esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Experimental Results and Analysis
#### 5.2.5 Summary of Overall Performance  
In general, as for traditional PF, the high complexity of the motion model in high-maneuvering target tracking requires a large number of particles to achieve the optimal accuracy, i.e., the accuracy and complexity of the algorithm, both of which must sacrifice one party to satisfy the other. UPF improves the accuracy to reduce the required sample size by establishing the importance sampling density through UKF. However, the extra computation of UKF increases the iteration time of each sample. The MMNF chooses GRNN as the basic tracking algorithm, and uses a set of models to match different motion patterns of the target. Its accuracy is slightly better than UPF, but the real-time performance is poor. The proposed PS-AUPF combines the adaptive sample-size strategy to adaptively adjust sample size according to the state variance at each time step, together with the partitioned sampling to reduce the dimension of the maneuver. Finally, with only about 13.6% of the running  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Running Time (s)** \\ \hline PF & 29.0031 \\ PS-PF & 3.2933 \\ UKF & 0.2361 \\ UPF & 7.4856 \\ MMNF & 13.1387 \\ PS-UPF & 1.8459 \\ PS-AUPF & 1.0191 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Running time of each algorithm.  
Figure 9: Changes of sample size in adaptive sample-size unscented particle filter with partitioned sampling (PS-AUPF) during a tracking process.  
time of UPF, PS-AUPF improved accuracy by about 6% over UPF, bringing about better real-time and effectiveness of tracking performance. In summary, the PS-AUPF proposed in this paper has better overall performance.
## 4 Simulations
### Impact of Dynamic Redistribution Strategies on Invulnerability Performance  
This subsection focuses on the impact of redistribution strategies on network invulnerability. We simulated three load-redistribution strategies (SMLD, ADD, SLD) on cascading failures, and the load distribution rate is reflected in Equations (4), (11) and (12), respectively.  
From the Figure 7, we can see that ADD and SLD are significantly better than SMLD, although SMLD is practical in other networks. The reason why SMLD performs poorly in this model is that this paper considers a hierarchical topology. Although the capacity is allocated according to the degree correlation, the cluster head node with a higher degree  
Figure 5: The influence of single-hop cluster head node capacity on G.  
Figure 6: The influence of single-hop cluster head node capacity on M.  
may have a higher amount of out-of-layer degrees and a lower amount of intra-layer degrees, resulting in unreasonable capacity allocation and serious cascading failures.  
We can also found that the network survival rate under ADD and SLD is significantly improved when \(\lambda\in[0.45,0.55]\), and ADD has better results in the early stage. However, compared to the threshold value for full network survival, the network can be fully survived under the SLD when \(\lambda=0.55\). In contrast, ADD can achieve the same effect when \(\lambda=1.1\).
# Smart Deployable Scissor Lift Brace to Mitigate Earthquake Risks of Soft-Story Buildings
## 3 Proof-of-Concept Experimental Investigation
### Experimental Setup and Instrumentation
The fundamental natural frequency increased to 4.50 Hz when the scissor lift brace was deployed. An increase in the natural frequency of a building generally indicates an increase in stiffness, a reduction in mass, or structural modifications. In this case, as we added an additional support to the original building in the form of a scissor lift, it was evident that the increase in the natural frequency was due to the increased stiffness of the building. Four historical earthquakes obtained from the PEER ground motion database were simulated. They are listed below and their ground acceleration time histories are shown in Figure 10a, while their corresponding spectra are shown in Figure 10b. * 1979 Imperial Valley (USGS Station 952)
* 1995 Kobe, Japan (Hik Station)
* 1994 Northridge (CDMG Station 24,514)
* 1992 Cape Mendocino (CDMG Station 89,005)  
Figure 9: Overview of the experimental setup—active condition.
