esa-sceva/satcom-chunk-collection huggingface dataset:
# Solid-Phase Microextraction Arrow for the Sampling of Volatile Organic Compounds in Milk Samples
## 3 Results and Discussion
### Evaluation of the SPME Arrow Procedure
#### 3.1.1 Selection of SPME Arrow Fiber  
The selection of the appropriate fiber coating material is crucial in the development of an SPME method. In this study, three different SPME Arrow fibers (100 \(\upmu\)m PDMS, 120 \(\upmu\)m CAR/PDMS, or 120 \(\upmu\)m DVB/PDMS SPME Arrow fibers) were evaluated for the extraction of volatile compounds from milk samples. The suitability of a particular fiber coating for a specific analyte is related to the polarity of the extraction phase and the overall selectivity toward the analyte over other matrix components [13]. The initial experiments were performed with 3 mL of sample, 30 min extraction time, 40 \({}^{\circ}\)C extraction time, 500 rpm stirring rate and no salt addition. Due to the different polarities of the coatings, various volatile compound patterns were observed. Among the examined Arrow fibers, the CAR/PDMS fiber provided a higher extraction efficiency for the volatile compounds. Therefore, this coating was chosen for further experiments. This observation agrees with other studies regarding the determination of VOCs in complex food matrices. The CAR/PDMS Arrow SPME fibers have recently also been used for the extraction of VOCs from brown rice vinegar [13] and Korean salt-fermented fish sauce [14].
# Smart Classroom Monitoring Using Novel Real-Time Facial Expression Recognition System
## 2 Materials and Methods
### Proposed Automatic Facial Expression Recognition System
#### 2.2.3 Constraints/Limitations  
It has been observed that among the pool of 100 selected students there were some students who were not clearly visible by the camera due to their seating positions, light issues in the classroom, or because they frequently kept their hands over their mouth, or were wearing a mask or glasses. Similarly, the facial expressions of some students could not be captured for some minutes due to their head being in a down position while they wrote important points of the lecture or used a cell phone for a long time. Therefore, the exact number of students with perfect data is 80.
###### Abstract  
The demand for non-destructive testing of carbon fiber-reinforced polymer (CFRP) is becoming increasingly pressing to ensure its safety and reliability across different fields of use. However, the complex structural characteristics and anisotropic bulk conductivity of CFRP make achieving high sensitivity in detecting internal defects such as delamination extremely challenging. To address this issue, a novel triple rectangular coil probe with high sensitivity developed for detecting delamination in CFRP is presented in this paper. A finite element model using COMSOL Multiphysics was developed for CFRP delamination eddy current testing with the designed probe. Based on this model, the probe parameters were determined through orthogonal experiments. By analyzing the eddy current distribution in CFRP samples, the scanning mode was defined. Following this, the detection voltage was evaluated for various delamination parameters, and the sensitivity of different probes was compared. Results indicate that, under the same excitation coil parameters, for a 5 mm delamination lateral dimension change, the single pancake and single rectangular coil probes exhibit sensitivities of 88.24% and 72.55%, respectively, compared with the designed probe. For a 0.5 mm delamination thickness change, their sensitivities are 49.04% and 56.69% of those of the designed probe. The designed probe meets the demand for high-sensitivity detection.  
non-destructive testing; eddy current testing; carbon fiber-reinforced polymer; delamination; high-sensitivity detection Article
## 4 Experiment and Discussion
### Discussion  
This experimental design is a comparison between our proposed ADCNN method and four currently popular methods. The differences between an excessively worn dental milling cutter and a normal cutter are not very noticeable. Because of this, classification errors often occur; the prediction is that the cutter is normal when in fact it is worn. Our results show that ADCNN can accurately predict the difference between the normal cutter and the excessively worn cutter, whereas the other methods predict incorrectly. The addition of white noise reduced the accuracy of the proposed method from 100% to 99%, 1 DCNN from 99.33% to 96.2%, LSTM from 99.05% to 94.1%, CNN-LSTM from 97.85% to 96.2%, and ConvLSTM from 77.32% down to 72.75%. The results show that, with or without noise interference, the proposed method has the highest prediction accuracy and adaptive anti-noise ability compared with the other methods.  
