esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Dataset  
Six healthy subjects (1 female and 5 males) volunteered to participate in the experiments. The mean \(\pm\) standard deviation age of the subjects was \(33\pm 8.7\) years. The experimental procedure was explained in detail for each subject. A signed consent form was collected from each subject before participating in the experiments. Furthermore, the participants had the chance to withdraw from the study at anytime during the experimental procedure. The experimental procedure was reviewed and approved by the Research Ethics Committee at the German Jordanian University. In this study, each subject was asked to simulate four activities related to the fall event. These activities are: walking, sitting, falling from standing and falling from sitting. The subjects performed the activities several times, and each time, the activities were carried out with various speeds and styles to capture the inter- and intra-personal variations between different subjects. This experiment was performed in a laboratory environment in which a mattress was placed on the ground to protect the subjects during falling. In order to record depth-map video sequences of the aforementioned four activities, a Kinect Sensor for XBOX 360 (Microsoft, Redmond, WA, USA) with the Kinect SDK v1.0 beta 2 (Microsoft, Redmond, WA, USA) was utilized.The collected dataset consists of approximately 19,300 frames that represent 229 activity sequences. In fact, different activities have various time durations depending on the subject and the type of the activity. The average length of the recorded activity sequences is approximately 84 frames. The activity sequences were captured at a rate of 15 frames per second (fps). The resolutions of the acquired RGB images and depth maps are \(640\times 480\) and \(320\times 240\), respectively. Simultaneous to the RGB images and depth maps, the Kinect sensor enabled the acquisition of the three-dimensional (3D) coordinates of twenty skeletal joints at a rate of 15 fps. The acquired sequences of RGB images, depth maps and 3D coordinates of the skeletal joints that correspond to each activity were manually annotated into multiple temporal segments, such that each segment represents a sub-activity. For example, the falling from standing activity was divided into three temporal segments. The first temporal segment represents the standing pose sub-activity. The second temporal segment represents the falling from standing pose sub-activity. Finally, the third temporal segment represents the fall pose sub-activity. The sub-activities associated with each of the four recorded activities are provided in Table 1. Figure 2 provides sample images of the different sub-activities associated with the fall-related activities.  
Figure 2: Sample images of different sub-activities associated with the four fall-related activities investigated in this study.
## 3 Attitude Controller Design
### Experiment Results and Discussion
#### 4.2.3 Comparison Between I&I and PI Control  
The comparisons of the roll, pitch and yaw control performance between I&I and PI control are shown separately in Figures 8-10. The I&I control performs higher accuracy than PI control under disturbance.  
Figure 6: Attitude of NTAUV under PI control with disturbance on each axis.  
Figure 7: PI control torque.  
Figure 8: Roll Control Comparison between I&I and PI Control.  
Figure 9: Pitch Control Comparison between I&I and PI Control.  
The hydrodynamic force and moment are complex as the underwater vehicle works at a low Reynolds number (Re) condition, especially when hovering and for low-speed horizontal moving. The actual hydrodynamic force is very complex, because there is no stable flow field around. The disturbance rejection performance of I&I control is validated.
# Rolling Bearing Fault Diagnosis Based on CEEMDAN and CNN-SVM
## 3 CEEMDAN and CNN-SVM Fault Diagnosis Models
### GWO-SVM Classifier  
For nonlinear classification problems, SVMs use kernel functions to map the data from the original space to a higher dimensional feature space, thus making the data linearly differentiable in the higher dimensional space. Among them, the radial basis kernel function has few parameters and a simple model and is most widely used. Therefore, in this paper, the radial basis function is chosen as the kernel function, and its expression is:  
\[K(x_{i},y_{i})=\exp\left(-\frac{\|x_{i}-y_{i}\|^{2}}{2\sigma^{2}}\right) \tag{16}\]  
where: \(k(x_{i},\,y_{i})\) is the kernel function of the SVM, and \(\sigma\) is the kernel function parameter.  
