esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed MIMU/GNSS UTC Integration Architecture
### Large Frequency Error Detector  
The limited operating range of the time domain frequency discriminator is an important reason for the degradation of system performance induced by large frequency errors. If the frequency error exceeds the pull-in range, the frequency discriminator outputs an erroneous estimate.  
In the EUTC system, the frequency error output by the frequency discriminator is first forwarded to the large frequency error detector. The main purpose of the detector is to detect whether the current frequency tracking error is anomalous. Additionally, if the frequency error exceeds the working range of the time domain frequency discriminator, a correct frequency error estimate is provided by the detector. Figure 5 depicts a detailed flow chart of the large frequency error detector.  
In the large frequency error detector, the frequency error \(\hat{f_{e}}\) output from the four-quadrant arctangent frequency discriminator is first compared with the preset upper limit \(f_{up}\). In our study, \(f_{up}\) is set to 20 Hz. If the absolute value of \(\hat{f_{e}}\) is less than the threshold \(f_{up}\), the current frequency tracking is normal, and the frequency error \(\hat{f_{e}}\) is passed to the channel prefilter. Moreover, the original coherent integration results are directly inputted to the NWPR \(C/N_{0}\) estimator. Otherwise, it means that the current epoch is affected by a large frequency tracking error.  
Subsequently, it is further determined whether the frequency error exceeds the range of the discriminator. If the absolute value of \(\hat{f_{e}}\) is greater than or equal to the discriminator threshold \(f_{sa}\), the discriminator output is unreliable. In our study, \(f_{sa}\) is set to the single-sided frequency pull-in range multiplied by 0.8. For example, with a 10 ms predetection integration time, the single-sided frequency pull-in range is 50 Hz, and \(f_{sa}\) is equal to 40 Hz. In order to obtain the correct frequency error, a transform domain frequency discriminator [15; 29] with a larger working range is applied to  
Figure 5: Flow diagram of the large frequency error detector.  
reprocess the coherent integration results. The new frequency error estimate is input to the refined frequency processor. In contrast, the frequency error that does not exceed the pull-in range of the discriminator is directly inputted to the refined frequency processor.
## 5 Simulation Analysis  
### Simulation Configuration  
The simulations are carried out on the SUMO platform. As an open-source software, SUMO provides higher privileges to do further development based on its framework. Meanwhile, SUMO can interact with Python programs through the Traci interface, whereas our algorithm and training procedure are coded in Python.  
A signalized corridor scenario is established in SUMO, which can be shown in Figure 6. The lane configuration for each intersection is the same as Figure 4. For each episode of train or evaluation, the traffic will be loaded in the first 100 s with a random arrival rate. Then the ego vehicle is inserted into the leftmost lane. The preloading procedure is to simulate the real traffic situation, that is, the ego vehicle can be influenced by some other vehicles. After that, the ego vehicle will cross the subsequent five signalized intersections with the support of the PPO algorithm. An episode of simulation will be terminated when the ego vehicle has crossed the last junction. Moreover, the transitions of each episode of simulation will be recorded and saved to prepare for the explanation process based on SHAP.  
Table 1 presents the general parameter settings for simulations. The parameters related to PPO are tuned manually through a series of simulations. We use a fully connected neural network architecture for both parametric policy and parametric value function. The units of the neural networks with three hidden layers are \([512,\ 256,\ 64]\). In addition, the default settings of weighting parameters \(\omega_{1}\), \(\omega_{2}\), and \(\omega_{3}\) are set to 5, 3, and 7. The sensitivity analysis of these parameters is presented after the general analysis.
