esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Damage Location Prediction Model Using AE Signal Data
### ANN Prediction Model  
The training of ANNs can be processed in a supervised or unsupervised manner [40]. An ANN model trained in supervised learning manner can output high accuracy prediction by given enough training data. Backpropagation ANNs are the most used supervised learning neural networks [17; 41]. The strong nonlinear mapping ability is one of the excellent advantages of backpropagation ANNs [14]. In the backpropagation ANNs, the signals propagate forward and the errors propagate backward until the output value is acceptable [42].  
Figure 4 illustrates the schematic diagram of the backpropagation ANN prediction model for the damage locations. The backpropagation ANN prediction model is composed of one input layer, one hidden layer and one output layer. The number of hidden layers and quantity of neurons are determined through the training process until the prediction accuracy cannot be further improved [43]. However, one hidden layer is commonly used for simple predictions [43]. The quantity of neurons of input and output layers is equal to the number of input and output variables, respectively [44]. In the case of the backpropagation ANN prediction model in this study, the quantity of neurons of input and output layers was one, since there was only one variable. One hidden layer and ten neurons were determined for the backpropagation ANN prediction model after pretesting.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Operating Specifications**} & **Value** \\ \hline \multirow{3}{*}{Dynamic} & Peak Sensitivity, Ref V(m/s) & 124 dB \\  & Operating Frequency Range & 1–30 kHz \\  & Resonant Frequency, Ref V(m/s) & 20 kHz \\ \hline \multirow{3}{*}{Environmental} & Temperature Range & \(-\)35 \({}^{\circ}\)C\(-\)75 \({}^{\circ}\)C \\  & Shock Limit & 500 g \\ \hline \multirow{2}{*}{Physical} & Dimensions & 28.6 mm OD \(\times\) 50 mm H \\  & Weight & 121 g \\ \hline \multirow{3}{*}{Electrical} & Gain & 40 dB \\  & Power requirements & 20–30 VDC @ 25 mA \\ \cline{1-1}  & Dynamic Range & \textgreater{}87 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operating specifications of R.45 IC type AE sensor.  
In the learning process of the backpropagation ANN prediction model, AE signals generated from the pile hit test were inputted from the input layer. Subsequently, the AE signals reached the output layer through the hidden layer. The damage locations were identified with the distance from ground level of each hit point at the output layer. The backpropagation ANN prediction model was trained using MATLAB 2020.
## 5 Experimental Evaluation
### Experimental Setup
#### 5.1.3 Experimental Environment Settings  
To evaluate the effectiveness of the MTD3 CO algorithm proposed in this chapter, this section simulated a comparison experiment of task offloading strategies when the application and MEC environments changed. Three scenarios were created to evaluate the energy usage and latency of all algorithms. To make the problem simpler, the data size and required calculation for each subtask were assumed to be within a certain range. For example, assume that each subtask's data size is between 5 kB and 50 kB, and that the CPU cycles required for each subtask are between 107 and 108 cycles. The computational communication ratio of the tasks is set to a random value between 0.5 and 0.8 since most mobile applications are computationally intensive. The time it takes to finish the computational part of a task depends on how high the computational communication ratio is.  
In this paper, a task offloading model for MEC was developed by considering the computational performance, signal range, and geographical location of the edge server. The simulation environment used was deployed under the Ubuntu18 system, and, to implement the meta reinforcement learning model, the mainstream Tensorflow machine learning framework was used, using datasets from [23]. Throughout the experiments, we divided the dataset simulated by the DAG task generator into training and test datasets, with each set of DAG parameters differing in width, density, and number of subtasks. Each dataset had 100 DAG tasks with the same parameters but different topologies, simulating an application's subtask relationships. The ultimate goal was to find the optimal offloading strategy for all offloading learning applications. The MTD3 CO algorithm performed inner- and outer-loop training on multiple training datasets using Algorithm 1, and the DAG tasks in each dataset were trained to learn the strategy by the same inner-loop to obtain the optimal offloading strategy for this application. The outer-loop training summarized the commonality of the inner-loop training to obtain the meta-strategy, which was used as the algorithm's initial strategy in the next inner-loop training to continue training specific policies for a specific DAG dataset, and the outer-loop training was continued until the meta-strategy converged. Lastly, the converged meta-strategy was used to set the initialnetwork parameters for a test dataset that was used to see how effectively the offloading strategy worked.
