esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Participants' Knowledge and Skills Assessment
\begin{table}
\begin{tabular}{c c c c} \hline  & **2017 Pre-Intervention** & **2017 Post Intervention** & **2019 Post Intervention** \\
**MCH Components** & **Assessment Mean\%, (MD\% ’, \(p\)-Value ’2)** & **Assessment Mean\%, (MD\% ’, \(p\)-Value ’2)** & **Assessment Mean\%, (MD\% ’, \(p\)-Value ’2)** \\ \hline Reproductive and sexual health & 56.9 (6.9, 0.005) & 73.3 (23.3, 0.000) & 70.7 (20.7, 0.000) \\ \hline Maternal and antenatal care & 61.0 (11.0, 0.000) & 79.3 (29.3, 0.000) & 60.5 (10.5, 0.000) \\ \hline Neonatal and child care & 43.0 (\(-\)7.1, 0.016) & 76.1 (26.1, 0.000) & 65.4 (15.4, 0.000) \\ \hline Infant growth assessment skills & 4 & 74.2 (24.2, 0.000) & 55.1 (5.1, 0.187) \\ \hline Overall average score & 53.6 (3.6, 0.045) & 75.7 (25.7, 0.000) & 62.9 (13.0, 0.000) \\ \hline [PERSON]’s Alpha A (N = 34) \({}^{3}\) & 0.575 & 0.745 & 0.776 \\ \hline \end{tabular} \({}^{1}\) Mean Difference—the difference between the university stipulated pass mark of 50% and the mean. \({}^{2}\) Significant at \(p\) < 0.05. \({}^{3}\) Number of items in the questionnaire. \({}^{4}\) No pre-intervention (baseline) assessment.  
\end{table} 
Table 3: Participants’ performance in the individual components and overall assessments relative to the university stipulated pass mark of 50%. The 2017 pre-intervention assessment was a baseline assessment; the 2017 post intervention and 2019 post intervention assessments were carried out eight weeks and two years later, respectively. N = 47. The mean difference between the 2017 and 2019 post intervention assessments showed significant decreases (\(p<0.05\)) in knowledge and skills retention in all the MCH components except in reproductive and sexual health where the decrease was not significant (\(p=0.283\)). The biggest decreases in knowledge retention were observed in infant growth assessment skills (19.1%) and maternal and antenatal care (18.8%). An assessment of participants knowledge changes from baseline (pre-intervention 2017) to the 2019 post intervention assessment showed a statistically significant increase of 22.5% in neonatal and child care, and 13.8% in reproductive and sexual health (\(p<0.001\)). Interestingly, participants maternal and antenatal care knowledge showed a non-significant decrease from baseline to 2019 (\(p=0.350\)). The overall average score also showed a non-significant increase in knowledge (\(p=0.151\)).
# Designing and Managing a Smart Parking System Using Wireless Sensor Networks
## 2 Existing Smart Systems for Parking Management
### Linear Protocols
This array of sensors consists of three types of sensors that communicate with the ZigBee technology. Sensor R detects the presence of a car in an available parking space, then it sends these data through varioussensors R until it reaches sensor C (the smart gateway). Sensor C collects all the information and sends it to the central server. The RR sensors detect the RFID tags installed on cars authorized to park in special parking spaces. Once the label has been detected, this information is sent via multiple hops by nodes R, until reaching node C, which passes it to the central server. The SPS uses two applications to better manage the parking system, one for drivers, which helps them find parking spaces quickly and easily using the Google Maps navigation system with an online payment system (NFC). Another application, for authorities, allows them to receive alerts from the central server via GCM (Google Cloud Messaging) on unauthorized cars that have parked in the special spaces. These linear protocols are not useful for the mass parking areas because these protocols will form longer chains which will create on the one hand delays in the delivery of data to the base station, and on the other hand, immense energy consumption during the transmission of the data. For this reason, mass protocols have been developed to deal with these problems in the efficient management of parking spaces in this type of parking area.