Experiments show that fusion architecture and multi-sensing data's feature extraction mechanism not only obtains subtler features but model classification accuracy is also improved through different physical signal characteristics. A white noise simulation model with an SNR of 7 dB is added to the adaptive ability of noise in the interference field of this experiment. The final classification accuracy is higher than other models, and it can also effectively classify normal tools and worn tools with minuscule characteristic differences. The traditional method requiring experts to de-noise the signal first and then extract features can also be bypassed, as some useful features may be lost during the de-noising process.  
Furthermore, for model prediction robustness, Figures 12-14 show that overfitting can be avoided with the proposed method. In comparison with other methods, the feature learning ability also has significant improvement. Feature fusion methods can improve the robustness and reliability of fault diagnosis.
## 4 Experimental Results and Analysis
### Comparison of Experimental Results
#### 4.2.1 Comparison of Experimental Results Based on Kitti Dataset
(**a**) Feature point extraction result of the SIFT algorithm; (**b**) feature point extraction result of this algorithm. Figure 10: The matching results of the \(11\)th group of pictures. (**a**) The matching results of the SIFT algorithm; (**b**) the matching results of the algorithm in this paper. Figure 9: Extraction results of feature points of the \(11\)th group of images to be registered. (**a**) Feature point extraction result of the SIFT algorithm; (**b**) feature point extraction result of this algorithm. Figure 11: Comparison of the Euroc dataset’s specific experimental data in four caegories: (**a**) the number of feature points of the reference image; (**b**) the number of feature points of the images to be compared; (**c**) matching time; and (**d**) Rems errors. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{4}{c}{**SIFT Algorithm**} & \multicolumn{4}{c}{**The Algorithm of This Paper**} \\ \cline{2-11}  & **RN** & **WN** & **MN** & **PT(s)** & **rems** & **RN** & **WN** & **MN** & **PT(s)** & **rems** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the results of Euroc.
# Study of the Error Caused by Camera Movement for the Stereo-Vision System
## 3 Camera Movement Errors
### Relative Translations  
As shown in Figure 4, the relative translation can be regarded as the case in which the left camera is fixed and only the translation of the right camera causes the position between the two cameras to change. The translation vector between the right camera coordinate  
Figure 3: The identical rotations of two cameras in a stereo-vision system.  
systems before and after camera movement is expressed by \(T_{rr^{\prime}}=\left[\Delta T_{x}\), \(\Delta T_{y}\), \(\Delta T_{z}\right]^{T}\). According to Equation (3), the 3D coordinate error \(\Delta\mathbf{X}_{w}\) can be described as  
\[\left\{\begin{array}{c}\Delta X_{w}=x_{nl}\Delta Z_{w}\\ \Delta Y_{w}=y_{nl}\Delta Z_{w}\\ \Delta Z_{w}=\frac{\Delta T_{x}-x_{nr}\Delta T_{z}}{x_{nr}(x_{13}x_{nl}+\tau_{ 32}y_{nl}+\tau_{33})-(\tau_{11}x_{nl}+\tau_{12}y_{nl}+\tau_{13})}\\ \Delta T_{y}-y_{nr}\Delta T_{z}\\ \end{array}\right. \tag{12}\]  
According to Equation (12), \(\Delta X_{w}\) is linear with the change of relative translation vector, and the coefficient depends on the normalized image coordinates \((x_{nl},y_{nl})\) and \((x_{nr},y_{nr})\), and the relative rotation matrix \(\ R_{lr}\). Since each point in the field of view of both cameras has a specific image coordinate, \(\Delta\mathbf{X}_{w}\) varies with different measuring points. Therefore, the 3D coordinate errors of any two points owing to the relative translation are described as  
\[\left\{\begin{array}{c}\Delta X_{w1}=X_{w1}^{\prime}-X_{w1}\\ \Delta Y_{w1}=Y_{w1}^{\prime}-Y_{w1}\\ \Delta Z_{w1}=Z_{w1}^{\prime}-Z_{w1}\end{array}\right.,\left\{\begin{array}[] {c}\Delta X_{w2}=X_{w2}^{\prime}-X_{w2}\\ \Delta Y_{w2}=Y_{w2}^{\prime}-Y_{w2}\\ \Delta Z_{w2}=Z_{w2}^{\prime}-Z_{w2}\end{array}\right. \tag{13}\]  
Based on Equation (13), the distance between two selected points after camera movement can be expanded with [PERSON]'s formula [39]. Ignoring the second-order term, the linearized equation of distance error is expressed as follows.  
