esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods  
### Experiment and Sensor Setup  
An office has been chosen as the experimental scene, as shown in the left side of Figure 1. The interior dimensions have been illustrated in the right side of Figure 1. The size of the room is 7 m \(\times\) 8 m, and the radar is located at the center of the left side of the office, approximately 1.2 m above the floor. On the right side of the office, there is a full-length bookshelf and a single desk with 0.7 m \(\times\) 1.4 m in front of the bookshelf.  
The human tracking and fall detection experiment utilizes an FMCW mmWave radar sensor, developed by Texas Instruments (TI), along with a laptop for data processing. The radar sensor chosen for evaluation is the IWR6843 ISK, which operates within a frequency range of 60 GHz to 64 GHz, offering a continuous bandwidth of 4 GHz [31, 32]. It is equipped with three transmitting antennas and four receiving antennas, providing 15\({}^{\circ}\) azimuth and elevation coverage. The mmWave radar employs FMCW technology to precisely measure velocity, range, and angle by transmitting continuous signals with varying frequencies. The radar signals of the sensor produce various scattering points after reflecting off a human body. These points, originating from different body parts, form a unique point cloud  
Figure 1: Real photo (**left**) and 2D layout (**right**) of experimental environment for fall detection using the mmWave radar sensor in a room.  
for each part. Typically, these points exhibit different velocities or Doppler shifts due to movements, such as leg or arm motions. This sensor captures x-y scatter data and Doppler measurements to identify detected objects, achieving a range resolution of 0.044 m and a maximum detection range of 9.02 m. The maximum measurable radial velocity is 1 m/s with a velocity resolution of 0.13 m/s. The images of x-y scatter and Doppler range plots for each activity were recorded for 10 s, generating 100 frames per activity at a sampling rate of 10 frames per second (FPS). The TI mmWave demo visualizer [33] was used to display x-y scatter plots and Doppler range plots, as shown in Figure 2, which illustrates measurements for five different activities: walking, sitting, lying down, inactivity, and falling.
# Review of Studies on User Research Based on EEG and Eye Tracking
## 4 Application Scope
### Spatial Environment  
Current life is a three-dimensional space that integrates geographical space, humanistic social space, and information space. The functional and expressive needs of space also present a precise and diversified development trend. Driven by new technologies and new demands, electroencephalography and eye-tracking technology are widely used to study the generalization characteristics of the subject, object, and expression mode of spatial cognition and behavior, in order to accurately understand users' experience and needs. Currently, EEG and eye-tracking technology are mainly used for user research in the field of spatial environments, including public navigation systems, traffic sign systems, urban spaces, and consumption spaces. This technology plays an important role in deepening the understanding of people's daily living space behavior and cognition, improving the efficiency of living space design and construction, and has important research value for providing satisfactory spatial experiences.
## 4 Results
### Boost Converter Emulator  
To validate the behavior of the BCE, an open loop test was carried out, changing the duty cycle from 0 to 50% with a constant input voltage of 49.25 V, which corresponds to the maximum power voltage of the emulated PVP and a load resistance 25 \(\Omega\) (this value was obtained in the converter design phase and was kept constant in the rest of the experiments presented in this section). Figure 20 shows the current behavior of the inductor \(i_{L}\) (signal in green color) of the BCE; furthermore, the same signal is illustrated using simulation (blue line). On the other hand, Figure 21 shows the behavior of the BCE output voltage signal \(V_{out}\) (green line) and the signal obtained in simulation (blue line).  
Figure 19: P-V curve of the PVPE.  
Figure 20: Current response from the boost convertor emulator (BCE).
## 3 Results
### scCGImpute Recovers Gene Expression in Real Data  
We use the Ziegenhain scRNA-seq data [37] to evaluate the resilience to the dropout events of scCGImpute. The Ziegenhain dataset is generated from 583 mouse embryonic stem cells sequenced using six different RNA-Seq protocols. The data generated by five sequencing methods, which contained 92 synthetic RNA molecules designed by the External RNA Controls Consortium (ERCC), were selected to be imputed. The imputation of ERCC genes was then compared with the known concentration of ERCCs using Pearson  
Figure 3: The Results of _ARI_, _NMI_, and Purity are shown to evaluate the clustering performance of scCGImpute with other methods on simulated data in line chart form. The value from 0 to 4.5 on the X-axis represents the value of the dropout.mid parameter in splatter, and the percentage in parentheses represents the corresponding dropout rate. The proportion of zero values in reference data is 13.5%.  
correlation analysis. The ERCCs are a group of external RNA control substances with known sequences and known concentration levels that can be used as reference standards in gene expression experiments. The results are shown in Figure 5. Compared with the original data generated by the five sequencing methods, the correlation between scCGImpute and the known concentrations of ERCCs is improved and significantly different from the original data.
