esa-sceva/satcom-chunk-collection huggingface dataset:
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 3 System Model
### Back-Pressure Routing
#### 3.1.1 Flow Selection  
At the initial stage, the aim is to decide a weight for each intermediate link on \((i,\ j)\in E\), so that the traffic packet flows to the next forwarding operation in the optimum path. The \(Q^{f_{c}}_{i}(t)\) is described as the number of packets for flow \(f_{c}\in N\) at the beginning of time \(t\) that is backlogged at the device \(i\in\ N\). Let \(Q^{f_{c}}_{(i,j)}(t)\) denote the backlog of flow \(f_{c}\) on the link \((i,j)\). In fact, \(Q^{f_{c}}_{f_{c}}(t)=0\) for all \(f_{c}\in N\), since no device forwards packets for itself. Each device \(i\in N\) computes weight for each outgoing link as a function of a local flow queue \(Q^{f_{c}}_{i}(t)\). For a given flow \(f_{c}\) on a link \((i,j)\), when the link is activated,packets from flow \(f_{c}\) will be scheduled if flow \(f_{c}\) achieves the maximal weight on the link \((i,j)\). The max weight has been denoted by \(W_{(i,j)}^{f_{c}}(t)\), also stated as BP, such that  
\[W_{(i,j)}^{f_{c}}(t)=Q_{i}^{f_{c}}(t)-Q_{j}^{f_{c}}(t) \tag{1}\]  
where \(W_{(i,j)}^{f_{c}}(t)\) is the maximum back-pressure weight on the link \((i,j)\) at slot \(t\), i.e.,  
\[W_{(i,j)}(t)=\max_{f_{c}\in N}W_{(i,j)}^{f_{c}}(t) \tag{2}\]
## 4 Results
### Audio-Recordings  
Audio recordings were extracted from the video recordings and transformed into audio files. The sections where participants were speaking in these audio files were cut, and these fragments were analysed using the software Praat (Version 6.1.03) [64], a software program that is often used in speech pathology studies [58]. These fragments could only be compared within participants, as pitch and intensity levels differ per person. Paired samples \(t\)-tests indicated that there were some differences based on the robot's behaviour. For example, the mean pitch was significantly higher for one participant when they interacted with the robot that displayed emotions compared to when they interacted with the robot that did not display emotions. Furthermore, the maximum intensity used to talk to the robot that displayed emotions was significantly lower for one participant compared to when they interacted with the robot that did not display emotions. However, as these findings were not consistent for all participants, no conclusions could be drawn from these results.
# Determining Factors Influencing Operating Speeds on Road Tangents
## 1 Introduction
Despite the existence of operating speed prediction models for road tangents, many studies have questioned their accuracy due to regional differences, variations in driving behavior, and site-specific conditions. Additionally, models that rely primarily on posted speed limits often fail to account for the impact of environmental factors, leading to discrepancies between the predicted and actual speeds [15]. While some operating speed prediction models for road tangents incorporate variables, such as the operating speed, on the preceding curve or previous road segment [26; 27; 28; 29; 30; 31; 32], this approach presents limitations when applied to newly designed roads. In such cases, the operating speeds on preceding segments are unknown, and thus must also be predicted, which introduces additional uncertainty into the model. The reliance on prior speed data increases the potential for errors, as these predictions may not accurately reflect real-world conditions without empirical speed data from the existing roads. Consequently, for newly designed roads, it is crucial to develop independent models that can predict operating speeds on tangents without relying on the operating speeds of preceding segments or curves, thereby minimizing the prediction errors and enhancing the accuracy of operating speed prediction. Without this consideration, models may be less reliable and less applicable to modern road design, where prior speed data is unavailable and must be estimated, leading to a higher margin of error in predictions. This research focuses on analyzing the operating speeds on tangent sections of both single- and dual-carriageway roads in Croatia, with the primary objective of examining the impact of various contributing factors on operating vehicle speeds. Road tangents were specifically chosen due to their distinctive influence on driver behavior and speed selection. Unlike horizontal curves, which impose physical constraints, such as centrifugal force, tangents are free from such limitations, enabling drivers to select speeds based on road geometry, visibility, and environmental conditions [15; 22; 33; 34; 35; 36; 37]. The lack of geometric constraints on tangents often results in higher operating speeds, making them crucial for understanding the variations in driving speeds and their implications for traffic dynamics. By concentrating on tangent sections, this study aims to enhance the understanding of how factors such as road design, surrounding environment, and traffic conditions interact to influence operating speeds. Through comprehensive data collection and subsequent statistical analysis, the research seeks to provide empirical insights into the factors shaping operating speeds, thereby contributing to a more nuanced understanding of speed behavior on road tangents.
