esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion  
### Parameters for Tunnel Flow Coefficient  
In the present study, the temperature, wind speed, number of vehicles, and types of vehicles inside of the tunnel, were analyzed to determine the seasonal value of tunnel flow coefficient. The temperature inside of the tunnel appeared as in the following order of \"Summer (38.69 \({}^{\circ}\)C),\" \"Fall (25.33 \({}^{\circ}\)C),\" \"Spring (20.38 \({}^{\circ}\)C),\" and \"Winter (8.13 \({}^{\circ}\)C),\" wherein the seasonal difference of temperature appeared exceeding 30 \({}^{\circ}\)C (Figure S1). With regard to the TFC, it is determined by variable parameters such as wind speed inside of the tunnel and number of vehicles passing through the tunnel rather than effects of fixed parameters, such as the area of cross section or length of the tunnel. Among the two kinds of variables, the traffic volume was counted accurately by using the HD Digital Wave Radar (Smart Sensor HD Model 126, Wavetronix, Provo, UT, USA). The measurements of wind speed inside of the tunnel may accompany uncertainties due to the measurements of hot wire anemometer (Digital Thermo Air Velocity Transmitter, FTS85, Eyc-tech Inc., New Taipei City, Taiwan). In the present study, the measurements of hot wire anemometer and pinwheel anemometer (Watchdog 2000 Series, Spectrum Technologies, Aurora, IL, USA) were compared to each other for the verification of measurements. The results revealed the uncertainty rate less than 2% from the comparison of wind speed of approximately 3.0 m/s, 4.8 m/s, and 5.8 m/s under the two temperature conditions of 19.8 \({}^{\circ}\)C and 36.8 \({}^{\circ}\)C. The wind speed inside of the tunnel appeared as in the following order of \"Fall (6.15 m/s),\" \"Winter (4.96 m/s),\" \"Spring (3.80 m/s),\" and \"Summer (3.47 m/s),\" wherein the difference of 2.68 m/s, between seasons of fall and summer, appeared. The traffic volume (#) in each season appeared as in the following order of \"Winter (3183 #/h),\" \"Spring (3048 #/h),\" \"Fall (3009 #/h),\" and \"Summer (2926 #/h).\" The speed of vehicles passing through the tunnel exhibited slight but insignificant seasonal differences to each other as in the following order, \"Winter (70.6 km/h),\"\" Spring (68.88 km/h),\" \"Fall (68.39 km/h),\" and \"Summer (67.02 km/h).\" In regard to the average number and speed of vehicles measured hourly (Figure S1), the number of traveling vehicles appeared decreasing from 2500 to 1000 vehicles from 00 to 04 AM, and thereafter, the number of traveling vehicles increased gradually to the 08 AM. Then, the number of traveling vehicles kept on the level of approximately 3500 vehicles constantly. On the contrary, the speed of vehicles manifested the aspects opposite to the number of vehicles. The speed of vehicles appeared gradually increasing from 00 to 04 AM and reached the peak speed of 92 km/h, and then it manifested the decreasing trend from 04 to 08 AM along with the increase in number of traveling vehicles. After 08 PM, the speed of vehicles was kept constant in the range 60-70 km/h.
# Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction
## 3 Experiments
### Results and Discussion
#### 3.4.2 Effectiveness of Graph Construction  
According to the analysis of relationship graphs between metro stations in the previous section, the network structure correlation graph and the passenger flow similarity graph are two important components in passenger flow prediction. In this section, we conduct ablation experiments to assess the significance of introducing these two components. With the intent of removing the graph components, we conducted four experiments as follows.  
MRDSCNN (No \(G\)): MRDSCNN without graphs \(G\).  
MRDSCNN (with \(G_{c}\)): MRDSCNN with passenger flow similarity graph \(G_{c}\).  
MRDSCNN (with \(G_{s}\)): MRDSCNN with network structure correlation graph \(G_{s}\).  
MRDSCNN: Complete MRDSCNN.  
