esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Hyperparameter Tuning  
It is necessary to set the parameters of GA before the LSTM is performed. After plentiful tests, the relevant parameters were finally determined as follows: maximum iteration number 100; chromosome number (for each generation) 20; algorithm chromosome selection; crossover probability 0.20; and mutation probability 0.02.  
The performance of the deep-learning network on the testing set was used as the optimization target in the intelligent hyperparameter optimization based on GA, and the specific reflection was that the MSE value of the network on the testing set was used as the fitness value of the GA. In the GA, one chromosome was used to represent a group of hyperparameters. The gene sequence was updated through chromosome evolution, to minimize the fitness value. The optimal hyperparameter values were selected after optimization of the GA. Finally, the LSTM model with the optimal hyperparameter values was validated experimentally.  
As above mentioned, the hyperparameters of the LSTM network were tuned by the GA. As shown in Figure 8a,b, the effectiveness of the hyperparameters was tuned by tracking the MSE value obtained from every iteration. The MSE values of both the prediction model for temperature at the end of the roadway and that for humidity at the end of the roadway decrease significantly in the initial stage of iteration, which is indicative of effectiveness of the GA in tuning the hyperparameters. It is worth mentioning that both the humidity and temperature models processed through dimension reduction by the PCA have a better fitness performance in the initial stage of parameter optimization than those not processed through dimension reduction by the PCA, which can also demonstrate to a certain extent that the dimension reduction by the PCA is of positive significance for improvement of model performance.  
Figure 8: Hyperparameters tuning using GA model for (**a**) temperature \(T_{f_{ent}}\) prediction and (**b**) relative humidity \(RH_{out}\) prediction.  
Figure 7: Total enthalpy difference variations in a roadway with water trickling at different water temperatures (**a**) and flow rates (**b**).
## 1 Introduction
Then, this process results in a set of vector representations for each word in the corpus, where each vector captures the meaning and context of the word, which can be used to perform various natural language processing tasks such as text classification, similarity and analogy tasks, and information retrieval. For example, if two words have similar vector representations, it means that they have similar meanings or contexts. Similarly, our neural network model trains on a collection of changelogs to predict co-changing elements for each element, resulting in a vector representation of software elements that can be used to determine the degree of changeability between software elements. Thus, if a developer modifies some set of elements, then the recommendation system can use this vector representation to suggest other elements that could be changed together. To validate our study, we used three publicly available datasets: Vuze (formerly Azureus) [16], Spring Framework [17], and Elasticsearch [18]. These datasets, which consist of open-source Java-based software development changelogs, were extracted from two types of VCS. Vuze was extracted from the concurrent version system (CVS), whereas Spring Framework and Elasticsearch were extracted from Git. Vuze is an open-source BitTorrent client that uses the BitTorrent protocol to transfer files. It is written in Java and has over a decade of development history in its changelog data. The Spring Framework is an open-source Java framework that facilitates the development of enterprise-level applications by providing features such as dependency injection, aspect-oriented programming, and support for web applications [17]. Similarly, Elasticsearch is an open-source Java-based distributed search and analytics engine [18]. We have trained the FCP2 Vec model using these datasets' changelogs and have shown that FCP2 Vec predicts potential changes, providing recommendations based on the current open file and detecting hidden dependencies that cannot be identified through program analysis. Comparative results also indicate that our model is highly scalable and can support a much deeper level of granularity, such as the file level, which was not possible with the dependency network [13]. This approach can help programmers predict the number of possible change propagations and the K files related to their current task, where K is a user-defined constant number. The remainder of this paper is organized as follows. Section 2 reviews the literature on change propagation related to the software domain review. Section 3 motivates our approach by providing an overview of the problem definition and the formal definition of Word2 vec and skip-gram. Section 4 describes our proposed approach. Section 5 presents an empirical case study of Vuze, Spring Framework, and Elasticsearch. Finally, we conclude and anticipate future work and limitations of our approach in Section 6.