\[\begin{array}{c}\Delta I_{w}=I_{r}^{\prime}-L_{w}\\ =\Delta X_{w1}\frac{\frac{\Delta X_{w1}-X_{w2}^{\prime}}{L_{w}}}{L_{w}}+\Delta Y _{w1}\frac{\frac{\Delta X_{w1}-X_{w2}^{\prime}}{L_{w}}}{L_{w}}+\Delta Z_{w1} \frac{\frac{\Delta X_{w2}-X_{w1}^{\prime}}{L_{w}}}{L_{w}}-\Delta X_{w2}\frac{ \frac{\Delta X_{w1}-X_{w2}^{\prime}}{L_{w}}}{L_{w}}-\Delta Y_{w2}\frac{\frac{ \Delta X_{w1}-X_{w2}^{\prime}}{L_{w}}}{L_{w}}-\Delta Z_{w2}\frac{\frac{\Delta X _{w2}-X_{w1}^{\prime}}{L_{w}}}{L_{w}}\end{array} \tag{14}\]  
Combining Equations (12) to (14), we find that the distance measurement error varies linearly with the change in the relative translation vector.
## 4 Implementation
### Proposed Scheme
#### 4.4.4 Implementation Results  
In this paper, we deployed the proposed scheme several times with various cameras (a webcam, CCTV camera, tablet, Point Gray rolling shutter camera, etc.) to analyze the effect of camera frame variation. The asynchronous process includes asynchronous decoding, the merging data technique, and the detection of missing parts. The length of  
Figure 13: Relationship between the camera focal length and communication distance.  
Figure 10 shows the relationship between communication distances, exposure times, and the intensity of the LED in the images (or SNR value). The exposure time can be increased to increase the communication distance, but as mentioned above, the exposure time should be controlled carefully. The image sensor operates as a low-pas filter. Then, if the exposure time is increased, the bandwidth of the OCC system will decrease due to the increasing power of the background noise.  
Figure 17 shows the results of using a Point Grey rolling shutter camera at different distances and exposure times. The BER is measured with varying times of exposure, with the same communication distance and noise condition. The communication bandwidth has a vital relationship with the exposure time; thus, both exposure time and the trade-off between exposure time and signal noise must be considered. To reduce the bit error rate or increase the communication distance and thus increase system performance, channel coding should be used. However, to calculate the working communication distance, Equations (7) and (8) should be used. The parameters of the proposed scheme are presented in Table 1. The summary of the comparison of MIMO-OCC methods will be presented in Table 2 to highlight the proposed scheme. We implement this scheme with 8 \(\times\) 8 LED matrix at the communication distance of 2 m, which can be visually assessed by the Supplementary Materials.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Transmitter Side** & \\ \hline LED matrix size & 8 \(\times\) 8 & 16 \(\times\) 16 \\ The number of LEDs & 64 & 256 \\ FEC & CC(3/4) & \\ Packet rate & 30 packet/s & \\ \hline \multicolumn{3}{c}{**Receiver Side**} \\ \hline Camera & Point Grey rolling shutter camera & \\ Camera Frame Rate & 60 fps & \\ \hline \multicolumn{3}{c}{**Throughput**} \\ \hline Uncode bit rate & 1.920 kbps & 7.680 kbps \\ Code bit rate & 1.440 kbps & 5.760 kbps \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristic parameters of the proposed scheme.  
Figure 16: Rx interface with the rear camera of tablet.
# Interval Extended Kalman Filter--Application to Underwater Localization and Control
## 6 Conclusions  
The probabilistic and the set membership approaches in the localization problems are often seen as different and in competition. In this paper, we have shown that they may collaborate to keep both the integrity of the interval filter and the accuracy of the Kalman filter.  
While in practice, the robots often rely on a probabilistic localization with a Kalman Filter for their online autonomous navigation, the set membership method is mainly used in post-process analysis. This paper proposes a new filter IEKF which benefits from the advantages of the EKF and the IF. By linearizing the EKF at a point given by the IF, the IEKF is more likely to converge. Moreover, the IF can be used to verify the consistency of the observer, and validate that the robot did not enter inside a forbidden zone.  