Tables 4 and 5 show the outcomes of experiments evaluating the individual contributions of pre-defined graph components. As we can see, any graphical component is important, and removing any component degrades the performance of the whole model. Specifically, the passenger flow similarity graph performs significantly well, especially on the RMSE, indicating that the incorporation of information from non-neighboring stations substantially enhances the model stability. In contrast to models without graph structures, our proposed approach exhibits remarkable enhancements in predicting passenger inflow across various time granularities, yielding reductions of 6.63%, 7.54%, and 8.03% on the RMSE. Similarly, predictions of passenger outflow also reap the benefits, with reductions of 7.27%, 4.00%, and 8.24% on the RMSE. Each graph component distinctly improves the performance of the MRDSCNN model. Crucially, the collaborative amalgamation  
Figure 13: The heatmaps of passenger flow similarity adjacency matrices. (**a**) A 10 min passenger inflow; (**b**) 15 min passenger inflow; (**c**) 30 min passenger inflow; (**d**) 10 min passenger outflow; (**e**) 15 min passenger outflow; (**f**) 30 min passenger outflow.  
of both graph components assumes a pivotal role in fostering heightened stability and predictive proficiency.  
Figure 14 shows the observed passenger inflow and outflow at Fengqi Road Station throughout the day. The comparison results of different ablation experiments prove that the proposed model and its variants are in very good agreement with the actual passenger flow. The model with the graph components at Fengqi Road Station performs better, especially during the evening peak in passenger entry and the morning peak in passenger exit.
## 2 Materials and Methods
### Statistical Analysis  
The values of the six adopted parameters and of the three reference parameters from the literature, \(J_{B}\), \(Fl_{B}\), and \(RMS_{G,B}\), were computed for all acquisitions. The mean value for each of the parameters was calculated for each task (three repetitions of each PT for all subjects). Descriptive statistical analysis was then performed on the obtained data (mean \(\pm\) standard deviation). Non-parametrical analysis was performed on the data, as the number of subjects in the included population was too low. Mann-Whitney test was performed on the parameters to test the presence of statistically significant differences between the healthy and pathological samples for each approach. No comparisons between the two approaches were performed. The computation was performed with the values obtained applying the parameter definitions adopted in this study and with the values of \(J_{B}\), \(Fl_{B}\), and \(RMS_{G,B}\) calculated according to their literature definitions ([PERSON] et al., 2018) for comparison.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Adopted Definition**} & \multicolumn{3}{c}{**Measurement Unit**} \\  & & **Adopted Definition** (\_B) & **Adopted Definition** & **Traditional Definition** \\ \hline _Total Time (TT)_ & \multicolumn{3}{c}{\(TT=\text{Stop}-\text{Start}\)} & [s] \\ _Smoothness (J)_ & \(J=\frac{1}{TT}\sum\limits_{t=Start}^{Stop}|j_{t}|\) & \(J_{B}=ln\left(TT^{3}.\sum\limits_{t=Start}^{Stop}|j_{t}|\right)\) & [m/s\({}^{4}\)] & [m] \\ _Fluency (Fl)_ & \(Fl=\frac{1}{TT}\sum\limits_{t=Start}^{Stop}|a_{bj}|\) & \(Fl_{B}=ln\left(TT^{2}.\sum\limits_{t=Start}^{Stop}|a_{b|t}|\right)\) & [m/s\({}^{3}\)] & [m] \\ \(RMS_{G}\) & \(RMS_{G}=\frac{1}{3}.\sum\limits_{i=1}^{3}rms\left(\omega_{i}\right)\) & \(RMS_{G,B}=\sqrt{\frac{\sum_{i=1}^{stop}\omega_{i}\omega_{i}^{2}}{f_{s}T}}\) & [\({}^{\circ}\)/s] & \\ \hline \(RMS_{j}\) & \(RMS_{j}=rms\left(j\right)\) & - & [m/s\({}^{4}\)] & - \\ Max. Jerk Variation (\(\Delta j\)) & \(\Delta j=j_{max}-j_{min}\) & - & [m/s\({}^{4}\)] & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definition of the adopted functional parameters (Adopted Definition) and of the reference parameters described in the literature (Traditional Definition, by [PERSON] et al. (2018)), according to the current notation. For the additional parameters \(RMS_{j}\) and \(\Delta j,j\) is evaluated within the time interval [start; stop].  
For the interpretation of the results, a significance level of 5% was considered. Finally, the presence of correlation among the adopted parameters in the two populations of healthy and pathological subjects was investigated through a correlation matrix (Spearman's Rho) and evaluated according to the conventions presented in Table 2.
## 4 The Preliminary Experiment of Atmospheric Density Inversion for SWARM-A
### Inversion of SWARM-A Atmospheric Density by Semi-Long-Axis Decay Method  
When calculating atmospheric density through atmospheric drag force according to Formula (1), the remaining atmospheric drag force, satellite mass, tangential velocity, and atmospheric density after multiplying the atmospheric damping coefficient and cross-sectional area are determined by 4.1 above. The satellite mass and tangential velocity can be directly read from orbit data, while the atmospheric drag force can be calculated through orbit variation and energy conservation law. These two calculation methods will be discussed in this section.  