# Effects of BIM-Based Construction of Prefabricated Steel Framework from the Perspective of SMEs
## 1 Introduction  
Building information modeling (BIM) enables integrated design [1], and it can be applied to various fields (e.g., design verification, quantity calculation, and prefabrication) throughout the construction life cycle by utilizing the three-dimensional BIM model that is built for each construction type [2]. In the preconstruction phase, performing collision detection and design review using BIM can help to reduce the losses that occur in the construction phase by identifying the problems that can be solved beforehand at the design phase (e.g., design errors) [3; 4]. Doing so improves the design quality by reducing design errors, design changes, and rework [5; 6]. In the fabrication phase, the procurement process of construction frame members through BIM-based prefabrication can be simplified, and the productivity of workflow between the designers and constructors can be improved [7]. In addition, in terms of production productivity, the workers that are involved in the production of frame members can utilize the information inputted to BIM objects to support the manufacturing process; such information is parametric and it is not provided in the existing two-dimensional (2D)-based production work [8]. In the construction phase, a four-dimensional (4D) simulation can be implemented by linking the BIM model with the schedule [9]. A BIM-based 4D simulation combines information that is related to the process, equipment, and space to identify various uncertainties that arise during the process, and provides information that can be reviewed in advance ([PERSON] et al., 2014). Thus, the effect of BIM on each phase of a construction project becomes clear.  
However, previous studies on the effects of BIM on construction projects were conducted for large-scale projects [10]. Previous studies analyzed the return on investment (ROI) that was attained by general contractors for BIM-based construction projects [11; 12; 13]. Of course, many studies have analyzed the effects of BIM application from the viewpoint of small- and medium-sized enterprises (SMEs) [14; 15; 16; 17]. However, studies on developing a BIM-based construction management system from the viewpoint of SMEs are lacking [18]. Due to the nature of SMEs, the sales and profits are not as large as those of large-scale enterprises, and many of them abandon the application of BIM, which involves high initial costs [19; 20]. Therefore, we begin this study by analyzing the difficulties of managing a construction project from the viewpoint of SMEs. We propose a BIM-based construction management system framework to support SMEs. We also conduct a case study to verify the major functions that will be installed in the system for the potential SME users and their perceived performance.
# Power Autonomy Estimation of Low-Power Sensor for Long-Term ECG Monitoring
## 3 Sensor Operating Modes and Energy Consumption Measurements
### Consumption Profiles in Active and Idle Mode  
Two types of communication events are defined in the BLE specification: advertising events and connection events. Figure 3 presents a short excerpt from one current recording representing the active mode where individual activity events can be identified: connection event, message-processing event, ECG sampling event, and system tick event. Among all the detected events, both MCU and BLE radio are in _sleep mode_, with both of them consuming almost no energy at all. Low current spikes near the noise floor represent the MCU activity bursts. In the following subsections, we describe all possible energy consumption modes that can be identified from the current profile recordings. Additionally  
Figure 2: Schematic presentation of the current consumption measurement circuit.  
to the active mode events, the consumption measurements for the advertising event in idle mode and in the sleep mode are also described.
# Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization
## 2 Materials and Methods
### Raw Dataset and Properties  
This study uses the dataset named \"UJIIndoorLoc\", which was obtained at Jaume I University [21]. The dataset was collected from 3 different buildings, each of them having 5 floors. A total of more than 20,946 samples were collected. This dataset is publicly available online [30], and the variables in it are explained in Table 1. features, and their range are outlined in this table below.  
\begin{table}
\begin{tabular}{c c c} \hline
**Feature** & **Properties** & **Range** \\ \hline
1–520 & WAP RSSI level & from –104 to 0 and +100 \\
521 & Longitude & from –7695.93875492992999000 to –7299.786516730871000 \\
522 & Latitude & from 4,864,745.7450159714 to 4,865,017.3646842018. \\
523 & Floor & Integer values from 0 to 4 \\
524 & Building ID & Categorical integer values from 0 to 2 \\
525 & Space ID & Where the capture was taken. Categorical integer values (office, corridor, classroom) \\
526 & Relative Position & 1—Inside, 2—Outside, in front of the door \\
527 & User ID & Categorical integer values from 0 to 18 \\
528 & Phone ID & Categorical integer values from 0 to 18 \\
529 & Timestamp & UNIX Time \\ \hline \end{tabular}
\end{table}
Table 1: Dataset properties.  
Figure 2: Hierarchical fusion algorithm.
