esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Computer Vision 3D Simulation Using Computer Vision  
In this study, we propose the integration of photogrammetry into the realm of design engineering to facilitate 3D modeling of the design domain using camera alone [66; 67; 68; 69; 70; 71; 72].  
Photogrammetry, a technique widely utilized in modern engineering for 3D modeling of objects, leverages the principles of optics, geometry, and computer vision to reconstruct physical objects from photographic images. This method enables engineers to create accurate digital representations of real-world objects, which are crucial for various applications such as industrial design, architecture, archaeology, and forensics. The process of photogrammetry begins with capturing multiple photographs of an object from different angles using calibrated cameras. These images, along with known camera parameters such as focal length and sensor size, serve as inputs for computational algorithms. Through feature extraction and matching techniques, corresponding points across images are identified, forming a point cloud representation of the object's surface, as presented in Equation (1). Subsequently, these points are used to generate a dense mesh, which accurately describes the object's geometry in three-dimensional space (as illustrated in Figure 1).  
Where \(P\) represents the perceived size of an object in an image, \(f\) is the focal length of the camera lens, \(B\) denotes the actual size of the object, and \(D\) signifies the distance between the camera and the object.  
\[P=f\cdot B\cdot D^{-1} \tag{1}\]  
The transformation in photogrammetry is depicted in Equation (2)  
\[X=T^{-1}\cdot\overline{x} \tag{2}\]  
where \(X\) represents the coordinates of a point in the object's 3D space, \(T\) denotes the transformation matrix derived from camera calibration and orientation, and \(\overline{x}\) corresponds to the homogeneous coordinates of the point in the image. This equation encapsulates the process of triangulation, enabling the computation of accurate 3D coordinates from 2D image data. The accuracy and resolution of photogrammetric models depend on several factors, including the quality of input images, camera parameters, and the computational algorithms used for point cloud generation and mesh reconstruction. Advanced photogrammetric software packages facilitate the automated processing and refinement of models, offering tools for texture mapping, surface reconstruction, and error minimization techniques.
## 3 Proposed Model  
In IoT-enabled ML for Water Quality Assessment, we use a temperature sensor, flow sensor, chlorine sensor, and solenoid valve. These sensors are integrated into a microcontroller such as the Raspberry PI 3 model, which then transmits the sensor readings to the Thing Speaks cloud service. After the data have been compiled in the cloud service, an appropriate machine learning model will be utilized to anticipate the amount of chlorine content, in addition to a forecasting strategy, essentially a subdivision of predictive modeling. The suggested model will primarily be used to measure and analyze the current level of pollution as well as to estimate what that level will be in the future.  
Chlorine level prediction using machine learning involves building a model that can accurately predict the amount of chlorine in a sample based on various inputs as mentioned in Figure 1. The process typically involves collecting data on chlorine levels and various factors that may affect chlorine levels, such as temperature, pH, and other chemicals. Once the data are collected, the next step is to preprocess the data to remove any outliers or missing values and to normalize the data if necessary. The preprocessed data are then divided into training and testing datasets. The machine learning algorithm is then trained using the training dataset. This involves adjusting the model parameters based on the input data so that the model can accurately predict the chlorine level for a given set of inputs. The model is then tested on the testing dataset, and the accuracy of the predictions is measured. If the accuracy is not satisfactory, the model may need to be adjusted, or a different machine-learning algorithm may need to be used. Once the model is optimized, it can be used to make predictions for new data points, providing accurate estimates of the chlorine level in a sample. This information can be used to make informed decisions about water treatment processes and ensure water is safe for consumption.  
Temperature can affect the solubility of the chlorine compounds in the water and the reaction kinetics between the chlorine and other substances in the water. For example, warmer temperatures can increase the solubility of chlorine compounds and can speed up the reaction kinetics, leading to a higher chlorine aggregate ratio. It is important to monitor the groundwater's temperature and consider it when assessing the chlorine aggregate ratio. This can help ensure that accurate predictions are made and that the water quality is properly maintained.
