esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Radio Network Information Service (RNIS) to MEC Framework in B5G Networks
## 4 Performance Evaluation and Results  
We start this section by describing the Agriculture 4.0 (see Figure 1) as the use case scenario where a smart farm uses technologies such as MEC, 5G and IoT to revolutionize agricultural practices. The farm is made up of agricultural equipment, such as tractors, harvesters and drones, which are all connected and equipped with sensors and communication devices. The equipment generates and collects operational data that is transmitted over the network. The data is processed by intelligent algorithms in order to analyze the information in real time. This analysis allows farming practices to be adjusted dynamically, taking into account factors such as climate, soil and the specific needs of the crops.  
Among the various use cases and agricultural applications, it is possible to highlight important radio information for optimizing these scenarios, such as network coverage, signal strength, network latency, and network availability. Integrating this RNIS information into MEC apps creates a more resilient, adaptable and efficient system. The RAB and PLMN requests can consist of the requirements of application messages. As we did not have access to the applications and equipment for a real use case, we used the RAB and PLMN information for the appropriate tests.  
In the aforementioned agricultural scenario, extremely strict requirements in terms of response speed are not necessary. Latencies of up to 100 ms are tolerable, and latencies of less than 1 millisecond are not critical. In this scenario, we have 100 to 200 agricultural devices, such as tractors, harvesters, drones and other equipment, which are controlled by edge applications. These applications analyze relevant data for each group of devices, providing specific information according to the activities carried out by each piece of agricultural equipment. In addition, there are two to four MEC apps that consume information from RNIS and contribute to improving decision-making. With applications responsible for various services within agriculture, each of the MEC apps can consume information and provide services for various types of UE.  
MEC apps can take advantage of RAN-level information to optimize their performance, supporting the control of agricultural use cases. Some of the MEC apps in this agricultural context include the following: autonomous vehicle fleet management; bandwidth management; traffic control; device tracking; harvest management; and agricultural transportation. Each of these applications can address several use cases with numerous types of agricultural UE.
# A Hybrid Architecture for Safe Human-Robot Industrial Tasks
###### Abstract  
In the context of Industry 5.0, human-robot collaboration (HRC) is increasingly crucial for enabling safe and efficient operations in shared industrial workspaces. This study aims to implement a hybrid robotic architecture based on the Speed and Separation Monitoring (SSM) collaborative scenario defined in ISO/TS 15066. The system calculates the minimum protective separation distance between the robot and the operators and slows down or stops the robot according to the risk assessment computed in real time. Compared to existing solutions, the approach prevents collisions and maximizes workcell production by reducing the robot speed only when the calculated safety index indicates an imminent risk of collision. The proposed distributed software architecture utilizes the ROS2 framework, integrating three modules: (1) a fast and reliable human tracking module based on the OptiTrack system that considerably reduces latency times or false positives, (2) an intention estimation (IE) module, employing a linear Kalman filter (LKF) to predict the operator's next position and velocity, thus considering the current scenario and not the worst case, and (3) a robot control module that computes the protective separation distance and assesses the safety index by measuring the Euclidean distance between operators and the robot. This module dynamically adjusts robot speed to maintain safety while minimizing unnecessary slowdowns, ensuring the efficiency of collaborative tasks. Experimental results demonstrate that the proposed system effectively balances safety and speed, optimizing overall performance in human-robot collaborative industrial environments, with significant improvements in productivity and reduced risk of accidents.  