# Modeling of Downlink Interference in Massive MIMO 5G Macro-Cell +
## 2 Multi-Beam Antenna System--Practical Aspects  
One of the key differentiators of 5G is the ability to utilize the benefits of the mMIMO technique, especially the simplification of multiple-user access [1; 2]. Due to a large number of antenna elements connected to multiple transmission-reception radio chains, fast fading, as seen by the gNodeB, gradually disappears, and the radio channel becomes flat in the frequency domain. This effect, called channel hardening, causes that in orthogonal frequency division multiplexing (OFDM) access each subcarrier has a similar channel gain. Therefore, different UEs from the same cell can be allocated to the whole available frequency bandwidth [6; 7].  
On top of this, mMIMO allows cell capacity in reference to conventional MIMO to increase significantly. Due to the spatial multiplexing of available resources obtained through energy focusing using large antenna arrays, i.e., beamforming, mMIMO allows the same frequency bandwidth to be reused by multiple UEs at the same time. However, such a multi-user scenario is only possible in the case of favorable propagation conditions, i.e., when propagation channel responses from the gNodeB are sufficiently different to simultaneously serve UEs (UEs are considered to be spatially orthogonal). From this viewpoint, the number of available resources in the cell are multiplied by the number of UEs. In less favorable propagation conditions, i.e., when the spatial orthogonality between UEs is not sufficient, the available radio resources have to be appropriately distributed. Usually, if different UEs are served by other beams, they can be allocated with full available bandwidth in different time slots to avoid intra-cell interference. In cases where the same beam serves multiple UEs, the available bandwidth is split between these UEs accordingly. It may also be possible that only a single UE will be under the coverage of two neighboring beams. This would result in a doubling of the resources available from a single beam, i.e., UE can be served in two consecutive time slots.  
Even though, due to beamforming, mMIMO significantly limits inter-cell interference in reference to legacy MIMO, the problem of unavoidable re-use of training sequences, i.e., pilot contamination, by UEs in different cells still exists, and the inter-cell interference grows along with the number of base stations in the network [19]. Therefore, it is crucial that inter-cell interference, on top of intra-cell interference, is accurately modeled in the network planning and optimization stages, as well as accurately estimated and limited during network operation through sufficient precoding.
## 3 Background of the Methods Used
### Random Forest (RF)  
[PERSON] [58] provided RF that is an ensemble machine learning method, which is a decision trees algorithm and has a proper performance for classification and regression purposes. The RF algorithm consists of many decision trees through bootstrap aggregation (bagging) [59; 60]. For arriving at a final decision, this algorithm includes the combination of a result set of various decision trees, and each tree is commonly trained with choosing variables as well as data samples, randomly, from an initial training database [61].  
RF should be applied as follows to estimate the HL of buildings:  
(a): For ensuring the forest richness, the number of trees indicated was determined.  
(b): The remaining amount that had been utilized for validation were named out-of-bag (OOB) data, bootstrap with replacement by the principal HL training database.  
(c): A non-pruning regression was expanded to enhance each node, for each bootstrap sample.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Kernel Functions** & **Type** \\ \hline \(K(x,y)=x\cdot y\) & Linear kernel \\ \(K(x,y)=[(x\cdot y)+1]^{d}\); \(d=(1,2,\ldots)\) & Polynomial kernel \\ \(K(x,y)=\exp[\frac{-|x-y|^{d}}{\delta^{2}}]\) & Radial primary kernel function \\ \(K(x,y)=\tanh[d(x\cdot y)-\delta]\) & Two-layer neural kernel \\ \hline \hline \end{tabular}
\end{table}
Table 1: Kernel functions.  
(d): OOB should be utilized to estimate the HL and mean of whole trees at each bootstrap iteration.  
(e): Performance indices including RMSE, R\({}^{2}\), MAE, VAF, and MAPE could be used to analyze the predicted HL amounts on OOB.
## 4 Dual-Band MIMO Antenna Designs  
A dual-band antenna transmits and receives radio signals at two different frequencies. These antennas may use any of the two frequencies separately or both at the same time, depending on their arrangement and applications. Dual-band antennas are widely used with 2.4 GHz and 5 GHz frequency bands. Dual-band allows for quicker speeds and greater versatility. As a result, the dual-band eliminates connection problems and provides more reliability, flexibility and stability. The high gain and omnidirectional Wi-Fi coverage of dual-band antennas [42] make them perfect for a wide range of applications, including vast interior areas, warehouses, buildings, naval installations and many more. Figure 9 shows a design approach for dual band MIMO antennas. The dual-band MIMO antenna designed for WLAN application is made up of closely spaced symmetric MIMO antennas with a 5.3 mm edge-to-edge spacing [43].