# A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis
## 4 Experiment  
### Datasets and Evaluation Metrics  
The SemEval-2013 [27] and SST-2 (the preprocessed SST-2 dataset was selected as an experimental dataset; the preprocessed data are at [[https://github.com/clairett/pytorch-sentiment-classification](https://github.com/clairett/pytorch-sentiment-classification)]([https://github.com/clairett/pytorch-sentiment-classification](https://github.com/clairett/pytorch-sentiment-classification)), accessed on 26 November 2021) [28] datasets were selected as experimental datasets. The SemEval-2013 dataset is a Tweet sentiment analysis dataset containing five categories of labels (positive, negative, neutral, objective, and objective-or-neutral). Our experiment only considered the positive and negative data by SemEval-2013 (binary). The SST-2 dataset contains movie reviews containing two labels: (1) positive and negative, and (2) very negative, negative, neutral, positive, and very positive. From this dataset, our experiment only used the positive and negative data. Tables 2 and 3 show the details of all the datasets.  
The accuracy and macro-F1 score [14] were adopted to evaluate the performance of the proposed S-EWE method. Let \(TP\) (resp. \(TN\)) be the number of samples whose true labels and model prediction labels are both positive (resp. negative), and let \(FN\) (resp. \(FP\)) be the number of samples whose true labels are positive (resp. negative) and model prediction labels are negative (resp. positive). Accuracy and macro-F1 score are defined as  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN}, \tag{7}\]  
and  
\[\text{Macro-F1}=\frac{1}{k}\sum_{i=1}^{k}F1_{i}, \tag{8}\]  
where \(P_{i}=\frac{TP_{i}}{TP_{i}+FN},R_{i}=\frac{TP_{i}}{TP_{i}+FN_{i}}\) and \(\text{F1}_{i}=\frac{2P_{i}R_{i}}{P_{i}+R_{i}}\) stand for the precision, recall, and F1 scores of the category \(i\); and \(k\) is the total number of categories.
# Analysis of Vibration Characteristics of the Grading Belt in Wolfberry Sorting Machines
## 3 Results and Discussion
### Analysis of the Results of Structural Optimization Design
#### 3.5.3 Optimum Fixture Length  
Dynamic simulations were conducted under four distinct working conditions of the fixed devices. The lateral vibration amplitudes at each point on the DE side belt for different lengths were compared, aiming to investigate the influence rule of the fixed device length on the belt's vibration characteristics.  
As presented in Table 7, adjusting the length of the fixed device leads to a reduction in the transverse vibration of the belt. Specifically, when the overall size of the transmission mechanism is 2200 mm, the amplitude attenuation rate reaches its maximum value when the total length of the fixed device is set to 1600 mm. In cases where the size of the transmission mechanism varies, it is necessary to conduct a specific analysis to determine the optimal length of the fixed device.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Radius of a Rounded** & \multirow{2}{*}{**Theoretical Reduction in Amplitude**} & \multicolumn{2}{c}{**F-Point Amplitude**} \\
**Corner R (mm)** & & **Simulation Reduced Value** & **Bell Deflection** \\ \hline
0 & 0\% & 2.1 mm & 0.67 \\
9 & 58.2\% & 0.9 mm & 0.45 \\
10 & 61.2\% & 0.84 mm & 0.38 \\
11 & 65.4\% & 0.78 mm & 0.59 \\
12 & 69.2\% & 0.71 mm & 0.56 \\
13 & 72.6\% & 0.67 mm & 0.58 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Amplitude measurement results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Serial Number** & \multicolumn{4}{c}{**Amplitude and Decay Rate at Each Point**} \\
**Point D** & **Attenuation Rate** & **Point F** & **Attenuation Rate** & **Point E** & **Attenuation Rate** \\ \hline Length 1200 mm & 0.35 & & 2.2 & 0.49 & \\ Length 1400 mm & 0.29 & \(17.1\%\) & 2.08 & \(5.5\%\) & 0.34 & 30.6\% \\ Length 1600 mm & 0.23 & 34.3\% & 1.75 & 20.45\% & 0.28 & 42.8\% \\ Length 1800 mm & 0.30 & 14.3 & 1.87 & 15\% & 0.35 & 28.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: DE Edge Amplitude Measurements.  
Figure 22: Frequency-domain response graph.
