esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Emulsion Stability
#### 3.2.1 Untreated CPC-Stabilized Emulsions
The three-modal PSD with both fine particles and two fractions of coarse particles in the range of a few to over 100 \(\upmu\)m resulted in phenomena such as gravitational separation (creaming and sedimentation), which had a strong impact on the physical stability of eC\({}_{\rm s}\) and was unsatisfactory (category D) from the first day (Table 1; Figure S2b). In turn, eC\({}_{\rm g}\) with monomodal PSD and fine particles in the range of 0.1 to 1 \(\upmu\)m were much more stable \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample 1**} & \multicolumn{2}{c}{**PSD**} & \multicolumn{6}{c}{**Turbiscan Stability 2**} \\ \cline{2-9}  & \(\mathbf{d_{43}}\) & _span_ & **TSI** & **Day** & **7.0** & **28.0** & **Day** \\ \cline{2-9}  & [\(\upmu\)m] & **[-]** & **[-]** & **[-]** & **Category** & **TSI** & **Category** \\ \hline eC\({}_{\rm g}\) & 0.70 \(\pm\) 0.02  & 3.19 \(\pm\) 0.08  & 0.4 \(\pm\) 0.02  & A+ & 3.0 \(\pm\) 0.1  & B & 5.8 \(\pm\) 0.2  & C \\ eC\({}_{\rm g}\)N & 1.10 \(\pm\) 0.03  & 4.64 \(\pm\) 0.16  & 0.1 \(\pm\) 0.0  & A+ & 0.7 \(\pm\) 0.0  & A & 2.8 \(\pm\) 0.2  & C \\ eC\({}_{\rm g}\)T & 0.80 \(\pm\) 0.00  & 3.68 \(\pm\) 0.07  & 0.4 \(\pm\) 0.2  & A+ & 2.7 \(\pm\) 1.0  & B & 6.4 \(\pm\) 0.3  & C \\ eC\({}_{\rm g}\)NT & 51.25 \(\pm\) 0.68  & 1.48 \(\pm\) 0.02  & 0.2 \(\pm\) 0.1  & A+ & 1.1 \(\pm\) 0.3  & B & 1.7 \(\pm\) 0.3  & B \\ \hline eC\({}_{\rm s}\) & 40.62 \(\pm\) 0.23  & 1.87 \(\pm\) 0.01  & 12.8 \(\pm\) 0.2  & D & 17.0 \(\pm\) 0.4  & D \\ eC\({}_{\rm s}\)N & 36.83 \(\pm\) 0.81  & 2.04 \(\pm\) 0.05  & 6.1 \(\pm\) 0.7  & C & 29.1 \(\pm\) 2.8  & D & 36.5 \(\pm\) 1.8  & D \\ eC\({}_{\rm s}\)T & 37.77 \(\pm\) 1.67  & 1.99 \(\pm\) 0.08  & 6.8 \(\pm\) 1.8  & C & 11.5 \(\pm\) 1.1  & D & 17.2 \(\pm\) 0.0  & D \\ eC\({}_{\rm s}\)NT & 38.05 \(\pm\) 0.22  & 1.97 \(\pm\) 0.01  & 5.0 \(\pm\) 2.4  & C & 11.4 \(\pm\) 0.8  & D & 14.5 \(\pm\) 0.0  & D \\ \hline \end{tabular}
\end{table} 
Table 1: The results of the volume PSD (\(d_{43}\) and _span_) and Turbiscan Stability Index (_TSI_) after 1, 7, and 28 days of storage for CPC\({}_{\rm g}\)- and CPC\({}_{\rm s}\)-stabilized emulsions. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample 1**} & \(\mathbf{\mu 100\) 8  } & **VRI \({}^{\dagger}\)** & **Model 1**  & **\(\mu\)** & **\(k\)** & **\(N\)** & \(\tau_{0}\)** \\  & [mPa\(\cdot\)s] & [-] & [r\(\geq\) 0.999] & [mPa\(\cdot\)s] & [mPa\(\cdot\)s] & [-] & [mPa\(\cdot\)s] \\ \hline eC\({}_{\rm g}\) & 3.42 \(\pm\) 0.22 [FOOTNOthroughout the storage period. Its stability changed over time from excellent after one day of emulsion preparation to poor after 28 days (Table 1; Figure S2a).
# Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density
## 2 Related Work
### Community Detection Evaluation Criteria  
If there is no scientific evaluation index of community detection results, then the community detection algorithm is at a loss, so how to evaluate the merits of community detection results is another important topic in community detection research. Especially for overlapping communities, the criteria for evaluation of community structure are very complex. It is difficult to give a unified and recognized evaluation index. [PERSON] (2019) extended the modularity of the traditional one-mode network to the bipartite network. [PERSON] et al. (2019) proposed another kind of modularity of bipartite networks, which can be represented by a pair of two numerical groups in two directions, allowing one-to-many correspondence of communities of different vertex types. [PERSON] et al. (2019) proposed a new modularity measurement method based on [PERSON], which applies to bipartite networks and non-uniform networks. [PERSON] et al. (2019) proposed a new modularity evaluation method, which extended the modularity to overlapping community detection based on [PERSON]'s method. This evaluation criterion can evaluate the findings of overlapping communities in bipartite networks. All evaluation criteria described above are limited by modularity resolution ([PERSON], 2019). The problem of resolution limit was first proposed by [PERSON] et al. (2019) in 2007. [PERSON] et al. (2019) find that multi-resolution modularity may not be able to prepare to identify a large number of network community structures with scale-free characteristics in reality. Specifically, multi-resolution modularity may merge some small communities into large communities and may split large communities into small communities. [PERSON] et al. (2019) proposed a mass function of community partition community density to measure the quality of community structure in a bipartite network. Still, this mass function is only applicable to the flying overlapping community structure in the bipartite network.
# Blockchain-Enabled NextGen Service Architecture for Mobile Internet Offload
###### Abstract
Smart contracts can act to bridge gaps between stakeholders and can provide subscribers with a new level of user experience. The rest of the paper is organized as follows. We begin by introducing the topic and discussing the associated research problem. This section highlights the motivation, objectives, contribution, and goal of the research. Section 2 discusses the work carried out by researchers in the related field. Section 3 explains in detail the five phases that comprise our proposed offloading protocol. To test the proposed framework, the offloading of a subscriber from 3G/4G/4G-LTE/5G networks to a fixed broadband WiFi network is simulated, and the results are analyzed. In Section 4 we present the results of our simulation which was carried out using the ns-3 simulator and Ethereum blockchain. Section 5 discusses the deployment of smart contracts on Ethereum's _Goerli testnet_ and shows the result obtained. We conclude with some final remarks in Section 6. To summarize, the goals, objectives, main contribution, and motivation of the research are given below:  
* The goal of the proposed research is to provide a blockchain-based offloading scheme and to improve the overall user experience of mobile customers. The users of network providers with slow speeds can temporarily access high-speed networks without formally leaving one network and moving to another network. * The objectives of the research are to provide the architecture of the proposed scheme along with associated discussion such as its implementation and various implications. The proposed scheme is also simulated, and the results are discussed, to present the overall feasibility of the proposed offloading scheme. * The main contribution of the proposed scheme is to provide a seamless and efficient blockchain-based offloading mechanism. An offloading mechanism is proposed using blockchain technology, which provides efficient offloading and improves user experience. * There are various offloading mechanisms available, which are discussed in the related work section, but there is no clarity on offloading allowing low-speed users to temporarily use available WiFi in the area where a user is roaming. The motivation of the research is to test the proposed offloading scheme, to check the feasibility and technicality of allowing users to temporarily use WiFi and improve user experience.
# Routing Schemes in FANETs: A Survey
## 1 Introduction
### Organization of This Paper  
The rest of this paper is organized as follows:  
\begin{table}
\begin{tabular}{l  
* Section 2 presents background and the motivation for the need to investigate routing schemes in FANETs.
* Section 3 presents the taxonomy and framework of routing schemes in FANETs.
* Section 4 presents the framework of routing schemes in FANETs.
* Section 5 presents a discussion of various routing schemes in FANETs based on the taxonomy.
* Section 6 presents open issues.
* Section 7 concludes the paper.
# Energy Efficient Dynamic Symmetric Key Based Protocol for Secure Traffic Exchanges in Smart Homes
## 5 Performance Evaluation
### Computation Overheads  
During the AKA procedures, one-way hashing and symmetric encryption are the only operations executed. For symmetric encryption, Advanced Encryption Standard (AES) is utilized. However, for one-way hashing operations, a secure hash algorithm (SHA-1) is used. To make a comparative evaluation with other related schemes straightforward, the values in [40] are used. As pointed out in [40], the execution time for symmetric encryption or decryption (\(\mathrm{T_{ED}}\)) = 0.0215 ms, ECC multiplication (\(\mathrm{T_{EM}}\)) = 0.4276 ms, message authentication code (\(\mathrm{T_{MA}}\)) = 0.0215 ms, one way-hashing (\(\mathrm{T_{H}}\)) = 0.052 ms andfuzzy extraction (\(\mathrm{T_{FE}}\)) = 0.0215 ms as shown in Table 9.  
