esa-sceva/satcom-chunk-collection huggingface dataset:
# FFT-Based Scan-Matching for SLAM Applications with Low-Cost Laser Range Finders
## 5 Experiment
### Dynamic Localization  
We also completed dynamic localization experiments based on the robot mobile platform. In the experiments, robot ran along a given trajectory under different moving speeds.  
As shown in Figure 11, when the speed exceeds 0.15 m/s, the accuracy of the ICP algorithm is poor and the error is up to 0.5 m (tracking lost). In addition, due to the vibration of the robot, the curves generated by the ICP algorithm fluctuate greatly. In contrast, the algorithm proposed by this paper demonstrates robustness with speed; the location error is less than 0.2 m when the speed is  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scenes**} & **ICP** & **NDT** & **FFT** & **FFT-ICP1** & **FFT-ICP2** \\  & **(ms)** & **(ms)** & **(ms)** & **(ms)** & **(ms)** \\ \hline Room & 23 & 108 & 355 & 198 & 201 \\ Office & 27 & 127 & 367 & 201 & 134 \\ Corridor & 26 & 120 & 365 & 197 & 101 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average run-time for different methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Avg. error** & **Variance** & **Avg. error** & **Variance** & **Avg. Rotation** & **Variance of** \\  & **of x (m)** & **of x error** & **of y (m)** & **of x error** & **error (deg)** & **rotation error** \\ \hline NDT [37] & 0.041 & 0.027 & 0.043 & 0.022 & 0.78 & 0.32 \\ \hline ICP [26] & 0.043 & 0.033 & 0.034 & 0.024 & 0.81 & 0.31 \\ \hline FFT [48] & 0.052 & 0.058 & 0.047 & 0.045 & 1.02 & 0.67 \\ \hline FFT-ICP1 [47] & 0.033 & 0.019 & 0.034 & 0.020 & 0.64 & 0.29 \\ \hline FFT-ICP2 [this paper] & 0.040 & 0.021 & 0.037 & 0.023 & 0.75 & 0.28 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the matching accuracy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Step**} & **Obtain** & \multicolumn{2}{c}{**Estimate the rotation**} & **Estimate** & **Precise** \\  & **scan** & **By missing** & **By FFT for** & **the** & **estimation** \\  & **images** & **data features** & **rotation (optional)** & **by FFT** & **by ICP** \\ \hline Average & & & & & \\ Run Time & 10 ms & 5 ms & 103 ms & 82 ms & 11 ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average run-time for each step of FFT-ICP2.  
0.2 m/s. The curves generated by the proposed algorithm are smoother, so it also has a good robustness to vibration.  
Figure 12 shows the results of generating maps directly from a series of scans. In the experiments, robot ran at a linear speed up to 0.3 m/s and a rotation speed of 25 \({}^{\circ}\)/s. The given robot trajectories are shown as red stars. As can be seen from Figure 12, the NDT method failed with rotation, while our proposed method gave a better result.  
The experiments show that the proposed method in this paper leads to an improvement of robustness and speed, compared with our previous methods. This technique has proven to be helpful for constructing an on-line SLAM system.
## 1 Introduction
The advancement of computer-aided drug discovery tools has markedly improved natural product-related drug discovery, effectively reducing the historically high attrition rates. Therefore, structure-based virtual screening (SBVS) tools including molecular docking and molecular dynamic (MD) simulation, together with in silico absorption, distribution, metabolism, excretion, and toxicity (ADMET) screening and network pharmacology analysis, have been frequently utilized by both academia and industry to accelerate the drug discovery process. Network pharmacology, a holistic approach, leverages the integration of systems biology, pharmacology, bioinformatics, and network analysis to predict theinteractions of compounds with multiple biochemical pathways involved in a specific disease condition [16]. Accordingly, network pharmacology analysis allows the opportunity to comprehensively understand the mechanism of action of a therapeutic candidate and provides the opportunity to repurpose existing bioactive compounds for novel therapeutic applications [17]. Molecular docking and MD simulation studies have become major elements in the SBVS [18]. Molecular docking analysis elucidates the binding interactions between drug candidates and biological targets, offering valuable insights into their binding affinity, intermolecular interactions, and optimal binding orientations [19]. Moreover, protein-based drug targets possess dynamic structures and are frequently susceptible to conformational changes. Hence, MD simulation studies are necessary for the investigation of the time-dependent dynamic interactions between therapeutic candidates and respective targets, as they allow the investigation of time-dependent dynamic interactions. A combination of both molecular docking and MD simulation approaches provides a more holistic view of biomolecular interaction [20]. In addition to efficacy, for a compound to be considered a viable therapeutic candidate, it should also demonstrate a favorable pharmacokinetic and toxicity profile. Therefore, we conducted a comprehensive assessment of the pharmacokinetics and toxicity of spilanthol through in silico ADMET screening tools. Subsequently, we employed network pharmacology to assess the potential of spilanthol in attenuating NDDs. Thereafter, we utilized molecular docking and MD simulation studies to examine the biomolecular interactions of spilanthol with key target proteins identified through the network pharmacology results. Finally, the in silico findings were validated using a lipopolysaccharide-induced BV-2 microglia cell model.