## 4 Proposed Empirical Models for Weather Factors  
In this section, we introduce empirical models for the extinction and backscattering coefficients for weather factors including fog, rain, snow, dust, and particulate matter. Both coefficients are highly dependent on the wavelength of the laser beam. For the purpose of this work, a wavelength of 905 nm was selected to calculate the models, which is commonly used in automotive lidars.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 5 Data Validation
### Data Validation Through Anomaly Detection Algorithms  
The validity of the dataset is further assessed using anomaly detection algorithms. These algorithms identify unusual or unexpected data points that could indicate errors in the data collection process, sensor malfunctions, or significant shifts in water quality.  
Figure 17: Bland–Altman plots for pH, TDS, and turbidity measurements. The red dashed line represents the mean difference, while the green dashed lines indicate the 95% limits of agreement.  
Anomaly detection helps to identify problematic data that might otherwise skew analysis and reduce the overall quality of the dataset.  
Anomalies in water quality data can be caused by a variety of factors. For example, a faulty sensor can lead to inaccurate readings, such as a pH sensor that consistently displays a neutral value of 7.0, which does not correspond to the actual water pH, indicating that the sensor is damaged or out of calibration. Similarly, electrical noise or signal interference can cause erratic fluctuations in readings from sensors such as TDSs, often due to poor grounding or nearby electronic equipment. Anomaly detection algorithms can help to identify these irregular data points by flagging readings that deviate significantly from expected values, allowing for accurate data validation and ensuring reliable water quality assessments.  
In this study, a standardized dataset partitioning strategy was used to structure the water quality monitoring data. The original dataset was split into training, validation, and testing sets in a 7:3:1 ratio.
## 3 Controller Design
### Nonsingular Terminal Sliding Mode Control  
The nonsingular terminal sliding surface used in this paper is shown in Equation (6).  
\[s=\alpha e+\dot{e}^{\lambda} \tag{6}\]  
\[\dot{s}=\alpha\dot{e}+\lambda\ddot{e}\cdot\dot{e}^{(\lambda-1)}=\alpha\dot{e}+( \ddot{x}_{d}-\ddot{x})\cdot\lambda\dot{e}^{(\lambda-1)} \tag{7}\]  
where \(e\) and \(\dot{e}\) are the system error and a derivative of the error, \(\alpha>0\), \(1<\lambda<2\). Equation (7) is the time derivative Equation (6). \(\ddot{x}_{d}\) is the second derivative of the reference signal, and \(\ddot{x}\) is the second derivative of the output. The sliding surface Equation (6) uses only the exponential term for the error derivative term. The conventional sliding surfaces use exponential terms and control gains. However, we use it as shown in Equation (6) to maintain the characteristics of NTSMC while resolving the disadvantage because it is difficult to tune the control gains for use in the additional controller described later.  
Next, to ensure system stability and design the controller, we consider a Lyapunov function candidate such as Equation (8).  