# UAV-Based Privacy-Preserved Trustworthy Seamless Service Agility for NextG Cellular Networks
## 1 Introduction
To facilitate reliable cooperative service provisioning in presence of intentional or maliciously induced failures and attacks, in this paper, we propose a lightweight privacy-friendly security mechanism for UAV-assisted inter-operator agility. We propose a network model where the supporting UAVs acts as a mediating authenticator between the User Equipment (UE) in need of service and the guest provider's base station. For this purpose, we adopt a delegation-based digital signature mechanism, or a proxy signature offering a lightweight UAV/UE and UAV/eNB authentication with low message exchange toward the home core network. In proxy signature authentication mechanism, a proxy signer signs a message using a delegated credentials of an original signer [5]. Proxy signature provides data security and user privacy without adding extra communication overhead to the home core network (original signer). The assigned UAVs are authorized by the home network core as its proxy delegated signer for the agility service. For user privacy-preserving purposes, we further assume a concealed user ID for all authentications and communications with the guest network as well as the supporting UAVs. Additionally, to limit collecting user information by the supporting UAV, the only data available for the UAV to use as a concealed user ID and the user's eligibility indicator to be a part of the inter-operator service. The proposed inter-operator agility cooperative cellular environment framework can be easily integrated under the 3 gpp standard for emergency sessions [6]. To this end, the main contributions of this work can be summarized as follows:  
* Enable privacy-friendly seamless inter-operator agility and secure user porting in a cooperative cellular environment. * Introducing a novel trustworthy authentication mechanism to facilitate all-party authentication in a zero-trust environment. The rest of the paper is organized as follows. The related works are summarized in Section 2. Then, the system model distribution and attack models are described in Section 3. Next, the proposed authentication scheme is introduced in Section 4. The experiment setup and evaluation are in Section 5. Then the security and performance analysis are in Section 6. Finally, concluding remarks are provided in Section 7.
# Carbon Nanocomposites in Aerospace Technology: A Way to Protect Low-Orbit Satellites
## 2 Corrosion Factors and Mechanisms in Low Orbit Environment
### Lab-Based AO Corrosion Experiments and Facilities
The LEO Atomic Oxygen Interaction Facility at the Australian National University, National Space Test Facility (NSTF) in Canberra, Australia, is an example of such efforts. Recently reported results of the atomic oxygen interaction experiments [45] were related to the assessment of the \(AO\) effect on silicone, with the nanoindentation technology used to characterize the mechanical properties of the material before and after \(AO\) exposure. Further development of this facility is planned to test various materials under space conditions, with the carbonous materials, e.g., such as carbon fibres, being in focus. Using this and similar set-ups, it will be possible to effectively and rapidly observe the potential change in materials characteristics under the effect of \(AO\) exposure without highly expensive space flight tests, greatly enhancing our ability to explore other more innovative materials and material architectures at a relatively low cost. Radio-frequency (RF) plasma technology is among the most promising techniques for designing the on-ground test facilities for \(AO\)-material interaction studies. Figure 7a-c illustrate an approach to build a thermal atomic oxygen source using a radio-frequency plasma system. The authors interpreted the decrease in ion current and ultra-violet flux as the electron flux from the metal walls of the test chamber, along with the absorption of the ultra-violet flux. Apparently, these factors would be absent under the real space conditions; thus, the process of building specific-conditions test facilities that would adequately reflect the complexity of different space environments represents a rather significant engineering challenge. Another approach to ground testing of space-related materials is based on the application of inductively coupled plasma systems, which are quite common in materials research. Figure 7d,e shows a scheme of an experiment and the measured mass loss due to the \(AO\) exposure. In this system, the authors reported a stable operation of this novel system, with the capability to support long-term tests with a highly regulated flux of atomic oxygen. Figure 7f,g illustrates the ground tests of an \(AO\) flux and ionizing radiation dose sensor. This is an example of the recent level of the ground tests, the aim of which was to predict the degradation of materials by the action of \(AO\) in space environment. In this approach, the \(AO\) flux was measured by detecting a change in the behaviour of thin layers of organic materials. These and other efforts focused on the design of novel, sophisticated material degradation test systems are likely to continue to gather momentum [46; 47; 48].
