esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Equipment  
Sample analysis was conducted using a high-performance liquid chromatography system equipped with a UV detector (HPLC/UV, 1200, Agilent Technologies, Santa Clara, CA, USA). All samples were separated using a C18 column (150 \(\times\) 4.6 mm). The mobile phase consisted of methanol/water (70/30, _v/v_), and the flow rate was set at 1 mL/min. The detection wavelength was set at 280 nm. Infrared spectra were obtained using a Fourier-transform infrared spectrometer (FT-IR, Spectrum One, PerkinElmer, Waltham, MA, USA). The morphology of the MIPs and coating was observed using a scanning electron microscope (SEM, S-4800/350, Hitachi Ltd., Tokyo, Japan).
# HPC Platform for Railway Safety-Critical Functionalities Based on Artificial Intelligence
## 2 Related Work  
The use of artificial sense (in real-time and via onboard embedded hardware) has been presented via a number of demonstrations in the railway industry.The Siemens autonomous tramway pilot case [5] was one of the first demonstrations that continues to inspire researchers today [6]. With this in mind, vision-based on-board obstacle detection and distance estimation in railroads have become the most pertinent scientific approaches [7]. To test and validate an autonomous obstacle detection system, various experiments and actual pilot cases have been implemented throughout the past few years, [8; 9; 10; 11; 12; 13]. Other applications have also been worked on, such as vehicle localization on light trains [14] or railway lateral signalling detection on mainline trains [15; 16].  
The majority of these demos, similarly to those for self-driving cars, concentrate more on the computing capability of those systems rather than how these platforms may be certified. However, certification of sophisticated computing systems is an active area of study, such as those necessary for fully automated train systems [3]. Major chip suppliers, such as Nvidia and Intel, are also creating particular platforms that support that purpose [17], by including built-in fault-tolerant mechanisms such as lockstep execution or error correction codes in memory structures. Finally, to certify complex systems affordably, a more comprehensive and cutting-edge safety certification technique is required [18].  
There are currently no commercial solutions that guarantee high-performance equipment with the safety requirements to be met in the railway sector. This work aims to be the first on-board HW prototype to execute AI functions safely (and in real time) in railway operations.
# Sesame Plant Disease Classification Using Deep Convolution Neural Networks
## 3 Methodology  
### System Architecture  
The major steps of the proposed system architecture are shown in Figure 1. The steps include image preprocessing, image segmentation, feature extraction, and sesame crop disease classification. In the image processing step, we resized the images to a standard size and employed filtering techniques for noise removal and image enhancement. In addition, image segmentation was performed to identify the region of interest and distinguish the foreground image from the background image. The CNN technique was used to extract features from the sesame image. In the feature extraction and classification steps, a deep convolution neural network was used. The architecture illustrates the overall process followed to train, validate, and test the proposed model.  
Figure 1 depicts the data collection and preparation pipeline of a deep-learning image classification problem. The following mathematical representation covers the step-by-step transformations of the input image through the preprocessing, feature extraction, and classification pipeline of the proposed CNN model.  
The 2D matrix input image representation:  
\[I(x,y)\in R^{M\times N\times C} \tag{1}\]  
where \(M\) and \(N\) are the spatial dimensions (height and width) of the image, and \(C\) represents the number of RGB channels.  
Preprocessing and segmentation: image preprocessing techniques such as normalization, contrast enhancement, and noise reduction can be represented mathematically as  
\[I^{\prime}(x,y)=\frac{I(x,y)-\mu}{\sigma} \tag{2}\]  
where \(\mu\) and \(\sigma\) are the mean and standard deviation of pixel intensities. Segmentation may involve thresholding:  
\[S(x,y)=\begin{cases}1,\;if\;I^{\prime}(x,y)\geq T\\ 0,\;otherwise\end{cases} \tag{3}\]  
where \(T\) is a threshold value.  
Convolutional layers apply a filter \(W\) (of size \(k\times k\) times) to the image:  
\[F_{ij}^{l}=\sum_{m=0}^{K-1}\sum_{n=0}^{K-1}W_{min}^{(l)}I^{\prime\prime}(i-m,j-n)b ^{l} \tag{4}\]  
where \(W^{(l)}\) is the weight of the \(n^{th}\) layer, \(b^{(l)}\) is the bias, and k is the filter size.  
In the pooling layers, max pooling and average pooling reduce spatial dimensions.  
