esa-sceva/satcom-chunk-collection huggingface dataset:
# A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification
## 3 The Proposed Indoor Localization Method  
### Overall Structure of the Proposed Method  
Figure 2 shows the procedures of the proposed positioning method. The ranging results and RSSIs are collected from the FTs and then input into the LOS/NLOS identification model. Then, the model identifies NLOS signals and only outputs the LOS ranging results. If the number of LOS ranging results is greater than the minimum number required for positioning, the LOS ranging results are used as the input of the single point positioning to calculate the location of the MT. If the number of LOS ranging results is less than the minimum number, all the ranging results, including LOS and NLOS, will be used as the input of the single point positioning to derive the location.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Obstacle** & \
\begin{tabular}{c} **Reinforced** \\ **Concrete Wall** \\ \end{tabular} &
\begin{tabular}{c} **Plasterboard** \\ **Wall** \\ \end{tabular} & **Wooden Door** & **Metal Door** & **Glass Window** \\ \hline Signal & & & & & \\ Attenuation & 15–16 & 3–5 & 3–5 & 10–12 & 6–8 \\ Value (dBm) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Signal attenuation through different media.
## 2 Materials and Methods
### Model Architecture  
#### 2.3.1 Basic Overview  
U-Net uses encoders and decoders to model the space of the segmentation target. U-Net is an effective deep learning model for segmentation [41]. However, during modelling, subtle changes in the target may be ignored and sensitivity to fine details can be low. The continuous use of single-scale convolutional filters and pooling operations during encoding makes feature extraction insufficient. Therefore, given the complex content and high-frequency spectrum of a remote sensing image, we developed a locally aggregated MSG-GCN for accurate image segmentation.  
Figure 3 shows the overall structure of the segmentation framework. There are three basic components: an encoder with three encoding blocks; a transitional MSGCN module between the contraction and expansion blocks; a decoder with three decoding blocks. The LA [79] integrates encoding and decoding features through an attention mechanism. LA effectively filters out irrelevant information, enhances the capacity to represent key information, and completes the segmentation task. For each encoding block and corresponding decoding block, we use 1 \(\times\) 1 convolution for compression and 1 \(\times\) 1 and 3 \(\times\) 3 convolution layers for excitation. Thus, each encoding and decoding block extracts local and global semantic features by splicing features at different graph scales to capture multiscale information from the segmentation target. Simultaneously, pooling operations at various scales are run using the final decoder, which aids description of the global and contextual semantics [84]. The MSGCN is used by both the encoder and decoder as a transitional layer (strictly speaking, the MSGCN is an encoding block). This accurately identifies multiscale local and global information of the segmentation target. The MSGCN determines interactions between pixels by merging and transferring node information, reduces the intraclass ambiguity caused by different characteristics, and thus aids the modelling of long-term dependence [85].  
Figure 2: Ground truth map of forest type classification, provided by the University of Tokyo Chiba, Forest (UTCBF).
# Smart Classroom Monitoring Using Novel Real-Time Facial Expression Recognition System
## 2 Materials and Methods
### Proposed Automatic Facial Expression Recognition System  
In this study, we have developed an automatic emotion recognition system using facial expressions that incorporates novel salient facial features. The facial features for each emotion are initially detected using HOG for face recognition, and automatic emotion recognition is then performed by training the convolutional neural network (CNN) that takes real-time input from a camera deployed in the classroom.  
The selected emotional states are happiness, sadness, and fear along with the cognitive-emotional states of satisfaction, dissatisfaction, and concentration. These facial expressions are identified using Python APIs. Figure 2 explains the facial features selected to identify each facial expression.  
Figure 1: Stages of existing FER systems.  
The proposed facial expression recognition system works in a real-time classroom environment. The camera is installed in the classroom to capture input for proposed FER. The preprocessing is performed on the input and HOG is used for face localization during this preprocessing. The proposed FER system then performs feature extraction of the proposed novel salient facial feature using a histogram of oriented gradients (HOG). The deep learning model convolutional neural network (CNN) is used for the classification of the selected emotions. The proposed FER is capable of displaying and storing the identified emotion in its database.  
The selected factors or variables used in this study are the duration of the lecture, difficulty of the subject, gender, department and seating position in classroom. The variable lecture duration is divided into three time slots of 15, 30 and 45 min. Similarly, the variable difficulty of the subject is selected on the basis of students' grades in the subject. The subject that has a greater average of lower grades is selected as a difficult subject. The subjects selected as difficult subjects include theory of automata, multivariable calculus, general science and modern programming languages. The variable gender consists of male and female. The variable seating position is divided into three categories: students seated in first rows; students seated in middle rows; and students seated on last rows of the lecture room. The variable department considers four departments: computer science; education; information technology; and computer engineering.  