# Design Procedure of a Mono-Anchor Head for 2360 MPa High-Strength Steel Strand
## 4 Finite Element Modeling of Mono-Anchor Head  
Finite element modeling that reflects the behavior of an actual anchorage device is important to establish the stress and strain characteristics occurring at a specific location of the mono-anchor head. Therefore, reliable analysis modeling of an anchoring system using a 2360 MPa strand and a high-strength mono-anchor head was performed based on the static load test and the material test used for the anchor head in advance.
# A 100-Gb/s PAM-4 DSP in 28-nm CMOS for Sredes Receiver
## 3 Crucial Techniques
### Lower-Power Techniques
#### 3.2.2 Sigma-Delta-Based Average
The following are the calculation formulas of the four ideal levels, where \(Ave_{up}\), \(Ave_{mid}\), and \(Ave_{down}\) are the three intermediate value levels obtained by the sigma-delta algorithm:  
\[ideal_{3}=2\times Ave_{up}-ideal_{1} \tag{5}\]  
\[ideal_{1}=\frac{Ave_{up}+Ave_{mid}}{2} \tag{6}\]  
\[ideal_{-1}=\frac{Ave_{mid}+Ave_{down}}{2} \tag{7}\]  
\[ideal_{-3}=2\times Ave_{down}-ideal_{-1} \tag{8}\]  
Figure 8:0 Averages. (**a**) Moving average filter. (**b**) 1 st-order sigma delta integrator.
# GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement
## 1 Introduction
For improving the performance of time-series clustering, four image feature representation methods were demonstrated to convert one-dimensional time-series into two-dimensional images, and the deep clustering network (DC), leveraging autoencoder, and K-Means were designed for feature learning and clustering. Therefore, the deep clustering model, named the GW_DC, was proposed in this study. First, time-series were converted into four kinds of two-dimensional images: grayscale images, recurrence plot (RP) images, Markov transition field (MTF) images, and Gramian Angular Difference Field (GADF) images. Then, for better cluster performance, wavelet transform was applied to extract and process the different signal components of the two-dimensional image for removing noise and enhancing the texture features of the images. Finally, the features of the enhanced images were represented by the autoencoder of the GW_DC, and the clustering process was completed by the clustering layer in the network. To verify the performance of the proposed GW_DC model, six UCR datasets were applied, and the clustering results were employed to reverse verify the characterization effects of different two-dimensional images. The comparative analysis revealed that the clustering results of GADF images were the best, and the proposed GW_DC model showed a better clustering effect than other deep clustering models.
## 3 Methodology
### Fatigue Resistance Estimation Using Linear Amplitude Sweep and VCCD Analysis  
The fatigue resistance of asphalt binders was evaluated using the Linear Amplitude Sweep (LAS) test combined with the Viscoelastic Continuum Damage (VCCD) model, following AASHTO T 391-20 [43]. To simulate in-service aging, binder samples were first conditioned using the Rolling Thin Film Oven Test (RTFOT).  
The testing procedure consisted of two main phases. First, in the frequency sweep phase, a Dynamic Shear Rheometer (DSR) was used to measure the complex shear modulus (\(\,\mathrm{I}\,\mathrm{G}^{*}\,\mathrm{I}\)), phase angle (\(\delta\)), and storage modulus over a range of frequencies at a constant strain amplitude. This phase helped establish the viscoelastic properties and stiffness of the binder.  
Second, in the linear amplitude sweep phase, the strain amplitude was gradually increased from 0.1% to 30% under a constant loading frequency of 10 Hz, allowing for the continuous monitoring of shear stress, strain, complex modulus, and phase angle as the binder experienced incremental cyclic loading. The test was continued until a significant reduction in \(\,\mathrm{I}\,\mathrm{G}\,\mathrm{I}\) indicated the initiation of fatigue damage, which served as the failure criterion.  
