esa-sceva/satcom-chunk-collection huggingface dataset:
# Soft Sensor Transferability: A Survey
## 4 Transfer Learning in SS Design
### Production Processes  
Quality prediction is tackled in [PERSON] et al. (2018) and [PERSON] et al. (2018) for production processes. Every time a change in production occurs, the process changes behavior, leading to the need for what is called incremental learning. In such a case, new target data are incrementally used to extend the source model's knowledge. In [PERSON] et al. (2018), an injection molding is considered. To assess the changes in the process behavior, an ANN is first trained on the source data and when the produced part is changed a new block of neurons, specially trained for the new part, is added, so that the model does not forget the knowledge from the previously learned parts. Such incremental learning approach is graphically compared, in terms of Correlation Coefficient (CC), to a baseline approach that does not adopt incremental learning but rather jointly trains the ANN on all data available to that point, when six different parts are produced. Results showed the incrementallearning approach maintains a value of CC over 0.95 with every part, while the baseline approach cannot handle the increasing complexity in the data with an incremental drop in CC for every newly produced part.  
In [PERSON] et al. [69], quality prediction on cement clinker is performed through the prediction of the concentration of free calcium oxide (f-CaO). Incremental learning is needed because of the process time variance. A data-driven model based on deep dynamic features extracting and transferring methods is applied to build a SS for cement quality prediction. A large semi-supervised dataset is used to extract nonlinear dynamic features through a deep Long Short-Term Memory (LSTM) encoder-decoder network with an attention mechanism. The features are then transferred to an eXtreme Gradient Boosting (XGBoost) regression model for output prediction. The method is compared to other different models, both static and dynamic, for f-CaO content prediction proposed in other researches, in terms of CC and RMSE. In particular, the proposed method showed an improvement of the 285% of the CC and a reduction of 74% in the RMSE with respect to a simple static PLS model, whereas an improvement of 67% in the CC and a reduction of 65% in the RMSE with respect of a dynamic LSTM model.
###### Abstract  
Precipitable water vapor (PWV) monitoring with real-time precise point positioning (PPP) is required for the improved early detection of increasingly common extreme weather occurrences. This study takes Hong Kong as the research object. The aim is to explore the accuracy of real-time global navigation satellite system (GNSS) PPP in estimating PWV at low latitudes and under different weather conditions. In this paper, real-time PPP is realized by using observation data from continuously operating reference stations (CORS) in Hong Kong and real-time products from the Centre National d'Etudes Spatiales (CNES). The Tm model calculated using numerical weather prediction (NWP) data converts the zenith tropospheric delay (ZTD) of real-time PPP inversion into PWV and evaluates its accuracy using postprocessing products. The experimental results show that compared with GPS, multi-GNSS can reduce the convergence time of PPP by 29.20% during rainfall periods and by 12.06% during non rainfall periods. The improvement in positioning accuracy is not obvious, and the positioning accuracy of the two is equivalent. Real-time PPP ZTD experiments show that there are lower average values for bias, standard deviation (STDEV), and root mean square (RMS) during nonrainfall periods than during rainfall periods. Real-time PPP PWV experiments show that there are also lower bias, STDEV, and RMS values during nonrainfall periods than during rainfall periods. The comparative study between rainfall and nonrainfall periods is of great significance for the real-time monitoring and forecasting of water vapor changes.  
GNSS; real-time PPP; ZTD; PWV; NWP Article
# Detecting Small Size Mass Loading Using Transversely Coupled SAW Resonator
## 4 Conclusions  
In the field of biosensors, small sizes of substances to be measured, such as cells and proteins, are common. However, small size loads often cause waveform distortion in longitudinally coupled resonators (LC-SAWR) and other sensitive chips. Further analysis shows that the cause of the waveform distortion is that the small size loads cannot completely cover the sensitive area of LC-SAWR, resulting in uneven load distributions on the surface of the sensor. In order to suppress the waveform distortion caused by the small size load, a transversely coupled SAW resonator (TC-SAWR) was designed and fabricated in this paper. For TC-SAWR, acoustic wave resonates transversely in its aperture, and the mass loading acting on the TC-SAWR will change the resonance frequency of each transverse mode, regardless of whether it covers the aperture completely or not, which avoids waveform distortions caused by the small size loads.  