Of course, our filter requires more computation with two observers instead of one, but it is the prize to pay if we want both the integrity needed to a safe navigation and the accuracy, needed to have smooth trajectories that do not overload the actuators too much.  
Figure 10: Situation where several sets are solution to the interval analysis.
## 5 Conclusions  
In this study, we introduced a state-of-the-art virtual medical assistant, MIRA, that interacts with the user in a spoken natural language, diagnoses a disease based on a user's chief complaint, and refers the user to a nearby appropriate medical specialist. The key contribution of MIRA includes disease identification based on chief complaint, understanding single and multiple intents, a voice-based authentication mechanism, conversational state tracking, and continuous monitoring of the system for detecting anomalies. Moreover, we designed a chief complaint dataset and stock phrases from the recorded dialogue corpora. MIRA is the first assistant of its kind that considers security aspects (such as authentication), which requires improvements in terms of transmission security and audit control to become HIPAA compliant. The designed knowledge source of MIRA considered glaucoma and diabetes chief complaints only, which can be extended to other medical conditions in the future.  
There are many challenges in developing these kinds of interactive systems such as privacy concerns, accuracy constraints, correct decision making, precise response generation, and gaining user trust. The compliance with standards may help in risk minimization. Besides these challenges, it is beneficial for society, especially in underdeveloped countries, where people are suffering from many diseases due to the lack of healthcare facilities. These kinds of virtual medical assistants help the patient identify an appropriate medical specialist and reduce healthcare cost. Also, it supports medical practitioners and students in clinical decision making.  
[PERSON] is the principal researcher, who proposed the idea, designed and developed the prototype version, conducted the experiments based on the designed scenarios, and wrote the paper. [PERSON] and [PERSON] provided the medical related information, supported in data acquisition and analysis. [PERSON] and M.A.R. contributed to participant management, English proofreading and finalized content flow in the manuscript. [PERSON] supervised the whole process, provided advisory feedback, and reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-0-01629) supervised by the IITP (Institute for Information & communications Technology Promotion). This work was supported by the Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00655). This work was also supported by the National Research Foundation (NRF) under the NRF-2016K1A3A7A03951968 and NRF-2019R1A2C2090504.  
The authors declare no conflict of interest.
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 3 Privacy Techniques
### Differential Privacy  
On the one hand, differential privacy (DP) is a privacy-preserving technique that provides a mathematical guarantee of privacy [74; 75]. It works by adding random noise to the data, making it difficult to identify individual data within the dataset [76]. This technique has been used in various contexts, including recommendation systems. For example, in [77], the study used DP to protect the privacy of users in a collaborative filtering-based recommendation system. The authors added Laplace noise to the user ratings to ensure privacy preservation. Similarly, in [78], the study used DP to protect the privacy of user ratings in a matrix factorization approach to recommendations.  
On the other hand, local differential privacy (LDP) is a variant of DP that is used for federated learning. Instead of adding noise to the entire dataset, LDP adds noise to each user's data before sending them to the server [79]. This ensures that the server never has access to the raw data, but still can obtain useful information. Several studies have been conducted in this context; for example, in [80], the study used LDP to protect the privacy of users in a matrix-factorization-based recommendation system. Similarly, in [81], the authors improved the matrix factorization using LDP for recommendation systems.  
The benefits of using DP and LDP in FRS include the ability to protect user privacy while still allowing the server to obtain useful information from the data [82; 83; 84]. Moreover, it ensures that the raw data are never exposed to the server, which is important for protecting user privacy. The limitations of using DP and LDP in FRSs include the fact that the noise added to the data can lead to inaccurate results, as well as increased computation time due  
Figure 5: Privacy-preserving techniques used in FRSs.  
to the need to add the noise [85]. Besides, it is difficult to determine the optimal noise level for a given dataset, which can lead to inaccurate results. Therefore, it is essential to design a system that can effectively trade off between recommendation accuracy and privacy.