VCCD analysis was then applied to interpret the LAS test results. In this model, the complex modulus (\(\,\mathrm{I}\,\mathrm{G}^{*}\,\mathrm{I}\)) and phase angle data were used to calculate the damage parameter (D), reflecting the binder's micro-damage accumulation under repeated loading. The damage evolution was modelled using the relationship (Equation (2)):  
\[|G^{*}|sin\theta=C0-C1(D)^{C2} \tag{2}\]  
where  
\(|G^{*}|\) is the complex shear modulus (Pa),  
\(\theta\) is the phase angle (degrees),  
D is the damage parameter (dimensionless), representing the degree of fatigue-induced degradation,  
and C0, C1, and C2 are regression parameters derived from experimental data.  
The VCCD model parameters, specifically the slope (\(\alpha\)) (expressed in 1/% strain) and its adjusted value (\(\alpha/(1+\alpha)\)), were computed to assess the binder's fatigue life and resistance to crack propagation.  
Integrating LAS testing with VCCD analysis provides a detailed evaluation of asphalt binder fatigue resistance, offering valuable insights into the binder's durability and expected performance under real-world cyclic loading conditions. This approach aids in predicting long-term pavement durability, enhancing the development of materials for more resilient infrastructure.
## 3 Grassmann MTS Clustering
### Extrinsic Grassmann Clustering  
Different from the intrinsic methods discussed in the previous section, the core idea of the extrinsic approach is to embed points (i.e., MTSs represented by linear subspaces) on the Grassmann manifold into a Euclidean space [49] or a feature space such as reproducing a kernel Hilbert space (RKHS) via kernel mapping. Clustering is then performed on the new space [47]. Each approach is discussed in the following subsections in greater detail.
# Asynchronous Flipped Grant-Free SCMA for Satellite-Based Internet of Things Communication Networks
## 3 Asynchronous Flipped SCMA Scheme
### AF-SCMA Transmitter Design  
With the codebook collision resolution method mentioned above, we design a novel asynchronous SCMA for the future grant-free satellite communication networks. In AF-SCMA, MTDs can transmit their packets as soon as the packets generated, and they do not need to send the request-grant signaling  
Figure 2: Codebook collision resolution in AF-SCMA. Different colors of packets represent the packets encoded with different codebooks. (**a**) Codebook collisions. (**b**) SIC procedure in AF-SCMA.  
for applying the resource. Therefore, AF-SCMA reduces the complexity of deploying the system and it is an efficient uplink multiple-access scheme. The transmitter design of AF-SCMA can be summarized as follows:  
1. The incoming original data are organized in packets of fixed size which is predefined by the system.
2. Each packet is segmented into \(N_{chunk}\) chunks with equal length which equals to \(\tau/N_{chunk}\), and each chunk is associated with a few bits of cyclic redundancy check (CRC) information and the codebook information of its flipped replica which are allocated in the known location in the packet payload.
3. Packets are first encoded with a powerful FEC coding scheme with coding rate \(r\). After channel coding, each packet is further encoded with two different SCMA codebooks which are randomly selected from the codebook set with size of \(N_{CB}\).
4. For the two encoded packets (the original one and its flipped replica) of a specific packet, a pilot sequence is appended at the head of each packet, which is used to signal acquisition, user detection, codebook identification and channel estimation. According to SCMA encoding rule, each encoded packet is mapped to \(N\) subcarriers, thus the pilot sequences on these \(N\) subcarriers are the same for one packet.
5. For each MTD, once the packets in physical layer are ready to be transmitted, they are transmitted in the pattern described in Figure 2b. Network-wide timing synchronization and request-grant signaling overhead are not required in AF-SCMA.
# Study on Underwater Target Tracking Technology Based on an LSTM-Kalman Filtering Method
## 3 Simulation
### Model Training
#### 3.2.2 Target Tracking Model  
The process parameter dataset of the Kalman filtering method that was obtained in Section 3.1.2 was input into the LSTM network for training and the resulting model after adjusting the model parameters is described below.  