On the other hand, accurate simulation of TC-SAWR has been a difficult problem for a long time. In this paper, 2D-COM theory and the finite element method were used to realize the precise simulation of TC-SAWR. The COM-parameters and the anisotropic parameters were calculated, then the 2D-COM equations of TC-SAWR were established. Finally, by using the PDE module of COMSOL, the 2D-COM equations could be solved numerically, and the response of TC-SAWR under SiO\({}_{2}\) load was obtained.  
For comparison and verification, TC-SAWR with a SiO\({}_{2}\) cylinder attached to its surface was fabricated and measured. Both the simulation result and the experiment result confirmed the effectiveness of the TC-SAWR device to conquer waveform distortions in the detection of small size load.  
This paper illustrated the advantage of TC-SAWR in the field of biochemical detection and introduced an effective method (2D-COM combined with FEM) to simulate the device's performance. Based on this investigation, TC-SAWR is expected to play an important role in biochemical detection.  
Conceptualization, R.Y. and J.L.; methodology, R.Y.; simulation, R.Y. and Z.C.; experiments, M.L. and Y.Z.; data analysis, R.Y. and J.L.; writing--original draft preparation, R.Y.; writing--review and editing, J.L. and S.H.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NO.12074404).  
Not applicable.  
This research was funded by the National Natural Science Foundation of China (NO.12074404).  
The authors declare no conflict of interest.
## 4 Discussion
Since the sensitivity of the sensor will inevitably be reduced, CGMS needs to be manually and algorithmically calibrated at regular intervals to maintain the accuracy of the monitoring. The SHPM-OS sensor in this study maintained stable sensitivity within 20 days, which could relieve the pain of people with diabetes caused by finger-sticking. Moreover, a typical amperometric glucose sensor has to experience complicated immobilization procedure and degradation of enzyme, oxygen limitation and fibrosis [5; 50]. Improving the loading enzyme capacity of the electrode [51; 52; 53; 54], optimizing the immobilization methods [55] and enhancing enzyme activity [32; 46] of GOD can slow down the degradation rate of the enzyme. The anti-inflammatory effect of nitric oxide and dexamethasone can increase the compatibility of tissue-sensor interface and improve the sensor lifetime [56; 57; 58; 59], but the duration of anti-inflammatory medication is affected by the pore size of membrane. Hence, a coat with anti-inflammatory function can be added to SHPM-OS membrane structure in the future to reduce biofouling-related problems and the speed of electrode fibrosis [18]. The adjustment of the combination of hierarchically porous layers to prepare another novel implantable glucose sensor can be then developed, which may optimize biocompatibility and monitoring lifetime while maintain the existing sensitivity and linearity.
## 5 Difference Analysis
### Difference Analysis Considering the Effect of the Incident Direction  
The difference in responses between the positive and negative incident angles can be described by the proposed Diff\({}_{\text{EDP}}\), which can be calculated by subtracting two physical quantities as follows:  
\[\text{Diff}_{\text{EDP}}=\text{EDP}_{\text{positive}}-\text{EDP}_{\text{ negative}},\quad\text{EDP}=\text{DVR}\quad\text{ or}\quad\text{U}_{\text{c}}, \tag{16}\]  
where \(\text{EDP}_{\text{positive}}\) and \(\text{EDP}_{\text{negative}}\) denote the EDP under NF motion in the positive and negative incident directions, respectively. Diff\({}_{\text{EDP}}\) can be rewritten as Diff\({}_{\text{DVR}}\) or Diff\({}_{\text{U}_{\text{c}}}\) for DVR and displacement indices.  
Figure 12 shows the variation in the average Diff\({}_{\text{EDP}}\) value with five incident angles for four seismic cases, including two types of motion and two seismic amplitudes. As shown in Figure 12a, the two types of NF ground motions have similar DVR differences at different seismic intensities, indicating that the pulse characteristics have little effect on the damage response in arbitrary incident directions. The response difference caused by the incident direction is almost negligible for the peak of the 0.3 g ground motion. The maximum Diff\({}_{\text{DVR}}\) values are 0.0425 and 0.0398 under 0.6 g PL and NP motion with an incident angle of 45\({}^{\circ}\), respectively.  