## 3 Proposed Approach
### Possibilistic Fuzzy C-Means Method  
Let us consider clustering of a given dataset \(X=\{x_{1},x_{2},...,x_{N}\}\in\mathbb{R}^{p}\) (where \(x_{i}\) is the \(i\)-th data instance and \(p\) is the number of features describing instances) as a partitioning of \(X\) into \(1<c<N\) subgroups such that each subgroup represents \"natural\" substructure in \(X\). In [8], this partition of \(X\), denoted as \(M\), is a set of \(2\times c\times N\) values that can be conveniently arrayed as two matrices \(U=\left(u_{ij}\right)_{c\times N}\) and \(T=\left(t_{ij}\right)_{c\times N}\):  
\[\begin{split} M=\Big{\{}T\in\mathbb{R}^{c\times N}:0\leq t_{ij} \leq 1\forall i,j;\forall j\exists i:t_{ij}>0\Big{\}}\cup\\ \cup\Bigg{\{}U\in\mathbb{R}^{c\times N}:0\leq u_{ij}\leq 1 \forall i,j;\sum_{i=1}^{c}u_{ij}=1\forall j;\sum_{j=1}^{N}u_{ij}>0\forall i \Bigg{\}}.\end{split} \tag{1}\]  
Equation (1) defines the set of possibilistic fuzzy partition of \(X\). Here, \(U\) is the fuzzy matrix while \(T\) is the possibilistic one. Both matrices are mathematically identical, having entries between \(0\) and \(1\). These matrices are interpreted according to their entries. For the fuzzy matrix, \(U\), each entry \(u_{ij}\) is taken as the membership of \(x_{j}\) in the \(i\)-th cluster of \(M\), and for the probabilistic matrix each entry \(t_{ij}\) is usually the (posterior) probability \(p(i|x_{j})\) that, given \(x_{j}\), it came from cluster \(i\). It should be noted that \(t_{ij}\) is a measure that shows how typical a given entry is for \(i\)-th cluster.  
In [71], the authors explained why both possibilistic and fuzzy matrices should be used for clustering and in [8] they also listed the disadvantages of both Fuzzy C-Means (FCM) [72] and Possibilistic C-Means (PCM) [73] methods. Originally, there was a constraint on the typicality values (the sum of the typicality values over all data points in a particular cluster was set to \(1\)). Thus, in [8] the authors relax this constraint, namely, in their algorithm, the row sum of typicality values is equal to \(1\), but the column constraint on the membership values is retained. This leads to the following optimization problem:  
\[\min_{(U,T,V)}J(U,T,V;X)=\min_{(U,T,V)}\sum_{j=1}^{N}\sum_{i=1}^{c}(au_{ij}^{m }+bt_{ij}^{\eta})\times||x_{j}-v_{i}||^{2}+\sum_{i=1}^{c}\gamma_{i}\sum_{j=1 }^{N}(1-t_{ij})^{\eta}, \tag{2}\]  
\[\sum_{i=1}^{c}u_{ij}=1\forall j, \tag{3}\]  
\[0\leq u_{ij},t_{ij}\leq 1\forall i=\overline{1,c},j=\overline{1,N}, \tag{4}\]  
where \(V=\{v_{i},i=\overline{1,c}\}\) is the set of cluster centres, \(a>0\), \(b>0\), \(m>1\) and \(\eta>1\) are algorithm's parameters chosen by the end-user, \(||.||\) is the standard Euclidean distance, and \(\gamma_{i}\) (\(i=\overline{1,c}\)) is also a user-defined constant. In (2) \(u_{ij}\), \(i=\overline{1,c}\), \(j=\overline{1,N}\), have the same meaning as membership values in FCM, and \(t_{ij}\), \(i=\overline{1,c}\), \(j=\overline{1,N}\), have the same interpretation as typicality values in PCM. The PFCM approach is iterative, and \(U\), \(T\) and \(V\) are updated over each iteration according to the rules presented in [8].
## 6 Use Case of One Smart Building and Its Applications
The results obtained from research articles, for example, on a solitary SB, are linked to the network architecture adopted for this scenario: where Edge Computing alone reduces latency and jitter in the growing data, voice, and video traffic. It is important to note that the local integration of various networks (Wi-Fi, WSN and wired network where necessary) within the SB still needs review, given that all critical computational aspects, including computing, processing and storage, are implemented at the network edge. A review of the cells marked with IEC in Table 1 is necessary at this point. This research culminates in Sections 5 and 6, where quantitative evidence is presented to substantiate the efficacy of a specific architectural strategy. For standalone SBs with a limited occupancy, estimated at up to 60 units per tower, the adoption of an Edge computing (IEC) architecture, complemented by IoT devices of types (1) and (2) deployed within the building, demonstrates a quantifiable superiority. This assertion is rigorously supported by the decision-making matrix presented in Table 1, encompassing thirty-two distinct solution cells. These findings are contrasted with an alternative strategy employing Fog Computing with NFV and IoT devices of type (3). The advent of 5G-IoT, 6G and 6G-IoT on cellular networks and massive fiber optic implementations within Smart Cities, SBs or a large group of federated SBs may bring both innovative solutions and new challenges, such as cyberattacks and new security strategies to the current landscape [49; 61].Certainly, this scenario will open a field for future research adopting NFV, network slicing and SDN and SD-WAN technologies in a new scale and reducing deployment and operation costs for SBs.
