esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Specific Application of Topic Identification Models and Algorithms
### Application of Text Mining and Its Associated Models and Algorithms
#### 5.4.1 Co-Word Analysis and Co-Occurrence Matrix  
The co-word analysis method was established in the 1970s, based on the concept of citation coupling and co-citation in bibliometrics. It is based on developing statistics on the frequency of common occurrences of word pairs or noun phrase pairs in the same document; these words or noun phrase pairs are hierarchically clustered to reveal the affinity between them, and then the structural changes of the disciplines and topics they represent can be analyzed.  
[PERSON] et al. used the Ucinet software to obtain a high-frequency co-word matrix and generate a topic network evolution map in the field of intelligence science [66]. [PERSON] utilized a subject word co-occurrence model combined with a community discovery algorithm to build a co-word matrix and identify the main topics in synthetic biology [67]. [PERSON] et al. obtained a word co-occurrence matrix by using co-word analysis, and derived term sets by combining professional terms and high-frequency term analyses [68]. [PERSON] et al. established associations between technologies based on the co-occurrence of patent classification codes [69]. He focused on the achievements of library and information science, as well as computer software and application science, and used co-word analyses methods to explore the topic intersections in the two subjects [70].
# Cooperative Friendly Jamming Techniques for Drone-Based Mobile Secure Zone
## 2 Related Work
### Friendly Jamming Security Model  
Various studies are underway on friendly jamming security techniques applying jamming signals for security purposes [11; 12; 13; 14; 15; 16; 17; 18]. A friendly jamming technique is a method of emitting friendly artificial interference signals to prevent malicious eavesdroppers from overhearing when the sender communicates with a legitimate receiver. A jammer is used to protect the wireless communication network and send messages with confidentially, allowing legitimate senders to communicate in a secure manner. In friendly jamming security technique research, security metrics are defined to demonstrate the effectiveness and validity of jamming. Friendly jamming security techniques include a method of using anti-jamming that automatically arranges multiple friendly jammers to deliver information safely between moving objects in a mobile communication network [16; 17; 18].
###### Abstract  
Traditional network communication methods lack endogenous security mechanisms, which is the root cause of network security problems, e.g., spoofing identity and address forgery. This paper proposes a secure communication method based on the message hash chain, referred to as the chain communication method or MHC method. We use the message hash chain to ensure that the transmission process is immutable, non-repudiation, reliability, and the integrity and synchronization of the message. At the same time, we can sign and authenticate data streams in batches through chain signature and authentication technology, which can significantly reduce the overhead of signature and authentication, thereby improving the efficiency of secure message transmission. This paper formally proves the security of the message hash chain, conducts an in-depth analysis of the reliability of the MHC method, and conducts relevant experimental tests. The results show that the average transmission efficiency of the MHC method applied at the network layer is about 70% lower than that of the IP protocol communication method without a security mechanism. However, it is about 5% higher than the average transmission efficiency of the non-repudiation IPSec protocol communication method. The average transmission efficiency of the MHC method is about 23.5 times higher than that of the IP protocol communication method with the packet-by-packet signature. It is easier to ensure the non-repudiation of the data stream.  
message hash chain; chain communication method; chain signature; endogenous safety
## 2 Methods
### Bench Tests
#### 2.3.2 Measurement Procedure  
During the execution of measurements within a single measurement series, the boiler was operated at constant parameters: power, grate feed rate, fuel layer height, excess air ratio, and air partitioning under the blowdown zones. The analyzer, according to the manufacturer's recommended procedure, was calibrated before each series of measurements at least once a day. Calibration was performed using a reference gas. The measurement procedure was as follows:  
* In the first step, the background was measured to determine the boiler's steady operating conditions. The analyzer measured the concentration of NOx and other flue gas components for approximately half an hour. The measurement was averaged over time.
* In the second step, reagent injection was initiated. NOx concentration measurement started after a transition period from the reagent injection start. The plant operator observed the NOx rate of change in the flue gas in response to the reagent injection and decided whether steady-state conditions had already occurred.
* In the third step, the actual measurement period followed. The reagent injection at fixed set points (injection lance configuration, insertion points, and operating parameters) and the NOx concentration measurement in the exhaust gas took about 20-30 min.
* In the following steps, the operational parameters of the injection lances were changed (e.g., pressure, reagent output, and urea concentration). Re-measurement with the exhaust gas analyzer started each time after a transition period in which the operator had to confirm the steady-state conditions.
* In the final measurement stage, the background level of NOx concentration in the flue gas was again verified after stopping the reagent injection. This measurement was started when steady conditions were reached. The duration of the measurement was approximately 0.5 h.  
