esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment  
### Apparatus and Participants  
The VR system was built using the Meta Quest 2 HMD (\"Oculus Quest 2\", Oculus, [[https://www.oculus.com/quest-2/](https://www.oculus.com/quest-2/)]([https://www.oculus.com/quest-2/](https://www.oculus.com/quest-2/)), accessed on 7 June 2021) and using the Touch controllers (\"Oculus Quest 2 Accessories\", Oculus, [[https://www.oculus.com/quest/accessories/](https://www.oculus.com/quest/accessories/)]([https://www.oculus.com/quest/accessories/](https://www.oculus.com/quest/accessories/)), accessed on 7 June 2021) (Figure 3, right). The Oculus Quest 2 device uses an inside-out tracking technology, which means that no base stations are required for tracking. The VR system was built on the Unity 3D software development system, using version 2021.1. (\"Unity Real-Time Development Platform\", Unity, [[https://unity.com/](https://unity.com/)]([https://unity.com/](https://unity.com/)), accessed on 28 April 2021).  
The skull model was segmented from the facial CBCT of an orthognathic surgery patient using Materialise ProPlan CMF 3.0.1. The CBCT was scanned with Promax Mid by Planmeca at the Department of Cranio- and Dentomaxillofacial Radiology, Tampere University Hospital, Finland. Voxel size was 0.3 mm. This study was based on a retrospective registry data set and did not involve human experimentation or the use of human tissue samples. No patients were imaged for this study. The study followed Finnish legislation, the Medical Research Act (488/1999), the Act on Secondary Use of Health and Social Data (552/2019), and the European General Data Protection Regulation 216/679. The use of data was approved by the research director of Tampere University Hospital, Finland, on 1 October 2019 (vote number R20558).  
Four expert participants were recruited (two female and two male participants, oral and maxillofacial radiologists, who were readily available) who all had experience of orthognathic-surgical planning. Two participants performed them weekly and one performed them monthly. One was familiar with the planning procedure. Their experience in dentistry and radiology varied from 7 to 35 years and in CASS from 5 to 20 years. These radiologists were responsible for in-house CASS in their own hospital. (In other hospitals, the division of responsibilities may be different.) Participants ranged in age from 33 to 62, with an average age of 46. All the participants had some experience in using VR equipment, but none identified as an expert user.  
\begin{table}
\begin{tabular}{l l} \hline S1 & How successful were you in accomplishing what you were trying to do? \\ \hline S2 & How confident were you in your ability to use the tools? \\ \hline S3 & How efficient was the system to use? \\ \hline S4 & How easy was the system to use? \\ \hline \multirow{3}{*}{S5} & Could you imagine using the system for your daily work? (How likely \\  & would you be to use the system?) \\ \hline \end{tabular}
\end{table}
Table 1: The questions about the participant’s subjective impressions.
# Application of an Electronic Nose as a New Technology for Rapid Detection of Adulteration in Honey
###### Abstract  
This work demonstrates the application of an electronic nose (e-nose) for discrimination between authentic and adulterated honey. The developed e-nose is based on electrodes covered with ionogel (ionic liquid + gelatin + Fe\({}_{3}\)O\({}_{4}\) nanoparticle) films. Authentic and adulterated honey samples were submitted to e-nose analysis, and the capacity of the sensors for discrimination between authentic and adulterated honey was evaluated using principal component analysis (PCA) based on average relative response data. From the PCA biplot, it was possible to note two well-defined clusters and no intersection was observed. To evaluate the relative response data as input for autonomous classification, different machine learning algorithms were evaluated, namely instance based (IBK), Kstar, Trees-J48 (J48), random forest (RF), multilayer perceptron (MLP), naive Bayes (NB), and sequential minimal optimization (SMO). Considering the average data, the highest accuracy was obtained for Kstar: 100% (k-fold = 3). Additionally, this algorithm was also compared regarding its sensitivity and specificity, both being 100% for both features. Thus, due to the rapidity, simplicity, and accuracy of the developed methodology, the technology based on e-noses has the potential to be applied to honey quality control.  
