leq 1.0. \tag{14}\]  
It is worth noting that values of \(\gamma\) approaching the unit indicates a closer correlation between the IVs and the DVs; when \(\gamma\) is over 0.7, there is a strong correlation between the IVs and the DVs; when \(\gamma\) is less than 0.5, the correlation between the IVs and the DVs can be considered negligible [81].
## 2 Gaussian Fitting  
For SUHI or LST map analysis, a 2D Gaussian intensity distribution can be a reliable representation of the expected pattern, especially when an urban area shows an evident heat island with respect to the surrounding rural areas. This fitting procedure can also be suitable for the analysis of maps describing the spatial distribution of quantities having clustered high values tending to decrease branching off into the map perimeter.  
For this purpose a fast and robust Gaussian fitting technique is implemented to analyze and parameterize the spatial distribution of the SUHI for a selected urban area. This technique also provides a tool to perform quantitative comparisons between different SUHI maps useful for inter-citycomparisons or for single city analysis over different time scales. The enclosed plotting procedure allows a further better understanding of the shape and orientation of the heat island for each image.  
Each SUHI map is processed using a method described by [PERSON] [1,8]. In this technique a Gaussian surface is used to fit the spatially distributed heat island signatures as follows:  
\[SUHI(x,y)=a_{0}\times\exp\left[-\frac{\left((x-x_{0})cos\phi+(y-y_{0})sin\phi \right)^{2}}{0.5a_{x}^{2}}-\frac{\left((y-y_{0})cos\phi-(x-x_{0})sin\phi \right)^{2}}{0.5a_{y}^{2}}\right] \tag{1}\]  
where (\(x\),\(y\)) represents the location of a pixel. Such a least-square fit provides not only an estimate of the magnitude (\(a_{0}\)) of the \(SUHI\) intensity for the selected area, but also of the spatial extent (\(a_{x}\) and \(a_{y}\)), orientation (\(\phi\)), and the central location (\(x_{0}\) and \(y_{0}\)) of the heat island.  
The two spatial extent parameters can be combined to determine the overall area or footprint of the SUHI. The footprint has the form of an ellipse (horizontal cross-section of the Gaussian surface) with the major and minor axes equivalent to the \(a_{x}\) and \(a_{y}\) extent parameters, \(\phi\) is the angle at which the major axis forms with the x-axis; the central location (\(x_{0},y_{0}\)) is defined as the shift with respect to the center of the map.  
The SUHI intensity within such an ellipse is maximum in the center (\(x_{0}\), \(y_{0}\)): the distance from the center at which the temperature decreases up to a level of e\({}^{\text{-}1/2}\), or 61%, of its maximum value \(a_{0}\), identifies a footprint that will be named A_61%. Also, can be useful to compute the area of the SUHI for which the temperature is greater than a constant threshold instead of a fraction of the maximum, for example greater than 1.0 K (A_1K).  
This fitting method is not influenced by localized high surface temperatures within the image, smoothing isolated peaks as will be illustrated in the result section.  
In the next section the main steps of the algorithm performing the Gaussian fit are described.
# Practical Estimation of Machine Tool Spindle Dynamics for Maintenance Decision Making
## 4 Discussion
### Speed Dependent Dynamics  
The modal characterization of the dynamic spindle test listed in Table 2 suggests a speed-dependent relationship with the modal characteristics of the spindle. The existence of this relationship is well established in the literature; however, much of the nuance in this relationship remains to be explored. In a review by [PERSON] et al., the most important  
Figure 30: A drop in magnitude observed between the free vibration tool tip in Condition 1 and the tool-IG tip contact in Conditions 2 and 3 during the static spindle test.  
Figure 29: Comparison of the force spectrum for three different impact tip materials showing that harder tips excited a broader range of higher frequencies compared to the softer tip material.  
speed-related dependency in the spindle, as identified by the authors, is concerned with bearing stiffness effects [12]. To understand bearing stiffness effects, it is first noted that increased axial preload in angular contact bearings increases the dynamic stiffness of machine tool spindle units [24; 25; 26]. Increased spindle speed causes increased centrifugal forces on the rolling elements, resulting in an increase in the contact angle and decrease in the effective axial preload. Therefore, it has been proposed that increased spindle speed should result in a reduction in dynamic stiffness of the spindle. However, increased spindle speed also causes a temperature increase due to friction. The increased temperature differential is proposed to conversely cause an increased preload in the bearings, resulting in increased dynamic stiffness [27]. From the results shown in Table 2, the root cause of the apparent increase in stiffness observed for the 10,000 RPM case is not obvious, and future investigative work should be conducted. While the relationship between the modal parameters of a spindle unit and the spindle speed is rich in complexity, this study demonstrated that the characteristics can be estimated from a rotating spindle using the proposed method. The ability of this mechanical IG and the automated test routine to observe these speed-dependent modal characteristics of the spindle suggests that the approach used in this study may have applications beyond spindle-condition monitoring, such as cutting parameter optimization.