The required calculations were made to determine the reagent flow rate for the boiler operation variant. Based on the actual flue gas flow rate and the background \(NO\) [mg/Nm\({}^{3}\)] measurement reading for a given measurement series, the stoichiometric urea requirement was calculated. The formula below shows how to determine the reagent flow rate \(\dot{m}\) with the desired urea concentration in water:  
\[\dot{m}=\frac{V_{r}\cdot NO}{10^{6}\cdot C}[\frac{l}{h}] \tag{1}\]  
where \(V_{r}\)--real exhaust gas flow rate, \(NO\)--nitrogen oxide measurement, \(C\)--urea solution concentration. The test was conducted at the stoichiometric reagent flow rate and with multiples of this flow rate.
# Communication Performance Assessment for Advanced Metering Infrastructure
## 1 Introduction
for AMI communication networks [12; 13; 14; 15; 16; 17; 18]; however, these proposed technologies are not suitable for communication performance assessment. Few papers proposed the methodologies suitable to evaluate the communication performance before AMI deployment [19; 20; 21; 22; 23; 24]; however, the question of how to supervise the communication performance continuously still needs to be further investigated. Some methodologies based on the Gale-Shapley algorithm were proposed to analyze the capacity sharing for a set of geographically distributed independent items to integrate their resources and demand forecasts for a specific production objective [25; 26]. The communication performance assessment wasn't included in the proposed capacity sharing. This paper proposed an effective Communication Performance Index (CPI) to assess on a daily basis and supervise the communication performance of each smart meter after the AMI has been implemented. Some useful communication quality measurements that can be easily acquired from a smart meter such as reading success rate and response time are used to design the proposed CPI. The reading success rate is used to record meter reading success rate on a per hour, day or month basis. A higher reading success rate usually indicates the better stability of a communication network. The response time is the elapsed time between the end of an inquiry on a DCU or meter data management system and the beginning of a response. Faster response time also implies a higher communication performance. Fuzzy logic is employed to integrate these measurements into the proposed CPI calculation. The CPIs for communication paths, DCUs and whole AMI can then be derived from meter CPIs. Simulation and experimental results for small-scale AMIs demonstrate the validity of the proposed CPI. The main contributions of this paper include:  
* Using some communication quality measurements easily acquired from a smart meter to design a CPI. * Through the calculated CPIs, the communication performance and stability of AMI can be effectively assessed and supervised. * The proposed CPI and communication performance assessment would be supportive of the future deployment and operation of AMI.
## 4 Conclusions  
In this paper, research on feature enhancement extraction technique based on DAE network is carried out, G-BRBM for real-valued signals is designed and programmed, and greedy algorithm is introduced to enhance the extraction of line spectrum features. The validity of the model is verified by the loss curve obtained by simulation experiment. Based on this, an underwater large vehicle sonar detection LOFAR spectrum reconstruction experiment is conducted, and the performance of the DAE method is compared with the conventional method. The findings indicate that the G-BRBM employed in our network model aids in converging the model to its optimal state. The LOFAR spectral features exhibit a notable enhancement following reconstruction, along with a satisfactory global noise removal effect. Additionally, weak line spectrum that are imperceptible to the naked eye are amplified, resulting in improved contrast and regularity. The extractable line spectral density within 10-300 Hz band is more than doubled compared to traditional time-accumulation-based algorithms. Our proposed method can be applied to the enhancement and extraction of weak line spectral features in the case of very low signal-to-noise  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Band (\(\Delta f\))**} & **Number of Line** & **Line Spectral** & **Minimum Frequency** \\  & & **Spectrums (\(N_{i}\))** & **Density (\(\rho_{i}\))** & **Interval (\(f_{m}\))** \\ \hline Traditional & & & & \\ method & 10–300 Hz & 11 & 0.037 & 7.0 Hz \\ DAE & 10–300 Hz & 18 & 0.062 & 2.5 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the effect of traditional method and DAE method.  
ratio, which has a positive effect on both the subsequent application of the features and the solution of other low-frequency and low signal-to-noise ratio hydroacoustic problems. However, we also found that the model still needs to be further improved for the characterization of statistical features such as line spectral frequency drift and energy fluctuation. We will focus on solving these problems in our subsequent studies by performing LOFAR line spectral enhancement work with different bandwidths and resolutions, and trying to solve this problem by introducing the attention mechanism and multiscale feature extraction.  
Conceptualization, F.J. and G.L.; methodology, F.J. and G.L.; software, F.J. and G.L.; validation, F.J., G.L. and S.L.; formal analysis, F.J.; investigation, S.L.; resources, F.J. and G.L.; data curation, G.L.; writing--original draft preparation, G.L. and S.L.; writing--review and editing, S.L. and J.N.; visualization, G.L. and J.N.; supervision, F.J. and J.N.; project administration, F.J. and G.L.; funding acquisition, F.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 52371356.  
Not applicable.  