Max pooling:  
\[Pmax(i,j)=\underset{(m,n)\in R}{max}F(i+m,j+n) \tag{5}\]  
Average pooling:  
\[Pavg(I,j)=\frac{1}{|R|}+\sum_{(m,n\in R)}F(i+m,j+n) \tag{6}\]  
Figure 1: Shows the proposed system architecture.  
where \(R\) is the pooling window size (\(2\times 2\)).  
Dropout regularization is applied to prevent overfitting:  
\[O=D\cdot F \tag{7}\]  
where \(D\sim\text{Bernoulli}(p)\) (binary mask with probability \(p\)).  
The extracted features are flattened into a vector and fed into a fully connected layer.  
\[Z^{(L)}=W^{(L)}P^{(L-1)}+b^{(L)} \tag{8}\]  
The final classification is performed using the softmax function:  
\[P(yi)=\frac{e^{zj}}{\sum_{j}e^{zj}} \tag{9}\]  
where \(z_{i}\) represents the logits (outputs before activation) for class \(i\).  
The trained model assigns a label to the input sesame leaf image based on the highest probability output from softmax.
## 3 Results
### Angular Deviation and Off-Axis Force  
Table 8 provides a summary of the angular deviation and off-axis force magnitudes observed on tooth 21. TC-85 demonstrated the lowest angular deviation and off-axis force values for both 0.25 mm and 0.50 mm activations. Meanwhile, ATMOS and Zendura FLX  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**TC-85**} & \multirow{2}{*}{\(p\)**-Value**} \\ \cline{2-2} \cline{5-8}  & \multicolumn{2}{c}{**0.25 mm**} & \multicolumn{6}{c}{**0.50 mm**} \\ \cline{2-2} \cline{5-8}  & \multicolumn{2}{c}{**Mean \(\pm\) SD**} & \multicolumn{6}{c}{**Mean \(\pm\) SD**} \\ \hline Fx (N) & 0.34 & F & \(\pm\) & 0.55 & 0.51 & F & \(\pm\) & 0.28 & \textless{}0.001 \\ \hline Fy (N) & 0.16 & D & \(\pm\) & 0.40 & 0.07 & M & \(\pm\) & 0.25 & \textless{}0.001 \\ \hline Fz (N) & 0.21 & G & \(\pm\) & 0.12 & 0.15 & G & \(\pm\) & 0.13 & \textless{}0.001 \\ \hline Mx (Nmm) & 3.42 & M & \(\pm\) & 0.97 & 3.09 & M & \(\pm\) & 1.53 & \textless{}0.001 \\ \hline My (Nmm) & 0.70 & F & \(\pm\) & 1.96 & 2.73 & F & \(\pm\) & 1.46 & \textless{}0.001 \\ \hline Mz (Nmm) & 5.46 & D & \(\pm\) & 4.10 & 3.38 & D & \(\pm\) & 3.31 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 7: _Cont._produced considerably larger angular deviations and off-axis force values, particularly at 0.50 mm activation.
###### Abstract  
A terahertz (THz) all-dielectric metasurface with crescent cylinder arrays for chiral drug sensing has been demonstrated. Through the multipole expansion method, we theoretically found that breaking the symmetry of the metasurface can excite higher-order resonance modes and provide stronger anisotropy as well as enhanced sensitivity for the surroundings, which gives a better sensing performance than lower-order resonance. Based on the frequency shift and transmittance at higher-order resonance, we carried out the sensing experiments on (R)-(--)-ibuprofen and (S)-(+)-ibuprofen solution on the surface of this metasurface sensor. We were able to monitor the concentrations of ibuprofen solutions, and the maximum sensitivity reached 60.42 \(\mathrm{GHz/mg}\). Furthermore, we successfully distinguished different chiral molecules such as (R)-(--)-ibuprofen and (S)-(+)-ibuprofen in the 5 \(\mathrm{\SIUnitSymbolMicro}\) trace amount of samples. The maximum differentiation was 18.75 \(\mathrm{GHz/mg}\). Our analysis confirms the applicability of this crescent all-dielectric metasurface to enhanced sensing and detection of chiral molecules, which provides new paths for the identification of biomolecules in a trace amount.  