# A Semantic Segmentation Method with Emphasis on the Edges for Automatic Vessel Wall Analysis
## 3 Results
[23], the proposed EVSegNet combines an additional edge stream that could supply the edge knowledge information before segmenting for extracting edge information. Table 1 also shows the effect of the edge stream, which boosts the performance of the MR vessel wall imaging segmentation. Compared to the U-net + DUC + HDC, the Dice, accuracy, recall, and precision scores are increased from 86.5, 97.4, 86.1, and 91.3 to 87.5, 97.6, 86.8, and 91.5, respectively. Experimental results showed that our method was effective and presented superior performance over other methods. It serves to improve the performance of the model noticeably. Furthermore, we also give a few examples for visual comparison in Figure 9 to show the segmentation results from 12 patients: the first column is the original image, and the second is the corresponding binary mask for references. The following columns are vessel wall segmentation images predicted by U-net (None), U-net (Res), U-net (DUC), U-net (DUC + HDC), and EVSegNet. Their corresponding evaluation results of Dice and mIoU are shown in Table 2. In particular, as can be seen from the second row and fifth row in Figure 9, all backbones do well in large-sized vessel wall segmentation. Meanwhile, in the first, third, and fourth row, the performance of U-Net is significantly lower than that of the other backbone networks and suffered from unsatisfactory segmentation accuracy due to the small vessel wall. The U-net is not precise enough to discriminate between the foreground and background, which leads to the edge being non-closed. After adding the HDC module, the performance of small vessel walls has been improved. This indicates that thanks to the advantages of employing the HDC module, we were able to obtain a better and more stable performance for multiscale vessel walls. In addition, for the first and third row image, there is low contrast surrounding the boundaries in the neighboring tissues that were essentially invisible to naked eyes; it is observed that EVSegNet was able  
Figure 8: The Dice comparison of 11 epochs for different models for the test dataset. to locate the vessel wall region and produce the cleanest and most accurate segmentation results and is superior to the other backbones.
## 2 Method
### Natural Frequency, Modal Shape, and Damping Ratio Test  
The main methods of structural dynamic property testing are the artificial excitation method and the environmental random vibration method. Among them, the structural response signal to environmental excitation can be used to identify the modal parameters of the system. This method requires the excitation signal to be white noise or an ergodic process. The excitation signals of actual projects can generally meet the requirements. The ambient random vibration method is one of the more used methods in building structure testing, and its biggest advantage is that it does not require artificial excitation and is especially suitable for measuring the dynamic characteristics of the whole structure [19,21].  
The modal test uses dynamic testing under ambient vibration excitation. The test network was established according to the site conditions and stadium shape and divided into two regions. There are 66 measurement points in this test, of which each region is set up with the reference point of measurement point 35. In this building, we only considered the acceleration comfort in the vertical direction, so vertical acceleration was used to identify the modal parameters. Dynamic characteristic parameters were tested using the modal parameter identification method with ambient random excitation, and the modal parameter identification was performed on the collected time-range data of the vertical acceleration of the structure surface in each region. Figure 2 shows the two modal test area measurement points.  
Damping testing methods are divided into time- and frequency-domain methods, such as the logarithmic-reduction, resonance-frequency, transfer-function, and half-power bandwidth methods. Because the time-domain method has strict requirements on the waveform, the traditional half-power bandwidth method in the frequency domain method is the most commonly used in engineering [22, 23, 24]. In this test, the natural vibration  
Figure 1: Instrument installation schematic. (**a**) Vibration pickup; (**b**) Analyzer System Instrument.  
frequency of the structure was determined according to the dynamic characteristic test, and the force hammer was used to excite the 23 points. Then, the free attenuation signal in single mode was extracted through the bandpass filter, and the damping ratio of the first vertical vibration mode of the floor was lastly obtained by using the logarithmic-reduction method.
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 4 Performance Evaluation
### Simulations  
In addition to experiments using real drones, we conducted simulations using network simulator 3 (ns-3) [40,41]. We first conducted simulations similar to the real experiments in Section 4.2 to support the experiment results and thoroughly verify GCS-routing performance. We then performed additional simulations to evaluate GCS-routing from more diverse perspectives.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **1 st Replacement** & **2 nd Replacement** & **Average** \\ \hline GCS-routing & 0.5 s & 1.5 s & 1.0 s \\ DSDV & 19.0 s & 16.0 s & 17.5 s \\ OLSR & 3.0 s & 3.5 s & 3.3 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network disconnected time.  