The data presented in this study are available on request from the corresponding author (privacy reasons).  
The authors declare no conflicts of interest.
## 3 Results
### Statistical Methods  
None of the alternative statistical analysis approaches: Principal Component Analysis (PCA) and Multivariate Analysis of Variance (MANOVA) were successful (Supplementary File S1: Presentation of multivariate results). PCA would enable dimensionality reduction and the identification of hidden patterns or correlations between mechanical and environmental variables, providing insight into the structure of the data without the need for a predefined outcome variable. However, PCA was ultimately used only for exploratory data analysis and not as a primary predictive tool, as it does not directly model the relationship between predictors and the target variable (tensile strength). Multivariate Analysis of Variance (MANOVA) was also evaluated to assess whether groups of samples with different  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Material** & **N** & \(\overline{\pi}\) & **SD** & **V[\%]** & **Min** & **Q1** & **Me** & **Q3** & **Max** \\ \hline
**Lyoplant** & 9 & 152.65 & 65.03 & 42.6 & 63.5 & 116.4 & 173.4 & 181.7 & 265.3 \\ \hline
**Neuro-Patch** & 7 & 45.01 & 5.35 & 11.9 & 39.6 & 41.6 & 43.1 & 47.2 & 54.8 \\ \hline
**Poliester** & 10 & 307.51 & 89.70 & 29.2 & 134.8 & 247.2 & 323.1 & 373.6 & 427.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Analysis of the force parameters required to destroy the material in a static tensile test, divided into material types.  
Figure 7: Comparison of the maximum force required to break samples according to material type.  
physical characteristics (e.g., thickness ranges or weight intervals) showed statistically significant differences in mechanical results. However, MANOVA was deemed inappropriate for this study due to the continuous nature of most independent variables and the lack of clearly defined categorical groups, which limited its applicability. Therefore, the ML regression model was retained as the primary method due to its superior ability to handle continuous predictors and provide quantifiable, interpretable forecasts, aligned with the Industry 4.0/5.0 goals of real-time optimization and digital feedback in production.
## 1 Towards Semantic GIS
### Issues and Challenges  
As previously mentioned, semantics could contribute to GIS progress by providing a wide range of formalizations and relations as an expression of complex conceptualizations involving space and data [23].  
The formal definition of semantics targeting a complex data and functional infrastructure is a difficult challenge especially if, as for GIS, application domains can be quite different in structure, scale and scope. A mix of generic, domain-specific and application-specific concepts and relations have to work together, requiring strong efforts in terms of knowledge engineering in order to assure intelligible and usable models.  
From a strictly product-oriented perspective, the minimal set of challenges to address can be summarized as:  
* Most emerging applications require an innovative understanding of space, overcoming the strongly physical model currently in use. An extended specification of space, including logic views and relations in the context of interoperability and reducing ambiguity, could be extremely helpful for a large number of business processes and applications involving GIS. In recent years, several projects have been proposed in order to combine themes of space and time using an ontological approach (e.g., [24]). Semantics are being extensively used in order to define the contextualization of geospatial information (e.g. [25]).
* At the same time, an improved model for data management [26] and sharing [22] is sought. Next generation GIS should reflect data ecosystems and not simply federation of data. Rich data models are required to provide capabilities for effective data management and sharing. An additional effort is required to manage complex data on a large scale (e.g., _Open Data_[27] and _social objects_[16]) as well as to provide multi-dimensional perspectives of data (such as in semantic similarity measurement [28]).
* Semantics, able to formalize current models, would be an important (and in most cases also comprehensive) result. Looking at ICT architectures and the speed of their evolution, a more consistent role for semantics is expected in the near future, mostly in order to provide a consistent level of interoperability [23, 29]. Big Data, mobility, social trends in information [30, 31] and all the other phenomena potentially involving the web, should provide a deeper understanding of semantics as an effective \(n\)-th dimension of space, enabling creation of innovative models for representation and interaction.
# Study on Image Classification Algorithm Based on Multi-Scale Feature Fusion and Domain Adaptation
## 4 Experiments
### Comparison Methods and Evaluation Metrics  
Several comparison methods were employed in the experiments:  
1. Transformer [42] (Baseline): the Transformer model, trained on source domain data, was directly applied to target domain data without any domain adaptation;
2. DANN [37]: The DANN is a domain adaptation method that trains a neural network to learn features that are both discriminative for the source domain task and invariant to domain differences. This is achieved by using a gradient reversal layer to promote domain invariance, allowing the model to generalize to the target domain without labeled data;
3. LPJT [43]: LPJT is a domain adaptation method that jointly optimizes feature and sample adaptation while preserving the local sample consistency. It utilizes label propagation to predict new instances and supports both homogeneous and heterogeneous domain transfers;CDAN [44]: The CDAN is a domain adaptation method that enhances adversarial learning by conditioning the adaptation on classifier predictions. It employs multi-linear conditioning to capture the cross-covariance between features and predictions, and entropy conditioning to manage uncertainty, achieving state-of-the-art results on multiple datasets.  