# Sparse Envelope Spectra for Feature Extraction of Bearing Faults Based on NMF
## 1 Introduction
The results denote that the performances are very dependent on the wavelet basis and the practical applicability is limited. In addition, the short time Fourier transform (STFT) has also been adopted to represent the non-stationary information of the vibration signal [17; 18; 19]. In view of the non-negative property of the TFD, the non-negative matrix factorization (NMF) is suitable for the analysis of vibration signals due to the ability of the parts-based representation. [PERSON] [19] proposed the constrained NMF of the spectral matrix and used the decomposed basis matrix to extract features. After utilizing the semi-binary NMF of the spectrum, [PERSON] [18] selected the appropriate decomposition frequency band for slicing analysis based on the principle of maximum kurtosis. Meanwhile, [PERSON] [17] also adopted standard NMF on spectrograms and utilized the basis vector to filter frequency bands. In addition to vibration signals, the speech signal has also been analyzed while performing NMF [20; 21; 22]. Unlike traditional band-pass filtering, the NMF-based methods can decompose frequency bands according to time-frequency distribution adaptively, and the interference component in the bands cannot be eliminated. By applying NMF to the TFD, the frequency distribution of the vibration response can be decomposed into the base matrix, which can be interpreted as the spectral bases and the weight matrix which is the time envelope corresponding to the spectral bases. For the periodic transient oscillations, the spectral bases can reflect the natural frequency band, while the magnitude change of the periodic transient oscillations is also represented by the weight matrix. Due to the influence of noise and the decomposed frequency band, neither the band-pass filtering based on the frequency base matrix nor the envelope of the time weight matrix can clearly extract the impact features. However, since the bases and the weights have a one-to-one correspondence, it means that the same feature component with different amplitudes can exist in the band-pass filtering and the envelope of time weights. Thus, the inner product of the above results can be used to obtain a sparse envelope spectrum, which can enhance the components of fault features and weaken interference components. The rest of this paper is organized as follows. Section 2 gives a brief description of NMF and provides the characteristics of NMF of time-frequency distribution, and then introduces the principles and processes of the proposed method. Section 3 verifies the proposed method with simulated signals and fault bearing data. Conclusions are drawn in Section 4.
# Arabic Cursive Text Recognition from Natural Scene Images
## 2 Complexities Involved in the Scene Text Recognition Process and Its Solutions
### Methods Designed for Text Localization
Although this algorithm is more suitable for text detection, in some situations, it might detect false positives, which can further be investigated by applying various checks for the purpose of eliminating regions that are not of interest from a given image. Toggle mapping: The work presented by [PERSON] et al. [9] is based on toggle mapping method to segment the text from natural scene images. It is defined as a morphological operator first introduced by [49]. Toggle mapping maps the function \(f\) onto a set of functions. It is primarily used for contrast enhancement and noise reduction. As explained in [9], the proposed method is used to segment the greyscale image \(I\) into two sets of functions \(h1\) and \(h2\). The morphological erosion of \(I\) was done by \(h1\), and morphological dilatation of \(I\) was performed by \(h2\). Hence, their proposed method can detect the boundary, but introduces salt and pepper noise on homogeneous regions. In order to control the noise appearing in homogeneous regions, it is essential to take a value that represents the homogeneous region. Therefore, three parameters were taken into consideration, i.e., \(h1\), \(h2\) and minimal contrast \(C_{min}\). They evaluated their proposed method on 501 readable characters and compared their resultswith the Ultimate opening, Sauvola and Niblack filters. They found a high percentage, i.e., 74.85% of correctly-segmented characters in their proposed method for text segmentation. Graph-cut method for scene text localization: The scene text localization method based on the graph cut approach was presented by [24]. The edges of an image are first extracted through the local maximum difference filter, and the,n the image is clustered based on colour information. The candidate regions contact the text that was identified by combining geometric structures. The spatial information of scene text in a skeleton image was generated by extracting the edges. The characters were realized by applying heuristic rules in addition to connected components. At the end, they applied the graph cut approach for the purpose to identify and localize text lines correctly. They performed comprehensive evaluation experiments on four datasets (i.e., ICDAR2003, ICDAR2011, MSRA-TD500 and the Street View Text (SVT)) for the purpose of validating their proposed method. They concluded that their approach produced state-of-the-art results on diverse fonts, sizes, and colours in different languages regardless of the impact caused by illumination.