electronic nose; sensors; honey quality control; honey adulteration; multivariate analysis; machine learning +
Footnote †: journal: Applied Sciences
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BAN & Body Area Network \\ BS & Base Station \\ CMTC & Critical Machine Type Communication \\ CoT & Cloud of Things \\ CDMA & Code Division Multiple Access \\ CQI & Channel Quality Indicator \\ D2D & Device-to-Device \\ EMB & Enhanced Mobile Broadband \\ ETSI & European Telecommunications Standards Institute \\ FDMA & Frequency Division Multiple Access \\ GSM & Global System for Mobile \\ GPRS & General Packet Radio Service \\ HetNets & Heterogeneous Networks \\ IoT & Internet of Things \\ ITU & International Telecommunication Union \\ IECR & IoT European Research Cluster \\ LTE & Long-Term Evaluation \\ LTE-M & Long-Term Evaluation Advance \\ LoraWAN & Long Range Wide Area Network \\ Lora & Long Range \\ M2M & Machine-to-Machine \\ mMTC & Massive Machine-Type Communication \\ MTCs & Machine-Type Communications \\ MBS & Macro Base Station \\ MIMO & Multiple-input multiple output \\ NFV & Network Function Virtualization \\ NB-IoT & Narrowband Internet of Thing \\ NFC & Near Field Communication \\ OFDMA & Orthogonal Frequency Division Multiplexing \\ OMA & Open Mobile Alliance \\ QoS & Quality of Service \\ SNR & Signal-to-Noise Ratio \\ SBS & Small Base Station \\ SDN & Software Defined Network \\ TDMA & Time Division Multiple Access \\ TCP & Transmission Control Protocol \\ UHD & Ultra High Definition \\ UEs & User Equipment's \\ URLLC & Ultra-Reliable and Low Latency Communication \\ WLAN & Wireless Local Area Network \\ WRAN & Wireless Regional Area Networks \\ WBAN & Wireless Body Area Network \\ WiMAX & Worldwide Interoperability for Microwave Access \\ Wi-Fi & Wireless Fidelity \\
3 GPP & Third Generation Partnership Project \\
4G & Fourth Generation \\
5G & Fifth Generation Mobile Network \\ \end{tabular}
# Adaptive TCP Transmission Adjustment for UAV Network Infrastructure
## 3 Asran
### How to Distinguish Transient Link Instability from Buffer Overflow  
The TCP cannot distinguish whether the segment loss occurs due to transient link instability or buffer overflow. Unlike original TCP, the _ASRAN_ distinguishes between transient link instability and buffer overflow based on the _last_max_cvnd_ presented in Section 3.2. _Last_max_cvnd_ represents the most recently available transmission capacity of network. Therefore, if the size of the CWND is smaller than _last_max_cvnd_ after the segment loss occurs, the _ASRAN_ determines that transient link instability occurs in the network and restores the throughput quickly. If the size of the CWND is larger than _last_max_cvnd_ after the segment loss occurs, the _ASRAN_ determines that the buffer overflow is caused by the data transmission exceeding the transmission capacity. Figure 1(a),b shows CWND fluctuation modeling of TCP using _ASRAN_. CWND fluctuation modeling follows the TCP-NewReno algorithm, and each figure shows how _ASRAN_ works with CWND fluctuations when transient link instability and buffer overflow occur. Detailed description of each situation is given in the following subsections.  
Figure 2: CWND fluctuation of ASRAN in transient link instability and buffer overflow scenario.
###### Abstract
Secondly, the data-energy-to-bit-energy ratio of SR-OBDR-DCSK is derived. The influence of system parameters on system performance is analyzed. Compared to the SR-DCSK system, we confirm the performance advantage of the proposed scheme. Thirdly, by analyzing and comparing the maximum achievable data rate per transmitted symbol of SR-DCSK and the proposed scheme, we find that SR-ODBR-DCSK proposed scheme can significantly upgrade the data rate.