As shown in Figure 12b, a larger seismic amplitude can lead to a difference in the displacement response at each incident angle. The Diff\({}_{\text{U}_{\text{c}}}\) index decreases with an increase in the incident angle under 0.3 g NF motion, suggesting that the response difference caused by the positive and negative directions increases considerably when the incident angle is 15\({}^{\circ}\) owing to the upstream and downstream asymmetry of the dam body. For a motion amplitude of 0.6 g, the PL motion Diff\({}_{\text{U}_{\text{c}}}\) value is greater than that of the NP motion before 45\({}^{\circ}\) oblique incidence. After the 45\({}^{\circ}\) oblique incidence, the displacement response difference  
Figure 11: Responses ratio for different seismic oblique incidences: (**a**) DVR response ratio; (**b**) displacement response ratio.  
of the NP motion at positive and negative incident angles increases considerably as the incident angle increases. It is much larger than the PL motion. The results show that the incident direction and pulse characteristics considerably influence the structural response under strong NF motion.
# Sentiment Analysis of Arabic Course Reviews of a Saudi University Using Support Vector Machine
## 5 Methods  
### 5.1 _Svm-Saa-Scr_  
To develop our system, we used a C-Support Vector Classification (C-SVC) approach, which is widely used for classification problems and allows for tuning of the kernel type  
Figure 6: Correlation between Teamwork and Communication.  
\begin{table}
\begin{tabular}{l c} \hline Number of variables & 30 \\ \hline Number of observations & 324 \\ \hline Missing cells & 517 \\ \hline Missing cells (\%) & 5.3\% \\ \hline Duplicate rows & 0 \\ \hline Duplicate rows (\%) & 0.0\% \\ \hline Total size in memory & 76.1 KiB \\ \hline Average record size in memory & 340.4 B \\ \hline \end{tabular}
\end{table}
Table 3: Dataset Statistics.  
Figure 7: Variables Overview.  
and regularization strength to find the best performing model on the training data [32]. The proposed sentimental analysis algorithm for students' Arabic course reviews is as follows:  
1. Collect a dataset of student reviews for an Arabic course.
2. Preprocess the reviews by removing any irrelevant information such as special characters and numbers.
3. Tokenize the reviews to create a list of words for each review.
4. Create a sentiment dictionary or use pre-trained sentiment lexicon to classify each review as positive, negative, or neutral.
5. Use a machine learning model to train on the preprocessed and classified reviews.
6. Use the trained model to classify new reviews.
7. Analyze the results to identify patterns and areas for improvement in the Arabic course based on the sentiment classification of the reviews.
8. Repeat the process with updated reviews and continue to make improvements to the course based on the analysis of the results.  
The suggested algorithm is a general framework and different techniques can be used to preprocess and classify the reviews. Additionally, the algorithm can be improved by using additional techniques such as removing stop words, using stemming or lemmatization, and using advanced machine learning models.
# Feasibility of a Quasi-Static Approach in Assessing Side-Wind Hazards for Running Vehicles
## 2 Quasi-Static Approach for the Evaluation of Tire Forces
### Effect of Road Conditions: Cant and Curvature  
When the vehicle is running on a curved section of a bridge deck, the vehicle is subjected to additional centrifugal forces. Furthermore, the deck in a curved section inclines from the horizontal plane due to the cant by an angle \(\alpha\), which changes the direction of external forces, as shown in Figure 4. Those factors are taken into consideration, as shown by Equations (19)-(23).  
\[F_{S}^{{}^{\prime}}\ =\ \left(F_{S}+\frac{m_{tot}v^{2}}{R}\right)\cos\alpha+ \left(F_{L}-m_{tot}g\right)\sin\alpha \tag{19}\]  
\[F_{L}^{{}^{\prime}}\ =\ F_{L}\cos\alpha-\left(F_{S}+\frac{m_{tot}v^{2}}{R} \right)\sin\alpha+m_{tot}g(1-\cos\alpha) \tag{20}\]  
\[M_{R}^{{}^{\prime}}\ =\ M_{R}-\left(m_{f}+m_{r}\right)\left(\frac{v^{2}}{R} \cos\alpha-g\sin\alpha\right)d \tag{21}\]  
\[M_{Y}^{{}^{\prime}}\ =\ M_{Y}+\left(m_{f}\alpha-m_{r}b\right)\left(\frac{v^{2} }{R}\cos\alpha-g\sin\alpha\right) \tag{22}\]  
\[M_{P}^{{}^{\prime}}\ =\ M_{P}+\left(-m_{f}\alpha+m_{r}b\right)\left(\frac{v^{2} }{R}\sin\alpha-g(1-\cos\alpha)\right) \tag{23}\]  
In Equations (19)-(23), \(R\) is the radius of the curvature for the curved section, and \(m_{tot}\ =\ m_{s}+m_{f}+m_{r}\), \(d\) is the vertical distance between the sprung mass and the center of the wheel. \(F_{S}^{{}^{\prime}}\), \(F_{L}^{{}^{\prime}}\), \(M_{R}^{{}^{\prime}}\), \(M_{Y}^{{}^{\prime}}\), and \(M_{P}^{{}^{\prime}}\) are the updated side, lift forces, and the rolling, yawing, and pitching moments along the tilted x, y, and z axes, respectively, as shown in Figure 4. For simplicity, the aerodynamic coefficients of the vehicle were assumed to have not changed due to the angle of the cant.  