## 4 Parsimonious Network Based on Fuzzy Inference System (PANFIS)
### Adaptation of Rule Consequent  
PANFIS utilizes the extended recursive least square (ERLS) method to update the rule consequent of the fuzzy rule. This approach differs from the original RLS method because of the insertion of a constant \(\alpha\) to improve asymptotic convergence of the weight vector. This approach is inspired by the work presented in [13], where the system error convergence and the weight vector convergence have been mathematically proven with the help of the Lyaponov stability criterion. The constant \(\alpha\) behaves like the binary function, where it is activated when the approximation error \(\widehat{e}\) is greater than the system error \(e\). It is worth mentioning that the approximation error refers to the system error before the adaptation process of one-step ahead PANFIS prediction, while the system error corresponds to after the tuning process \(e_{n}=|T_{n}-y_{n}|\). In other words, the weight vector remains unchanged if the approximation error is lower than the system error.  
\[\alpha=\left\{\begin{array}{l}1,\left|\widehat{e}\right|\geq|e|\\ 0,otherwise\end{array}\right. \tag{16}\]  
\[L=Q_{i}x_{e}{\left({\Psi_{i}}^{-1}+{x_{e}}^{T}Q_{i}x_{e}\right)}^{-1} \tag{17}\]  
\[Q_{i}=(I-\alpha L{x_{e}}^{T})Q_{i} \tag{18}\]  
\[W_{i}=W_{i}+\alpha L(t-x_{e}^{T}W_{i}) \tag{19}\]  
where \(Q_{i}\), \(L\) are the covariance matrix of the \(i\)-th rule and the Kalman gain, respectively. For the global learning scenario, the covariance matrix \(Q\) is global and embraces the covariance matrix of all fuzzy rules. Since PANFIS is evolving in nature, fuzzy rules can be dynamically added Sequentially, the rule consequent is set as \(W_{C+1}=W_{winner}\) if a new rule is introduced. This setting reflects where the winning rule represents the most adjacent region to the new rule. On the other hand, the covariance matrix of a new rule is set as \(Q_{i}=\alpha I\) in the case of local learning, whereas it is assigned in the same way for the global learning case, but the dimension of the covariance matrix might have to be expanded. Note that \(\alpha\) must be set as a large positive constant to assure convergence of the predictive model. The ERLS method can also be seen as a variation of the FWRLS method in [3], where the binary function is used to enhance the convergence of the adaptation process.
## 4 Experimental Results and Analysis
For dataset S00 without containing loitering behavior, our algorithms successfully detected them as normal. It is good to pick a rectangle algorithm to detect suspects in emptiness and high activity areas, learning from the result of dataset S01. Dataset S05 is a video with a thief in public: the suspect loitered around the interest point of the package; we used the sector loitering detection algorithm. The suspicious loitering in a small area in dataset S03, we choose the ellipse loitering algorithm. All the loitering detecting algorithms can effective and accurately detect the suspects. Table 4 is the loitering detection results using the PETS2007 dataset compared with [PERSON]'s method [55]. First of all, our algorithms can accurately detect loitering in the PETS2007 dataset. All our three algorithms performed much better and achieved much higher detection accuracy than the [PERSON]'s algorithm. Our algorithms successfully detected the loitering which could not be detected by the [PERSON]'s method, such as those in dataset S05 and S08. Figure 16: The different algorithms to test the same scenarios to distinguish the advantages and differences of the three algorithms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Categories**} & \multirow{2}{*}{**Samples**} & \multicolumn{4}{c}{**Successful Detection**} \\ \cline{3-5}  & & **Rectangle Loitering** & **Sector Loitering** & **Ellipse Loitering** \\ \hline Rectangle & 30 & 29 & 1 & 5 \\ Sector & 30 & 2 & 28 & 3 \\ Ellipse & 30 & 3 & 1 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of the three-algorithm comparison.