The kernel function can effectively solve the problems of dimensional catastrophe and nonlinear separability, but the classification performance is easily affected by the penalty factor \(C\) and kernel parameter \(\sigma\). Therefore, in this paper, we used the optimisation-seeking ability of GWO to find the optimal parameter penalty factor \(C\) and kernel function parameter \(\sigma\) of SVM in order to improve the classification performance of SVM. The specific steps are as follows:  
1. Initialise the number of wolves \(M\) and the number of iterations \(N\), set the penalty factor \(C\) and the range of values of the kernel function parameter \(\sigma\), initialise the location of the wolves and calculate the value of the individual initial fitness.
2. Using the error rate as the objective function, the parameter penalty factor \(C\) and the kernel function parameter \(\sigma\) are used as the prey for the optimisation search.
3. When the objective function value is smaller than the individual fitness value of the grey wolf, update the individual fitness value to the current optimal objective function value.
4. When the maximum number of iterations is reached, the optimal penalty factor \(C\) and kernel function parameter \(\sigma\) are obtained, and the optimal parametric SVM classifier is built.
## 2 Materials and Methods
### ESEM-EDX Evaluation  
A high-speed, water-cooled handpiece with a diamond cylindrical bur was used to cut two longitudinal grooves in each root from the apex to the coronal section. To divide the root into two longitudinal parts, grooves were cut with a chisel. To conduct the ESEM-EDX analysis, one of the two halves was randomly chosen.  
ESEM analysis was carried out at 100\(\times\) and 3000\(\times\) magnification to observe macro- and micro-morphology of root canals dentin surface and the location of remnants.  
Without any prior preparation, the specimens were placed straight on the ESEM stub and analyzed (uncoated samples). Operative parameters were as follows: low vacuum (100 pascal), accelerating voltage of 20-25 kV, working distance 8.5 mm, and 133 eV resolution in Quadrant Back-Scattering Detector (QBSD) mode (0.5 wt% detection level, amplification time of 100 \(\upmu\)s, and measuring time of 60 s).  
EDX spectroscopy was used to identify the elemental composition of remnants. The ZAF correction approach, which involves calculating independent corrections for the atomic number effect (Z), absorbance (A), and fluorescence (F), was used to investigate the qualitative and semiquantitative element (weight% and atomic%) content [10]. The  
Figure 4: CBCT representation after root canal filling (**A**) and after SRCTs with RB #40 (**B**). Remnants are still present in the middle third of the root canal after SRCTs. Each CBCT representation was examined with HOROS software to measure the volume of filling material after SRCTs (**C**). The volume of remnants was identified using a semiautomatic segmentation based on the values of the grayscale (gray values for dentin, black values for the root canal, and white for filling material). The region of interest (ROI) was then determined and reconstructed in 3D to calculate the volume of remnants (**D**).  
compositions of sealer and gutta-percha (i.e., Zr, W, Zn, etc.) were used to identify the type of remnants (sealer, gutta-percha, or dentin smear layer). Three measurements were taken for each sample.
## 4 Performance Evaluations
### Traffic Analysis
Figure 6 shows the same results for a link bandwidth of 200 Mbps with and without the _ProxyTV_ approach. According to Figure 6b, an ISP can archive an acceptable packet loss rate (<5%) in the service up to 250 users even at two third of the required maximum link bandwidth for all the channels with _ProxyTV_. To achieve the same loss rate, it can only serve up to 50 customers using _StdTV_. Figure 6c shows the number of multicast sessions that consume the DiffServ EF bottleneck for this link bandwidth is also significantly lower for _ProxyTV_ than for _StdTV_.