# An Improved Weighted and Location-Based Clustering Scheme for Flying Ad Hoc Networks
## 6 Conclusions  
Owing to the high speed of UAVs and dynamically changing network topology, the traditional clustering schemes cannot be applied to FANETs directly. In this paper, we have proposed the IWLC scheme to enhance the performance of the networks, within the resource limitations. A location-based K-means++ clustering algorithm was firstly developed to form the initial UAV clusters, and a weighted summation-based cluster head selection algorithm was proposed, considering the remaining energy ratio, adaptive node degree, relative mobility, and average distance. The simulation results indicated that, as compared to the WSCA and ACO algorithms, the proposed IWLC scheme enhanced the performance of the number of clusters, packet delivery ratio, network lifetime, cluster head changing ratio, throughput and energy consumption dramatically.  
Figure 14: Throughput in different schemes with varying speeds of UAV nodes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Cluster** & \multirow{2}{*}{**CH Selection**} & **Cluster Maintenance** & **Overall Complexity** \\ \hline IWLC & \(O(KNI)\) & \(O(N)\) & \(O(N)\) & \(O(KNI)\) \\ WSCA & \(O(KNI)\) & \(O(N)\) & - & \(O(KNI)\) \\ ACO & - & \(O\big{(}N^{2}\big{)}\) & \(O\big{(}N^{2}\big{)}\) & \(O\big{(}N^{2}\big{)}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Time complexity analysis.  
**Author Contributions:** Conceptualization, X.Y. and T.Y.; methodology, X.Y. and T.Y.; software, X.Y. and T.Y.; validation, X.Y., T.Y. and J.Y.; formal analysis, X.Y., T.Y. and Z.C.; investigation, X.Y. and Y.W.; resources, T.Y. and J.Y.; data curation, X.Y.; writing--original draft preparation, X.Y.; writing--review and editing, T.Y.; visualization, X.Y. and T.Y.; supervision, T.Y. and J.Y.; project administration, T.Y., J.Y. and J.H.; funding acquisition, T.Y. and J.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of China, grant number 62101373; and the Natural Science Foundation of Jiangsu Province, grant number BK20200858.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results and Discussion
### Prediction Models Using Factors That Can Be Collected without Instruments  
Thus far, regression models were applied to all factors in the data to investigate the relationship between retroreflectivity and the other predictors, as well as find the best prediction models. To achieve the aim of building a model to calculate retroreflectivity using predictors that can be collected easily, quickly, safely, and cheaply, further regression models were generated. Predictors that need expensive measuring instruments and complex methods to be collected should not be included in the predicting models. Therefore, X, Y, and \(\beta\) were removed from the linear and logarithmic regression models, with the results shown in Tables 7-9. Firstly, linear and logarithmic regression were conducted on the datasets (see Table 7), using age, color, class, direction, and GPS position as predictors for models on the VTI data, while age, color, and class were predictors for models on the NMF data. Secondly, the regression models were further applied on the datasets that were divided according to classes (see Table 8). Finally, regression models were applied on the data divided according to class and color (see Table 9). The number of observations in the divided data is shown in Table 10.  
The divided data, according to colors and classes, have few observations and give less information (see Table 10). Therefore, a decrease in the performance of models, expressed by lower R\({}^{2}\) values, was expected in models including only one color or one class (see Tables 8 and 9). The most accurate regression models producing the highest R\({}^{2}\) values (0.50 and 0.95) are logarithmic regression models, which include the class and color as predictors together with age (see Table 7). As shown in Table 7, using logarithmic regression models produces R\({}^{2}\) values of 0.50 and 0.95 for VTI and NMF data, respectively, when omitting X, Y, and \(\beta\). Removing these significant predictors (X, Y, and \(\beta\)) from the predicting models has no impact on the performance of models, with the R\({}^{2}\) values unchanged when compared with those for Equations (2) and (3). The best regression models using only the factors that can be collected without instruments were the logarithmic regression models in Equations (4) and (5), as shown in Table 7:  
Based on VTI data (R\({}^{2}\) = 0.50):  
\[ \begin{array}{l}\log(1+\text{R}_{\text{A}})=-3.45+0.43\times\text{G}+0.34 \times\text{R}+0.39\times\text{W}-0.39\times\text{Ye}-0.26\times(\text{Dir}/360 )+0.12\times\text{lat}+0.01\\ \times\text{long}-0.05\times\text{Age}+1.39\times\text{Cl2}+1.89\times\text{Cl3 }\\ \end{array}  \tag{4}\]  