# Review of Photogrammetric and Lidar Applications of UAV
## 3 Classification of UAV
### Based on Wings and Rotors
#### 3.4.1 Multi-Rotor UAV  
Multi-rotor UAVs are the simplest and cheapest UAV option. Multi-rotor UAVs are especially useful in flight as they offer more control over the UAV's position and are ideal for aerial photography and surveillance. Since they have more than one engine, they are called multi-rotors. Classification divides them into quadcopters (four rotors), hexacopters (six rotors), octocopters (eight rotors), and others. Quadcopters are by far the most popular multi-rotor UAVs. The advantage of multi-rotor UAVs is their availability, ease of use, reasonable camera control, support for vertical take-off and landing (VTOL), and the ability to work even in limited space. Compared to other UAVs, the disadvantage of multi-rotor UAVs is their lower energy consumption efficiency so that they can fly for only about 30 min with a small payload capacity. The procurement price of multi-rotor UAVs varies between EUR 5000 and EUR 65,000. These UAVs can be used to monitor bridges, power lines, wind turbines, offshore rigs, and more, as well as or 3D scanning and photogrammetric measurement of urban areas and small structures [23; 24].
# Physicochemical Characterization and Antioxidant Activity of Jara Honey Produced in Western Georgia
###### Abstract  
The purpose of this research article was to study the physicochemical characteristics of semi-wild Jara honey grown in Western Georgia. Jara honey is produced in the alpine and sub-alpine forest zone of high mountain Adjara, which is distinguished by its variety of honey plants. The physicochemical characteristics were examined concerning the Alimentarius Code and EU regulations: moisture content, total carbohydrates, free acidity, pH, electrical conductivity, microelements (Li, Na, K, Mg, Ca), color, total phenols, total phenolic acids, total flavonoids, proline, diastase activity, proteins, and microscopic study of pollems. Using the UPLC-MSB method, grayanotoxin-III was identified in the semi-wild Jara honey samples. The findings demonstrated that the honey has significant concentrations of phenols, phenolic acids, and flavonoids. A directly proportional relationship was established between the quantitative content of phenolic compounds and the antioxidant activity of honey. This article is the first study of the characteristics of Jara honey produced in Western Georgia.  
honey; antioxidant activity; physicochemical; melissopalynology; grayanotoxin
## 4 Results
### Comparing Model Results  
The results were obtained from a dataset consisting of 2634 samples and one predicted class. However, the dataset was reduced to 1920 samples by applying undersampling to ensure balanced categories of the predictor class. The predicted class has four categories that represent the level of risk. A clustering method was applied to obtain these predicted classes using the representative features of the dataset.  
The models were developed to evaluate their efficiency and performance in predicting traffic accident risk levels. All models used the same dataset for testing and validation. Table 4 displays the evaluation metrics and the elapsed time obtained for each evaluated model.  
The results indicate that the CNN-RF model achieved the highest accuracy (0.9604) and better classification capability, but it took longer to execute (695.9 s) than the other models. The CNN model achieved the second-best accuracy (0.9411) and had a similar execution time (694.2 s). The MLP model achieved an accuracy of 0.9156 and had a short execution time of 10.7 s. The SVC-RBF model had the best run time, taking only 1.7 s, and a similar accuracy to the MLP model of 0.9140. Finally, the GPC-RBF model achieved a comparable accuracy score of 0.9015 and an execution time of 323.3 s. Figure 12 shows the accuracy values obtained by each model.  
It is important to note that while the accuracies of the rest of the models are comparable to the best model accuracy, that of CNN-RF with 0.9604, the SVC-RBF model achieved a significant accuracy (0.9140) in 1.7 s of evaluation, obtaining similarly good results to the MLP and CNN models. Finally, the inferior but not worst performance was that of the GPC-RBF model, which achieved less accuracy with 0.9015.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **ACC** & **SPE** & **SEN** & **Et (s)** \\ \hline CNN & 0.9411 & 0.9801 & 0.9398 & 694.2 \\ \hline CNN-RF & 0.9604 & 0.9867 & 0.9603 & 695.9 \\ \hline GPC-RBF & 0.9016 & 0.9672 & 0.9026 & 302.3 \\ \hline SVC-RBF & 0.9140 & 0.9713 & 0.9139 & 1.7 \\ \hline MLP & 0.9156 & 0.9720 & 0.9162 & 10.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the evaluation models.  