As per Figure 4, the MD executes \(\mathrm{1T_{H}}\) and \(\mathrm{1T_{ED}}\) operations, while the TA executes \(\mathrm{4T_{H}}\) and \(\mathrm{2T_{ED}}\) operations. On the other hand, the SHD executes \(\mathrm{2T_{H}}\) operations. As such, the total computation cost is \(\mathrm{7T_{H}}+\mathrm{3T_{ED}}\), which takes 0.4285 ms, as evidenced in Table 10.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**Scheme**} & **Cryptographic Operations** & **Total Computation Time (ms)** \\ \hline [PERSON] et al. [39] & \(\mathrm{16T_{H}}+\mathrm{3T_{EM}}\) & 2.1148 \\ \hline [PERSON] et al. [40] & \(\mathrm{16T_{H}}+\mathrm{3T_{EM}}\) & 2.1148 \\ \hline [PERSON] et al. [43] & \(\mathrm{29T_{H}}+\mathrm{4T_{ED}}+\mathrm{T_{FE}}\) & 1.6155 \\ \hline [PERSON] et al. [47] & \(\mathrm{17T_{H}}+\mathrm{6T_{ED}}\) & 1.0130 \\ \hline [PERSON] et al. [56] & \(\mathrm{17T_{H}}+\mathrm{16T_{EM}}\) & 7.7256 \\ \hline Proposed & \(\mathrm{7T_{H}}+\mathrm{3T_{ED}}\) & 0.4285 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Computation costs.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Cryptographic Operation** & **Execution Time (ms)** \\ \hline Symmetric encryption or decryption (\(\mathrm{T_{ED}}\)) & 0.0215 \\ \hline ECC point multiplication (\(\mathrm{T_{EM}}\)) & 0.4276 \\ \hline Message authentication code (\(\mathrm{T_{MA}}\)) & 0.0215 \\ \hline One way-hashing (\(\mathrm{T_{H}}\)) & 0.052 \\ \hline Fuzzy extraction (\(\mathrm{T_{FE}}\)) & 0.0215 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Cryptographic execution time.  
As shown in Table 9, the scheme in [56] requires 17 one-way hashing operations and 16 ECC point multiplication operations and hence incurs the highest computation overheads, as shown in Figure 5. The schemes in [39; 40] have the second highest computation costs of 2.1148, followed by the protocols in [43; 47] with 1.6155 ms and 1.0130 ms, respectively.  
Conversely, the proposed protocol requires only seven one-way hashing operations and has the lowest computation overheads. It is, therefore, the most efficient in terms of computation complexities during the authentication and key negotiation phase.
# Secure PBFT Consensus-Based Lightweight Blockchain for Healthcare Application
## 4 Experimental Results
### Transaction Latency  
Transaction latency is the time interval from transaction submission to transaction confirmation. It is used to measure the computing communication efficiency and runtime of the blockchain's consensus algorithm. The reduced latency will increase the transaction confirmations and decrease the chances of blockchain bifurcation.  
\[\mathrm{T_{delay}}=\mathrm{T_{complete}}-\mathrm{T_{submit}} \tag{11}\]  
where \(\mathrm{T_{delay}}\) is the latency, \(\mathrm{T_{complete}}\) is the block confirmation time, and \(\mathrm{T_{submit}}\) is the transaction initiation time. Figure 8 shows the relationship between latency and the number of nodes, the latency of the proposed algorithm is less than the traditional PBFT algorithm, indicating that nodes' consensus speed is faster than the PBFT algorithm. The main reason for achieving the minimal latency is grouping the nodes into the consensus node list and backup node list; therefore, the number of nodes participating in the consensus process is minimal. During the simulations the number of nodes are varied from 2, 4, 6, 8,..., 30. As the number of nodes in the network increases, the transaction latency will also increase in both PBFT and the proposed algorithm. This is due to rise in internal communication required during verification. We also tested the proposed algorithm by considering the effect of block size on latency by considering the varied number of transactions on different blocks. Since the number of transactions is more in a block, it takes a longer time to propagate, and verification leads to higher latency.  
Figure 7: Throughput Analysis.