Figure 13: Throughput for GCS-routing, DSDV, and OLSR for the replacement scenario.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Period** & **A** & **B** & **C** \\ \hline GCS-routing & 47.9 & 494.6 & 302.0 \\ DSDV & 187.3 & 506.2 & 342.9 \\ OLSR & 356.5 & 585.6 & 521.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Throughput standard deviation for each time period.
# Distributed Localization with Complemented RSS and AOA Measurements: Theory and Methods
## 3 Existing Methods
### Linear Least Squares (LLS) Method  
In [38], the authors considered the problem of distributed target localization based on fused RSS and AOA measurements, with the main difference (in comparison with the problem studied in [61]) that they assumed that all nodes in the network are capable of measuring both RSS and AOA quantities. The authors in [38] proposed an algorithm based on LLS approach. LLS is a special subclass of convex optimization, where the objective function is of the form \(\|\mathbf{A}\mathbf{x}-\mathbf{b}\|^{2}\), and the solution is given in closed form as \(\mathbf{x}=(\mathbf{A}^{T}\mathbf{A})^{-1}\mathbf{A}^{T}\mathbf{b}\). It is a standard approach in regression analysis, where the main idea is to give the best fit that minimizes the sum of squared residuals, where a residual is considered to be the difference between an observed value and the fitted one provided by the model, as illustrated in Figure 8. LLS requires the model to be linear, which is evidently not the case for RSS nor AOA. Nevertheless, rather than applying a sequence of approximations to the model, the authors in [38] cleverly took advantage of the geometry of the problem at hand. More precisely, for each of the anchors, they estimated the distance to a target of interest, and together with the measured AOA, obtained an estimate of the target location, much like the illustration shown in Figure 4. Of course, distance and angle estimates were employed, rather than their true values. It was considered that there exists some node synchronization protocol which allowed targets with direct edges to anchors to localize themselves first, after which they played a role of quasi-anchors (reference points with imperfectly known locations, also called _helpers_[64]) to help localize the targets with no direct link to anchors, iteratively.  
Figure 7: Illustration of the process of convex relaxation.
# Atmospheric Turbulence Study with Deep Machine Learning of Intensity Scintillation Patterns
## 7 Turbulence Models Evaluation via Machine Learning
Variations in \(l_{0}\) within a few millimeters (from \(l_{0}~{}=~{}1.0\) mm to \(l_{0}~{}=~{}4.0\) mm) only marginally effected DNN performance and, hence, spatial features of the scintillation patterns utilized by Cn'2 Net for \(C_{n}^{2}\) prediction. With further turbulence inner scale \(l_{0}\) increase (\(l_{0}~{}=~{}8.0\) mm and \(l_{0}~{}=~{}16.0\) mm), discrepancy between the true and predicted \(C_{n}^{2}\) values was quite obvious. Similar machine learning experiments were performed to estimate the impact of the turbulence outer scale \(L_{0}\) in the Von Karman power spectrum model on scintillation patterns. In this study the turbulence outer scale \(L_{0}\) was varied from \(L_{0}\)= 0.25 m to \(L_{0}~{}=~{}4\) m. The obtained results (see Table 5) demonstrated minor change in the \(C_{n}^{2}\) prediction error that increased to about 5% with decrease in outer \begin{table}
\begin{tabular}{c c c} \hline \hline \(l_{0}\)**(mm)** & \(L_{0}\)**(m)** & \(\overline{\epsilon}_{val}\) \\ \hline
0 & Infinity & 0.072 \\
1.0 & 1.0 & 0.091 \\
4.0 & 1.0 & 0.094 \\
8.0 & 1.0 & 0.170 \\
16.0 & 1.0 & 0.380 \\
1.0 & 0.25 & 0.10 \\
1.0 & 0.5 & 0.09 \\
1.0 & 2.0 & 0.08 \\
1.0 & 4.0 & 0.074 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation of turbulence inner \(l_{0}\) and outer \(L_{0}\) scales impact on averaged \(C_{n}^{2}\) prediction error \(\overline{\epsilon}_{val}\) in the cross-dataset training-inference machine learning experiments with Cn’2 Net training performed using the SIM-KO dataset (Kolmogorov turbulence model) and inference (\(C_{n}^{2}\) prediction) using the SIM-VK datasets (Von Karman power spectrum model). The first row corresponds to the deep neural network (DNN) training and prediction using SIM-KO datasets. scale up to 0.25 m. These results indicate that outer scale variation within a reasonable (from physics viewpoint) range does not significantly affect spatial features of speckles in the scintillation images.