# An Interoperable UMLS Terminology Service Using FHIR
## 3 Background
### Comparing UMLS Knowledge Structure and FHIR ConceptMap
#### 3.3.2 Differences
\begin{table}
\begin{tabular}{c c} \hline \hline
**UMLS REL Values** & **ConceptMapEquivalence Values** \\ (Description) & **(Description)** \\ \hline \hline \multicolumn{3}{c}{_REL set value with equivalence in ConceptMapEquivalence_} \\ \hline PAR (has parent relationship) & subsumes (The target mapping subsumes the meaning of the source concept) \\ \hline CHD (has child relationship) & specializes (The target mapping specializes the meaning of the source concept) \\ \hline SIB (has sibling relationship) & specializes \\ \hline RB (has broader relationship) & wider (The target mapping is wider in meaning than the source concept) \\ \hline RN (has narrower relationship) & narrower (The target mapping is narrower in meaning than the source concept) \\ \hline XR (Not related, no mapping) & unmatched (There is no match for this concept in the target code system.) \\ \hline SY (source asserted synonymy) & equivalent (The definitions of the concepts mean the same thing (including when structural implications of meaning are considered) (i.e., extensionally identical). \\ \hline RQ (related and possible synonymous) & relatedto (The concepts are related to each other, and have at least some overlap in meaning, but the exact relationship is not known.) \\ \hline RL (The relationships are similar or “alike”. & relatedto \\ The two concepts are similar or “alike”) & \\ \hline \multicolumn{3}{c}{_REL set value with no equivalence in ConceptMapEquivalence and vice versa_} \\ \hline RO (has relationship other than & — \\ synonymous, narrower, or broader) & — \\ \hline AQ (Allowed qualifier) & — \\ \hline DEL (deleted concept) & — \\ \hline QB (can be qualified by) & — \\ \hline RU (related, unspecified) & — \\ \hline (empty relationship) & — \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mapping between REL set and ConceptMapEquivalence valueset values.
# Quantitative Study on the Impact of Energy Consumption Based Dynamic Selfishness in MANETs +
## 5 Simulation Setup
### Simulation Parameters  
The general simulation parameters are shown in Table 1. Each simulation runs for 500 s. The simulation area is 300 m \(\times\) 1000 m, and the node movements follow a random waypoint mobility (RWP) model [37], which is the most common used one in related articles. At the initial stage of simulations, all nodes are randomly distributed in the constraint area.  
In this paper, the IdealWirelessNic module from INET Framework is used instead of a specific MAC layer protocol implementation. This module is a highly abstracted wireless network interface card that consists of a unit disk radio and trivial medium access control protocol. We are not interested in lower-layer communication effects in this work; instead our main objective is the communication impact on the network layer. So the IdealWirelessNic module is the best choice for our simulation because of its simplicity and encapsulations.  
The energy-related parameters are listed in Table 2. There are mainly three energy management modules in INET Framework, namely Energy Storage, Energy Generator, and Energy Consumer. Energy Storage is similar to a real battery and uses charge and current. The Energy Generator module alternates between active and sleep states. It starts in the active state, and while there, it generates a given amount of power. In the sleep state, it generates no power. Energy Consumer is based on power consumption values for various transceiver mode and states, and the time it stays in these states is described in Section 3.2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Simulation Time & 500 s \\ Simulation Area & 300 m \(\times\) 1000 m \\ Number of Nodes & up to 50 \\ Transmission Range & 250 m \\ Mobility & Stationary, Random Waypoint \\ Node Speed & 20\(\sim\)50 m/s \\ Packet Size & 100 bytes \\ Packet Rate & 1 Pkt/s \\ Routing Protocol & AODV \\ Bit Rate & 2 Mbps \\ NIC & _IdealWirelessNic_ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Network parameters.  
Note the following in our simulations:  
* Each error bar shows the 95% confidence interval of the corresponding data.
* The number of nodes does not include the sender and receiver; it includes intermediate nodes only.
* The receiver is fixed far from the sender, and the multi-hop is the only way to communicate with each other.
* Dynamic selfish nodes mean the nodes behave selfishly only if the residual energy level is lower than the selfish threshold.
* Altruistic nodes mean the nodes behave cooperatively during whole network procedure.