# Fractional Order Processing of Satellite Images
###### Abstract  
Nowadays, satellite images are used in many applications, and their automatic processing is vital. Conventional integer grey-scale edge detection algorithms are often used for this. This study shows that the use of color-based, fractional order edge detection may enhance the results obtained using conventional techniques in satellite images. It also shows that it is possible to find a fixed set of parameters, allowing automatic detection while maintaining high performance.  
satellite images; fractional derivative; automatic detection; color-based detection; grey-scale detection; fractional processing; aerospace; very-high-resolution satellite 26A33; 68U10; 94A08 +
Footnote †: journal: Applied Sciences  
1
# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 3 Results
### What Are the Main Technological Uses of Audio Features in Educational Research? (RQ1)
#### 3.1.2 Emotion and Behavior Analysis  
This category encompasses studies that analyze group behavior, student engagement, and emotional dynamics in classroom settings. Audio features are leveraged to extract information about interaction patterns, social regulation processes, and affective states, offering insights into how students collaborate and how emotions manifest during learning.  
A subset of studies focuses on collaborative behavior and group dynamics. For instance, [PERSON] et al. [24] investigates socially shared regulation in small groups, emphasizing the pedagogical role of silent pauses. Other studies, such as [25], aim to identify different interaction phases (cognitive, metacognitive, and social) during group work.  
Figure 5: Main uses detected in the analyzed literature (RQ1).  
Diarization-based methods are employed by [26] to analyze participation while preserving student privacy.  
Another line of work targets engagement and attention. [PERSON]. [27; 28] examine acoustic cues to infer students' interest and concentration levels throughout lessons, aiming to identify moments of disengagement or heightened attention.  
Emotional analysis represents a parallel but related focus. [PERSON] et al. [29] examines classroom expressions of encouragement and warmth, while [PERSON] [30] explores student emotions in politically oriented instruction. Teacher emotions are also considered in [31], which classifies emotional tones in teacher speech to understand how effective expression contributes to classroom climate.  
Taken together, these studies illustrate how behavioral and emotional insights derived from audio features can illuminate aspects of classroom functioning that are not easily observable through traditional metrics. By capturing how students interact, regulate, and respond emotionally, this category provides critical inputs for the design of supportive learning environments. Moreover, these analyses often serve as a foundational layer for more complex educational interventions, such as those aimed at providing personalized feedback or informing pedagogical adjustments.
## 5 Conclusions  
Cellular mobile network operators often explore point-to-area radio wave connections in the telecom industry for their terrestrial network deployment. The focus of this research contribution was geared towards conducting a robust empirically based prognostic modeling of different attenuation impacts on key atmospheric impairment variables. These variables are mainly cloud, rain, gases, and free space at ultra-high frequencies over terrestrial cellular communication links. Firstly, the motivation was to employ relevant empirical atmospheric data and their appropriate propagation models for prognostic modeling. Secondly, the extrapolative attenuation estimation results and the performance analysis were accomplished by engaging different stepwise propagation models and computation parameters often utilized in Earth-satellite and terrestrial communications. Steady attenuation loss levels rise in correspondence with increasing signal carrier frequency, and free space is more dominant. The results noticeably showed that the attenuation levels attained due to rain, cloud, atmospheric gases, and free space depend on droplet depths, sizes, composition, and statistical distributions. The results also revealed that the attained attenuation intensities from the atmospheric impairment effects are directly proportional to the signal quality degradation at increasing propagation distances. This trend defines the trend of results except for an angle of elevation, which is inversely proportional. Generally, the results revealed that atmospheric impairment-induced attenuation is the prominent source of signal degradations on the microwave communication channels, particularly at higher frequencies of operations. Our future work would focus on optimizing the projected models for optimal performance.  
The manuscript was written through the contributions of all authors. [PERSON] was responsible for the conceptualization of the topic; article gathering and sorting were carried out by [PERSON], [PERSON] and [PERSON]; manuscript-writing and original drafting and formal analysis were carried out by [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]; writing of reviews and editing were carried out by [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]; and [PERSON] led the overall research activity. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Figure 12: Attenuated signal power plot versus T-R propagation distance.  
Data Availability Statement: The data that supports the findings of this paper is available from the corresponding author upon reasonable request.  
The work of [PERSON] is supported by the Nigerian Petroleum Technology Development Fund (PTDF) and the German Academic Exchange Service (DAAD) through the Nigerian-German Postgraduate Program under grant 57473408.  
The authors declare no conflict of interest related to this work.
# A Tool Support for Model-Driven Development: An Industrial Case Study from a Measurement Domain
## 4 Proposed Method: Tool Support for End-User Programming
### Model-Based Testing of Measurement Procedures  
Testing of the acquired data is extremely demanding in any measurement system. There are several reasons for that. One of them is that data acquisition is a complex combination of software, electrical and mechanical components. These parts need to be synchronized throughout the whole measurement process. A lot of testing with repetitions is needed to be confident in a whole measurement package. A logical next step for DEWESoft was to automate the testing of measurement procedures [29].  