# Cardiac Arrhythmia Classification Based on One-Dimensional Morphological Features
## 2 Materials and Methods
### Proposed One-Dimensional Feature Extraction Method
#### 2.2.2 GLCM features  
Let \(G_{\delta}(i,j)\) denote the GLCM and represent the number of times that quantized value \(i\) is neighbors with quantized value \(j\) at a distance \(\delta\). The GLCM has the size of \(p\times p\), where \(p\) describes a pre-defined number of quantized levels of the ECG signal. Both the offset distance \(\delta\) and the quantized level \(p\) are hyperparameters to be tuned. In this study, the offset distance \(\delta\) ranges from 1 to 10 with an interval of 1 and the quantization level \(p\) was chosen from 16, 32, 64, and 128 levels. Both hyperparameters were optimized by the grid search. For each ECG lead, the \(p\times p\) GLCM \(G_{\delta}(i,j)\) can be defined as follows:  
\[G_{\delta}(i,j)=\sum_{x=1}^{n-\delta}\left\{\begin{array}{ll}1,&\text{if }y(x)=i\text{ \& }y(x+\delta)=j\\ 0,&\text{otherwise}\end{array}\right., \tag{1}\]  
where \(y\) is a quantized ECG signal, \(i\) and \(j\) are the quantized values of \(y\), \(x\) is a temporal position of \(y\), and \(n\) is the end position of the quantized ECG signal. Figure 2 illustrates how to generate the GLCM when the offset distance \(\delta\) is 3. After computing the GLCM, we obtained nine features (energy, contrast, entropy, homogeneity, correlation, dissimilarity, autocorrelation, average, and variance). The mathematical definition and the meaning of the features are presented in Table 2[25].  
Figure 2: The process of generating the GLCM for a quantized ECG signal.
# Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review
## 3 Review of DL and ML Safety Applications
### Jsm
#### 3.2.2 Analysis of Construction Safety Documents  
On-site managers can benefit from analyzing safety reports, as they can acquire details about the events and circumstances that result in safety accidents. Hence, corresponding actions can be taken to prevent similar accidents in the future [106]. With the development of safety management, scholars have proposed various text classification methods to classify and analyze accident causation [107]. More recently, researchers have tried to apply various ML algorithms to construction-related accident analysis. [PERSON] et al. [108] developed a predictive model which used random forest (RF) [109] and stochastic gradient tree boosting (SGTB) methods [110]. This model can forecast the different types of injuries recorded by on-site injury reports. Later, [PERSON] et al. [111] presented an improved NLP method to extract outcome variables and injury precursors from the unstructured injury-related text. This method can reduce the labor cost of text analysis. [PERSON] et al. [112] adopted an unsupervised learning approach for injury report classification based on K-means clustering. The text mining approach presented outstanding results regarding recall and precision. [PERSON] et al. [113] applied six ML approaches to classify the near-miss accident reports. The results illustrated that ML algorithms performed better than traditional text classification methods. However, ML requires hand-crafted featuring, which limits the generalization of the classifier and also may affect the adaptability.  
DL-based methods perform excellently, in regard to text mining and classification, when compared with traditional ML [114]. Word2 Vec [115] is a popular DL method that adopts word embedding technology. Word2 Vec can be trained by public semantic resources such as Wikipedia when utilizing it for text classification [116]. It avoids the manual featuring process and augments knowledge concurrently. However, there is relatively limited research focusing on this topic. Future research can be conducted by combining Word2 Vec and computer vision in ISM. For example, CNN can be used to detect unsafe events from the construction site images taken from surveillance cameras. Then, image captioning technology provided by Word2 Vec can be used to explain the event in text formation. In this way, an automated safety report generation system based on construction images can be developed.