* In order to intuitively express selfish thresholds, the ratio (%) of residual energy capacity to the nominal energy capacity is used in our simulation instead of \(S_{th}\) defined in Joules.
# Smart Mobility with Big Data: Approaches, Applications, and Challenges
## 3 Methods and Technologies for Smart Mobility
### Machine Learning
#### 3.1.2 Traffic Flow Prediction
The proposed method adopts the ABC algorithm and analyzes a nonlinear time series to optimize the weights and thresholds of the radial basis function (RBF) neural networks. Therefore, this model performed better than the KNN, RBF neural network, IBP neural network, and CARBF neural network models. The author's goal in [31] was to propose a new statistical modeling method that determines the optimal historical dataset through various analyses for each link and provides more accurate traffic flow predictions by day of the week. A three-step filtering algorithm based on change point analysis, correlation analysis, and Monte Carlo simulations was suggested to determine the optimal historical date range. Finally, Sipresk, a platform that analyzes urban traffic data to gain insight into traffic patterns, was suggested in [32]. Sipresk consists of data, analysis, and management layers and has several verified use cases, such as finding the average speed and congestion sections on specific highways.
## 7 Conclusions  
1. The OATPM and KNN methods are driven by the historical data of the energy monitoring platform, which can effectively separate the power consumption of lighting sockets and air-conditioning in public buildings. According to the three kinds of air-conditioning state (closed/slightly open/fully open) and three kinds of data state (located/above/below), \(K\) is evaluated at 3 for KNN throughout the paper. The identification error for three public buildings utilizing the method was less than 15%, and the proportion of error greater than 10% was less than 15%.
2. The OATPM method is suitable for identifying and separating daily data, and the calculation speed is high, but it cannot identify and separate hourly data. Thus, it is suitable for scenes with low identification accuracy, such as research on total energy consumption statistics. The KNN method is suitable for identifying and separating not only daily data but also hourly data; however, the calculation is complex and slow. Therefore, it is suitable for scenes with high identification accuracy, such as research on the correlation between human behavior characteristics and energy consumption.
3. The methodology proposed in this study is suitable for public buildings with different functions and climates, especially for buildings with high power consumption values for lighting sockets or large differences between power consumption characteristics of air-conditioning and lighting sockets. For example, in commercial buildings, the power consumption of air-conditioning is relatively large. Therefore, the power consumption of lighting sockets is far less than the power consumption of lighting sockets (mixed air-conditioning) in the cooling season, which means that the number of clustering iterations is fewer, and the distances between different clusters is greater. Thus, the identification results of this separation methodology for commercial buildings are accurate.  
Methodology, validation, writing--original draft, T.Z.; investigation and modeling, C.Z. and T.U.; conceptualization and supervision, L.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Project of China (Grant No. 2017 YFC0704203), National Natural Science Foundation of China (Grant No. 52078096) and \"the Fundamental Research Funds for the Central Universities\" (Grant No. DUT20 JC47).  
Studies do not involve humans or animals.  
Studies do not involve humans.  
The authors declare no conflict of interest.
# Effect of Polycarboxylate Admixture on the Performance of Fluorogypsum-Based Self-Leveling Material
## 3 Results and Discussion
### Strength  
Figure 4a,b shows the results of the effect of PCE on the strength of FSLM; the dashed line in Figure 4 represents the minimum strength requirement for the gypsum-based self-leveling mortar according to JC/T 1023-2021. From Figure 4a, it can be seen that the flexural and compressive strengths of FSLM show an overall trend of increasing first and then decreasing with the increase in the superplasticizer dosing, and the peak strength at each age corresponds to 0.14% of the superplasticizer dosing. When the PCE dosage is less than 0.14%, the 28 d strength of the specimen is improved, the effect on 1 d and 3 d strength is smaller, and the strength growth is slow. When the PCE dosage is greater than 0.14%, the compressive strength of the specimen at each age starts to show a linear decreasing trend and reaches the minimum value when the PCE dosage is 0.2%. When the PCE dosage increased from 0.14% to 0.2%, the strength at each age decreased significantly, and the flexural strength decreased by 60.0%, 34.6%, and 25.7%, and the compressive strength decreased by 50%, 31.1%, and 26.3%, respectively, indicating that PCE has a  
Figure 3: The setting time and fluidity of FSLM: (**a**) Setting time, (**b**) Fluidity.  
greater negative impact on the mechanical properties of FSLM under the condition of large dosage, especially the early strength.  