\[V_{1}=s^{2}/2 \tag{8}\]  
Its time derivative is  
\[\dot{V}_{1}=s\dot{s} \tag{9}\]  
Substitute Equation (7) into Equation (9)  
\[\dot{V}_{1}=s\dot{s}=s\cdot\left\{\alpha\dot{e}+(\ddot{x}_{d}-\ddot{x})\cdot \lambda\dot{e}^{(\lambda-1)}\right\} \tag{10}\]  
Substitute Equation (5) into Equation (10)  
\[\dot{V}_{1}=s\dot{s}=s\cdot\left\{\alpha\dot{e}+(\ddot{x}_{d}+M^{-1}(C+G)-M^{- 1}u-D)\cdot\lambda\dot{e}^{(\lambda-1)}\right\} \tag{11}\]  
To realize asymptotic stability \(\dot{V}_{1}<0\), we obtain the control input as  
\[u=M\cdot(\ddot{x}_{d}+\alpha\dot{e}/\lambda\dot{e}^{\lambda-1}-D+k\cdot\text{ sgn}(s))+(C+G) \tag{12}\]Equation (12) is the nonsingular terminal sliding mode control input. \(k\cdot\text{sgn}(s)\) is switching term, and \(k\) is switching term positive control gain. If \(D\) can be measured perfectly, the control gain \(k\) can be lowered, and we can design a controller that produces good control performance and less chattering. However, it is difficult to measure \(D\). Therefore, in this paper, we propose to compensate by approximating \(D\) using the RBF neural network.
# A Method for Building a Mixed-Reality Digital Twin of a Roadheader Monitoring System
## 3 Construction of the Digital Twin Monitoring System
### Construction of the Main Modules of the Digital Twin Monitoring System
#### 3.1.3 Human-Computer Interaction Interface  
The position and behavior information of the roadheader during underground operations is perceived through sensors, and then sent to the digital twin monitoring system via the network for analysis and identification, and the results are stored in a temporary array. The system then reads the corresponding data to complete the dynamic update of the information on the human-computer interaction interface.  
The human-computer interaction interface is divided into two parts, as shown in Figure 10. The right side shows the real-time position of the cutting head in the roadway section, as well as the running trajectory and cutting range of the cutting head. The left side shows information such as the yaw angle, pitch angle, and roll angle of the roadheader in the roadway, allowing the driver to fully understand the body posture of the roadheader.  
The gesture recognition function of Hololens2 is utilized and a program control panel is developed to complete the interaction between humans and machines. The program control panel is used to receive the control signals of the user, thereby achieving the behavior control of the digital twin of the roadheader, including the network information input module, parameter display components, functional menus, and the control panel of the roadheader (Figure 11).  
Figure 9: The scene of the roadheader in Unity3D.
## 3 Results and Discussion
### Validation of the HPLC Analytical Method Performance
#### 3.2.2 Accuracy of the Test Method  
Under the high-performance liquid chromatography conditions described in Section 3.1, spike recovery experiments were conducted by comparing measured concentrations with known standard concentrations. Recovery rates were calculated as shown in Equation (3) to assess the accuracy of the test conditions. Tests were typically performed at different addition levels (80%, 100%, and 120% of the analyte content in the sample), with each level sampled in triplicate. The average recovery rates were calculated using the external standard method. According to the data in Table 5, the recovery rates for HNS ranged from 99.78% to 100.35%, with average recovery rates between 99.93% and 100.17%, indicating that the method is highly accurate.  
\[\text{Recovery Rate}=\left(\frac{\text{Measured Concentration}-\text{Baseline Concentration}}{\text{Spiked Concentration}}\right)\times 100\% \tag{3}\]  
where Measured Concentration is the concentration of the analyte found in the sample after spiking; Baseline Concentration is the concentration of the analyte present in the  
Figure 6: Depicts the standard curves correlating the concentrations of (**a**) HNS and (**b**) DMF with their respective peak areas.  
sample before spiking; and Spiked Concentration is the known amount of analyte added to the sample.
# Characterization of Natural Gypsum Materials and Their Composites for Building Applications
###### Abstract  
Building retrofitting plays a key-role in energy saving and a growing interest is focused on insulating materials that allow a reduction in heat loss from envelopes with low thickness, by a process of reducing heating and cooling demand. In this context, a complete characterization of the physical properties of Moroccan natural gypsum materials was carried out. Basic information on the mineralogical, microstructure, thermal, mechanical, and acoustic characteristics of the rocks sampled from two Moroccan regions is provided. It was found that mineralogy, porosity, and water content are the main factors governing the development of the structure and the strength of the samples. The measured values of the porosity were 8.94%, the water content varied between 2.5-3.0% for the two studied typologies, coming from Agadir and Safi, respectively. Gypsum powder was used for fabricating samples, which were investigated in terms of thermal and acoustic performance. Thermal properties were measured by means of a hot disk apparatus and values of conductivity of 0.18 W/mK and 0.13 W/mK were obtained for Agadir and Safi Gypsum, respectively. The acoustic performance was evaluated in terms of absorption coefficient and sound insulation, measured by means of a Kundt's Tube (ISO 10534-2). The absorption coefficients were slightly higher than the ones of conventional plasters with similar thickness. A good sound insulation performance was confirmed, especially for Safi Gypsum, with a transmission loss-value up to about 50 dB at high frequency.  