We utilize level-1b data from the SWARM-A satellite for atmospheric density retrieval. Based on our analysis above, we attribute changes in orbital altitude primarily to work done against thermospheric atmospheric drag force. Under this drag force's influence, variations occur over time in the semi-major axis of the satellite's orbit; such changes are typically employed to describe alterations in orbital altitude. Furthermore, there exists a close relationship between atmospheric drag force and atmospheric density. Consequently, we employ variations in semi-major axis rate as a means to retrieve thermospheric atmospheric density.
###### Abstract
These features are considered to automatically build models with well-known classification algorithms [18]. In detail, supervised machine learning algorithms can be exploited in order to build predictive models starting from labeled radiomic characteristics. In this paper, a method that aims to detect the \(3+4\), \(4+3\) and \(4+4\) prostate cancer Gleason score is provided. We consider a set of radiomic features and, through supervised machine learning classification techniques, we label MRIs as related to \(3+4\), \(4+3\) or \(4+4\) Gleason score. With regard to the current state of the art, machine learning techniques were explored by researchers in this field. For instance, [PERSON] and colleagues [19] consider deep learning to discriminate between low- and high-grade localized tumors. They obtain an accuracy equal to 70%. They do not provide details about the considered architecture, anyway discriminating between low-grade and high-grade localized tumors. Differently, the method we propose is able to detect between the low and the high-grade cancers (for instance, the ones labelled with the \(3+4\) and the \(4+3\) Gleason scores). The authors in [20] experiment with three different state-of-the-art supervised machine learning algorithms with the aim to identify several types of cancer using gene expression as a feature vector. With regard to prostate cancer, they obtain the following results in terms of accuracy: 67.65%, 73.53%, and 67.65%, respectively, using the C4.5 decision tree, bagged algorithms. The main contribution of this paper is the detection of the \(3+4\), \(4+3\) and \(4+4\) Gleason scores exploiting calibrated machine learning through a set of radiomic features. The paper proceeds as follows: the next section presents the proposed method, Section 3 discusses the experimental analysis results, and conclusions and future works are drawn in the last section.
# Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms
## 4 Model Training and Evaluation  
In this work, we followed the machine learning life cycle to develop the models for location estimation. We started with data pre-processing, where row RSSI data were linearized and filtered. Additionally, we trained our models with the row RSSI dataset to understand the proposed filtering effect in location estimation accuracy. Then, the dataset was trained using ML models implemented using the scikit-learn ML library available in Python 3. All the models were developed on a Jupyter Notebook. Each stage of the implementation is explained as follows.
Our contribution is threefold: (i) to validate the gPCE surrogate model for a highly non-linear constitutive law such as the VN visco-elasto-plastic model, (ii) to perform a global SA and rank the influence of the model parameters and their combinations, and (iii) based on sensitivity results, to adapt accordingly the probabilistic framework for the ultimate goal of solving a parameter identification problem via ensemble-based Bayesian updating schemes. The gPCE surrogate is used in combination with the ES (gPCE-ES) and ES-MDA (gPCE-MDA) to solve the inverse problem based on measurements of vertical displacements, by sampling the ensemble members from the gPCE expansion. This is possible at a very low computational cost, because only direct polynomial evaluations are involved instead of running a full ensemble of forward models, provided that the gPCE approximation is sufficiently representative of the actual geomechanical outcome. The paper is organized as follows. Section 2 includes the description of the governing equations of the forward simulation model, their approximation via gPCE expansion, the derivation of the Sobol' indices and the Bayesian-based updating schemes (ES and ES-MDA). Section 3 presents the numerical test case focusing on the variability range of the significant uncertain parameters. The results are provided in Section 4, where uncertainty propagation via gPCE approximation is first presented, with further assimilation of surface displacements to update the model outcome and provide the best estimate of the model parameters. The result of the gPCE-ES updating is compared with the solution from the classical ES formulation and with the gPCE-MDA approach, showing the computational efficiency and algorithmic effectiveness of the proposed surrogate model for uncertainty quantification and numerical prediction purposes. Finally, some conclusive remarks close the presentation.