collaborative robots; intention estimation; industrial robotics; AI-based decision-making Article
## 4 Simulation Results
### Results on the Benchmark Test-Suite
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Function**} & \multirow{2}{*}{**PopSize**} & \multirow{2}{*}{**Gens**} & \multicolumn{2}{c}{**Best-of-Run Fitness**} & \multicolumn{2}{c}{**FirstHitEvals**} \\ \cline{3-8}  & & & \(t\)**-Statistic** & \(p\)**-Value** & \(t\)**-Statistic** & \(p\)**-Value** \\ \hline Ackley & 100 & 3000 & 21.3800 & \(1.3355\times 10^{-19}\) & — & — \\  & 150 & 5000 & 17.4636 & \(3.1280\times 10^{-17}\) & 88.1720 & \(3.7508\times 10^{-56}\) \\ Rosenbrock & 100 & 3000 & 0.1865 & 0.4264 & — & — \\  & 150 & 5000 & 2.5958 & 0.0060 & — & — \\ Chu-Rey & 100 & 3000 & 4.5314 & \(4.6542\times 10^{-5}\) & 53.5110 & \(2.3156\times 10^{-51}\) \\  & 150 & 5000 & 5.1236 & \(8.9954\times 10^{-6}\) & 63.4031 & \(1.1548\times 10^{-47}\) \\ Step & 100 & 3000 & \(-1.4639\) & 0.0770 & 34.7952 & \(1.9600\times 10^{-38}\) \\  & 150 & 5000 & — & — & 72.0480 & \(2.6003\times 10^{-50}\) \\ Alpine-1 & 100 & 3000 & 1.8655 & 0.0336 & — & — \\ SumSquares & 100 & 3000 & 11.2285 & \(2.2360\times 10^{-12}\) & 79.3863 & \(4.1571\times 10^{-61}\) \\  & 150 & 5000 & 11.6045 & \(1.0180\times 10^{-12}\) & 81.1938 & \(3.3244\times 10^{-61}\) \\ Sphere & 100 & 3000 & 10.3116 & \(1.6374\times 10^{-11}\) & 85.0016 & \(4.2333\times 10^{-54}\) \\  & 150 & 5000 & 8.2938 & \(1.9158\times 10^{-9}\) & 73.3631 & \(3.1842\times 10^{-45}\) \\ Boha-3 & 15 & 5000 & 1.0171 & 0.1588 & 0.6234 & 0.2678 \\ Boha-2 & 20 & 5000 & — & — & 0.4915 & 0.3125 \\  & 15 & 5000 & 1.0171 & 0.1588 & 1.7071 & 0.0472 \\  & 20 & 5000 & — & — & 2.4494 & 0.0087 \\ Bartels & 15 & 5000 & — & — & 7.5641 & \(1.6549\times 10^{-10}\) \\  & 20 & 5000 & — & — & 4.4699 & \(1.9412\times 10^{-5}\) \\ Gold-Pr & 15 & 5000 & 1.0676 & 0.1452 & 0.2496 & 0.4042 \\  & 20 & 5000 & 0.7407 & 0.2309 & \(-2.8765\) & 0.0217 \\ Matyas & 15 & 5000 & 1.0171 & 0.1588 & 0.8954 & 0.1875 \\  & 20 & 5000 & 1.0171 & 0.1588 & 1.9494 & 0.0280 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Smith–Satterthwaite tests: [PERSON] vs. [PERSON] on the benchmark functions. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Metric** & **\#zero Diff.** & \(n\) & \(W+\) & \(W-\) & \(W\) & \(\bullet\) & **Critical \(W\)** & **Mean of \(W\)** & **Std. Dev. of \(W\)** & \(z\)**-Statistic** & **Left Tail \(p\)** \\ \hline Mean of Best-of-Run Fitnesses & 5 & 19 & 175 & 15 & 15 & 0.05 & 53 & 95 & 24.8495 & \(-\)3.2194 & 0.0006 \\ Mean of FirstHitEvals & 0 & 19 & 180 & 10 & 10 & 0.05 & 53 & 95 & 24.8495 & \(-\)3.4206 & 0.0003 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Wilcoxon signed rank tests: [PERSON] vs. [PERSON] on the 12-function benchmark suite.
## 4 Case-Study Building
### Modal-DBD Design Steps
#### 4.2.8 Equivalent Linearization of Backbone Curves  
The Modal-DBD provides a simplified procedure to define the stiffness of the equivalent linear system. This assumes that the energy stored in each non-linear shear-wall (ENL) must equal the energy of the corresponding linear system (EL). The latter is thus defined for each shear-wall as:  
\[E_{NL}=\int_{0}^{d_{L}}F_{b}(d) \tag{18}\]  
with \(F_{b}\)_(d)_ previously defined (Equation (17)).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Log Wall** & \(F_{y}\) **= \(F_{u}\) (kN)** & \(d\) **(mm)** & \(d_{u}\) **(mm)** & \(k_{0}\) **(kN/mm)** & \(\mu\) \\ \hline P1\_GF & 130.52 & 52 & 198 & 2.51 & 3.8 \\ P2\_GF & 144.57 & 79 & 199 & 1.83 & 2.51 \\ P3\_GF & 104.96 & 128 & 200 & 0.82 & 1.56 \\ P4\_GF & 138.7 & 73 & 199 & 1.9 & 2.72 \\ P1\_FF & 133.40 & 58 & 197 & 2.30 & 3.42 \\ P2\_FF & 134.52 & 76 & 197 & 1.77 & 2.59 \\ P3\_FF & 110.24 & 106 & 199 & 1.04 & 1.87 \\ P4\_FF & 138.51 & 81 & 196 & 1.71 & 2.43 \\ P1\_SF & 131.44 & 53 & 200 & 2.48 & 3.75 \\ P2\_SF & 146.25 & 25 & 195 & 5.85 & 7.71 \\ P3\_SF & 127.19 & 79 & 200 & 1.61 & 2.54 \\ P4\_SF & 148.98 & 26 & 195 & 5.73 & 7.48 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Shear-wall backbone parameters.  