Keywords:Moroccan gypsum; ultrasonic waves; Vickers hardness; gypsum plasters; thermal conductivity; acoustic performance +
Footnote †: journal: Applied Physics and Chemistry
# Semi-Supervised Time Series Anomaly Detection Based on Statistics and Deep Learning
## 3 Proposed Anomaly Detection Framework  
The proposed anomaly detection framework, Tri-CAD, for univariate time series is introduced in this section. As shown in Figure 3, Tri-CAD first classifies time series into three classes: (Class 1) periodic, (Class 2) stationary, and (Class 3) non-periodic and non-stationary time series. The classification is based on the Pearson product-moment correlation coefficient [36] and Dickey-Fuller test [37]. The threshold settings in Figure 3 for the Pearson product-moment correlation coefficient \(\rho\) and the Dickey-Fuller test \(p\)-value will be described later. The first \(D\) (say, \(D\) = 1000) data are assumed to be normal and are used for the classification. Below, in this paper, the set of the first \(D\) data is called the normal dataset or the training dataset, whereas the set of the rest of the data is called the test dataset.  
Tri-CAD first relies on the normal dataset to divide time series into three classes. Then, different schemes that use statistics, wavelet transform, and the autoencoder are applied to  
Figure 3: The proposed anomaly detection framework.  
Figure 2: The architecture of an autoencoder.  
different classes of time series. The schemes are applied to the normal dataset to generate statistic and deep learning features of the time series. Afterwards, the same schemes are applied to the test dataset to generate statistic and deep learning features. Finally, the concept of standard deviation is applied for anomaly detection. Specifically, if the statistic and deep learning features of the normal dataset and the test dataset have a large difference that exceeds a certain threshold, defined by the standard deviation concept, anomalies are assumed to occur. How Tri-CAD performs the classification and anomaly detection is described below for each class of time series.
# The Use of Artificial Intelligence for Assessing the Pro-Environmental Practices of Companies
## 3 Results
### Analysis of Data Obtained from the Questionnaires
The fact that the number of positive answers is far from the maximum reflects the specific character of business activity or the company's industry of operation--the implementation of only certain environmental solutions is sufficient from the perspective of environmental protection. In the group of SMEs, the results are comparable in all stages of the product's life cycle. The differences present in the implementation of environmental solutions at different stages are not great. The questions in the area of design are often referred to as highly advanced solutions, which are not in frequent use in SMEs. The questions referring to the \"Product recycling\" area were designed to obtain information on how the manufacturing companies are involved in handling products at the end of the life cycle and how much they understand regarding the handling of products after they are withdrawn from use. The participation of SMEs in recycling activities is minor. The results show that LEs are more advanced than SMEs in the implementation of environmental solutions. The most pronounced differences were observed during the \"product design\" stage. The LEs scored almost twice the score of SMEs. The stage of \"Product use\" was treated equally in both types of enterprises (the difference in the results is insignificant). On many occasions, the survey was conducted in the form of an interview. During the conversations, the respondents often revealed that little or no environmental activity in SMEs was often caused by insufficient funds or human resources. SMEs cannot afford to implement environmental solutions or have no qualified personnel to take charge of the implementation. Unlike SMEs, most of the LEs have personnel dedicated to environmental issues. Such an analysis of the results aims to present the level of environmental activity of the respondents at various stages of the product's life cycle, and the differences between SMEs and LEs in this respect.