The proposed automatic facial expressions recognition system is installed in the classroom and a camera takes the face of the student as an input for the system. The camera and the system are set up to be ready before the beginning of each lecture. The system is capable of identifying the facial expressions of the students with the help of the facial expressions used to train the system. The system continuously saves the facial expressions of each student and the changes in facial expression during the lecture in a database. Figure 3 explains the automatic facial expression recognition system. While the proposed FER system is depicted in Figure 4.
# Microalgae as Potential Sources of Bioactive Compounds for Functional Foods and Pharmaceuticals
## 3 Results and Discussion
### Evaluation of Bioactivities of Microalgal Extracts
#### 3.1.3 Anti-Tumoral Activity
Contrary to our expectations [54], this extract displayed more cytotoxic effects towards S17 cell line than the tumorous THP-1 cell line (Selectivity Index = 0.844), having no anticancer activity for THP-1 cells. As a comparison, etoposide had a SI of 49.4 \(\pm\) 1.20, which is according to reported data [55; 56]. However, more research with other non-tumoral non-adherent cell lines is needed, as we only compared THP-1 cells to an adherent cell line (S17), and adherent and non-adherent cells are likely to have distinct responses. The healthy control cell line employed in the study was generated from murine bone marrow; as drug reactions might differ slightly between human and murine cell cultures, additional healthy controls produced from humans should be added in the subsequent research. The MTT in vitro cytotoxicity test was used to assess the toxicity of the microalgal crude extracts and their fractions on HepG2, THP-1 and S17 cell line. A promising novel anticancer therapy should not only have a high cytotoxicity towards cancerous cells but also have a high selectively towards killing cancer cells while not harming normal healthy cells [57]. Low selectivity is a characteristic of most traditionally used broad-spectrum cytotoxic drugs, which lead to significant toxicity and undesired side effects [58]. Therefore, only the extract with significantly higher selectivity values and lower IC\({}_{50}\) as etoposide (a widely used chemotherapeutic) for HepG2 cells (1.56 \(\pm\) 0.11 and 29.0 \(\pm\) 3.2 \(\mu\)g/mL, respectively) was considered promising--ethanol extract from _Phaeodactylum tricornutum_ (SI = 4.40 \(\pm\) 0.15 and IC\({}_{50}\) = 19.4 \(\pm\) 2.2 \(\mu\)g/mL)--and investigated further after fractionation (Table 7). These promising findings were confirmed by the microscopic evaluation of the cells, which were pre-incubated with _P. tricornutum_ ethanol extract and its dichloromethane fraction (125 \(\mu\)g/mL) and the control cells (HepG2, THP-1 and S17, which did not undergo any treatment), where a decrease in number and size of the cells was visible. In the case of HepG2 cells, a detachment from the surface of the wells was also observable. Prior to fractionation, a new crude ethanol extract of _P. tricornutum_ was freshly prepared from dry biomass and its cytotoxicity against all three cell lines was re-tested. Surprisingly, the IC\({}_{50}\) values for all cell lines were higher in the freshly prepared extract than the values reported in the initial screening. This discrepancy of activity between the extracts could be explained by the origin of the biomass, which is grown in an industrial setting for other applications (e.g., aquaculture feed, cosmetic industry) and where it is standard practice to perform constant optimization of culture conditions (e.g., light, temperature, nutrient availability, salinity) [59; 60; 61; 62] to enhance productivity. This leads to fluctuation in
# Real-time Neural Networks Implementation Proposal for Microcontrollers
## 4 Results  
### XOR Operation  
The binary code for the XOR operation, which is generated from this implementation as seen in Figure 1, resulted in 6.672 KBytes of program memory occupation (equivalent to 2.6%), a pretty compact solution if compared to the maximum program memory available for the ATMega-2560 and also compared to the 5.904 KBytes presented in [28] and the MLP implementation without the BP training, considering that this 2.6% already includes the training algorithm.  