Based on NMF data (R\({}^{2}\) = 0.95):  
\[ \begin{array}{l}\log(1+\text{R}_{\text{A}})=2.14+0.57\times\text{G}+0.91 \times\text{R}+2.34\times\text{W}+2.02\times\text{Ye}-0.04\times\text{Age}+0.9 \times\text{Cl2}+1.89\times\text{Cl3}\\ \end{array}  \tag{5}\]
# Gaussian Distribution Model for Detecting Dangerous Operating Conditions in Industrial Fish Farming
## 2 Materials and Methods  
### Acquisition of Fish Hatchery Water Quality Parameters  
To carry out this research, data were collected from two of the main phases of the development of Sengealese sole (_Solea senegalensis_) over two years: 2018 and 2019. These phases comprise the weaning stage when the fish larvae substitute their diet, changing from live food to artificial and inert feed. Moreover, the other one, denominated pre-fattening, designated by being a stage before the fattening phase, responsible for the fish development until they reach a suitable size and weight for sale and consumption. Figure 1a,b displays the tanks of these two important fish development phases.  
The SEA8 group is responsible for this industrial-scale fish farming. In this aquaculture facility, RAS are employed to treat the water, eliminate the waste products excreted by the fish, and add oxygen. The RAS's operating principle is to continuously draw water from fish tanks and take it to a mechanical filter and then to a biological filter. In addition,the water is aerated and stripped of carbon dioxide to finally return to the fish tanks. Furthermore, all stages of fish growth are monitored daily, i.e., 365 measurements over a year. Part of this process consists of measuring a set of physical and chemical parameters such as temperature, pH, ammonia, bromine, and nitrogen dioxide to assess the water quality in the fish hatcheries. Any parameter measured has its own sensor, like temperature sensor, salinity sensor, ammoniacal nitrogen sensor, where each sensor element is positioned at critical points of the circuit.  
Specifically, the weaning phase has 59 quadrangular tanks, with a water column of 20 cm in a dark room, and the fish stay in this phase until they weigh 1 g. The pre-fattening phase has 52 tanks with a length of 12 m and a water column between 15 and 20 cm. The fish stay in this phase until they weigh 40 g. Fish growth is determined with weekly weightings that are also used to calculate the right amount of food that needs to be provided. Periodically, or when the growth of the fish is very heterogeneous, screenings are carried out. These screenings are also used to detect abnormalities and diseases. In the weaning phase, the screenings are done manually, while in the pre-fattening phase, they  
Figure 1: Industrial Senegalese sole production (SEA8 group–2021). (**a**) Weaning room. (**b**) Pre-fattening area.  
are done by a machine. A full description of the data set collected and analyzed for the two main Senegelese sole development phases is presented in Table 1.
## 4 Security Threat Landscape in 5G Networks
### New Challenges and Threats  
The advent of mMTC in 5G networks brings forth new challenges and threats. Primarily, the massive increase in device connections leads to an enormous volume of data transmission, leading to higher network congestion [65]. This can potentially be exploited by malicious entities for DDoS attacks. Another challenge arises due to the resource-constrained nature of many mMTC devices. They often operate on limited power and computational resources, making it challenging to deploy robust security measures like advanced encryption and real-time monitoring [66]. As a result, these devices can be more vulnerable to various security threats, including device spoofing, man-in-the-middle attacks, and data breaches. Device heterogeneity further complicates the security landscape, as a multitude of device types, with varying levels of security, try to interact within the same network. This situation can lead to inconsistent security measures, creating weak points that can be exploited by attackers [67].  
Further, the need for low-latency communication in many mMTC applications can conflict with the implementation of robust security measures. Security processes such as encryption, authentication, and intrusion detection can introduce delays, potentially compromising the performance of time-sensitive applications [66]. These new challenges and threats necessitate novel approaches to security in mMTC, balancing the need for robust security measures with the unique constraints and requirements of mMTC applications in 5G networks [67].