Road roughness and a driver model were not reflected in the quasi-static approach. [PERSON] [8] has shown that the degree of the surface roughness effect on the vehicle behavior is negligible compared  
Figure 4: External forces acting on the vehicle on a curved road with a cant.  
Figure 3: Vehicle speed \(v\), true wind speed \(w\), and apparent wind speed \(V\).  
with wind turbulence. The impact of roughness on vehicle stability could also be further mitigated by the vehicle's suspension and dampers. Therefore, leaving these minor factors as a future task, the next section focuses on an overall behavioral assessment. On the other hand, a driver behavior model is one of the influential factors that can induce large lateral and yaw displacement. Side-slip accidents can occur due to this large movement even though the friction forces of the tires have not reached a friction-limited state. As [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) pointed out, however, since no driver model has been well established, it would be difficult to consider this complex factor during the risk assessment procedure. Therefore, in the following sections, the driver behavior model was not taken into consideration for the evaluation of tire forces.
## 4 Results
### Baselines vs. Feed-Forward Neural Network
#### 4.1.1 Neck y-Moment  
We first present results corresponding to the neck y-moment. This particular curve is interesting because of the two different behaviors that are present in \(t\approx 0.9\) that can be observed in Figure 2. Models are expected to identify whether the direction of the second peak goes upwards or downwards. The training parameters are depicted in Table 1. Moreover, the optimized architecture that has been found with Hyperas is in Table 2.  
Once the model has been trained, its performance is assessed with the validation set. Two predicted curves for the three different models are presented in Figure 5. It can be observed that the two proposed multi-output regression models are incapable of accurately predicting the behavior of the second peak, while the FFNN does. This is also reflected in the evaluation metrics presented in Table 3, where it can be observed that FFNNs offer the best performance, while SST and ERC provide similar results, worse than the FFNN, as expected.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Selected Value** \\ \hline Learning rate & \(10^{-3}\) \\ Optimizer & Adam \\ Objective function & MSE \\ Number of epochs & 350 \\ Batch size & 100, all training samples \\ Validation split & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected training parameters for FFNN.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Selected Value** \\ \hline Number of layers & 5 \\ Neurons per layer & \((1024,512,64,1024,1150)\) \\ Activation per layer & (ReLU, ReLU, Sigmoid, ReLU, Linear) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected hyperparameters for FFNN in the neck y-moment.  
Figure 5: Comparison between predicted curves for the three proposed methods and the real curve chosen for two different test samples for the neck y-moment. “Real” stands for the ground truth, “Dense” for the FFNN prediction, “Baseline” for SST prediction, and “75 metamodels” for ERC prediction. Note that the best prediction is clearly given by the FFNN method, which is the one that best predicts the second peak in the first curve.
# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 3 Proposed Fingerprinting System
### Proposed Fingerprinting Strategies  
The four strategies introduced below provide the definition of distance \(d(i,j)\) and a criterion to break ties in the distances associated with two or more RPs during the sorting procedure. As anticipated in Section 3.1, the distance is also used to define the weight of the coordinates of the \(k\) selected RPs in (1), i.e., \(w(i,j)=1/d(i,j)\). The four strategies explore different ways to combine two pieces of information in defining \(d(i,j)\): the number of cells detected in both positions \(\mathbf{p}_{i}\) and \(\mathbf{p}_{j}\), and the similarity between the sets of measurements for the selected RF parameter collected in the two positions.
# A Roadside and Cloud-Based Vehicular Communications Framework for the Provision of C-ITS Services
## 3 Roadside and Cloud Architecture
### Roadside Architecture
#### 3.1.1 Roadside Unit (RSU)
In the implemented design, some RSUs are equipped with smartmicro's traffic radars [42]. The connections between them are established by short Ethernet cables, which are used by the RSU to connect via a local IP to fetch the sensed objects' processed and classified radar data. RSUs with IP cameras do not currently establish a direct connection with them, although they are connected to the same local Ethernet network. The IP camera feed is streamed over the Internet to the C-ITS platform.