Figure 7: Contour plot of in-plane lateral displacement of the P1-GF portion of log-wall (ABAQUS, legend values in mm).  
Given that the energy stored in the equivalent elastic system is:  
\[E_{L}=\frac{1}{2}k_{sq}d_{t}^{2} \tag{19}\]  
with \(d_{t}\) the target displacement, the equivalent elastic stiffness \(k_{sq}\) can be hence calculated as:  
\[k_{sq}=\frac{2E_{NL}}{d_{t}^{2}} \tag{20}\]
## 4 Findings and Discussion
### ITS/IoV Intrusion Detection Systems (IDS)
#### 4.1.1 Artificial Intelligence (AI) Frameworks and Result Evaluation  
Vehicular networks or IoV constitute a critical part of the ITS. Its dynamic nature, as well as the exponential increase in the number of connected devices as a result of B5G technologies, has resulted in cyberattacks and the need for security. AI has played a critical role in securing connected devices/things such as the IoV, and vehicular ad hoc networks (VANETs). In [23], the authors proposed an optimized support vector machine for the protection of VANET from DDoS attacks. Although the proposed model achieved an accuracy of 99.33%, it cannot be said to be an XAI as no evidence of the XAI principles was presented by the authors.  
Additionally, the authors in [32] proposed a random-forest-algorithm-based IDS for detecting false basic messages in IoV. The accuracy of the proposed model was 99.6%. The work was based on the vehicular reference misbehavior (VeReMi) dataset. Using the same VeReMi dataset, the authors in [116] developed a distributed IDS for the detection of misbehavior and position falsification of VANETs using an ensemble of k-nearest neighbor (KNN) and random forest. Although the trio of [117, 118, 119] proposed ML and deep learning (DL) solutions for the security of ITS (such as autonomous vehicles (AVs), and IoV), their choice of the dataset can be challenged as there could have been more suitable ITS-specific datasets such as the VeReMi [120], BursTADMA [121], or VeReMi extensions, as used by the authors in [115].  
Moreover, excessive demands for handcrafted feature engineering are a common challenge of ML research, particularly intrusion detection [122]. Moreover, traditional ML schemes are not well suited for multi-classification problems. However, studies have shown the adaptability and flexibility of ML-based IDS over DL-based IDS. ML-based IDS has shown tremendous potential in improving detection accuracy with minimal computational complexity and time [123, 124]. It has led to the adoption of deep learning approaches [125].  
ML algorithms can detect seemingly tricky attack patterns. An ML-based IDS has the operational aim of delivering a secure network. Because attacks and attack methods are continually developing and new vulnerabilities occur daily, the capacity of ML models to adapt and grow is precious. It is another reason signature-based IDS are declining while anomaly-based IDS based on ML are advancing.
# Modelling and Assessment of a New Triple-Frequency IF1213 PPP with BDS/GPS
## 3 Results and Discussion
### Influence of the Satellite PIFCB
#### 3.2.2 Influence of the BDS-2 PIFCB in BDS  
BDS-2 satellites have time-varying characteristics of phase hardware bias [19]. However, this section demonstrates that the influence of BDS-2 PIFCB can be ignored when BDS-2 and BDS-3 are used as BDS. The TF-CF model is shown in Table 3 with BDS-2 as the first system and BDS-3 as the second system.  
This section describes four BDS static PPP models for positioning. Figure 5 shows the static positioning error curves of stations SUTH and ULAB on DOY127. It is observed that the positioning error curves of the four PPP models largely overlap after convergence. A boxplot of the distribution of positioning accuracy and convergence time for the 60 selected stations for different static PPP models within DOY121-127 is shown in Figure 6. The median positioning accuracy and convergence times, representing the positioning performance of this model, are presented in Table 5. From Figure 6, the positioning performance of the four PPP models is similar. From Table 5, the accuracy of the four static PPP models after convergence is better than 1.0 cm in the horizontal direction and 1.5 cm in the elevation direction. The convergence accuracy of the TF-F, TF-C and TF-CF models deviates from 0.05 cm, and the convergence time deviates from 2 min. The experimental results demonstrate that the influence of BDS-2 PIFCB is negligible when BDS-2 and BDS-3 are used as the BDS.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Convergence Accuracy (CM)**} & \multicolumn{3}{c}{**Convergence Time (Min)**} \\ \cline{2-7}  & **E** & **N** & **U** & **E** & **N** & **U** \\ \hline DF & 0.61 & 0.37 & 1.26 & 30.86 & 10.17 & 20.14 \\ TF-F & 1.56 & 0.94 & 3.00 & 86.36 & 40.64 & 54.43 \\ TF-C & 0.66 & 0.42 & 1.12 & 36.93 & 14.00 & 22.21 \\ TF-FP & 0.86 & 0.45 & 2.01 & 84.25 & 38.21 & 51.79 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics on the positioning results of the four GPS static PPP models.  