Terahertz sensing, 2021, 11, 8892. +
Footnote †: journal: Applied Sciences
# Validating Precise Orbit Determination from Satellite-Borne GPS Data of Haiyang-2D
## 3 Results and Discussion
### Accuracy Assessment of RD Orbits by Using the Three New Methods  
In addition to the common external methods including SLR range validation and validated by DORIS, the carrier difference validation, inter-satellite differential, and SLR 3D validation proposed in this paper are also used for external validation.
# A Portable Non-Motorized Smart IoT Weather Station Platform for Urban Thermal Comfort Studies
## 4 Efficiency and Performance of the Proposed Weather Station Platform
### Test 3: Latency/Timestamp Accuracy  
To understand the delay in sending the measured values to the server, the difference between the server time and the time recorded by the GPS sensor was calculated (Server time minus GPS time). For this, only the data stored on the server was considered, as the data stored on the device does not have a server timestamp.  
Table 4 shows the latency test results, with the mean, minimum, maximum, and standard deviation of the time difference in seconds. Overall, on average, it takes 2.5 to 2.9 s for data to reach the server, irrespective of the neighborhood building density in the 'Stop-and-Go' scenario. Even though the highest latency was 8.7 s, the standard deviation indicates that the latency is close to the mean. A similar average was noticed in Parts 4 and 5 and Route B of the 'On the move' scenario. On the other hand, a much lower average, between 1.0 and 1.5, was noticed in Parts 1, 2, and 3 of the 'On the move' scenario. This lesser latency might be due to the greater openness and the very few buildings in these locations compared to the other locations in both scenarios. As we have encountered a potential relationship between the urban setup and the latency, we suggest developing an optimization algorithm, similar to [53], by identifying the critical impacting factor and adjusting the data transmission accordingly.
## 4 Comparative Analysis of Theoretical and Experimental Results
### Seismic-Response Analysis under Single and Bidirectional Seismic Motion
#### 4.3.2 Comparison between Floor-Displacement Responses  
Figure 27 shows the comparison between the average floor-displacement responses of the rigid and soft soil interlayer isolation structures under four types of seismic motions with a 0.2 g input in the X-direction and bidirectional directions.  
Figure 27 shows that (1) under the four types of ground motions with a bidirectional input, the floor-displacement response of the interlayer isolation structures is greater on soft soil foundation than rigid foundation. With the increase in the number of floors of the lower structure, its displacement in the case of the soft soil interlayer isolation system changes slightly more than that of the rigid foundation. Under a long-term seismic motion, the top displacement response of the soft soil interlayer isolation system is significantly greater than that of the rigid foundation. This indicates that the coupling of the long-term seismic motion with the soft soil foundation significantly enhances the displacement response of the upper part of the interlayer isolation structure. Therefore, when designing isolation structures, the focus must be on the issue of exceeding the displacement limit of the upper structure under the coupling of long-term seismic motion and the soft-soil foundation. (2) Under long-period ground motion with bidirectional input (PGA = 0.1 g), the displacement change in the isolation layer is slightly different than that in the case of a unidirectional input. However, as PGA increases, the displacement change of the isolation layer is close to that for the case of unidirectional input. With the increase of PGA, the displacement value of the upper structure of the soft soil interlayer isolation system under long-term seismic motion is closer to that in the case of the unidirectional input. However, for other cases, there is still a certain gap between it and unidirectional input. (3) When considering the influence of the SSI effect, the floor-displacement response of the interlayer isolation structure is significantly amplified. Under the action of 0.2 g ordinary ground motion, the displacement response of the top layers of the upper and  
lower structures built on soft soil foundation is 1.52 and 2.01 times that for the structures on the rigid foundation, respectively. Under the action of 0.2 g long-period seismic motion, the displacement response of the top layers of the upper and lower structures is 1.88 and 2.39 times that of the structure on the rigid foundation, respectively.
# Utilizing Machine Learning for Detecting Harmful Situations by Audio and Text
## 4 Methodology Description
### ML Methods Used for Combined Text and Audio  
In the next set of experiments, we used both audio and textual features. We assumed the combination of audio and text for enhancing the results of the classification task. We applied different combinations of textual data and data extracted from the audio recordings.  
To train both features, we built a _Wav2 vec Emotion Vector_ model that shrank the given audio Wav2 Vec embedded vector to a size of 100, and we concatenated it with the text Heb-BERT embedded vector with size of 768, which was followed by the text MLP model. This model is shown in Figure 8. In our research on detecting insulting and harmful content, we discovered that the context of the text was more relevant in determining its sentiment than the tone in which it was delivered. Although a recording may depict a dangerous situation, it may still be spoken in a calm tone. To better account for the relevance of the text compared to that of the audio, we chose to reduce the size of audio inputs to a certain extent as they contribute less than text data. Nevertheless, it seems important to retain information from the audio as it still holds valuable sentiment analysis information.  