## 5 Results  
This section presents the \(\mathbf{Q}\) matrix values derived from the proposed novel approach for Kalman filter tuning in both direct and indirect INS/GNSS integration. Additionally, the other parameters used to initialize the EKFs are detailed, along with the results obtained from the filter tunings in both the standard (STD) form and the proposed new approach (NA). These results are then compared with those obtained from the platforms.  
To calculate the variances in the \(\mathbf{Q}\) matrix using the NA tuning method, inertial data from a roller coaster trajectory, employed in the SIA project (Inertial Systems for Aerospace Application, Sistemas Inerciais para Aplicacao Aeroespacial in Portuguese) at the Brazilian Institute of Aeronautics and Space (IAE, Instituto de Aeronautica e Espaco in Portuguese), are used as the reference trajectory in the Monte Carlo simulation. This trajectory induces more pronounced accelerations and angular velocities, leading to larger variances. This approach is specifically designed to tune the filter for worst-case scenarios, thereby ensuring robustness under extreme conditions. The variance results, truncated to the fourth decimal place, are presented in Table 3 and Table 4 for the DEKF and IEKF, respectively. For the DEKF variances, it can be observed that the position variance values were high for the vertical channel due to its instability, a phenomenon discussed in detail by [PERSON] et al. (2019) in the context of inertial navigation systems.  
For the other EKF parameters, all filters used the same calibrated inertial sensor data from the platforms, the same value for the matrix \(\mathbf{R}\), and identical initial point values in the INS for each run, with all data originating from the platforms. For the initial values of the covariance matrix \(\mathbf{P}\), all filters used the same values, except for those compared with the PINA-F platform. This exception was necessary because the PINA-F platform does not provide standard deviation data in its log.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**PINA-F**} & \multicolumn{2}{c}{**PINA-M**} \\ \cline{3-6}
**Parameter** & **Axis** & **STD** & **NA** & **STD** & **NA** \\ \hline \multirow{3}{*}{Accel Errors} & \(x\) & \(4.7155\times 10^{-5}\) & \(6.5378\times 10^{-5}\) & \(1.9247\times 10^{-6}\) & \(3.3675\times 10^{-4}\) \\  & \(y\) & \(4.7155\times 10^{-5}\) & \(6.5378\times  
The following subsections present the results obtained from the filter tunings. The reference trajectory, shown in some graphs, was obtained using Google Earth and is therefore only applicable to latitude and longitude plots. In the graph legends, \"STD Q\" denotes the standard tuning, while \"NA Q\" represents the tuning based on the novel approach proposed in this work. To enhance visualization, areas where the GNSS signal experienced significant degradation are enlarged in the graphs.
# S2p: Provenance Research for Stream Processing System
## 4 Online Provenance
### IDs Propagation during Transformations
#### 4.2.1 Stateless Operators  
For stateless operators, their results depend entirely upon the nearest input data only; namely, no historical data would affect the results. We divide stateless operators into four categories, shown in Figure 7. Each represents a pattern of data transformation. Then, we will illustrate how the output data acquire their source ID list from the input data for different categories.  
Before that, we will give some expressions for future convenience. The \(g(.)\) represents the function that extracts the source ID list attached in each stream data. The \(P(.)\) represents the transformation in operators.  