Figure 5: Positioning error of the four BDS static PPP models (DOY127, 2022). The stations are (**a**) SUTH and (**b**) ULAB, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Convergence Accuracy (CM)**} & \multicolumn{3}{c}{**Convergence Time (Min)**} \\ \cline{2-7}  & **E** & **N** & **U** & **E** & **N** & **U** \\ \hline DF & 0.74 & 0.46 & 1.31 & 51.64 & 29.57 & 33.64 \\ TF-F & 0.93 & 0.54 & 1.46 & 47.59 & 21.69 & 30.36 \\ TF-C & 0.96 & 0.56 & 1.46 & 46.14 & 21.00 & 28.29 \\ TF-CF & 0.98 & 0.53 & 1.44 & 47.89 & 22.75 & 29.96 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistics on the positioning results of the four BDS static PPP models.
## 5 Experimental Results
### Network Set-Up Time Tests  
#### 5.4.1 Test Explanation  
The objective of these tests was to obtain information about the network setup time. This time was defined as the time between the first beacon transmission and the last node's reception and its association response.  
Figure 13: Network Device State Diagram.  
The test was repeated five times for every configuration and all the nodes were turned on concurrently. At the end of the tests, the data related to each configuration was averaged.  
A star topology was used in all occasions and the nodes were located on the chest, waist and ankle of a subject's clothing. The subject was located 1 meter from the coordinator, which was connected to a PC. Two AA batteries powered the devices.  
Two types of tests were performed. Only one star was active in the first test and there was no additional activity in the channel. In the second test, two stars actively worked in the same channel, but with different PANIDs.  
Each time measurement was performed with beacon order (BO) values of 7 and 9. For each of these values the duty cycle values were set at 0.25, 5 and 1. The duty cycle is the relation between the standard deviation (SD) and BO. The transmitted frames were analyzed using the Texas Instruments Sniffer and a CC2531 USB Dongle, which was 15 cm distant from the coordinator.
## 1 Introduction
The relatively new MCRAT method has been investigated across multiple industries, including the mining sector [18], logistics [19], and banking [20], as well as in different topics including 3 DP machine selection [21] and material selection [22; 23]. In addition to its advantages such as clarity, rationalism, logic, adaptability, and dependability [19], the MCRAT technique has not previously been employed in LPI evaluation. Among the methods employed for evaluating the alternatives, SAW, TOPSIS, and FUCA are also included. The SAW, also known as the scoring method, is one of the simplest MCDM methods [24]. In the literature, the SAW method is applied to solve various problems, such as institutional ranking [25], and cloud service selection [26]. The TOPSIS method, which is distance-based, is a popular MCDM method and has been applied to various topics such as financial performance measurement [27], credit rating prediction [28], and site selection [29]. The FUCA method, which is quite simple, ranks all solutions from best to worst [30; 31]. Examples of its application in literature include the development of wood and pallet production processes [32]. This study contributes to the existing literature in several ways: (i) This MCDM model is being applied for the first time to evaluate the logistics performance of nations. (ii) This study demonstrates that robust results can be obtained using MCDM methods without normalization. (iii) To evaluate the model's robustness, a holistic sensitivity analysis incorporating an innovative approach was conducted, examining the impact of criterion weights on the results across 105 scenarios. (iv) The impact of variations in criterion weights on MCDM outcomes was tested not only for the MCRAT method but also for the SAW, TOPSIS, and FUCA methods as part of a comparative analysis. (v) A thorough analysis was conducted by examining all World Bank LPI reports, except for the 2007 edition. (vi) The research findings can assist countries in assessing their current logistics performance and formulating business strategies to enhance it.