## 3 DeENT  
This section presents the DEENT approach comprising DEENT-Generic, DEENT-Bert, and a tweets dataset built for evaluation purposes and labeled (non-depressive and depressive) with BERT and K-means. DEENT is based on the encoder-only Transformer architecture to detect depression from Twitter data collected during the COVID-19 pandemic. Detecting depression in social network data is a binary classification problem. The encoder-only Transformer's architecture has proven particularly effective for tasks requiring text comprehension [30,31], as binary classification is; note that tweets expressing depression messages rose exponentially during the COVID-19 pandemic. By following the encoder-only Transformer architecture, DEENT involves five elements [22,32,33,34]: Input Embedding, Positional Encoding, Multi-head Attention, Feed Forward, and Add & Norm. Input embedding represents the input text in a vectorial way, in which a token represents a word; it helps a model to process and understand patterns from the text input data. The Positional Encoding occurs after input word embedding and before the encoder. This encoding is necessary since, without positional information, an encoder-only model might believe that two sentences have the same semantics. Multi-head Attention involves implementing attention mechanisms in parallel; an attention mechanism improves model performance by focusing on relevant information, meaning the Transformer selectively attends to different parts of the input text data by assigning weights according to the relevance of features for the model's output. Each head attention processes the input independently, allowing it to capture the different aspects of the relationships between words. The Feed Forward consists of two linear neural networks separated by a nonlinear activation function, helpful for learning more complex patterns. Add & Norm combines a residual connection (Add) and a normalization layer (Norm). Add facilitates the training of
## 5 Conclusions  
In the present work, we have established a novel hybrid model for day-ahead solar radiation prediction, based on the mesoscale meteorological WRF model and the clearness index-based Kalman filter post-processing procedure. The hybrid model has been validated at two study sites, i.e., Maizuru and Qinghai, in Japan and China, respectively.  
The WRFv3.9.1 model, with a high horizontal resolution of 0.5 km \(\times\) 0.5 km, is chosen. A sensitivity test of the radiation parameterization schemes was firstly conducted to tune the WRF model for solar radiation forecasts. Based on the results of sensitivity tests, the RRTM longwave scheme is adopted for both sites; the schemes of RRTMG shortwave and Dudhia shortwave are chosen for Maizuru and Qinghai sites, respectively.  
Then, the forecasting ability of the WRF model for solar radiation has been validated at two sites. The results indicate that the WRF model has a relatively high ability of forecasting short-term solar radiation, though there are differences among different sky conditions and different locations.  
For the sake of improving the raw WRF forecasts of solar radiation, the KF_Kt is conducted, and the result was compared with the corresponding observations, as well as the results of KF_TS. The KF_Kt is able to largely improve MBE by around 80% and RMSE by 20% in day-ahead solar radiation under clear and cloudy sky conditions at the Maizuru site. The performance of KF_Kt is better at the Qinghai site than the Maizuru site. The result also shows the notable advantage of KF_KT against KF_TS for both sites. A relatively small impact of KF_KT under overcast conditions can be found at both study sites.  
The hybrid solar radiation model proposed in this present work can be expected as an effective tool for day-ahead operational control for any target sites. It also indicates some new directions worthy of further investigations, for example, development of a more reliable approach which is able to search and group the solar radiation forecasts with similar characteristics of forecasting errors.  
Conceptualization, Y.C. and F.X.; Methodology, Y.C.; Formal analysis, Y.C.; Investigation, L.Y. and L.C.; Validation, L.C., Y.C. and J.Z.; Supervision, F.X.; Writing--original draft, Y.C.; Writing--Review and Editing, F.X. and Y.C.  
This research was funded by the National Natural Science Foundation of China (Grant Nos. 41905084 and 2018 YFC1507000) and the Scientific Research Foundation of Chengdu University of Information Technology (Grant No. KYTZ201813).  
The authors declare no conflict of interest.  
Figure 9: The same as in Figure 8, but for the Qinghai site.
# A Continuous Monitoring and Reallocation Method for Successful Decisions in Change Management
###### Abstract  
Businesses and organizations often struggle to cope in a rapidly changing and competitive environment, requiring changes with increasing frequency and complexity. The evolution of information systems has enabled direct access to information and data, which can be utilized for the effective implementation of changes. This usually requires managing resistance to change, which is often presented by employees. The aim of this study is to address the problem of change management in order to limit the decline in overall performance during the critical period of change and to accelerate the implementation of change. This study provides a new innovative approach to managing employee resistance during the change process based on a continuous monitoring and reallocation algorithm. The proposed method introduces the idea of solving the human resource allocation problem by continuously readjusting staff allocation during the change process. The Hungarian algorithm is used to optimize assignments. In this way, the resistance of each employee is taken into account regularly, and the assignment can be altered in the early stages of change. The new method is mathematically formulated and described in detail through an algorithm, which is then used in experiments. The proposed method of frequently reallocating human resources to tasks leads to better overall performance and improves decision making during change processes. The experimental results show that the new approach significantly increases the total performance by up to 124% when compared to existing change management approaches and reduces the time required to achieve the desired state by up to 20%. Thus, the enhanced management of human resistance to change provides distinct advantages over traditional methods by ensuring more dynamic, timely, and adaptive resource allocation during the change period, ultimately leading to successful decision making and sustainable change management.  