## 3 Results and Discussion  
This section presents the results and analysis of the second-order time-difference values related to the STG cycle slip detection and repair method, and investigates the performance of the proposed method using the GPS observations of the GOCE satellite.
# IoT-Based System for Improving Vehicular Safety by Continuous Traffic Violation Monitoring
* Empirical proof shows that the sensor-based approach achieves high accuracy in speed estimation, passenger counting, and seatbelt fastening checks, while maintaining a low computational demand on the edge side.
# Experimental Evaluation and Numerical Simulation of the Thermal Performance of a Green Roof
## 2 Materials and Methods
### On-Site Measurements
In addition, using surface temperatures instead of air temperatures, the thermal conductance (C-value or only C) of the roof can be deduced, by applying the following equation  
\[C=\frac{\sum_{j=1}^{N}q_{j}}{\sum_{j=1}^{N}\Bigl{(}T_{sij}-T_{sej}\Bigr{)}} \tag{3}\]  
where \(T_{si}\) and \(T_{se}\) are the internal and external surface temperatures, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Measuring Instrument** & **Manufacturer** & **Model** & **Measuring Range** & **Resolution** & **Accuracy** \\ \hline Heat-flow sensor & Hukseflux & HFP01 & \(-2000\) \(\times\) \(2000\) W/m\({}^{2}\) & 0.01 W/m\({}^{2}\) & 5\% on 12 h \\ Thermometer & LSI & Pt100 & \(-40\) \(\times\) \(80\)\({}^{\circ}\)C & 0.01 \({}^{\circ}\)C & 0.10 \({}^{\circ}\)C (0 \({}^{\circ}\)C) \\ Surface temperature & LSI & EST124 & \(-40\) \(\times\) \(80\)\({}^{\circ}\)C & 0.01 \({}^{\circ}\)C & 0.15 \({}^{\circ}\)C (0 \({}^{\circ}\)C) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Measuring instruments technical data. Figure 3: Measurement apparatus schema. Moreover, internal and external surface temperatures were also used to obtain information about the dynamic thermal performance of the roofs, in terms of heat waves' phase shift and decrement factor. The heat waves' Phase Shift (PS) can be evaluated as the time difference between the maximum value of the internal surface temperature and the maximum value of the external surface temperature of the roof [22]  
\[PS=t_{T_{si}^{MAX}}-t_{T_{se}^{MAX}} \tag{4}\]  
The Decrement Factor (DF) can be defined as follows [22]  
\[DF=\frac{T_{si}^{MAX}-T_{si}^{MIN}}{T_{se}^{MAX}-T_{se}^{MIN}} \tag{5}\]  
where \(T_{si}^{MAX}\) and \(T_{si}^{MIN}\) are the maximum and the minimum internal surface temperatures registered in a day, and, in turn, \(T_{se}^{MAX}\) and \(T_{se}^{MIN}\) are the maximum and the minimum surface external temperatures registered in a day.
# VIS-HAPT: A Methodology Proposal to Develop Visuo-Haptic Environments in Education 4.0
## 4 The VIS-HAPT Methodology
### The Definition of the Technological Model
#### 4.2.4 Application Programming  
Once these requirements are considered, developers need to decide how the visuo-haptic environment can be best programmed. There are diverse programming languages that can be used to program 2D and 3D applications (C++, C#, Java, Python, etc.). Works on incorporating haptic devices in simulation frameworks can be found in the research of [PERSON] et al. [34]. They created Chai3D, a C++ cross-platform graphic simulation framework. It is open source and supports a variety of commercially available haptic devices. Moreover, efforts focused on incorporating haptic devices' in-game engines can be found in the work of [PERSON] et al., where the authors added the connectivity of haptic devices (Novint Falcon, Omega, and Geomagic Touch) with Unity game engine [9]. The latter is an example of how current approaches are considering the use of game engines for the development of visuo-haptic simulators. Game engines are software applications that facilitate the implementation of graphics rendering, collision detection, and physics simulation, which allow the developer to focus on the game logic and interactions.  