# Design and Implementation of a Contextual-Based Continuous Authentication Framework for Smart Homes
## 5 Evaluation Results
### Evaluation 2: Authentication Weights and Device Thresholds  
For the second experiment, the system framework is evaluated with regards to the calculated confidence levels, based on the assigned weights and the subsequent access levels given to the various services. Table 6 shows some example weights for contexts that are assigned by default, and security levels that are assigned by the homeowner which will be applied to smart devices of varying concern. The confidence level is considered as the total of the assigned weight of the available context information.  
As seen in Table 6A, when providing both credentials of username and password, the assigned weight is 40 whereas, when achieving access to only the calendar, the assigned weight is only 10. In contrast, as seen in Table 6B, the highest security level is 4 with threshold 100, whereas the lowest security level is 1 with threshold 30.  
Table 7 reflects some examples of calculating a confidence level by adding together the assigned weights and determining if the system should grant the user access to the requested service. As can be seen from the same table, should the confidence level (calculated by combining the available parameter weights) be sufficient to meet the security level/access threshold of the requested service, access is granted. If the calculated confidence level is insufficient to meet the security level/access threshold of the requested service, access will be denied.  
As an example, as seen in Table 7, with the ability to access calendar information as well as the location based on the network, the total calculated weight will be 30. Thus, if the user is requesting access to a service with an assigned security level of 3 or higher, the request will be denied, since the achieved security level is lower than that required. In addition, having only a username and password would allow login and access to only those services that do not require a high threshold. As an example, weight 40 will not allow access to important services by setting a higher access threshold.
# Evaluation and Comparison of Spatial Clustering for Solar Irradiance Time Series
## 2 Solar Irradiation Time-Series Analysis
### Data Measures and Stations  
The Spanish Ministry of Agriculture, Fisheries, and Food [31] make available an Agroclimatic Information System for Irrigation (SIAR) with measured data recorded every 30 min on 542 sites (only 448 are active; [[https://eportal.mapa.gob.es/websiar/Inicio.aspx](https://eportal.mapa.gob.es/websiar/Inicio.aspx)]([https://eportal.mapa.gob.es/websiar/Inicio.aspx](https://eportal.mapa.gob.es/websiar/Inicio.aspx)), accessed on 2 June 2021). The position of the meteorological stations used in this work is shown in Figure 1. Solar irradiance measurements obtained by pyranometers are verified according to the International Standard ISO 9847 Solar energy Calibration. For the purposes of this study, global horizontal radiation time series (GHI) from 1 January 2017 to 31 December 2020, with a 30 min time step, were used. It is obvious that a reliable measure of meteorological data is not always an easy task, and even the maintenance of the station is well realized; quality control must be applied, mainly on solar radiation data.  
Before explaining the quality-control method (Section 2.3), the clear-sky solar radiation model (Section 2.2) is briefly described here.
## 3 ML-Based Routing Algorithms in WSNs
### System Model  
In the proposed system model, WSN is considered to be an agent within the context of ML and uses feedback signals that can identify the system environment. Then, the system learns and tries to achieve the best decisions with ML algorithms to design the optimal policies. Hence, the routing decision problem can be efficiently represented as a Markov decision process (MDP). A hypothetical cooperative routing model which is formulated as a multi-agent MDP is proposed as shown in Figure 7. In addition, any of the ML algorithms discussed below can be used to formulate the optimal solution. The study considers only a WSN consisting of \(N\) sensors that observe the action of the routing.  
The proposed system model is considered to function in a time-slotted manner. At the beginning of each time slot, one of the agents, which is also the fusion center (FC), activates an ML-based distributed cooperative routing decision and combines the local decision of each agent. Each sensor node senses its own neighbors' link state information (e.g., energy efficiency, overhead, packet loss rate, bit error rate) and makes decisions locally. Then, they constantly sense the link state information and inform the FC of their local decisions. Typical routing issues such as energy efficiency and QoS could be minimized by employing spatial diversity through cooperative and distributed routing decision. In this case, the sensor broadcasts a request for cooperative routing to all neighbors. Then, the agent combines all the independent local decisions and gives the final decision of routing.  
Focusing on ML-based routing algorithms in WSNs, the existing schemes are classified into four categories: distributed regression (DR)-based routing algorithms, artificial neural network (ANN)-based routing algorithms, reinforcement learning (RL)-based routing algorithms, and ensemble learning (EL)-based routing algorithms.