## 4 Conclusions  
In this study, changes of odor and taste in Daokou braised chicken during processing were comprehensively analyzed by GC-MS, GC-IMS, e-nose and e-tongue. The study showed that braising promotes the release of flavor compounds, and was key for the formation of the characteristic flavor of Daokou braised chicken. Of the 75 and 55 volatile compounds identified in Daokou braised chicken by GC-MS and GC-IMS, respectively, the majority were hydrocarbons, aldehydes, alcohols, terpenes, ketones, heterocyclics, esters, acids and phenols. Aldehydes, alcohols and ketones were detected in higher proportions throughout processing, but their changes were not consistent. With the development of processing, the proportion of volatile compounds with animal fatty odor, such as aldehydes and alcohols, decreased, while the proportion of esters, ketones and terpenes from spices with fruity fragrances increased, especially in the braising stage. The results from the e-nose and e-tongue also showed that the chicken flavor changed significantly during the braising process. The e-nose results showed that oxides were more abundant after stewing, which was consistent with the detection result of GC-MS. It should be noted that braising was found to be beneficial to flavor formation, salt penetration and umami release, but that long braising times could lead to reduced flavor intensity and some bitterness. Therefore, more in-depth research on the changes of flavor compounds in braising and the critical braising time will be the main focus of future research.  
F.Z.: Methodology, Writing--original draft preparation, Data treatment, Figure; L.S.: Methodology, Writing--original draft, Writing--review and editing; G.Z.: Writing--review and editing; C.Z. and M.L.: project administration, Writing-review. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Key R & D and promotion project of Henan Province (202102110137).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to thank Shandong Hai-neng Scientific Instruments Co., Ltd. for the assistance in GC-IMS and Ensoul Technology Ltd. for the assistance in e-tongue. The authors also thank the editor for their valuable comments and suggestions on this manuscript.  
The authors declare no conflict of interest.
## 3 System Model
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol/Acronym** & **Description** \\ \hline \(\tau_{i}\) & \(i\)th Task. \\ \(P_{i}\) & Period of \(i\)th Task. \\ \(\phi_{i}\) & The release time of \(i\)th Task’s zeroth instance. \\ \(\tau_{ik}\) & The release time of \(i\)th Task’s \(k\)th instance. \\ \(c_{ik}\) & The active execution time of \(i\)th Task’s \(k\)th instance. \\ \(end_{ik}\) & The ending execution time of \(i\)th Task’s \(k\)th instance. \\ \(WR_{i}\) & Worst case response time of \(i\)th task. \\ \(BR_{i}\) & Best case response time of \(i\)th task. \\ \(H\) & Hyperperiod of set of \(n\) tasks. \\ \(n\) & Total number of tasks. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the symbols and acronyms and their description. Figure 2: An example of the effect of fixed-priority preemptive scheduling on the execution of three periodic tasks \(\tau_{1},\tau_{2},\tau_{3}\), with release periods 10, 19, and 56, and computation times 3, 11, and 5, respectively. We assume that task \(\tau_{1}\) has the highest priority, and task \(\tau_{3}\) has the lowest priority. The numbers to the top right corner of the boxes denote the response times of the respective releases. Note that the response time is counted from the moment of release (indicated by small arrows) up to the corresponding completion. _where \(WR_{i}\) is the worst case response time of task \(\tau_{i}\), \(end_{ik}\) is execution end time of task instance \(k\) and \(r_{ik}\) is the release time. It is worth to note that \(WR_{i}\) must fall within the given deadline of the task for all the execution to meet their deadline._  
**Definition 2**. : _The best case response time of any task \(\tau_{i}\) which is also called the minimum response time is denoted by Equation (2)_  
\[BR_{i}=min_{\phi,k}(c_{ik}-r_{ik}) \tag{2}\]  
_where \(BR_{i}\) is the best response time, \(end_{ik}\) is execution end time of task instance \(k\) and \(r_{ik}\) is the release time. Best response time is not necessary in most cases for the analysis of the system, but it is sometime good to keep track of it._  
**Definition 3**. : _The hyperperiod of periodic input tasks are defined as the time interval after which all the periodic tasks executions are repeated. Hyperperiod \(H\) of set of \(n\) tasks \(\tau\) with the period \(P\) can be found by the Equation (3)_  
\[H=lcm\{P_{i}|1\leq i\leq N\} \tag{3}\]  
_where lcm is the least common multiple of tasks' period._  
The hyperperiod is a very crucial factor in a sense that it is a function of CPU computation cycles. The higher the hyperperiod will be the more CPU clocks the scheduling footprints will consume and vice versa. As embedded devices have scarce resources, therefore, it is essential to cautiously model the period of tasks in order to reduce the hyperperiod [34]. For instance, the result of the hyper period of the periods \(\{12,11,19,23,17\}\) is 980,628 which is considered very high, and the CPU will at least iterate this amount of time to cover just one hyperperiod.