Keywords:resistance to change; human resource allocation; change management; decision-making algorithms; information systems implementation +
Footnote †: journal: _Algorithms_
## Abbreviations  
RMSRoot mean squareASOSMAdaptive second-order sliding modeGNSSGlobal Navigation Satellite SystemPDProportional-differentialPIDProportional-integral-differentialMATLABMatrix LaboratoryCANController Area NetworkEfiMPCEfficiency-oriented path tracking control algorithmMPCModel predictive controlIA'--F-SMCImproved A'algorithm--fuzzy sliding mode variable structure control
# 2D Triangulation of Signals Source by Pole-Polar Geometric Models
## 3 Experimental Cases
### Quality of Acquired Data  
Figure 15 shows three sets of acquired data quality. Figure 15a,b are poor data quality and Figure 15c shows satisfactory data quality. Figure 15a shows that AP6 and AP7 overestimate and AP4 e AP5 underestimate the respective radial signal range. Although AP8 is more specific, the incoherence of data diverts the solution from the desired precision, independent of the method used to formulate the estimation. In light of this, [27] provided an alternative to improve the quality of the acquired data, [28] discussed the limits of localization using signal strength by a comparative study and [29]  
Figure 14: Structure composed of five Access Point (AP) devices and their respective position on an outdoor environment.  
performed sensor localization under limited measurement capabilities. References [15; 30] confirm that an optimal solution involves high computational cost, but obtaining the optimal solution is not guaranteed. This is because the errors are not in the methods that perform the triangulation of the signal data. The errors are in the signal data.  
With regard to the problem of localization in wireless sensor networks [31; 32; 33; 34] presented alternative approaches and concerns regarding signal data behavior. To achieve better results, [34] preprocessed data after processing a set of partial results to decide which the best one was. As it can be noticed, acquiring accurate signal data is an arduous task. References [5; 35] proposed methods for sampling signal data acquisition and discussed uncertainty and signal location. Besides, the environment is not predictable with multipath, fading, interference and shading effects [16].  
In Figure 15a, AP6 generates a signal range that covers an area of \(\approx\)1200 m\({}^{2}\) on the ground, while the area covered by the APs system is \(\approx\)150 m\({}^{2}\) (Figure 14). Therefore, this data incoherence leads to solutions with larger errors.
# Machine Learning-Assisted Secure Random Communication System
## 5 Reception
### AWGN Channel  
The AWGN channel **A**, as shown in Figure 4, distorts the transmitted noise sequences via the distribution given below  
\[\textbf{A}\ \sim\ S_{\alpha_{A}\sim 2}(\beta_{A}=0,\ \gamma_{A}\,,\ \mu_{A}\ =\ 0) \tag{5}\]  
The impairments introduced by **A** are similar to the random characteristics inherently built into **R**. Hence, the conventional criterion'signal-to-noise ratio' is not applicable for measuring the performance of unconventional RCSs. Therefore, the effects of channel impairments on **R** are usually analyzed via the criteria of the dispersion ratio, scale ratio, or mixed signal-to-noise ratio (MSNR). This was introduced in [63] as follows:  
\[\text{MSNR}_{\text{dB}}\ =10\text{log}\frac{\gamma}{\gamma_{A}} \tag{6}\]  
Using (6), the ML-RCS can be analyzed under various channel settings by changing \(\gamma_{A}\) to imitate the noise variants encountered by **R** while propagating through the AWGN channel. The ranges of \(\gamma_{A}\) and the corresponding MSNRs utilized in this study are summarized in Table 3.
# Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa
## 2 Materials and Methods  
In this study, a simulation based on fuzzy logic techniques embedded within the MATLAB version R2017b software simulation environment was applied. In order to simulate the proposed model, the MATLAB Fuzzy Logic toolbox was used. Then, the fuzzy prediction model was modeled and its performance behavior was observed, using a set of three input parameters: indices of PM\({}_{2.5}\), indices of SO\({}_{2}\) and indices of NO\({}_{2}\). The estimated Kampala Air Quality Index (KAQI) was considered as the output parameter. In order to process the fuzzy logic model, a rule-based [PERSON]'s fuzzy inference system was used and later, defuzzification processes followed.