# Enhancing Energy Efficiency in Retail within Smart Cities through Demand-Side Management Models
## 5 Conclusions  
The retail industry, classified as a non-production sector electricity consumer, faces unique challenges compared to its production sector counterparts. As a provider of services to consumers, the retail sector's electricity usage cannot be stored, setting it apart from high-pressure electricity users in the production sector with cogeneration functions. Despite these challenges, the retail sector falls under the residential and commercial user category, exhibiting significant diversity in electricity usage among numerous households. This diversity allows for relatively low implementation costs for energy-saving and carbon reduction measures, presenting an opportunity to achieve maximum effectiveness in suppressing electricity consumption. This study introduces a forward-looking DSM model tailored to the retail industry in Taiwan. Through small-scale experiments, the study not only assesses the model's applicability but also verifies its feasibility. Aggregators play a pivotal role in collecting and analyzing electricity consumption information from the users they oversee. By conducting a small review of DSM programs globally and understanding the execution status and willingness of the retail industry to engage in such initiatives, this study designs an appropriate retail industry DSM aggregation model. By applying deep learning techniques to analyze the effectiveness of the aggregator model, the experiments also demonstrate that the MSPE of this research is below 2.05%. This indicates a high level of accuracy and provides meaningful reference value for ESCO. The study offers valuable insights into contemporary debates surrounding energy efficiency and DSM policies and methodologies. Simultaneously, it provides practical recommendations for DSM energy management practices in smart cities.  
Conceptualization, C.-Y.K. and C.-B.Y.; methodology, C.-Y.K.; validation, C.-Y.K.; formal analysis, C.-Y.K. and C.-B.Y.; investigation, C.-Y.K.; data curation, C.-Y.K. and C.-B.Y.; writing--original draft preparation, C.-Y.K.; writing--review and editing, C.-B.Y. and C.-Y.K.; visualization, C.-Y.K.; supervision, C.-B.Y. and C.-Y.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to this study has data usage rights only, and does not have data ownership.  
The authors declare no conflict of interest.
## 4 Performance Evaluation
### Simulation Setup
#### 4.2.5 Results Comparison  
After proving the competence of the CRSSA in different and varying channel parameters and numbers of nodes, we evaluate its performance by comparing the NRMSE against state-of-the-art anchor-free localization techniques proposed in recent studies, namely, [PERSON] et al. [21], SFO [17], and EAFLA [13]. [PERSON] et al. employed a time division multiple access (TDMA) scheduling algorithm, where each node broadcasts a beacon message containing its unique node ID. RSS measurements are then utilized to estimate distances between nodes, and the centroid and average distance are computed for localization.  
The SFO technique applied in [17] involves an approach using the DV-Hop algorithm. This technique leverages the principles of SFO to optimize node positions based on hop distances obtained from the DV-Hop algorithm.  
Figure 6: Inter-node distance estimation NRMSE for different number of nodes, \(N\), with (\(\gamma=3,\sigma=4\), m = 4).  
The EAFLA proposed in [13] begins by electing cluster heads and forming clusters according to the low-energy adaptive cluster hierarchical (LEACH) protocol. Distances between nodes are then estimated using a combination of the RSS signal transmission model and the Kleinrock and Sylvester model [14].  
In Figure 7, we evaluate the NRMSE under specific parameter settings (\(\gamma=3\), \(\sigma=4\), and \(m=4\)). We present results in blue boxplots. Our proposed approach consistently outperforms existing benchmark methods found in the literature. The NRMSE achieved by our approach falls within an impressive range of \([0.03,0.058]\). In comparison, errors obtained using the SFO method vary between 0.16 and 0.189, [PERSON] et al.'s approach yields errors within \([0.45,0.1]\), and errors with the EAFLA range from 0.07 to 0.26. This demonstrates the superior accuracy and precision of our proposed method, consistently achieving lower NRMSE values across the specified parameter configurations when compared to existing state-of-the-art approaches.
# Lightweight Frequency Recalibration Network for Diabetic Retinopathy Multi-Lesion Segmentation
## 2 Methods
### Residual Attention Module (RAM)  
This paper introduces residual attention modules into the decoder to improve the segmentation performance of our LFRC-Net, as illustrated in Figure 5, which combines a residual block and a hybrid channel-spatial attention block. The residual block is incorporated to mitigate the issue of gradient vanishing, the channel attention block efficiently identifies and utilizes pivotal feature channels, and the spatial attention block concentrates on prominent local area features.  
Figure 4: Frequency recalibration module.  
The features \(x\in R^{H\times W\times C}\) are inputted into a residual block comprising a depthwise separable convolution layer, dropblock layer, BN layer, and ReLU activation function to obtain the output \(CL(X)\). Notably, the kernel size of both layers of depthwise separable convolution in the decoder is set to 3. Then \(CL(X)\) proceeds through a hybrid channel-spatial attention module with a channel attention mechanism and a spatial attention mechanism, as depicted in Figure 6.  
In the channel attention mechanism, \(CL(X)\) is fed into average pooling and max pooling to capture the global information of each channel. Then, the channel attention weight vector is obtained by shared-weight one-dimensional convolution and sigmoid function and multiplied with \(CL(X)\) to obtain the output of the channel attention block \(CA(x)\).  