The information presented in Table 5 shows that the values obtained up to \(H^{1}=38\) neurons in the hidden layer. The \(H^{1}\) parameter is limited by the available memory in the \(\mu\)C used: ATMega-2560. However, this value is quite reasonable for most real-time applications in robotics and industrial automation. Another noticeable result is that the processing times are also reasonable. Analyzing the network without the training time results (EM, BPM-1, and BPM-2), it possible to see that the iteration for a batch mode of \(\mathbb{N}=4\) samples takes 42.91 ms in the worst case, \(H^{1}=38\). If you analyze the duration of each iteration including the network being trained on the online mode, it takes 69.88 ms, again in the worst case of \(H^{1}=38\). This shows that the implementation herein presented is indeed suitable for commercial applications in fields such as industrial automation, robotics, automotive industry, and others.  
An important result regarding these tests is the behavior of the fitted curves in Figures 5 and 6. Figures 5 and 6 present the curves of the results described in Table 5 where curve-fitting was performed on the measured points with polynomial regression. The processing time grows linearly with the number of neurons in the hidden layer (\(H^{1}\)). This result is quite significant since you can estimate if a certain \(\mu\)C can be used with this implementation, and if the network architecture that was chosen will fit or not in the \(\mu\)C. This result can be used as a reference by other groups that refute the usage of MLP-BP applications on \(\mu\)Cs.  
Figure 4: Hardware-In-the-Loop measurements of the embedded XOR operation using an oscilloscope.  
Figure 5: (**a**) Execution time of FFM-1 by number of neurons in the hidden layer, \(H^{1}\). (**b**) Execution time of FFM-2 by the number of neurons in the hidden layer, \(H^{1}\).  
The MSE curve was compared by varying the number of neurons in the hidden layer (\(H^{1}\)), as shown in Figure 7. This shows that for 2 and 20 iterations, the MSE was really close to zero, and that with 38 neurons, 80 iterations were needed. According to Table 5, it took approximately 8.28 seconds to fully train the XOR case with 38 neurons.  
Figure 6: (a) Execution time of Backpropagation Module (BPM)-1 by the number of neurons in the hidden layer, \(H^{1}\). (b) Execution time of BPM-2 by the number of neurons in the hidden layer, \(H^{1}\).
## 3 How to Calculate Accurate Airtime at the AP Side
### Analysis of TCP Flows  
TCP ensures reliable data transmission through sequence numbers, acknowledgments, and retransmission mechanisms. The difference between TCP and UDP in terms of wireless airtime is that TCP includes an acknowledgment mechanism, which allows the receiver to inform the sender of the sequence number of the next data segment expected. Therefore, the airtime for a TCP stream includes both the airtime for TCP data packets and the airtime for TCP ACKs:  
\[T_{tcp}=T_{data}+T_{ack} \tag{6}\]  
When considered separately, the transmission logic for TCP data packets and TCP ACKs over wireless is the same as for UDP. They both include direct airtime and indirect airtime. Thus, using the method for calculating UDP airtime described in Section 3.2, combined with the frame sizes of TCP data and TCP ACK, their respective airtime can be calculated.  
TCP can adjust the sending frequency of TCP ACKs through parameters. Typically, after successfully receiving a data segment, the receiver will combine acknowledgments for multiple data segments and then reply with one ACK message. This reduces the number of acknowledgment messages and improves network efficiency. In the short term, the position of the Sta is relatively fixed, so it can be assumed that the wireless transmission rate for both TCP data packets and ACKs is equal. If \(d\) (Delayed Acknowledgment) represents the reply frequency of TCP ACKs, that is, \(d\) (default is two) TCP data segments for one ACK, then the single airtime for a TCP stream can be expressed as:  
\[T_{tcp}=\frac{s_{data}+s_{ack}/d}{r}+(1+\frac{1}{d})\times t_{ind} \tag{7}\]  
where \(s_{data}\) and \(s_{ack}\) represent the frame sizes of the TCP data packet and ACK, respectively, \(r\) represents the wireless rate of the stream, and \(t_{ind}\) is calculated using Formula (5). So far, we have been able to calculate the total airtime for both TCP and UDP. This paper will address the following question in Section 4.2: How can we design an appropriate queue scheduling algorithm that, in combination with TCP ACKs, targets both upload and download traffic separately?
# Development of a Path Tracker Based on a 4 WS Vehicle for Low-Speed Automated Driving Systems
## 4 Evaluation
### Real-Car Experiment
#### 4.2.2 Real-Car Experiment Results  
The real-car experiment results compared the performance of each path tracker at different vehicle velocities. The comprehensive results, which were compared using the RMS, MAX, and SD of the absolute result values, are shown in Table 4, and the graphs for each performance metric are shown in Figure 13.  
Figure 11: 4 WS platform for real-car experiment.  