Figure 12: Accuracy model scores.  
Upon analyzing these results, it is evident that two models, CNN-RF and CNN, stand out in terms of accuracy. Based on the evaluation time, it is evident that only the SVC-RBF model allowed for the generation of new models in a shorter amount of time and with a comparable performance. Figure 13 shows the execution and evaluation times obtained for each model.
# Hybrid NOMA Protocol with Relay Adaptive AF/DF Collaboration and Its Modeling Analysis in NB-IoT
## 3 Performance Modeling Analysis of H-NOMA Protocol
### Relay-Based Adaptive Collaboration and H-NOMA Signal Transmission Process
#### 3.1.1 Broadcast Stage  
This phase considers that the eNB transmits the signal only once, i.e., the phase ends if the user still cannot receive the corresponding signal after transmission. The transmission protocol in the broadcast phase is described as follows:  
1. The eNB sends superimposed signals \(X_{s}\) to the relay and users at the same time and waits for the feedback response, which is a combination of the signals \(X_{1},X_{2}\) required by the user \(U_{1}\) and \(U_{2}\), respectively, and \(E(\left|X_{1}\right|^{2})=1,E(\left|X_{2}\right|^{2})=1\).
2. If the signal \(X_{1}\), \(X_{2}\) are successfully decoded and obtained by \(U_{1}\), \(U_{2}\), and at the same time, the two users immediately feedback to the source node. When the eNB receives the ACK frames from both users, it will broadcast the next new superimposed signal. If the eNB receives a NACK frame from either user's feedback, it determines the next time slot where the collaborative transmission should take place according to the relay decoding situation.
# A Novel Hybrid Genetic-Wale Optimization Model for Ontology Learning from Arabic Text
## 1 Introduction
Secondly, the best solution quality provided by the GA is superior to the SA and is comparable to the TS. Moreover, the domain-specific knowledge can be incorporated by the GA in all combinatorial or optimization phases to dictate the strategy of search, in contrary to TS and SA, which lack this feature. Therefore, based on the proven superiority of the GA and WOA in many applications [30; 31; 32; 37; 38] and to overcome the drawbacks of the ordinary WOA, this work further demonstrates the robustness of the proposed hybrid genetic-whale optimization algorithm (G-WOA) to optimize ontology learning from Arabic texts, in which the GA algorithm is used to optimize the exploitation capability of the ordinary WOA algorithm and solve its premature convergence issue by combining the genetic operations of GA into the WOA. This paper contributes to the state-of-the-art of Arabic ontology learning through the following:  
* Firstly, a text mining algorithm is proposed particularly for extracting the concepts and their semantic relations from the Arabic documents. The extracted set of concepts with the semantic relations constitutes the structure of the ontology. In this regard, the algorithm operates on the Arabic documents by calculating the concept frequency weights depending on the term frequency weights. Thereafter, it calculates the weights of concept similarity using the information-drivenfrom the ontology structure involving the concept's path distance, the concept's distribution layer, and the mutual parent concept's distribution layer. Eventually, it performs the mapping of features by assigning the concept similarity to the concept features. Unlike the ordinary text mining algorithms [9; 10], this property is crucial because merging the concept frequency weights with the concept similarity weights supports the detection of Arabic semantic information and optimizes the ontology learning. * Secondly, this is the first study to propose bio-inspired algorithms for optimization of Arabic ontology learning, in which a hybrid G-WOA algorithm is proposed in this context, to optimize the Arabic ontology learning from the raw text, by optimizing the exploration-exploitation trade-off. It can benefit from a priori knowledge (initial concept set obtained using the text mining algorithm) to create innovative solutions for the best concept/relation set that can constitute the ontology. * Thirdly, investigating the comparable performance between the proposed G-WOA and five other bio-inspired optimization algorithms [32; 39; 44; 45; 46], when learning ontology from Arabic text, where its solutions are also compared to those obtained by the other algorithms, across different Arabic corpora. To the best of our knowledge, the proposed and compared bio-inspired algorithms have not been investigated in Arabic or non-Arabic ontology learning yet. * Fourthly, the proposed ontology learning approach is applicable with the other languages, where it can be applied to extract the optimal ontology structure from the non-Arabic texts.