In the spatial attention mechanism, the spatial location features of \(CA(x)\) are obtained by summing the results of average pooling and max pooling. The spatial attention weight vector is acquired through a \(7\times 7\) convolution and sigmoid function and multiplied with \(CA(x)\) to attain the ultimate output of RAM \(x_{1}\in R^{H\times W\times C}\). The residual attention module is calculated from Equations (5)-(7).  
\[CL(x)=Relu(BN(Dropblock(Sep(x)))) \tag{5}\]  
\[CA(x)=CL\times\sigma(1 DConv(MP(CL))+1 DConv(AP(CL))) \tag{6}\]  
\[x_{1}=CA\times\sigma(Conv(MP(CA))+1 DConv(AP(CA))) \tag{7}\]  
where \(Sep\) denotes depthwise separable convolution, \(CA\) channel attention, \(BN\) batch normalization, \(CL\) the first convolution layer of output, and \(1 DConv\) one-dimensional convolution, with \(Conv\)\(7\times 7\) convolution. The features of input and output are \(x\in R^{H\times W\times C}\) and \(x_{1}\in R^{H\times W\times C}\), respectively.  
Figure 5: Residual attention module (RAM).  
Figure 6: Channel spatial attention module (CSAM).
# Resilient Scheduling as a Response to Uncertainty in Construction Projects
## 2 Resilience Approach to Manage Uncertainty in Construction Projects  
The methodological approach taken in this research is presented as an algorithm for resilient scheduling to manage uncertainty in construction projects, as depicted in Figure 1. At the start, uncertainty sources in a construction project are analyzed to clarify how uncertainty impacts project objectives. From here, the multi-objective optimization (MOO) model is developed to serve as a response to uncertainty in construction projects. The objective functions in the model stem from the uncertainties related to variability in the durations of activities. In such a way, the optimization method overcomes problems of the deterministic approach which cannot account for activities' completion times with absolute certitude. The optimization problem consists of three objective functions and complementary constraints. Decision variables in the MOO model define the start times for activities in a project once the optimization problem is solved by the use of a hierarchical approach. The solution is obtained in a form of a resilient baseline schedule. Finally, the baseline schedule is validated by the simulation analysis, where recognized uncertainty sources are modeled as stochastic variables.  
The structure of this study is organized as follows. The following subsection begins with a discussion on the nature of uncertainty in construction projects. In Section 2.2, the taxonomy development process is described. Resilience framework is discussed in more detail in Section 2.3. In Section 2.4, the MOO model for resilient scheduling is presented. The Section 3 consists of two parts, where the solving process is discussed in more detail in Section 3.1, while the resilient scheduling approach is validated on the construction  
Figure 1: Algorithm for the resilience approach to manage uncertainty.  
project example by simulating two baseline cases in Section 3.2. The overall conclusions and suggestions for future research are given in Section 4.
## 2 NB-IoT System Detailed Design and Implementation
### NPUSCH Transmitter Chain Implementation  
In the uplink, the physical (PHY) layer receives a data transport block of a specified size from the MAC layer along with other upper layer parameters. The maximum transport block size \(\text{TBS}_{\text{max}}\) for NB-IoT is 2536 bits [9]. The following subsections present the detailed implementation of each block in the transmitting chain.
## 1 Introduction  
A polymer gel dosimeter acts as a chemical dosimeter that will polymerize after photon or proton irradiation. It principally consists of water, gel, a monomer, a cross-linker, and an oxygen scavenger. After irradiation, water is resolved into free radicals, hydroxide radicals, and hydride radicals. These radicals react with the monomer in the polymer gel, resulting in polymerization. The cross-linker facilitates a chain reaction, resulting in the formation of a cross-linker polymer and a more stable polymer. Polymerization of the gel increases with the irradiation dose. Employing magnetic resonance imaging (MRI) readout [1, 2], we can characterize the correlation between polymerization and dose. Polymer gel dosimeter is employed as a relative dose measurement and investigated widely in X-ray irradiation [3, 4, 5, 6]. It was employed for the dose measurement of the scattered proton beams [7, 8]. While the use of MAGAT-f and PAGAT gels has been evaluated for photons, thus far its feasibility as a 3D high-resolution range measurement has not been tested for the synchrotron-based proton pencil beam. Compared with the one-dimensional ionization chamber and two-dimensional high-resolution radiochromic film, polymer gel dosimeter possesses the unique potential to measure the three-dimensional dose distribution [9] for clinical investigations.  
Pencil beam scanning (PBS) is more conformal to the tumor shape than the conventional scattered beam. However, the spot size and scanning position of the pencil beam must be measured during the quality assurance (QA) procedure [10]. The spot size and the actual scanning positions are of importance for clinical treatment delivery. In this paper, polymer gel dosimeters PAGAT [2, 11] and MAGAT-f [12] were used to measure the full width at half maximum (FWHM), scanning positions, and Bragg peak range of the pencil beam.