Figure 12: Real-car experiment environment: (**a**) PCD map; (**b**) path on satellite photo.  
Based on the RMS results of the lateral deviation and heading angle error, it was confirmed that the proposed 4 WS path tracker outperformed the other path trackers in terms of tracking performance. This indicates that the autonomous vehicle is likely to demonstrate superior performance in confined spaces.  
Regarding the RMS of the side-slip angle, it performed better than the other path trackers at 10 KPH. However, at 5 KPH, it decreased compared to the conventional 2 WS path tracker and increased compared to the conventional 4 WS path tracker. This is believed to be due to oscillations in the front steering angle caused by issues with control gain optimization in the straight section.  
The RMS of the yaw rate increased at 5 KPH compared to the other path trackers. At 10 KPH, it increased compared to the conventional 2 WS path tracker but decreased compared to the conventional 4 WS path tracker. This is attributed to the use of counter-phase steering, which results in a higher rotational angular velocity than a vehicle using conventional 2 WS. However
## 1 Introduction
### The Implementation of the Key-Agreement Process Based on ECDH Protocol  
The security protocol generates a secure session key for the communicating entities after the secure completion of the protocol process under mutual authentication conditions for the communicating entities.  
Figure 1: Internet of Things communication.
# A Spatial Prediction-Based Motion-Compensated Frame Rate Up-Conversion
## 2 Proposed SP-MCFI Algorithm
### MV Prediction for Absent Block  
After tracking the MVs of the basic blocks by a full search ME, the motion vectors \(\mathbf{v}_{a1}\), \(\mathbf{v}_{a2}\), \(\mathbf{v}_{a3}\) and \(\mathbf{v}_{a4}\) of the four basic blocks adjacent to the current absent block are selected as candidates. The candidate vector set \(\mathbf{V}_{c}\) is composed of the above vectors as follows:  
\[\mathbf{V}_{c}=\{\mathbf{v}_{a1},\mathbf{v}_{a2},\mathbf{v}_{a3},\mathbf{v}_{a4}\} \tag{7}\]  
The coordinate of the upper left pixel of the current absent block is set to \(\mathbf{p}\), and then the motion vector \(\mathbf{v}_{p}\) of the absent block is calculated based on the Sum of Bi-direction Absolute Difference (SBAD):  
\[\mathbf{v}_{p}=\underset{\mathbf{v}\in\mathbf{V}_{c}}{\text{argmin}}\|\mathbf{B}_{i}(\mathbf{p}- \mathbf{v})-\mathbf{B}_{i+1}(\mathbf{p}+\mathbf{v})\|_{1} \tag{8}\]  
where \(\mathbf{B}_{i}(\mathbf{p}-\mathbf{v})\) is the vector in which all the pixels of block \((\mathbf{p}-\mathbf{v})\) in \(\mathbf{f}_{i}\) are arranged in rows, \(|\bullet|\mid_{1}\) is the \(l_{1}\)-norm of the vector and \(\mathbf{v}\) is a candidate vector.  
After the MV prediction of the absent blocks is completed, all the MVs of the two kinds of blocks are assembled into the MVF \(\mathbf{V}_{t+0.5}\) of the interpolated frame \(\mathbf{f}_{t+0.5}\). The OBMC technique is used to calculate the value of the pixel \(\mathbf{p}=(i,j)\) in \(\mathbf{f}_{t+0.5}\) as follows:  
\[\mathbf{f}_{t+0.5}(\mathbf{p})=\frac{1}{2}\omega(k)(\mathbf{f}_{i}^{k}(\mathbf{p}-\mathbf{v}_{i,j} )+\mathbf{f}_{t+1}^{k}(\mathbf{p}+\mathbf{v}_{i,j})) \tag{9}\]  
where  
\[k=1,2,3\ ;\omega=\begin{cases}1,&k=1\\ \frac{1}{2},&k=2\,,\\ \frac{1}{4},&k=3\end{cases}\]  
\(\mathbf{v}_{i,j}\) is the MV of \(\mathbf{V}_{t+0.5}\) at \(\mathbf{p}\); \(k\) represents the three types of the overlapping part: it denotes the non-overlapping part when it is set to 1, the overlapping part of two blocks when it is set to 2 and the overlapping part of four blocks when it is set to 3; and the value of coefficient \(\omega\) is set to the corresponding value based on the change of \(k\).