**1-1 operators**: This type of operator, e.g., _map_, takes one single data as input and produces another single data as output. Under this one-to-one transformation, the source ID for output data \(Y_{j}\) is the source ID in its corresponding input data \(X_{i}\), i.e.,  
\[g(Y_{j})=g(X_{i}) \tag{1}\]  
where  
\[\forall X_{i},Y_{j}|Y_{j}=P(X_{i})\]  
**1-N operators**: This type of operator, e.g., _flatmap_, takes one data as input and produces multiple outputs. The source ID for the \(k\)th output data \(Y_{j}^{k}\) is the source ID of its corresponding input data \(X_{i}\), i.e.,  
\[g(Y_{j}^{k})=g(X_{i}) \tag{2}\]  
where  
\[P(X_{i})=Y_{j}^{1}\bigcup Y_{j}^{2}\bigcup...\bigcup Y_{j}^{N}\]  
**M-1 operators**: This type of operator takes multiple input data as input and produces one output. The source ID list for the output data \(Y_{j}\) is the group of the source ID list from its corresponding input data set \(\{X_{i}^{1},X_{i}^{2},...,X_{i}^{M}\}\), i.e.,  
\[g(Y_{j})=g(X_{i}^{1})\bigcup g(X_{i}^{2})\bigcup...\bigcup g(X_{i}^{M}) \tag{3}\]  
where  
\[Y_{j}=P(X_{i}^{1}\bigcup X_{i}^{2}\bigcup...\bigcup X_{i}^{M})\]  
**M-N operators**: This type of operator, e.g., **Union**, takes input data from two or more streams and produces multiple output data as output. We can regard this as the composition of the other three categories as above. Therefore, we can determine the source ID list by decomposing their mapping relation and aggregating the individual analysis result.
## 4 Discussion  
Recent research [8] suggests that a Deep CNN (EfficientNet) provides promising accuracies when applied to RFI in predicting CV risk. The study was performed on a large scale cohort of Asian patients, mostly non-diabetic, obtaining scant correlation (0.47) between the RetiCAC CNN score and the CAC assessed by CT scan. In the present pilot study we have focused on type 2 diabetic population, thus using a more homogeneous dataset for making the training. For this reason and due to the use of VGG and ResNet DL architectures the accuracy has been significantly improved.  
Results using methods over both clinical and image data show acceptable accuracies in the assessment of a high CAC score. Yet, the two established protocols showed complementary capacities in comparison to independently use both modalities. Experimental studies suggest that it is possible to tailor a specific ensemble to the particular needs of the application, improving either the precision or the recall, although there is a trade off between both performance indicators.  
The protocol shows that an application defined specifically for clinical diagnosis, guarantees missing only few patients with CAC \(>\) 400, reducing considerably the false negatives and maximizing the recall (75% aprox.). However, this procedure has a significant number of false positives which will impact on resource costs but not in individual diagnosis capabilities.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classifier** & **Accuracy** & **Recall** & **Precision** & **F1** & **Conf Mat** \\ \hline KNN & 0.61 & 0.55 & 0.55 & 0.55 & 28 & 15 \\ Protocol & 0.68 & 0.30 & 0.91 & 0.45 & 42 & 1 \\  & 0.68 & 0.30 & 0.91 & 0.45 & 23 & 10 \\ \hline SVM & 0.70 & 0.45 & 0.75 & 0.57 & 38 & 5 \\ Protocol & 0.64 & 0.21 & 0.88 & 0.34 & 42 & 1 \\  & 0.71 & 0.48 & 0.76 & 0.59 & 17 & 16 \\ Protocol & 0.66 & 0.24 & 0.89 & 0.38 & 42 & 1 \\  & 0.67 & 0.58 & 0.63 & 0.60 & 32 & 11 \\ Protocol & 0.68 & 0.30 & 0.91 & 0.45 & 42 & 1 \\  & 0.62 & 0.48 & 0.57 & 0.52 & 31 & 12 \\  & 0.67 & 0.24 & 1.00 & 0.39 & 43 & 0 \\  & 0.68 & 0.58 & 0.66 & 0.61 & 33 & 10 \\ Protocol & 0.68 & 0.30 & 0.91 & 0.45 & 42 & 1 \\  & 0.68 & 0.30 & 0.91 & 0.45 & 23 & 10 \\  & 0.69 & 0.48 & 0.57 & 0.57 & 38 & 5 \\  & 0.67 & 0.24 & 1.00 & 0.39 & 18 & 15 \\  & 0.68 & 0.58 & 0.66 & 0.61 & 33 & 10 \\  & 0.68 & 0.30 & 0.91 & 0.45 & 42 & 1 \\  & 0.69 & 0.45 & 0.75 & 0.57 & 38 & 5 \\  & 0.64 & 0.21 & 0.88 & 0.34 & 42 & 1 \\  & 0.64 & 0.21 & 0.88 & 0.34 & 26 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._A second application is conceived for image retrieval of large databases where resources for further cardiological testing are scarce. The ensemble may maximize the precision by reducing the number of false positives, but the application clearly worsens predictions at individual level. Nevertheless, it may find utility when prioritization is urgently needed, as is the case of low income countries.