## 3 Proposed Approach
### Class Weights
#### 3.3.2 Auto Encoder (AE)
\[\overline{y}=\textit{softmax}\,\left(W^{(out)}\,h^{(l)}+b^{(out)}\right) \tag{17}\]  
In this context, \(\overline{y}\) represents the vector of predicted probabilities across multiple classes. The weight matrix \(W^{(out)}\) and bias \(b^{(out)}\) are associated with the output layer. The softmax function is utilized to convert the logits into probabilities, ensuring that the predicted values sum to one across all classes. (i) Binary Classification  
The architecture outlined in Table 21 presents the layers of the Auto Encoder model designed for binary classification, tailored for both the NF-UNSW-NB15-v2 and CICIDS2017 datasets. The input block is configured to accommodate the unique features of each dataset. The NF-UNSW-NB15-v2 dataset processes data with 25 features, while the CICIDS2017 dataset handles 69 features. This input layer serves as the entry point for data, providing the foundation for the model's operations. The encoder structure comprises three dense layers, with 128, 64, and 32 neurons, respectively. Each dense layer utilizes the ReLU activation function, which introduces non-linearity and facilitates the extraction of complex patterns within the data. These layers effectively compress the input data into a lower-dimensional latent space, capturing the most critical features necessary for effective classification. The final output block consists of a single neuron activated by a sigmoid function. This layer generates a probability score indicating the likelihood of the input data belonging to the positive class, enabling binary classification. The architecture is designed to distinguish effectively between the two classes, ensuring robust performance across both datasets. This carefully structured model leverages its shared architecture to handle the unique characteristics of the NF-UNSW-NB15-v2 and CICIDS2017 datasets, enhancing its overall classification effectiveness.
# GNSS-Assisted Low-Cost Vision-Based Observation System for Deformation Monitoring
## 3 Results
### In-Field Experimental Study of the Displacement Measurements for a Single Chain
When determining the target displacement, the target image was cut from the first image and assigned as an input image. The target template of the same size was used as a reference image (Figure 21c). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Target**} & \multirow{2}{*}{ \begin{tabular}{c} **Target Center** \\ **(px)** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **Relative Target** \\ **Center (px)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Target Displacement between** \\ **Observation Epochs (mm)** \\ \end{tabular}  } & \multicolumn{2}{c}{**Manual**} & \multicolumn{2}{c}{**Displacement Error (mm)**} \\ \cline{2-13}  & \(\mathbf{n_{0}}\) & \(\mathbf{m_{0}}\) & \(\mathbf{n^{\prime}}\) & \(\mathbf{m^{\prime}}\) & \(\mathbf{\delta n}\) & \(\mathbf{\delta m}\) & \(\mathbf{\delta a}\) & \(\mathbf{\delta  
The QR target displacements were obtained by processing the image series. The displacement accuracy determination was found as the difference between measured and manually fixed displacement. The results for each test are presented in Tables 4-6. Displacement determination errors allow calculating the root mean square errors (19) of the displacement determination for each test measurement and tentatively estimating the accuracy of the phase correlation algorithm. \[m_{a}=\sqrt{\frac{\sum_{i}\Delta a_{i}^{2}}{i}};\ m_{b}=\sqrt{\frac{\sum_{i} \Delta b_{i}^{2}}{i}}. \tag{19}\]  
The results of the accuracy estimation are summarized in Table 7.  
The results in Table 7 are pretty close to the values in Figure 10 which describes a total error of measurements for the single chain. It was found that the larger the displacement, the larger the error of its determination. However, this dependency must not mislead, as for shorter distances we manually assigned larger displacements. In general, we may accept the RMS error equal to 6.5 mm and use this value for the simulation of the VOS that contains multiple chains with interrelated measurements.
# A Novel Adaptive PID Controller with Application to Vibration Control of a Semi-Active Vehicle
## 2 New Adaptive Hybrid Controller  
As mentioned above, the proposed control is established based on the fuzzy model, H infinity technique, and PID control. The combination of these controls relies on the advantages of each controller; the fuzzy model is used for the formulation of the dynamic model associated with the uncertainty, the PID controller is used to adaptively adjust control gains, and the H infinity technique connected with the PID controller is used to guarantee the robustness to uncertainties such as parameter variations and external disturbance. By combining several control algorithms, the stability of the control system subjected to heavy uncertainties can be improved and control performance can be easily tuned by adjusting control gains of each control logic.