Before selecting the haptic rendering module, researchers should select which technology they will use in the visual rendering and visuo-haptic environment modules. As stated above, game engines are great tools for the development of high-end applications such as games and simulations. In the case of Unity, it is a cross-platform game engine developed by Unity Technologies. It can be used for 2D or 3D graphics applications, as well as interactive virtual environments. This game engine is recommended for its ease of use and scripting language, which facilitates the definition of the simulated elements' behaviors.  
Finally, _HaDUI_ Unity plugin is recommended as the haptic rendering module in the pipeline [9]. This plugin enables the proper connection between a haptic device and the Unity game engine. A challenge that researchers face during the development of visuo-haptic applications is the proper addition of haptic devices in the simulation (see Section 4.2.2). Developers should consider that for haptics devices to work properly, these devices need to run at a minimum rate of 1000 Hz to avoid anomalies in their performance. This plugin has been tested and used to develop electromagnetism simulators and it obtained proper and stable interactions between the user and the visuo-haptic virtual environment [6].
# A Novel IEEE 802.15.4e DSME MAC for Wireless Sensor Networks
## 5 Performance Analysis
### Energy  
In this section, the energy consumption, which is defined as the average energy consumption by a device to transmit one slot amount of data successfully is analyzed. We consider \(P_{RX}\) and \(P_{TX}\) are the energy consumption to receive and to transmit a packet, respectively. Sensor devices are assumed to be in sleep state during the backoff procedure by which they do not consume any energy. The energy consumption due to turnaround process from the sleeping state to the sensing one is assumed to be \(\frac{P_{RX}+P_{TX}}{2}\). Taking \(T_{L}\), \(T_{A}\), \(T_{CCA}\), \(T_{ta}\) and \(\delta_{max}\) as the time duration of transmitting a packet, receiving an acknowledgement, each successful CCA, turnaround time, maximum time to wait for the acknowledgement, respectively, the total energy consumption per device can be analyzed as follows.  
\[E=\frac{\alpha T_{CCA}P_{RX}+2(1-\alpha)\beta T_{CCA}P_{RX}+(1-\alpha)(1- \beta)\{(1-P_{S})E_{c}+P_{S}E_{s}\}}{(1-\alpha)(1-\beta)P_{S}T_{data}} \tag{35}\]  
where, \(E_{s}\) is the energy consumption of successful transmission, and \(E_{c}\) is energy consumption due to collision. \(E_{s}\) and \(E_{c}\) can be calculated as  
\[\begin{array}{rl}E_{s}&=2T_{CCA}P_{RX}+2T_{ta}\frac{P_{RX}+P_{TX}}{2}+T_{L} P_{TX}+T_{A}P_{RX}\\ E_{c}&=2T_{CCA}P_{RX}+2T_{ta}\frac{P_{RX}+P_{TX}}{2}+T_{L}P_{TX}+\delta_{max }P_{RX}\end{array}\Bigg{\}} \tag{36}\]
# Dynamic Response of Electro-Mechanical Properties of Cement-Based Piezoelectric Composites
## 3 Results and Discussion  
### Quasi-Static Compression Test  
The quasi-static sensitivity calibration of the type 1-3 and type 2-2 cement-based piezoelectric composite was performed on the MTS universal testing machine by using compression tests. The quasi-static sensitivity calibration results of the two composites are shown in Figure 4. The relevant parameters obtained by linear fitting are shown in Table 1.  
By combining the results of Figure 4 and Table 1, a nonlinear mutation point (point G) is observed for the quasi-static sensitivity of type 1-3 and type 2-2 cement-based piezoelectric composites: the stress value of type 1-3 and type 2-2 is approximately 21.7 and 35.0 MPa, respectively. When the applied load is lower than the critical value, a good linearity is observed between the output electric displacement and stress of the sample. When the applied load is higher than the critical value, a non-linear characteristic is observed between the output electrical displacement and stress of the sample. That is, as loading increases, the rising amplitude of the output electric displacement tends to decrease or even attenuate. This may be because loading reaches the critical value associated with the electric domain switching of the piezoelectric ceramic. Owing to the electric domain deflection, the piezoelectric performance of the sample tends to reduce. In the linear range, the quasi-static sensitivity of the type 1-3 and type 2-2 cement-based piezoelectric composites is 157 and 169 pC/N; the difference between the two is not obvious. However, the linear range associated with the quasi-static sensitivity of the type 2-2 is significantly larger (by  
Figure 3: Schematic diagram of the SHPB device.  
61.3%) than that of type 1-3. Overall, the piezoelectric sensing performance of type 2-2 is better than that of type 1-3.