# Deep-Learning-Based Recovery of Frequency-Hopping Sequences for Anti-Jamming Applications
## 4 Performance Analysis
### Performance of FH Reception  
Now, we analyze the bit error rate (BER) of the frequency-hopping receiving system. The CNN-GRU network built in this paper was applied to the receiver of the frequency-hopping communication system, and the 100,000-bit source code sequence was received under five kinds of single jamming and mixed jamming. The BER of the frequency-hopping signal intelligent receiving system based on CNN-GRU is shown in Figure 10, where the ideal frequency-hopping reception refers to an ideal situation in which the receiver knows the frequency-hopping sequence used by the transmitter in advance.  
We can see from Figure 10 that the BER performance of the intelligent receiving system was generally close to the ideal frequency-hopping reception. When the signal-to-jamming ratio was lower than \(-14\) dB, because the CNN-GRU-based frequency-hopping signal intelligent receiving system estimated the accuracy of the frequency-hopping sequence at about 80%, the BER was slightly higher than that of the ideal frequency-hopping reception. When the signal-to-jamming ratio was greater than 0 dB, the BER of the frequency-hopping signal intelligent receiving system based on CNN-GRU was slightly higher than that of ideal frequency-hopping reception. This is because the GRU module of the CNN-GRU network had no signal features that could be used to assist inference when estimating the frequency of the first hop, so it was possible to misestimate the frequency of the first hop, resulting in bit errors in the intelligent receiving system.  
It should be indicated that when the intelligent frequency-hopping transmitting system adjusted the frequency-hopping sequence according to the electromagnetic environment, the actual conventional frequency-hopping receiving method could not know the frequency-hopping sequence in advance. Therefore, the BER of conventional frequency-hopping reception was close to 50%, while the intelligent receiving system of the frequency-hopping signal based on CNN-GRU could reach the BER close to the ideal conditional reception under the constraints of the known frequency-hopping period.  
Figure 9: Frequency-hopping sequence estimation accuracy.