## 3 Model Formulation and Scheme Design
### Communication Strategy
#### 3.4.2 Adjustment of the UAV's Moving Status  
After the planned path is obtained by the TGA algorithm, the UAV flies to each cluster in the order of the planned path. As shown in Figure 5, the area covered by the signal of the cluster head is a circle with a radius of \(r\) and the center at the position of the cluster head. The UAV begins to fly at a constant speed and altitude.  
According to DCMDR, the communication range of the cluster head is divided into different regions with different DRs. With the region closer to the cluster head, the DR assigned is higher.  
The longest distance the UAV can fly within the signal coverage of the cluster head is \(2r\) m. The UAV's flight speed is \(v\) m/s, the data rate obtained is \(m\) bit/s. Therefore, when the UAV flight speed is constant, the theoretical maximum amount \(b_{1}\) of data received by the UAV within the signal range of cluster head is as follows:  
\[b_{1}=2\sum_{DR=0}^{6}m_{DR}\cdot\frac{r_{DR}-r_{DR+1}}{v} \tag{14}\]  
According to (14), it can be seen that \(b_{1}\) is affected by \(m_{DR}\) and \(v\). When the data rate is certain, the faster the UAV's speed is, the smaller \(b_{1}\) will be. Thus, we assume the data size stored by the cluster head is \(b\) bits: there will be two cases. The first case is \(b\leq b_{1}\), when the UAV crosses the signal coverage area of the cluster head, data collection can be  
Figure 5: Adjustment of the planned flight path.  
completed without adjusting the speed. In addition, in order to reduce unnecessary flight distance, after receiving the data of the current cluster head, the UAV will fly to the next cluster head according to the visit sequence of the planned path, as shown in cluster 1 in Figure 5. The second case is when \(b>b_{1}\), that is, the UAV is moving at the current speed and cannot collect the data completely within the signal coverage of the cluster head. In order to complete data gathering efficiently, the UAV needs to reduce the speed within the signal coverage of DR6, as shown in cluster 2 in Figure 5. On the basis of obtaining the planned path through the TGA algorithm, using DCMDR and Adjustment of UAV's Moving Status, we call it the TGA-MOVING-DCMDR method.
## Data and Study Area  
### Site Description  
The study area is located in the province of Navarra in northeastern Spain, as shown in Figure 1. It comprises of 9052 crop parcels and occupies approximately 215 km\({}^{2}\).  
The Pyrenees Mountains extend over the northern half of the province, as they stretch southward from France. Navarra's landscape is characterized by a mixed relief of watered valleys and forested mountains. Its agricultural yield is comparatively low compared to the rest of the country due to farm fragmentation; however, the fertilizer usage in the community is far greater than the national average [25]. In this study, crop type maps are produced for the nine main crop types found in the available dataset, and this entails soft wheat (9217 ha), barley (4835 ha), oats (1554 ha), corn (255 ha), sunflower (602 ha), vineyards (250 ha), broad beans (826 ha), and rapeseed (1000 ha). These refer to the lowest level of the nomenclature hierarchy, as shown in Figure 2, and explain nearly 90% of the total number of parcels (9052/10,274 parcels) that is found in the regional agricultural zone.  
The average parcel area in the study site is approximately 2 ha, which according to [PERSON] et al. (2016) belongs to the smallholding category [26]. This can be interpreted as 200 Sentinel-2 pixels  
Figure 1: Study area located in the Spanish province of Navarra and the corresponding Sentinel-2A tile (S2A tile) covering it. The site refers to the agricultural zone surrounding the city of Pamplona.  
Figure 2: Crop type nomenclature adopted for Navarra, covering the nine main crop types that amount to almost 90% of parcels of the local agricultural zone. Crops are separated based on season, family, and type of cultivation.  
and less than 25 Landsat-8 pixels per parcel. The small average parcel size forms a challenging test site to showcase the capacity of methods and proves ideal for comparisons among sensors of different spatial resolution.
## 3 Results
### Consistency of Precise Products from Different Analysis Centers
#### 3.3.2 Glonaass
The single-satellite SISRE differences range from 3.5 to 10.1 cm, and the SISRE statistics are 5.2 cm for the GLONASS-K satellite R09, which are close to the results taking all GLONASS satellites into consideration (see Table 3). Thus, it seems that the consistency of precise products for the GLONASS-K satellite (R09) is comparable to that for GLONASS-M satellites. Figure 4: Epoch-wise orbit and clock differences for GLONASS satellites between GFZ and WHU precise products on DOY 122 of 2020.