## 2 Technical Contributions  
In this paper, we proposed a convolution neural network for LED detection and deep neural network for data decoding based on the RS-OFDM scheme considering mobility environment and long-range communication with low bit error rate. Some outstanding contributions of the proposed system can be represented as the following:  
* The problem of complex noise (blur picture, interference, irregular attenuation) in an OCC system is difficult to tackle in the time domain, but it is simple to solve in the frequency domain by disregarding the DC component. The complex noise becomes more difficult in the mobility environment then; we need another algorithm to resolve this issue. Deep learning can help to reduce the noise effect.
* Mobility support: the RS-OFDM scheme based on the rolling-shutter effect revealed concerns for recognizing numerous LEDs since it was more sensitive to the mobility effect. While utilizing a rolling-shutter camera, the LEDs appeared in an image as black and white strips; when employing RoI algorithms, the number of LEDs could not be clearly recognized. As a result, we presented a neural network for increasing the OCC system's performance.
* By using deep learning for OFDM symbol detection, the bit error rate is reduced considering the mobility effect. As we mention below, the optical channel varies in the mobility environment, and then, the [PERSON] algorithm proved ineffective for RS-OFDM OCC system, especially the mobility environment. It is presented in Section 5. By collecting the real data from various cases (different distances, velocity, and exposure times), the input dataset was collected to train the deep learning model, which shows good performance compared to the conventional method we proposed in [9].
## 3 Results  
### Synthesis and Characterization of Hapten-Protein Conjugate  
The development of a competitive enzyme immunoassay requires the availability of basic reagents such as a hapten-protein conjugate, a free hapten, specific antibodies (or antiserum), and anti-species antibody conjugate labeled with peroxidase to detect formed immune complexes.  
The conjugate of nonylphenol (the structure is shown in Figure 1A) with soybean trypsin inhibitor (STI) as the carrier protein (hapten-protein conjugate, NP-STI) was obtained by the Mannich reaction (Figure 1) at the molar ratio of 30:1. During the reaction, formaldehyde is transformed into CH\({}_{2}\) group as a substituent in orthoposition next to the phenolic hydroxyl group (Figure 1B). This group in the conjugate is a link between the protein amino group and the NP core (Figure 1C). The resulting conjugate was characterized by spectrophotometry and FT-IR spectroscopy.  
The absorption spectrum of the NP-STI conjugate has a characteristic peak at 278 nm and a shoulder at 284 nm, which is characteristic of both the protein (280 nm) and native nonylphenol (278 nm) (Figure 2). The data were used to determine conjugate concentrations. The resulting NP-STI conjugate had a concentration of 7.4 mg/mL.  
Figure 1: Scheme of conjugation of nonylphenol with carrier protein by Mannich reaction. (**A**)—nonylphenol structure; (**B**)—intermediate compound having active group reacting with primary amines to form the hapten–protein conjugate—(**C**).  
Figure 2: Absorption spectra of the NP–STI conjugate, NP and STI.  
The STI protein, hapten (nonylphenol), and synthesized NP-STI conjugate were characterized by FT-IR spectroscopy, demonstrated in Figure 3. The spectrum of the NP-STI conjugate shows bands in the region of 1700-1600 cm\({}^{-1}\), 1575-1480 cm\({}^{-1}\), and 1230-1300 cm\({}^{-1}\) associated with the amide group of proteins [40] (Figure 3, FT-IR spectrum of STI). The characteristic bands in the region of 2831-3000 cm\({}^{-1}\) in the spectrum of nonylphenol, attributed to asymmetric and symmetric stretching vibrations of CH\({}_{2}\)[41], are repeated in the spectrum of the NP-STI conjugate. Thus, FT-IR spectroscopy data confirm the successful preparation of the NP-STI conjugate.  
Comparison of the titrations of free amino groups for STI and STI-NP demonstrated that the number of nonylphenol molecules on the surface of the NP-STI conjugate is about two. Taking into account the fact that the entire STI globule contains nine lysine residues, only approximately 20% of the remainder of the total. Such a relatively small load is adopted in competitive ELISA, since it makes it possible to more effectively prevent the binding of antibodies to the conjugate when a free hapten is introduced [42].