The evaluation metrics used in the experiments included the accuracy, precision, recall, and F1 score. The accuracy measures the proportion of correct predictions out of the total number of cases. The precision (positive predictive value) measures the proportion of true-positive predictions among all positive predictions. The recall (sensitivity) measures the proportion of true-positive predictions among all actual positives. The precision and recall are often conflicting metrics, and the F1 score balances both, giving them equal weight. The accuracy and F1 score are the most important metrics. The formulas for the evaluation metrics are as follows:  
\[accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{14}\]  
\[precision=\frac{TP}{TP+FP} \tag{15}\]  
\[recall=\frac{TP}{TP+FN} \tag{16}\]  
\[F1 score=\frac{2\times precision\times recall}{precision+recall} \tag{17}\]  
where TP, TN, FP and FN represent the numbers of true positives, true negatives, false positives, and false negatives, respectively. In this experiment, the normal class was treated as the positive class.
# Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features
## 3 Material and Methods
### News Datasets
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Language** & **Class** & **Content** \\ \hline \multirow{5}{*}{EN} & fake & “Voters on the right have been waiting for this for a long time! Police have finally \\  & legitimate & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\  & & raided a Democratic strategic headquarters, and the results are devastating! (...)” \\  & legitimate & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\  & & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\  & & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\  & satirical & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\  & & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\  & & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\  & & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline \multirow{5}{*}{ES} & fake & “La Universidad de Oxford da mas tiempo a las mujeres para hacer los examenes (...)” \\  & legitimate & “El PSOE reactiva el debate sobre la eutanasia El partido del Gobierno llevara su propuesta para regularla al Pleno la proxima semana y cree que saldra adelante (...)” \\  & satirical & “Mucha euforia ha generado el gran lanzamiento del nuevo reality Colombiano \"Yo me abro\" que se estrena hoy y en el que los particlipantes demostraran sus habilidades para escapar de la justici colombiana. (...)” \\ \hline \multirow{5}{*}{PT} & fake & “Ministro que pediu demissão do governo [PERSON] explica o motivo: “Não faco maracutaias. Não tenho rabo preso” (...)” \\  & legitimate & “Governo federal decide decretar intervenca no asguranca publica do RJ. Decreto serd publicado nesta sexta-feira (16), segundo o presidente do Senado, [PERSON]. Decisão foi tomada em me meio a escalada de violencia na capital carioca. (...)” \\  & satirical & “A senadora [PERSON] e uma mulher que não tira o corpo fora de polemicas. Ela dú uma tora de arvore para não entrar numa briga mas derruba uma floresta inteira pelo prazer de não sair. (...)” \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Examples of news content for all classes of each language.
# Interconnecting Haptic Interfaces with High Update Rates through the Internet +
## 5 Simulation Results  
### Protocol Efficiency  
Protocol efficiency is uninfluenced by the network status and is determined by the payload of each application and the header of each protocol Equation (1). As real-time haptic applications enforce very high update rate, the protocol efficiency should be examined. Haptic applications use small payloads The transport protocols should use as small overhead as possible so as not to overshadow the payloads of the haptic applications.  
Table 4 illustrates the efficiency of the examined protocols. The UDP protocol shows the highest efficiency As it has the smallest header.
## 4 Discussion  
Existing research presupposes static topology. However, in real-world environments where LEACH is applied, nodes change dynamically over time. Thus, in this study, the efficiency of LEACH within the dynamic topology was increased through Q-LEACH.  
As the reinforcement learning is an alternative solution for optimizing the routing. It can achieve low latency, high throughput, and adaptive routing [60]. Various type of reinforcement learning  
Figure 10: The comparison with related work.  
method are applied to routing [61]. Among these RL methods, Q-table was applied in this paper to perform Q-learning in OpenAI environment. Existing research required extra effort to build its own environment, but we can contribute to performance improvement by configuring an optimized situation with an open source environment applying OpenAI's gym environment.  
In order to optimize routing in each network, it is necessary to develop this study by detailing the specific tasks defined for existing networking problems. In addition, it is necessary to introduce a number of recently developed reinforcement learning methods (Deep Deterministic Policy Gradient, Advantage Actor Critic, etc.) into LEACH to be applied in real life.  
In the static topology model, there is the need to consider LEACH with multi-hop, whereas, in the dynamic topology model, a multi-hop system is not efficient in the aspect of energy consumption as nodes composing the topology are constantly moving. It seems necessary to calculate LEACH through more well-defined dynamic modeling by applying various variables that occur in actual situation.