## 2 Preliminaries and Problem Formulation
### Problem Formulation
As such, the slotted channel is modeled as a discrete Markov chain whose states, namely idle-idle, idle-busy, busy-idle, busy-busy, are on an adjacent slot-pair basis [5]. Each node \(i\) has a unique channel state model, which reflects the traffics of all other nodes. Its state evolves as follows. If none of other nodes perform any transmissions, the channel state will be idle-idle. This happens with the probability \(\prod_{j\
eq i}(1-\alpha_{j})\). Otherwise, when at least one of the other nodes starts transmitting, the state goes to idle-busy and subsequently busy-busy. The state evolves to busy-idle once a packet transmission is completed, which happens with probability \(\frac{1}{L-1}\). Otherwise, the state stays at busy-busy. Finally, once the state changes to busy-idle, it will go to idle-idle with probability 1. From the state evolution, the stationary channel state can be solved as \(P_{II}{}^{i}=\frac{1}{1+\tau_{i}(1+L)}\), \(P_{BB}{}^{i}=\frac{(L-1)\tau_{i}}{1+\tau_{i}(1+L)}\) and \(P_{IB}{}^{i}=P_{BI}{}^{i}=\frac{\tau_{i}}{1+\tau_{i}(1+L)}\), where \(P_{II}{}^{i}\), \(P_{BB}{}^{i}\), \(P_{IB}{}^{i}\) and \(P_{BI}{}^{i}\) are the probabilities of node i that the slot-pair state is in idle-idle, busy-busy, idle-busy and busy-idle respectively, where \(\tau_{i}=1-\prod_{j\
eq i}(1-\alpha_{j})\). The channel is considered busy when the slot-pair state is not \(P_{II}{}^{i}\). Therefore, \(\beta_{i}\) can be determined as:  
\[\beta_{i}=1-P_{II}{}^{i}=\frac{\tau_{i}(1+L)}{1+\tau_{i}(1+L)}=\frac{\big{(}1- \prod_{j\
eq i}\big{(}1-\alpha_{j}\big{)}\big{)}(1+L)}{1+\big{(}1-\prod_{j \
eq i}\big{(}1-\alpha_{j}\big{)}\big{)}(1+L)} \tag{3}\]  
The difference from the work in [5] is that instead of viewing the channel state as a whole, we model the channel from the perspective of an arbitrary node \(i\). Each node has a unique channel state model. This slight yet important modification allows us to characterize the channel states in a distributed manner. Figure 1: Channel state model.
## 3 Methodology
### Overlay-Based Approach: Post Hoc Comparison
#### 3.1.2 Overlay and Damage Labeling  
A rule-based overlay function, \(\mathcal{O}\), is applied to the two masks to generate the final 3-class damage assessment map, \(\mathbf{Y}_{\text{pred}}\in\{0,1,2\}^{H\times W}\). This logic is implemented in the project's 'utils/labeling.py' utility. For each pixel \((h,w)\),  
\[\mathbf{Y}_{\text{pred}}^{(h,w)}=\mathcal{O}(M_{\text{pre}}^{(h,w)},M_{\text{ post}}^{(h,w)})=\begin{cases}2&\text{if }M_{\text{pre}}^{(h,w)}=1\text{ and }M_{\text{post}}^{(h,w)}=0 \quad(\text{Damaged Road})\\ 1&\text{if }M_{\text{pre}}^{(h,w)}=1\text{ and }M_{\text{post}}^{(h,w)}=1 \quad(\text{Undamaged Road})\\ 0&\text{otherwise}\quad(\text{Background})\end{cases} \tag{3}\]  
This approach is computationally simple and allows for the reuse of existing segmentation models but is highly susceptible to image misalignment and may fail to capture subtle damages not significant enough to alter the binary segmentation outcome.
## 2 System and Signal Characterization  
This article considers a multi-layer MIMO system, which requires a number of \(R\) receiving antennas equal to or higher than \(T\) transmitting antennas. It is considered that each transmitting antenna sends a different flow of symbols. On the other hand, the number of \(R\) receiving antennas is responsible for providing diversity. SC-FDE signals are assumed [18], associated with quadrature phase shift keying (QPSK) modulation.  