In textual programming, we use unit testing to automate the process of testing and gain the possibility of running the tests over and over again. This technique is based on the idea of testing parts of a program and comparing the obtained results with those expected. The same idea was applied to the SeTT (Sequencer Test Tool) [29]. The main difference between traditional unit testing and SeTT are testing artefacts. Since measurement procedures in the Sequencer are models, SeTT tests models. For tests we could use textual notation similar to [36], however, DEWESoft customers are used to developing their measurement procedures with models, therefore, we decided to support the development of measurement tests using Model-Based Testing (MBT) [37].  
From the DEWESoft user's point of view, we wanted to separate the development of tests completely from measurement procedures. Therefore, DEWESoft users do not have the testing support by default in their MDDE. They have to switch to the testing environment to define tests. However, we kept the same notation for models in MBT as used in the measurement procedures. All building block and connections from the MDDE can also appear in the MBT environment. The difference between the MDDE and MBT environment is that, later, we included support for testing in the form of a new build block, \"Assert\", that tests the values from measurements. There are several options to define the structure of a DSML (grammar, metamodel, etc). The Sequencer notation was defined by a metamodel [38; 39; 40]. SeTT's notation is derived from the Sequencer's metamodel, with the extension--the Assert building block (Figure 4). Our implementation of SeTT is a non-invasive extension of a DSL [41; 42]. In this case, Sequencer kept the original notation. Note that non-invasive DSL extension is possible for those DSLs where new concepts are orthogonal to base DSL concepts. In our case, the Assert concept did not require any changes in other concepts from the base metamodel.
## 4 The Proposed Model
### Graph Attention Embedding Representation
#### 4.3.3 Multi-Layer Attention Propagation  
To capture higher-order dependencies, we stack multiple GAT layers, where the output of layer \(l-1\) serves as the input to layer \(l\):  
\[\mathbf{e}_{u}^{(l)}=\sigma\left(\sum_{i\in\mathcal{N}(u)}\alpha_{ui}^{(l)} \cdot\mathbf{e}_{i}^{(l-1)}\right),\mathbf{e}_{i}^{(l)}=\sigma\left(\sum_{u\in \mathcal{N}(i)}\alpha_{iu}^{(l)}\cdot\mathbf{e}_{u}^{(l-1)}\right). \tag{25}\]  
In our implementation, we adopt a two-layer GAT architecture \((L=2)\) to balance expressiveness and computational cost. Through layer-wise propagation, GAT enables the model to integrate multi-hop semantic and behavioral signals, while dynamically adjusting the importance of neighbors based on learned attention.
# Molecular Dynamics Simulation Reveal the Structure-Activity Relationships of Kainoid Synthases
## 2 Results and Discussion
### Structural Flexibility of Kainoid Syntheses  
In order to explore the structural flexibility of kainoid synthases, root-mean-square fluctuations (RMSF) of the substrate-free state were calculated. In the substrate-free state, the flexible regions of PnDabC included the 122G-145V loop, 218P-231R loop, 258V-266V loop, and 330I-341C loop. For DsKabC-PKA, the flexible regions included the 32W-44A loop, 86A-130D loop, 204Y-218R loop, and 282H-344Y loop. For GfKabC-PKA, the flexible regions included the 95T-123D loops, 129F-148H loops, 180A-213L loops, and 313T-325Y loops (Figure 9). The conformational flexibility of the loops around the active center provided the necessary microenvironment for catalysis. The embedding area of the substrate in kainoid synthases reflected the size of the binding interface between the substrate and kainoid synthases, and the binding state of the substrate and kainoid synthases could be determined by analyzing the embedding area. The buried solvent accessibility surface area (Buried SASA) reflected the size of the binding interface between the substrates and kainoid synthases. According to the analysis, the cNGG in PnDabC and the PKA in DsKabC remained stable, indicating that the contact area between cNGG and PnDabC and between PKA and DsKabC remained stable; that is, the combinations remained stable. For GfKabC-PKA, the buried SASA was relatively stable during 0-350 ns but fluctuated sharply during 350-500 ns and decreased to 0, indicating that PKA was stable when combined with GfKabC in the first 350 ns. After 350 ns, the contact area decreased, and the combination was no longer stable (Figure 10).  
Figure 8: Superposition of the kainoid synthases and substrates in simulation.  
Figure 9: RMSF of kainoid synthases.