## 4 Results
### Experimental Comparison with Existing Techniques  
The boosting algorithm, 'PA_B-J48', is compared with EM-Stat [79], TA [80], AM-Mouth [81], Ali [78], Lee [58], and Frid [82]. The comparison is made in terms of accuracy (A), sensitivity (Se), Precision (P), F-Measure (F), TP-Rate, FP-Rate, ROC-Area, and time efficiency with 10-Fold cross validation using Distraction (D) and No-Distraction (ND). Sensitivity, Precision, and F-Measure are defined in Equations (15)-(17), respectively.  
\[Sensitivity=\frac{TP}{(TP+FN)}, \tag{15}\]  
\[Precision=\frac{TP}{(TP+FP)}, \tag{16}\]  
\[F-Measure=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{17}\]  
where (\(TP\)) is the true positive, (\(FN\)) is the false negative, (\(TN\)) is the true negative, and (\(FP\)) is the false positive.  
The 'PA_B-J48' uses geometric and spatial features with J48 under a boosting framework. Facial fiducial points and motion vector-based interpolation for frontal and non-frontal faces are used to compute the geometric features. AdaBoost, which incorporates an ensemble learning approach, is used to combine various weak classifiers to form a strong classifier.
## 4 Application to Etna
### 12 April 2012  
On 12 April 2012, the volcanic plume detected by WR was foretold by almost three hours and two and a half hours, by IS and TC, respectively (Figure 7). This activity started in the evening of the day before as Strombolian activity, evolving to a lava fountain in the early afternoon of April 12. The fountaining was concentrated within an hour, in which the three systems presented a rapid and simultaneous rise of the estimated values. It was interesting to observe that: (1) both EW1 and EW2 of TC anticipated the IS values, highlighting a prolonged Strombolian activity, (2) both the EW levels of WR were quite close in time, and (3) all three systems showed a simultaneous decrease in the estimated values.  
Figure 6: Time series of \(Q_{M}\) (kg/s), IP (-), and \(v_{ex}\) (m/s) in blue, red, and magenta lines, respectively, for the 10 April 2011 Etna lava fountain. Values are plotted together with the EW1 and EW2 (vertical dashed lines of different colors).
# A Comprehensive Survey on Knowledge-Defined Networking
## 6 Data Plane
### Forwarding Devices
#### 6.2.3 Virtual Switches  
A virtual switch is a software component that connects virtual machines and physical network devices, which allows network administrators to manage network topologies in a dynamic manner without having to make changes to the underlying physical network. Virtual switches have the ability to create network segments and isolate traffic between different virtual machines or groups of virtual machines, which will help improve network security. Open vSwitch and VMware NSX virtual switches are popular examples of virtual switches [420].
# Design and Evaluation of Flooding-Based Location Service in Vehicular Ad Hoc Networks+
###### Abstract  
Location-based routing protocols for vehicular ad hoc networks (VANETs) use location information to determine routing decisions. This information is provided by a location service that is queried by nodes in order to properly forward packets to communication partners. This paper presents the semiflooding location service, a proactive flooding-based location service that drastically reduces the number of update packets sent over the network compared to traditional flooding-based location services. This goal is achieved by each node partially forwarding location information. We present both deterministic and probabilistic approaches for this algorithm, which remains very simple. A mathematical model is proposed to show the effectiveness of this solution. The cases of homogeneous 1D, 2D, and 3D networks were studied for both deterministic and probabilistic forwarding decisions. We compare our algorithm with simple flooding and with the multipoint-relay (MPR) flooding of the optimized-link-state-routing (OLSR) protocol, and we show that our algorithm, despite being very simple, has excellent scalability properties. The mean number of generated messages ranges with the mean number of the neighbors of one random network node.  
Location Services; VANETs; Flooding; Semiflooding; MPR Flooding; Scaling; Analytical Model +
Footnote †: This paper is an extended version of our paper published in [PERSON]; [PERSON] A flooding-based solution to improve location services in VANETs. In Proceedings of the 2016 IEEE international conference on communications (ICC), Kuala Lumpur, Malaysia, 22-27 May 2016; pp. 1-6.