The LSTM network was a three-layer network structure. The hidden layer size was 20 and the hidden layer state had bias. The loss function was the categorical cross-entropy loss function of mean absolute loss (MAE) and the training batch size was 600. The expression for the MAE was as follows:  
\[\text{MAE}=\frac{\sum\limits_{i=1}^{n}\left|f(x)-y\right|}{n} \tag{8}\]  
where \(n\) is the number of epochs, \(f(x)\) is the real value, and \(y\) is the predicted value after neural network training.  
The MAE trend of the LSTM network after 1000 epochs of training is shown in Figure 9.  
It was concluded from the training results that after 1000 epochs of training, the MAE decreased to 0.01. The batch training test of the model showed that the estimation error of the Kalman filtering method that was based on LSTM network correction, as presented in this paper, was small.  
Figure 8: Accuracy and loss of the CNN model after 100 epochs of training: **(a)** the accuracy of the CNN model after 100 epochs of training; **(b)** the loss of the CNN model after 100 epochs of training.
## 5 HUVEC Model Applications
### Other applications  
#### Extracellular Vesicles  
Extracellular vesicles (EVs) are nuclear vesicles surrounded by a membrane, classified according to biogenesis and size. Based on size, EVs can be classified as exosomes (40-100 nm), microvesicles (100 nm -1 \(\upmu\)m), or apoptotic bodies (> 1 \(\upmu\)m) [158; 159; 160]. EVs have been proposed as vectors of intercellular communication, with important roles under physiological and pathological conditions, including CVD. EVs transport bioactive molecules, such as proteins, lipids, and nucleic acids that can regulate different cellular processes and signaling pathways [159; 161; 162].  
HUVEC have been used to evaluate how different stress conditions affect EVs composition, production, or content, as well as their communication with other endothelial or other cell types [161; 162; 5, 163; 164]. HUVEC exposed to high glucose concentrations increased EMV production and induced changes in their composition and functionality [165; 162], contributing to endothelial dysfunction development and progression. Moreover, TNF-\(\upalpha\) stimulation of HUVEC caused changes in EVs protein [163; 164; 165; 166; 167; 168; 169], and microRNA expression [168; 169; 170]. These changes induced functional alterations in the recipient cells, suggesting that EC-derived EVs have a vital mediator role in CV homeostasis [163; 164; 165; 166; 167; 168; 169; 170].
## 4 Sensor Network Design
### WSN Working Principle  
The objective of our research is to provide a dynamic and robust event driven protocol for soil moisture monitoring so that the results can be used in the future to manage irrigation resources throughout Jordan. The overall data flow of our system comprises of (1) sampling soil moisture and rainfall data readings at predefined time intervals based on particular events; (2) sending sampled data to the gateway node; (3) sending the data from the gateway node to the base station; (4) moving to sleep mode; and (5) waking up and running whole process again. We use a mesh topology for network connectivity because of its advantages in our application scenario. Mesh topologies are able to route data and messages to the final gateway node through several different nodes. Even if the connection to any RF node is lost because of any environmental tragedy or loss of power, etc. still all the important data can reach to the gateway node and the base station due to the mesh topology.  
Each sensor node is directly connected to the other nodes and also to the gateway node and also synchronized to each other so that they send data simultaneously after 3 h. When there is rainfall the sensor node which is affected by the event sends a STATUS message to all nodes indicating that it is receiving rainfall, and hence the priority of the messages of this node to is increased and this gives it precedence to use routes until there is no longer any rain.
## 4 Remote Sensing Platforms Used in Peatland Research
### Satellite-Based Remote Sensing
#### 4.1.2 Vegetation Dynamics and Productivity Monitoring  
Several studies have explored using satellite data to analyse peatland vegetation dynamics and productivity [66; 70]. The utilisation of NDVI derived from satellite data has become a prevalent method for evaluating the health and productivity of vegetation [64; 71]. Moreover, integrating high-resolution optical and radar imagery enables the detection and monitoring of vegetation changes associated with peatland degradation and restoration efforts [11; 60].  