# A DFT-Based Running Time Prediction Algorithm for Web Queries
## 5 Experimental Evaluation
### Performance Evaluation  
In this section, we evaluate the execution time and the memory consumption of the query running time prediction algorithms. At run time, for each incoming query, Table 6 shows (i) the average execution time required to compute the vectors for queries with two and five terms and (ii) the average execution time required by the neural network that predicts the running time of queries. We present results for a neuronal network with 5, 10, 25 and 50 neurons in the hidden layer. The results show that the proposed algorithm reduces the on-line query vector construction time in a significant manner (90-92%) as it handles a smaller number of attributes than the baseline algorithm (6 vs. 42 attributes, respectively). Furthermore, the proposed algorithm is able to reduce in at least 32% the execution time of the neuronal network that uses the small query vector to predict each query running time. In total, considering the query vector construction time and its use in the neural network for predicting the respective query running time, the improvement  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data-Scoring**} & \multicolumn{2}{c}{**Real**} & \multicolumn{3}{c}{**Baseline**} & \multicolumn{3}{c}{**DFT-Based**} \\ \cline{2-11}  & **QRT** & \(max_{t}\) & **QRT** & \(max_{t}\) & **AAE** & \(max_{e}\) & **QRT** & \(max_{t}\) & **AAE** & \(max_{e}\) \\ \hline \hline \multicolumn{11}{c}{WAND} \\ \hline ClueWeb09—BM25 & 0.197 & 3.030 & 0.197 & 2.602 & 0.034 & 1.444 & 0.197 & **2.901** & **0.030** & **0.731** \\ Gov2—BM25 & 0.107 & 1.772 & 0.108 & **1.740** & 0.028 & 1.738 & 0.107 & 1.501 & **0.024** & 0.609 \\ ClueWeb09—TF-IDF & 0.731 & 4.867 & 0.734 & **4.867** & 0.033 & 4.094 & 0.730 & 4.077 & **0.030** & **1.075** \\ Gov2—TF-IDF & 0.370 & 3.486 & 0.371 & **3.486** & 0.032 & 3.166 & 0.369 & 2.847 & **0.029** & **1.034** \\ \hline \multicolumn{11}{c}{BM-WAND} \\ \hline ClueWeb09—BM25 & 0.133 & 1.062 & 0.133 & 0.839 & **0.025** & 0.376 & 0.134 & **0.909** & **0.025** & **0.370** \\ Gov2—BM25 & 0.074 & 0.881 & 0.075 & **0.811** & 0.015 & 0.750 & 0.075 & 0.563 & **0.014** & **0.362** \\ ClueWeb09—TF-IDF & 0.064 & 0.692 & 0.064 & 0.492 & 0.006 & 0.309 & 0.064 & **0.599** & **0.005** & **0.253** \\ Gov2—TF-IDF & 0.066 & 0.411 & 0.066 & 0.336 & **0.005** & 0.098 & 0.066 & **0.383** & **0.005** & **0.178** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Query running time in seconds obtained with the real execution, the baseline and the proposed prediction algorithms. Numbers in bold font indicate better results.  
in execution time of the proposed algorithm over the baseline algorithm is in the range 77-82%.  
Table 7 shows the memory consumption in bytes required to store (1) the descriptors of the term-vector and (2) the descriptor of the query-vector for each query being solved in the processor. As expected, the proposed algorithm also reduces the memory consumption requirements of each incoming query in a significant manner in about 70%.
# Privacy-Preserving Data Sharing in Telehealth Services
###### Abstract
As the healthcare industry continues to embrace digital transformation, the importance of implementing robust data security measures cannot be overstated [16]. Protecting sensitive health information is essential not only for compliance with regulatory requirements but also for maintaining patients' trust and safeguarding the integrity of healthcare systems. By adopting comprehensive security strategies, healthcare organizations can significantly mitigate the risk of data breaches and build a more resilient healthcare infrastructure [17]. This framework introduces a novel integration of homomorphic encryption, blockchain-based data integrity, and secure multi-party computation (SMPC), specifically tailored for telehealth applications. Unlike existing models that often address these security and privacy components in isolation, our approach provides a comprehensive solution by enabling encrypted data computations, immutable data storage, and secure access control through smart contracts within a single framework. This ensures that patient data remain confidential throughout the data sharing and processing lifecycle, while also maintaining compliance with stringent healthcare regulations such as the HIPAA and the GDPR. Our work advances the state of the art by enabling collaborative, privacy-preserving data sharing among multiple healthcare entities without compromising data security, setting a new standard for secure, scalable telemedicine systems. While blockchain technology and cryptographic methods like homomorphic encryption are well established, our framework integrates these technologies in a manner specifically tailored to telehealth applications, addressing unique challenges such as real-time data access and compliance with stringent healthcare regulations. We enhance the conventional use of these technologies by integrating secure multi-party computation (SMPC) to allow multiple healthcare entities to safely interact with data without compromising patient privacy. This novel integration creates a robust environment where data privacy is maintained even during complex data processing tasks, which is not extensively covered in the existing literature.