# Landsat Surface Product Validation Instrumentation:
## 3 ASD and UAS Technology Assessment
### Accuracy  
The accuracy of the surface reflectance measurements is a primary factor for the operational validation of surface reflectance products. Four targets in the BigMAC exercise (two Permaflects panels and two Mystery panels) had recently been calibrated for accuracy. These panels were used to assess the accuracy of ASD- and UAS-based reflectance measurements. Different calibration procedures and traceability paths were used for the two sets of targets. This is described in detail in the following sections.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multicolumn{10}{c}{UAS Representative-SWIRE Band} \\ \hline \multicolumn{10}{c}{Input-based \(\mathrm{
# Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications
## 4 Estimation of GHI from Other Meteorological Data
### Results
A bibliographical study [5] was realized on ANN methods used for such an estimation of GHI from exogenous meteorological data and this study showed that:  
* For the estimation of monthly mean values of daily GHI, the nRMSE was between 4.07% and 9.4%, but the process, on average, monthly, allows smoothing of the anisotropic effects and sometimes linear relationships are sufficient to link GHI with other parameters. * For the estimation of the daily GHI, nRMSE around 6% and nMAE around 5% were found. The time granularity was much higher than in our work. Note that the determination coefficient (R\({}^{2}\)) between measured and estimated data was between 0.86 and 0.95 for the four graphs in Figure 5.  
The mean bias error (MBE) was also computed for Figure 5 and was equal to \(-\)0.08 Wh/m\({}^{2}\), for 10 and six inputs, \(-\)1.14 Wh/m\({}^{2}\) for nine inputs without S, and \(-\)1.87 Wh/m\({}^{2}\) for five inputs; thus, all the ANN models slightly underestimated GHI.
# Electronic and Transport Properties of Epitaxial Graphene on SiC and 3C-SiC/Si: A Review
## 4 Electronic and Transport Properties: Effect of Interactions with the Surroundings
### Epitaxial Graphene-Substrate Interaction
#### 4.1.4 Impact of Growth on (100) and (111) 3C-SiC/Si
The graphene is strongly p-type doped (carrier concentration \(\sim\)10\({}^{13}\) cm\({}^{-2}\), mobility \(\sim\)80 cm\({}^{2}\cdot\)V\({}^{-1}\cdot\)s\({}^{-1}\) in EG/3C-SiC(100)) as a result of the EG-substrate interaction, which comprises of silicates (charge transfer from EG into the silicates) produced at the interface by the alloy-mediated synthesis. The buffer layer presence in EG/3C-SiC (111) reduces the charge transfer and improve the mobility almost five times compared to EG/3C-SiC (100) at a value of  
Figure 11: XPS C 1s core-level spectrum for (**a**) EG/3C-SiC (100) and (**b**) EG/3C-SiC (111). Buffer layer components are visible for EG/3C-SiC (111). Reprinted with permission from Reference [21]. Copyright (2020) American Chemical Society. 330 cm\({}^{2}\cdot\)V\({}^{-1}\cdot\)s\({}^{-1}\) and carrier concentration of 10\({}^{12}\) cm\({}^{-2}\) (p-type). The effect of interface silicates on the charge transport of EG on (100) and (111) 3C-SiC are in agreement with DFT calculations [21]. The transport measurements on EG also indicated that, within the observed diffusion regime (mean free path 3-10 nm), the grain sizes (\(<\)100 nm) and the number of graphene layers has no effect on the charge transport of EG. Further support to the lack of correlation of the grain sizes with the EG charge transport characteristics can also be found in the work by [PERSON] et al. [9; 10]. A domain size of about 1 \(\upmu\)m for EG/3C-SiC(111) was reported for the EG at a sheet carrier concentration of \(\sim\)\(2\times 10^{13}\) cm\({}^{-2}\) via the linear dispersion of DOS [66]--a value in line with the carrier concentration of EG/3C-SiC(111) from Reference [21] with grain sizes \(<\)100 nm.