## 4 Experiments and Results
### Simulation
\begin{table}
\begin{tabular}{|p{142.3 pt}|p{142.3 pt}|} \hline Scenario: & Net Intra-campus of 2000 x 2000m2 \\ \hline Nodes clients: & Fixed and Pieces of furniture with different mobilities (1, 2 m/s) \\ \hline Channel: & Single Radio \\ \hline Model of Propagation: & Two Ray Ground \\ \hline Type of Interface: & Wireless NIC \\ \hline Layer PHY: & 802.11g \\ \hline Model of the Antenna: & Omni-Antenna \\ \hline Model of Mobility: & Random Waypoint \\ \hline Protocol of Transport: & TCP AND UDP \\ \hline Amount of Gateways in Static Mesh: & 5 \\ \hline Size of the net: & Scales small, medium, large \\ \hline Model of traffic: & HTTP, FTP, VOIP, REAL \\ \hline Width of Band of the Nodes: & 2 Mbps \\ \hline Routing protocols: & AODV AND OLSR \\ \hline Type of the Layer of Connection: & LL \\ \hline Type of Line: & FIFO \\ \hline Maximum of Packages in Line: & 50 \\ \hline Time of Simulation: & 60 minutes \\ \hline Trust interval & 95\% \\ \hline \end{tabular}
\end{table} 
Table 4: Description of the simulation parameters used in the scenario intra-campus. for both nodes. It is interesting to emphasize that the customer associates to a node gateway according to the covering area of the same. When there is a node customer's mobility, this associates to the node closer gateway. Each node gateway has a single number of association.
## 4 Analysis of Test Results
### Pile Top Load-Top Displacement Curve
It is worth noting that the settlement of the pile foundation does not increase in tandem with the augmentation of its load-bearing capacity; indeed, the pile top displacement under the ultimate load condition of test pile TP2, post-combined grouting, undergoes a slight decrement. The test results demonstrate that combined post-grouting improves the bearing performance of bored piles. The cement grout injected at the pile side effectively reduces the impact of the mud skin formed during construction, thereby enhancing the exertion of side resistance. Meanwhile, the grout infused at the pile tip consolidates the slag present at the borehole's base, resulting in a substantial enhancement in the pile tip resistance's expression. Additionally, the reinforcing influence of combined post-grouting on the soil adjacent to the pile serves to further elevate the individual pile's bearing capacity and diminish settlement.
# A Survey of the Effects of Vehicle Configuration on Urban Air Mobility +
## 2 Aerial Urban Vehicle Configurations and Concepts
### Vertical Take-Off and Landing (VTOL)  
Vertical take-off and landing vehicles can be considered the most popular category of the UAM. They are able to hover flight, they do not need any runway, and are suitable for personal use and few passengers. Due to the critical issues in the infrastructure section, and the fact that there are not enough airports for HTOL aircraft specifically for urban applications, and short distances they seem to be the best solution. In the following section, different VTOL configurations including helicopters, multirotors, and cyclocopters are reviewed.
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
## 1 Introduction
In this pilot study, we introduce an automated method for measuring pulse rate from video recordings using a representation-learning approach: 3D convolutional neural networks. The video, considered here as a consistent ensemble of frames, is directly introduced in the neural network and no prior image processing (e.g., automatic face detection and tracking) is required. Simultaneous mapping of relevant PPG pixels, and consequently skin pixels, is additionally provided by the system. The volume of either uncompressed and labeled (with reference pulse rate values) video data being very limited, a synthetic generator of pseudo-PPG videos is proposed to train the models (Figure 1). The remainder of the manuscript is organized as follows. Section 2 presents an overview of studies that relate to imaging photoplethysmography and remote pulse-rate measurement from videos. The materials and methods are presented in Section 3. Experimental results are presented and discussed in Section 4, just before Section 5, where conclusions are derived from the potential and limits of the methods developed in this work.
## 2 Materials and Methods
### Proposed Modification in LEACH
#### 2.6.3 Q-Leach
```
1:Save node Location, CH Location on Coordinate basis
2:Source (\(x\), \(y\)) -> target (\(x\), \(y\)) initial position input to csv (or generate random position) # q table generation (Update one space of Q each time)
3:Make a map to unit dimension
4:Initialize Q table
5:Set the \(\gamma\) value multiplied by max Q (state max, action max)
6:discount rate = 0.99, maximum iteration number before regrouping (episodes \(i\) = 2000)
7:Repeat (for each episode \(i\)):
8:state reset (reset environment to a new, random state)
9:Initialize reward value
10:Initialize While flag done = false
11:while not done:
12:\(\varepsilon=1/((i/100)+1)\)
13:Create a probability value for \(\varepsilon\)-Greedy (Set to Decay as step passes)
14:if Random action when \(\varepsilon<1\) (small probability)
15:else Action to the side where max state aggregate is present end
16:return new (state, reward, done) from action <<Q table update>>
# Update Q-Table with new knowledge using learning rate
17:Q table = Max value of reward + (discount * new state)
18:Upon arrival from Node to CH, Reward +1
19:Save reward value, update state
20:reward value appends in array
21:Move node to higher reward in Q table
22:if node = CH end
23:if duplicate nodes exist or move to another location end
24:Explore other dimensions
25:While complete the solution in total field (\(1000\times 1000\))
```  
**Algorithm 2** Q-LEACH  
Declaring target matrix from the original topology is preceded as shown in Figure 7. Before applying Q-table, total field is divided into several unit dimension (which is the same method as D-LEACH) and Q-table is calculated in each unit dimension in the space of target matrix, in this study, defined as \(15\times 15\). Q-probability, drawn out in four directions at node position on target matrix, gives the direction of node (toward direction with the highest probability). In the developed Q-table, state demonstrates the Q-probability that of approach (4 direction: up, down, left, right) of node toward CH, as the hole and other nodes in the dimension can act as the causes of lowing the probability. 1 step action occursaccording to the state-direction with the biggest probability. Eventually, the system earns 1 reward when node arrives to CH. The detailed parameters were chosen according to previous studies that analyzed WSN parameters in order to select the optimal value for the experiment [52; 53; 54; 55]. Proposing LEACH protocol simulation parameters are shown in Table 2.