The combined model was aimed at utilizing both textual and audio information to achieve greater accuracy in detecting challenging situations. First, we processed the text data using text Heb-BERT vectorization, as explained in Section 4.1. In addition, the audio vector, given in one of the formats described in Section 4.2.1 except the MFCC, was passed through a fully connected layer with 100 neurons and a ReLU activation function, with a 30% dropout layer before and after the fully connected layer. Then, both outputs (textual-based input and audio-based output) were merged, and three additional fully connected layers were used. This was followed by a softmax layer for the detection of unsafe/insulting/neutral situations and a sigmoid neuron for binary classification detection. This architecture provided the most accurate results, as described in the next section. For additional information, we refer the reader to the GitHub website ([[https://github.com/MLspecialNeeds/harmful_sentence_detection](https://github.com/MLspecialNeeds/harmful_sentence_detection)]([https://github.com/MLspecialNeeds/harmful_sentence_detection](https://github.com/MLspecialNeeds/harmful_sentence_detection)), accessed on 15 March 2023).  
Figure 8: An illustration of the text and audio model.  
Figure 7: An illustration of the audio MLP model.  
Based on these various ML methods, in the next section, we compare the accuracy of the different models: text-based, audio-based, and combined text-and-audio-based models, in preset challenging situations.
## 3 Application Results  
As mentioned before, the streamflow dataset of two stations, namely Dizaj and Tapik, in north-western Iran, contained 31 years of data (372 months) from January 1986 to December 2016. In order to establish models, 70% and 30% of data from the beginning (260 months and 112 months) were used for calibration and validation stages of the proposed models. First, the quality of modeling starts with data pre-processing (data normalization). In the second step, two standalone models including OPELM and CHAID models were used to predict monthly streamflow. To improve predictive models, the stochastic term of streamflow models was calculated by ARCH model that described the variance of the current error term or innovation as a function of the actual sizes of the previous time periods' error terms, and afterward, the results of hybrid (ARCH-OPELM and ARCH-CHAID) models were reported.
## 5 Experiment Result
### Second Scenario: Patient Position Tracking Based on Different Camera Locations
However, the visibility of this system is not efficient when patients are out of the camera's range or the transmitter is not visible to the receiver, preventing the capture of information from each patient. By using this proposed system, this issue can be addressed. As mentioned before, in this system, when one camera is unable to obtain data from the transmitter, other nearby cameras can receive the data. Figure 12: Results of implementing the OCC-IoMT system. (**a**) the detection performed by the YOLO algorithm in the first receiver’s FOV; (**b**) the patient moving out of the first receiver’s FOV and then being detected by the second receiver, demonstrating how the system identifies and tracks the patient’s movement and the transition of FOV from the first receiver to the second; (**c**) a view of the database, displaying the data received from both receivers; and (**d**) a web interface accessible to patients, providing real-time information about patients (the second scenario).
## 2 Materials and Methods
### Box-Behnken Optimization  
Box-Behnken design (BBD) with 3 factors and 3 levels ranging from low (\(-\)1) to medium (0) to high (\(+\)1), consisting of 15 experimental runs, was employed for the optimization of DES-UAE procedure. The corresponding codes and real values for each variable are presented in Table 3.  
The effects of the independent variables--ChCl:U ratio, WC in DES, and extraction time (t)--on the dependent variable--AC of the prepared extracts determined by the CUPRAC, ABTS, and DPPH methods--were evaluated. After obtaining the data, response surface methodology (RSM) was used to determine the optimal processing setting for each independent variable.  