# FLINT: Flows for the Internet of Things
## 4 Flint Architecture
### Device Based Context  
FLINT uses the _Mapper_ adapter as a hub for data distribution. The Mapper is a simple processing adapter with multiple sink inputs and outputs. Incoming data are processed on a per-device basis. The agent matches a device's Medium Access Control (MAC) address with an adapter based on the input topic. A routing scheme is constructed from the configured information for a particular device. The sink then forwards the original payload and the routing scheme to the next adapter in the chain. From there, based on the routing scheme, the processed packet is distributed to the next hop.  
Adapters have a unique identifier. These identifiers are used to construct chains of adapters. Every adapter, except for the Mapper adapter, subscribes to their input topic, i.e.,  
Figure 3: The different types of adapters in a sample configuration. The central adapter is the Mapper. This is a processing adapter that forwards data from input adapters to processing adapters and to output adapters.  
Figure 2: The basic components of an adapter. Triangular ports connect to the platform, filled rectangular ports connect the agent and the sink. Open rectangular ports connect the agent to non-FLINT sources.  
_urn:uuid:adapter-uuid/in_. The Mapper, on the contrary, is subscribed to the output topic of every adapter, i.e., _+/out_.  
Figure 4 shows a directed graph using the different types of adapters. The _io-1_ and _io-2_ adapters are I/O adapters. Adapter _p-1_ is a processing adapter and _d-1_ is a direct adapter. In this example, adapter _io-1_ publishes its input data to the output topic _urn:uuid:io-1/out_ to request a routing scheme from the mapper. An exact match for a device's MAC address and the requesting adapter will return the configured routing scheme. The packet from adapter _io-1_ is sent to the next hop in the chain, i.e., _p-1_. From there, _p-1_ forwards the modified data to the next adapter in the chain--_io-2_. At this point, data are output using the adapter's agent. Similarly, communication flowing in the other direction is published to the adapter's output topic to request routing information from the mapper. The scheme in the other direction excludes _p-1_ from the chain and is directly forwarded to _io-1_. Direct adapters, such as _d-1_, forward data directly to their destination. This can be useful to serve a fleet of devices.
# Applications of Pulsed Light Decontamination Technology in Food Processing: An Overview
## 5 Role of Pulsed Light Technology in Food Safety
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Food Products** & **Pulse Energy (f/cm\({}^{3}\))** & **Treatment Time (ss) \& Log\& Reductions** & **Reference** \\ \hline \multirow{2}{*}{Cantaloupe melon} & \multirow{2}{*}{15.6} & \multirow{2}{*}{-} & 1.39 of total plate count & [112] \\ \cline{3-4}  & & & 1.45 of yeasts and molds & \\ \hline \multirow{2}{*}{Raspberries} & \multirow{2}{*}{28.2} & \multirow{2}{*}{30 s} & 45 of Salmonella & \\ \cline{3-4}  & & & 3.97 of E. coli O187E+H7 & [51] \\ \hline \multirow{2}{*}{Powdered infant formula} & 0.199 & - & \(>\)99s of _L. innocu_ & [70] \\ \cline{2-4}  & 0.398 & - & \(>\)99s of _B. subtilis_ spores & \\ \hline \multirow{2}{*}{Coconut water} & \multirow{2}{*}{19.2} & \multirow{2}{*}{-} & 52 of _E. coli_ & [113] \\ \hline \multirow{2}{*}{Strawberry} & \multirow{6}{*}{22.5} & \multirow{6}{*}{24 s} & \(0.9\pm 0.2\) of Murine neocovirus & \\  & & & 1.9 \(\pm 0.3\) of all O187E+H7 & \\ \cline{3-4}  & & & \(2.1\pm 0.4\) of Salmonella & \\ \hline \multirow{2}{*}{Blueberry} & \multirow{2}{*}{22.5} & \multirow{2}{*}{24 s} & \(3.8\pm 0.6\) of Murine neovirus & \\  & & & \(5.7\pm 1.2\) of _E. coli_ O157E+H7 & \\ \cline{3-4}  & & & \(4.2\pm 0.2\) of Salmonella & \\ \hline \multicolumn{4}{c}{Tomatoes (slices packed in} & \multirow{2}{*}{8} & \multirow{2}{*}{30 min} & About 1.17 of _L. innocu_ C1171262 & [114] \\ \cline{3-4}  & & & About 1.34 of E. coli 1.107 & [114] \\ \hline \multirow{2}{*}{Fresh tomatoes} & \multirow{2}{*}{8} & \multirow{2}{*}{-} & Up to 2 of psychrophichetic bacteria & \\  & & & Up to 13.4 years and molds & \\ \hline \multicolumn{4}{c}{Melors} & \multirow{2}{*}{9} & \multirow{2}{*}{-} & The shelf life was extended 12 days & [116] \\ \hline \multicolumn{4}{c}{Spinach} & \multirow{2}{*}{15.75} & \multirow{2}{*}{15 s} & 2.7 of _E. coli_ O187E+H7 & [177] \\ \hline \multicolumn{4}{c}{Strawberries} & \multirow{2}{*}{3} & \multirow{2}{*}{10 s} & Up to 0.8 for _Salmonella_ & [118] \\ \hline \multicolumn{4}{c}{Tomato stem scar} & \multirow{2}{*}{31.5} & \multirow{2}{*}{30 s} & 2.3 for _S. enterica_ & [119] \\ \hline \hline \end{tabular}  
* PVR = Percentage of viability reduction; calculated by the following formula: [1– (visible cell counts on treated sample/viable cell counts on control)] \(\times\) 100\({}^{\rm h}\) Factors: 10 = 1 log reduction, 100 = 3 log reductions; Maximum energy per pulse; Treatment time in sulfate; 45 s treatment/side; For Aspergillus size fluence of 1.30) [f/cm\({}^{2}\); 8] Time mentioned as \(\approx\) unless specified.  
\end{table} 
Table 4: _Cont._
# A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data
## 3 High-Efficiency Mobile Positioning Method  
A high-efficiency mobile positioning method is proposed to analyze and determine the location of each cell-RSSI pair from CVO data. The following subsections present two stages: (1) a pre-deployment stage and (2) a runtime stage.
## 1 Introduction
The position differences of TCI are within 1.0 m, and such errors of LCI are within 5.0 m. The studies above are mainly based on undifferenced GNSS observations. The Dual Frequency (DF) PPP/INS integration using Single-Difference Between-Satellites (BSSD) GPS observations was applied in [28]. During the simulated outages of 10 s-30 s, the position accuracy of BSSD PPP/INS TCI can be decimeter-level. Such accuracy is higher than those using undifferenced observations. Owing to the evolution of multi-constellation GNSS (multi-GNSS), more available observations can be adopted to enhance the integration performance. [PERSON] et al. [21] developed the multi-constellation (GLONASS, BDS, and GPS) TCI of SF PPP/INS, and it was verified by a set of land-borne experiment data. Results showed that significant positron improvements in terms of accuracy, continuity, and reliability could be obtained by INS aiding. Anyway, the performance of conventional GPS SF-PPP can be improved by utilizing the multi-GNSS observation. The enhancement of multi-GNSS on PPP/INS is also illustrated in [29]. According to the results, the positioning and convergence performance of PPP is enhanced significantly by multi-GNSS and INS. However, such impacts in terms of velocity and attitude are invisible. Based on the works in [20; 21; 22; 23; 24; 25; 26; 27; 28; 29], continuous solutions with high accuracy can be provided by the PPP/INS integration during GNSS outages. However, the positioning errors of PPP/INS still accumulate rapidly, especially for a low-cost IMU when the GNSS signal seriously deteriorates, even interrupts completely around high buildings or under tunnels [30; 31]. Such a circumstance can be facilitated by using the velocity information from an odometer. In [31], GPS + GLONASS DF-PPP was integrated with INS and odometer, and simulated GNSS outages were utilized to evaluate the performance in challengingcircumstances. According to the results, the position accuracy was furtherly ameliorated by the odometer. In this paper, we implied PPP/INS/odometer tightly coupled integration model. In comparison with previous works, the contribution of this paper is that such a tightly coupled integration is based on the BSSD model and the BDS-3 PPP-B2b orbit/clock corrections. In order to assess the performance of this algorithm, vehicle-borne data acquired in urban environments are processed and analyzed. The enhancements of a low-cost INS, BSSD BDS-3 PPP model, and an odometer on positioning and attitude determination are discussed in detail.