# Assessment of Different Stochastic Models for Inter-System Bias between GPS and BDS
## 4 Results and Discussion
### Pseudorange and Carrier-Phase Observation Residuals
Figure 3: Ionospheric-free pseudorange observation residuals of GPS and BDS systems at STR1 station as an example on DOY 251, 2017, it is the results for the first 3h period with kinematic PPP mode. Figure 4: Ionospheric-free carrier-phase observation residuals for GPS and BDS satellites in STR1 station, the result in the first 3h period on DOY 251, 2017 with static PPP mode is taken as an example, left panels are the results for GPS system, the right ones denote the results of BDS system, from top to bottom represents different schemes. Figure 5: Ionospheric-free carrier-phase observation residuals in the first 3h period of DOY 251, 2017 for the kinematic PPP mode, left panels show the results for GPS system, the right ones are the results of BDS system.
# Application of the Reed-Solomon Algorithm as a Remote Sensing Data Fusion Tool for Land Use Studies
## 3 Results  
Calculation of the RS algorithm was not quite routine for all of the aforementioned spectral bands. There were computer software applications that were not able to handle the huge integer number calculations. The results of RS algorithm geocomputation in the form of a report are presented in Table 1. The algorithm was computed as an expression of the raster calculator (using the Golden Software Surfer mathematical function).
## 3 Results and Discussion
### Antimicrobial Activity of Hemp Seeds Fermented with LAB  
The unfermented hemp seed flour did not exhibit antimicrobial activity against the tested bacteria and fungi, whereas after fermentation with LAB, there was antimicrobial activity against some foodborne pathogens (Table 2). The hemp seed product fermented with _L. acidophilus_ presented the highest antimicrobial activity (against _S. aureus, Salmonella typhimurium, B. subtilis_, and _B. cereus_, with a clear inhibition zone of 3-5 mm). There was lower antimicrobial activity in the hemp seed product fermented with _L. plantarum_, with a clear inhibition zone of 3-5 mm for _Salmonella typhimurium_ and 1-3 mm for _E. coli_, _S. aureus_, _B. subtilis_, and _B. cereus_. Finally, the hemp seed product fermented with _L. brevis_ exhibited the lowest antimicrobial activity. The growth of _A. versicolor_ was inhibited only by the hemp seeds fermented with _L. plantarum_, while _Penicillium_ sp. growth was suppressed after fermentation with all three tested LAB strains, with a clear inhibition zone of 1-3 mm. The antimicrobial effect of hemp seeds fermented with _L. acidophilus_ on the growth of _Salmonella typhimurium_ is shown in Figure 1. A clear inhibition zone could be seen on the agar plates. The antimicrobial effect of LAB is mainly related to the production of lactic and acetic acids, as well as propionic, sorbic, and benzoic acids; hydrogen peroxide; and diacetyl, ethanol, phenolic, and proteinaceous compounds. Moreover, some LAB strains are able to synthesise antimicrobial substances (bacteriocins) [45]. Our findings agree with [PERSON] et al. [40] that during hemp seed fermentation with LAB strains, the antimicrobial substances produced and activity depend on the LAB strain.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**LAB Used for Hemp Seed Fermentation**} \\ \hline
**Foodborne** & \multirow{2}{*}{_L. plantarum_} & \multirow{2}{*}{_L. brevis_} & \multirow{2}{*}{_L. acidophilus_} \\
**Pathogens** & & & \\ \hline \multicolumn{4}{c}{_against fungi_} \\ \hline _A. versicolor_ & & & \\ _P. chrysogenum_ & \(-\) & \(-\) & \(-\) \\ _P. cyclobium_ & \(-\) & \(-\) & \(-\) \\ _A. terreus_ & \(+/-\) & \(-\) & \(-\) \\ _Penicillium_ sp. & \(+\) & \(+\) & \(+\) \\ \hline \multicolumn{4}{c}{_against bacteria_} \\ \hline _E. coli_ & \(+\) & \(+/-\) & \(+/-\) \\ _St. aureus_ & \(+\) & \(-\) & \(++\) \\ _S. typhimurium_ & \(++\) & \(-\) & \(++\) \\ _B. subtilis_ & \(+\) & \(-\) & \(++\) \\ _B. cereus_ & \(+\) & \(+\) & \(++\) \\ \hline \hline \end{tabular} Evaluation of the antimicrobial activity: (\(-\)) no clear inhibition zone around the sample (no inhibition); (\(+/-\)) up to a 1 mm clear zone; (\(+\)) a 1–3 mm clear zone; (\(++\)) a 3–5 mm clear zone.  
\end{table}
Table 2: The antimicrobial effect of hemp seeds fermented for 48 h on foodborne pathogens.