The partial cubic model (PCM) was assumed for predicting three responses: CUPRAC, ABTS, and DPPH. The proposed model for each response of \(\mathrm{Y_{n}}\) was expressed according to Equation (3):  
\[\begin{split}\mathrm{Y_{n}}=\beta_{0}+\beta_{1}\times\mathrm{ ChCl:U}+\beta_{2}\times\mathrm{WC}+\beta_{3}\times\mathrm{t}+\beta_{11}\times( \mathrm{ChCl:U})^{2}+\beta_{22}\times\mathrm{WC}^{2}+\beta_{33}\times\mathrm{t} ^{2}\\ +\beta_{12}\times\mathrm{ChCl:U}\times\mathrm{WC}+\beta_{13}\times \mathrm{ChCl:U}\times\mathrm{t}+\beta_{23}\times\mathrm{WC}\times\mathrm{t}\\ +\beta_{112}\times(\mathrm{ChCl:U})^{2}\times\mathrm{WC}+\beta_{113 }\times(\mathrm{ChCl:U})^{2}\times\mathrm{t}\end{split} \tag{3}\]  
where \(\mathrm{Y_{n}}\) is one of the three predicted responses: CUPRAC, ABTS, and DPPH; ChCl:U, WC, and t represent the independent variables; \(\beta_{0}\) is the constant; \(\beta_{1}\), \(\beta_{2}\), and \(\beta_{3}\) are the linear term coefficients; \(\beta_{11}\), \(\beta_{22}\), and \(\beta_{33}\) are the quadratic term coefficients; and \(\beta_{12}\), \(\beta_{13}\), \(\beta_{23}\), \(\beta_{112}\), and \(\beta_{113}\) are the cross-term coefficients.  
The quality of the developed PCM was estimated by the calculation of the determination coefficient (R\({}^{2}\)) values, whereas analysis of variance (ANOVA) was used to evaluate the statistical significance of the proposed model by the values of regression and the mean square of residual error.
## 5 Conclusions  
This paper presents an investigation of the proposed radio link model for underground mine sensor networks. The analytical radio link model is proposed to determine the best node position and node separation in underground mines, considering different mining structural schemes. The proposed model was validated using wizzi motes operating at 433 MHz, which were obtained from Wizzi Lab, France. The experimental characterization of the radio link model was determined in terms of RSSI and PER, which were obtained from the signal strengths of transmitted and received radio waves. RSSI and PER were measured for tunnel and room-pillar mining, along with different propagation modes, to verify the optimized node position defined in the node position matrix. The presented model gives freedom to radio link designers, allowing them to place a stationary node at the rooftop or near the side wall of the mine, which eventually improves link reliability and increases node separation distance without affecting the received signal strength. Furthermore, the effectiveness of the DASH7 protocol in localizing miners among multiple stationary nodes in a salt mine was demonstrated, highlighting its potential in the deployment of application-specific sensor networks. The radio link model presented in this study can be utilized by design engineers to address concerns regarding node placement and separation when designing sensor networks that utilize the DASH7 protocol for use in underground mines.  
Figure 9: RSSI metric for the localization of the miner in (**a**) a mine tunnel and (**b**) a mine room–pillar.  
**Author Contributions:** Conceptualization, S.S. and I.H.N.; Methodology, S.S., H.Z. and S.B.Q.; Software, S.B.Q.; Validation, H.Z., S.B.Q., S.M.A. and S.M.; Formal analysis, S.S. and I.H.N.; Investigation, S.S. and I.H.N.; Resources, S.B.Q.; Data curation, S.S., H.Z. and I.H.N.; Writing--original draft, S.S. and I.H.N.; Writing--review & editing, H.Z., S.B.Q., S.M.A. and S.M.; Supervision, S.M.A. and S.M.; Project administration, S.M.A. and S.M.; Funding acquisition, S.M.A. and S.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Routing Protocol for LLNs
### Downward Traffic
#### 2.3.1 Non-Storing Mode  
In non-storing mode, a message destined to a node within the DODAG is first sent to the root, which attaches hop-by-hop routing information to it [18; 19]. Downward routes are formed through recursive look-ups in the root's source routing table. To build this table, nodes address DAO messages to the root's global/unique-local unicast address. DAOs thus follow the default route resulting from upward topology construction, as will data traffic destined to other nodes before being routed down from the root. Each node appends the DAO with a Target option for each of the self-owned (routable) addresses via which it wishes to be reachable (conditions apply; see RFC 6550 [4] (Section 9.4)), followed by a single Transit Information option containing the global address of its single DAO parent (i.e., its preferred parent).  
Figure 4: Format of an RPL Destination Advertisement Object (DAO). Note that a DAO is an ICMPv6 informational message with Type = 155 (RPL Control Message) and Code = 0x02 (DAO). The asterisk (*) indicates the absence of the DODAGID field in certain cases (see [4] (pp. 28–29)).