[PERSON] et al. (2021) examined remotely sensed vegetation phenological parameters in Chinese bogs using MOD13Q1-EVI data, highlighting the potential of satellite imagery in monitoring long-term phenological changes [70].  
[PERSON] et al. (2019) explored mapping above-ground biomass using very high spatial resolution (VHSR) satellite imagery from platforms like QuickBird, WorldView-2, and WorldView-3. They underscored the significance of integrating precise in situ data to enhance the precision of biomass estimation [71].  
However, limitations arise when analysing dense and tall vegetation in peatland areas, as the penetration of radar signals is reduced. Consequently, satellite-based SAR/LiDAR remote sensing may underestimate vegetation biomass and structure. Moreover, as NDVI is mainly used not only in peatland research but in remote sensing in general, such an index is relatively stable; for instance, in peatland vegetation, NDVI is well-correlated with the vascular plants' phenology, but the correlation with _Sphagnum_ spp. is not well determined.
# Wall Structure Geometry Verification Using TLS Data and BIM Model
###### Abstract  
Building information modeling (BIM) represents significant progress in the field of digitalization and informatization of the construction process. The virtual model (BIM model) is the source of graphic data among other information, which are applicable for geometry verification of the building's structures. For this purpose, data and information about the building should be collected. Comparison of the BIM model (design) with as-built 3D models enables the evaluation of the quality of the as-built structures. The most effective methods for spatial data collection are terrestrial laser scanning (TLS) and close-range photogrammetry. Using both methods, measurement can result in a point cloud. The paper describes an approach for verifying the geometry of wall structures. The graphic data of designed structures are represented by the existing BIM model. The approach presented uses the Industry Foundation Classes (IFC) file format from which the designed geometry is derived. The as-built models of the structures are created from point clouds. Point cloud segmentation uses a combination of regression, filtering based on local normal vectors, and curve segmentation. Consequently, the designed and the as-built models (segmented from the point cloud) are compared.  
building information modeling; IFC; terrestrial laser scanning; automation; geometry verification +
Footnote †: journal: Applied Sciences
## 3 Methods for Assessing the Impact of Vibrations on People in Buildings Selected for Analysis
### The Vibration Dose Value (VDV) Method  
The best additional method for assessing the impact of vibrations on people in buildings in the case of sudden changes in acceleration, known as peaks in the recorded signal, is the Vibration Dose Value (VDV) method. This method is primarily used for general and local vibrations that actively affect humans. Its sensitivity to the occurrence of peak values in the recorded signal arises from the formula used to calculate it, where vibration acceleration is raised to the fourth power:  
\[\text{VDV}=\left[\int\limits_{0}^{\text{T}}a_{w}^{4}(\text{t})\text{dt}\right] ^{\frac{1}{4}} \tag{4}\]  
Figure 2: Illustration of HPVI value based on an example of RMS analysis during the metro train passage.  
The transformation of the signal using Equation (4) results in the Vibration Dose Value (VDV) being expressed in units of m/s1.75. The procedure for determining the VDV value is similar in essence to the procedure used in the RMS method. However, the problem arises at the very beginning, where after applying the appropriate correction filter (values available in standards [23; 24], or [25]), it is necessary to introduce weighting values corresponding to the directions of vibrations. According to standard [23], weighting values assigned to the directions of vibration reception by humans should be applied. Standard [24] allows for the use of the so-called combined weight when assessing the impact of vibrations on people in buildings. There are still differences in weighting values for the same directions in various standards. This is evident when comparing information from two standards, which were the first to introduce the VDV method, namely the British standard [26] and ISO [6]. Both standards show differences in weighting values in the vertical direction, while the differences in the horizontal directions are minimal and result from the accuracy of determining the provided values. In the analyses conducted in connection with this study, according to the Polish standard [25], weighting functions were used for the \"z\" and \"x\" and \"y\" directions. The weighting function values specified in ISO [6] were applied because the Polish standard does not provide them.  
Table 2 presents threshold values for the probability of complaint occurrence according to the appendix of the Polish standard [15].