The aforementioned phenomenon suggests that PCE has a critical doping value of 0.14% for FSLM. When the PCE dosage is below this value, the superplasticizer chemically reacts with the calcium ions of gypsum to form complexes that can fill the pores, thereby improving the pore structure of the hardened body [29; 40]. As a result, the hardened body exhibits a dense structure and low porosity, which positively impacts strength enhancement. However, two main effects occur when the PCE dosage exceeds this value. Firstly, the pores formed due to the gas-entraining effect of PCE reduce the stacking between gypsum crystals [41]. Secondly, the chelation and chemisorption of anionic groups with calcium ions in PCE diminish the excitation effect of the admixture, leading to a decrease in the hydration rate and affecting the strength development.
# Aerial Robotic Solution for Detailed Inspection of Viaducts
## 4 Experimental Results
### Relative Localization  
In the experiments to test the relative localization, the position data estimated by the algorithm were compared with the ground truth from the total station. It should be taken into account that the latter can only provide positions without orientation, so only the translation part is compared. The comparison between the two sets was made with a time association of the positions and a scale-free alignment was performed with the Umeyama algorithm [38]. The EVO framework [39] was used to facilitate this task. The metrics used are the APE (_Absolute Position Error_), to evaluate the global consistency, and the RPE (_Relative Position Error_), to evaluate the local one. For the RPE, an increment of 0.5 m was selected for the calculations. For both metrics, data such as maximum peak, mean, standard deviation, and RMSE (_Root Mean Square Error_) were obtained. Tables 5 and 6 show the results.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Seq**} & \multicolumn{3}{c}{**Ground Truth [m]**} & \multicolumn{3}{c}{**Algorithm Estimation [m]**} & \multicolumn{3}{c}{**Distance [m]**} \\ \cline{2-11}  & **x** & **y** & **z** & **x** & **y** & **z** & **Ax** & **\(\Delta\)y** & **\(\Delta\)z** & **Total** \\ \hline take-off\_0 & \(-\)13.99 & \(-\)5.20 & 2.92 & \(-\)14.10 & \(-\)5.35 & 2.37 & 0.11 & 0.15 & 0.55 & 0.58 \\ \hline take-off\_1 & \(-\)10.24 & \(-\)7.39 & 4.97 & \(-\)10.59 & \(-\)7.37 & 4.43 & 0.35 & \(-\)0.02 & 0.54 & 0.64 \\ \hline beginning\_0 & \(-\)5.19 & \(-\)7.75 & 5.13 & \(-\)5.46 & \(-\)7.87 & 4.72 & 0.27 & 0.12 & 0.41 & 0.51 \\ \hline middle\_0 & 4.62 & \(-\)9.74 & 5.59 & 4.60 & \(-\)9.78 & 5.10 & 0.02 & 0.04 & 0.49 & 0.49 \\ \hline ending\_0 & 23.45 & \(-\)20.41 & 5.24 & 23.37 & \(-\)20.35 & 4.76 & 0.08 & \(-\)0.06 & 0.48 & 0.49 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative table between the ground truth obtained by the total station and the output of the global location algorithm for the Álora viaduct.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Seq**} & \multicolumn{3}{c}{**Ground Truth [m]**} & \multicolumn{3}{c}{**Algorithm Estimation [m]**} & \multicolumn{3}{c}{**Distance [m]**} \\ \cline{2-11}  & **x** & **y** & **z** & **x** & **y** & **z** & **Ax** & **\(\Delta\)y** & **\(\Delta\)z** & **Total** \\ \hline take-off\_0 & \(-\)9.30 & \(-\)27.64 & 0.51 & \(-\)9.20 & \(-\)27.59 & 0.34 & \(-\)0.10 & \(-\)0.05 & 0.17 & 0.20 \\ \hline middle\_0 & \(-\)0.29 & \(-\)27.75 & 2.29 & \(-\)0.38 & \(-\)27.63 & 2.12 & 0.09 & \(-\)0.12 & 0.17 & 0.23 \\ \hline ending\_0 & 14.73 & \(-\)31.02 & 2.39 & 14.70 & \(-\)31.18 & 2.27 & 0.03 & 0.16 & 0.12 & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative table between the ground truth obtained by the total station and the output of the global location algorithm for the Algodonales viaduct.