# Development of a Social DTN for Message Communication between SNS Group Members
## 1 Introduction  
Today, people at almost any place can access the Internet using mobile terminals by subscribing to a mobile communication carrier. They can also access the Internet via public wireless local area networks (LANs) that are provided at public places. Thus, pervasive social networking (PSN) [1] environments, in which people can access social network services (SNS) anytime from anywhere, are gradually expanding. However, public wireless LANs are not yet widely available in Japan and thus visitors from abroad can exchange messages via the Internet only in limited places.  
Smartphones, which are highly sophisticated, have the ability to set up an ad hoc connection. A variety of applications are being studied to exploit this ability. The authors proposed to use a social MANET (mobile ad hoc network) [2], which is constructed by identifying terminals that belong to a certain SNS community, to expand the area where Internet access is possible beyond public wireless LAN areas.  
DTN (delay, disruption, disconnection tolerant networking) [3; 4; 5; 6; 7; 8; 9; 10; 11] has been studied to achieve reliable data transfer even in an inferior communication environment in which network interruptions or long delays occur frequently. However, if a malicious node participates in a DTN, it can disturb the network operation. Therefore, it is necessary to study how to limit nodes that participate in a DTN.  
We have developed an Android OS (operating system) application that enables members of an SNS community to build a DTN and exchange messages through it. A community token is used to identify members of the same group. We proposed methods of creating and sharing a community token to identify community members in [2]. Use of this token can limit terminals that participate in a DTN and thus ensure network security. To reduce flooding of message copies for constructing a DTN, control messages are forwarded to only those nodes that hold the same community token. In addition, when a source node sends a communication request to its destination node, these nodes exchange control messages to establish a communication route. Relay nodes use these messages to create and hold routing information for these nodes in their routing tables. Thereafter, relay nodes can forward data messages based on their routing tables. This again reduces flooding of message copies. We have developed a social DTN manager that incorporates these functions, Facebook Graph API [12] and Google Nearby Connections API [13], and installed it in Android terminals and confirmed that a social DTN can successfully be built. Section 2 presents related studies and the position of the method proposed in this paper. Section 3 describes how a social DTN can be built. Section 4 explains the experimental social DTN manager we have built. Section 5 verifies that the experimental social DTN manager operates as expected and evaluates route establishment time in a DTN that was built with Android terminals in which the experimental social DTN manager was installed. Section 6 presents conclusions and future issues.
# An Empirical Study of the Code Generation of Safety-Critical Software Using LLMs
## 4 Experiments  
### Software Requirements Analysis in Nuclear and Automotive Domain  
Typically, in safety-critical domains such as nuclear energy and the automotive industry, requirements engineers conduct the software requirements analysis according to industry standards. In the nuclear energy field, the relevant standards for software development include IEC 61508 [37] and IEC 60880 [38]. In the automotive field, software development standards such as ISO 26262 [39] are followed. Software requirements are usually derived from upstream system requirements and conceptual designs, which are primarily described in natural language. Requirements engineers transform the upstream natural language documents into more detailed software-specific requirements. Due to the strong natural language understanding capabilities of LLMs, in theory, it is possible to generate code from both system requirements and conceptual design documents as starting points. Clearly, the well-established requirements analysis and the requirement documents in safety-critical domains provide a solid foundation for code generation based on LLMs.
# Two-Party Quantum Private Comparison Protocol Based on Rotational Encryption
## 6 Discussion  
The qubit efficiency [47; 48; 49; 50] can be expressed as follows:  
\[q_{e}=\frac{q_{c}}{q_{t}} \tag{22}\]where \(q_{c}\) represents the total number of classical bits compared, and \(q_{t}\) denotes the total number of qubits used for comparison, excluding those designated for eavesdropping detection [27; 31]. In our protocol, each participant uses a single-photon state to encode one classical bit, thus achieving a qubit efficiency of 50%.  