As depicted in Figure 1, the \(n\)th transmitted block, of \(N\) data symbols, sent by the \(t\)th antenna is denoted as \(x_{n}^{(t)}\), while the received block by the \(r\)th antenna is denoted as \(y_{n}^{(r)}\). The mapping between the time domain signal and the frequency-domain signal for the transmitted block is defined as \(DFT\Big{\{}x_{n}^{(t)};n=0,1,\ldots,N-1\Big{\}}=\Big{\{}X_{k}^{(t)};k=0,1, \ldots,N-1\Big{\}}\), i.e., by performing the discrete Fourier transform (DFT) of the time-domain block. Similar mapping is assumed for the received block as \(DFT\Big{\{}y_{n}^{(r)};k=0,1,\ldots,N-1\Big{\}}=\Big{\{}Y_{k}^{(r)};k=0,1, \ldots,N-1\Big{\}}\).  
After removing the cyclic prefix, and assuming a cyclic prefix longer than the overall channel impulse response of each channel, using the matrix-vector representation, the received frequency-domain signal comes:  
\[\mathbf{Y}_{k}=\left[Y_{k}^{(1)},\ldots,Y_{k}^{(R)}\right]^{T}=\mathbf{H}_{k} \mathbf{X}_{k}+\mathbf{N}_{k} \tag{2}\]  
where \(\mathbf{X}_{k}=\left[X_{k}^{(1)},\ldots,X_{k}^{(T)}\right]^{T}\) stands for the frequency-domain transmitted data symbols, where \(\mathbf{H}_{k}\) denotes the \(R\times T\) channel matrix for the \(k\)th subcarrier, with \((r,t)\)th element \(\mathbf{H}_{k}^{(t,r)}\), and where \(H_{k}\) denotes the channel frequency response for the \(k\)th subcarrier (assumed invariant during the transmission of a given block). Note that the mapping between the channel time and frequency domains is defined by \(\left\{H_{k};k=\ 0,1,\ldots,N-1\right\}=DFT\{h_{n};n=0,1,\ldots,N-1\}\). Moreover, \(N_{k}\) is the frequency-domain block channel noise for that subcarrier.
# Evaluation of Water and Carbon Estimation Models in the Caatinga Biome Based on Remote Sensing
## 3 Results and Discussion
### Spatial Analysis
Areas such as the Agreste and Sertao of Paraiba showed greater variations in ET throughout the year, with a standard deviation close to 3 mm-day\({}^{-1}\). These areas that showed greater variations throughout the year were also the areas where the maximum reached close to 10 mm-day\({}^{-1}\) (Figure 6b), where these values are considered high for the  
Figure 4: Annual ET derived from the products MOD16A2 (**a**) and SEBAL (**b**) for the state of Paraiba for 2013.  
region. It is possible to observe that the central region of the map reached a maximum of \(\sim\)1 mm-day\({}^{-1}\), which makes sense for this region to reach SD close to 0 (Figure 6a), as these are regions where the ET values are very low throughout the whole year. Observing Figure 6c, it is noted that most of the state of Paraiba has a median value of between 0.5- and 1.0-mm-day\({}^{-1}\), except for the Mata Paraibana mesoregion, where the median values are higher, above 2 mm-day\({}^{-1}\), reaching values close to 10 mm-day\({}^{-1}\) in some areas. This happens because it is a region with the presence of the Atlantic Forest biome, where ET rates are higher than in the Caatinga biome as stated in [12]. Figure 7 shows the annual spatial statistical analysis (SD, maximum-max, and median) of the GPP derived from the MOD17A2H product for Paraiba in 2013. Through the SD map (Figure 7a), it is possible to observe that the central region of Paraiba has a good portion of GPP homogeneity throughout the year, reaching a variation below 1 g-C-m2-day\({}^{-1}\) throughout the year. The greatest variations occurred in the Agreste and Sertao Paraibano mesoregions, reaching a maximum SD of 4 g-C-m2-day\({}^{-1}\). These results support the statistical map of the maximums (Figure 7b), which shows that the maximum GPP was low in areas where there were no significant variations in GPP throughout the year and that the maximum was close to 10 mm-day\({}^{-1}\) in areas where there were significant variations (Agreste and Sertao Paraibano). The map showing the GPP median (Figure 7c) presents an  
Figure 5: Annual GPP derived from the product MOD16A2 (**a**) and Modeled (**b**) for the state of Paraiba for 2013.  
almost homogeneous map with median values below 2.5 mm-day\({}^{-1}\), except for the Mata Paraibana mesoregion, where the median was slightly higher, above 5 mm-day\({}^{-1}\). Figure 6: Statistical analysis (standard deviation—SD, Maximum—max, and median) of the ET for the state of Paraiba in 2013 derived from the product MOD16A2.