A comparison between our protocol and several existing QPC protocols is presented in Table 1, focusing on the following aspects: quantum states used, quantum measurements, the need for entanglement swapping, the need for unitary operations, quantum communication, and qubit efficiency.  
From Table 1, it is evident that the proposed QPC protocol offers several advantages over other representative QPC protocols, including the following:  
1. The proposed protocol exclusively employs Z-basis measurement technology, as opposed to the entanglement measurement technology utilized by most existing protocols. For instance, the protocols outlined in Refs. [21; 22; 24] incorporate both Z-basis measurements and Bell-basis measurements, which impose higher requirements for measurement.
2. Our protocol only uses single photons and Z-basis measurements as foundational elements, which are easier to realize than the entanglement of Bell states [26], multi-qubit quantum states [20; 21; 22; 23; 24; 25], Bell-basis measurements [19; 21; 22; 23; 24], and GHZ basis [26].
3. All qubits are transmitted in a one-way communication manner, significantly decreasing the likelihood of the Trojan horse attacks, thereby enhancing the security of the communication.
4. Our protocol achieves a qubit efficiency of 50%, surpassing many existing QPC protocols in Refs. [19; 20; 21; 22; 23; 24; 25].
## 4 Simulation Results
### Evaluation of 23 Additional Benchmark Models  
In this part, the comparisons of the performance of the regular MGO with the suggested IMGO approaches are extended to consider 23 additional benchmarking. The optimization performance evaluations on multiple commonly used benchmarks, including the Sphere Function, Schwefel 2.22 Function, Schwefel 1.2 Function, Schwefel 2.21 Function, Rosenbrock Function, Step Function, Quartic Function, Schwefel Function, Rastrigin Function, Ackley Function, Griewank Function, Penalized Function, Penalized2 Function,  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**No. **} & \multicolumn{3}{c}{**Standard MGO**} & \multicolumn{3}{c}{**Proposed IMGO**} & \multicolumn{3}{c}{**IMGO vs. MGO (Improvement**} \\  & **Best** & **Mean** & **Worst** & **STD** & **Best** & **Mean** & **Worst** & **STD** & **Best** & **Mean** & **Worst** \\  & & & & & & & & & **Score** & **Score** & **Score** \\ \hline \(Fn_{1}\) & 147.8916 & 1064.324 & 3179.816 & 853.3595 & 100.0378 & 1592.515 & 4746.031 & 1447.195 & 32.357\% & 4  
tion, Foxholes Function, Kowalik Function, Six Hump Camel Function, Branin Function, GoldStein-Price Function, Hartman 3 Function, Hartman 6 Function, Shekel 5 Function, Shekel 7 Function, and Shekel 10 Function. Table 3 lists the boundaries and the dimensions of each benchmarking function. The proposed IMGO algorithm was evaluated alongside the original MGO algorithm using 30 search material agents and 1000 generations. The results, summarized in Table 3, are based on 50 independent runs. Additionally, Figure 3 provides a detailed visual comparison of key performance indicators, including the best, mean, worst, and standard deviation (STD). These comparisons are categorized into three possible outcomes, better, equal, and worse, highlighting the performance of IMGO relative to MGO. The analysis reveals that IMGO consistently achieves significant improvements across various benchmarks. Specifically, IMGO outperforms MGO in achieving better fitness indicators for 12 functions in the best, 18 in the mean, 15 in the worst, and 16 in the STD. This accounts for superior performance in 52.17%, 78.26%, 65.22%, and 69.57% of the functions, respectively. Furthermore, in some cases, both IMGO and MGO perform equally well. For example, the algorithms exhibit equal fitness indicators in 10 functions for the best, 3 for the mean, 5 for the worst, and 3 for the STD, corresponding to an equivalent performance in 43.48%, 13.04%, 21.74%, and 13.04% of the functions, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{ \begin{tabular}{c} **Function No. and** \\ **Function Name** \\ \end{tabular}