esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Improved Mass-Conserved and Interface-Corrected DI Method
#### 2.1.2 Improved Cahn-Hilliard Model with Mass Conservation and Interface Correction
The derived equation can be written as:  
\[\int\limits_{\Omega_{L}}\frac{\partial C}{\partial t}dV+\int\limits_{S}(uC) \cdot dS=\int\limits_{\Omega_{L}}\Gamma\
abla^{2}\mu_{C}dV-\int\limits_{\Omega _{L}}q_{1}dV+\int\limits_{\Omega_{L}}q_{2}dV, \tag{12}\]  
Here, \(S\) represents the border of the zone \(\Omega_{L}\). The term \(\int\limits_{S}(uC)\cdot dS\) ought to be zero due to conservation of mass; i.e., there is no mass transfer across the interface:  
\[\int\limits_{S}(uC)\cdot dS=0, \tag{13}\]  
Furthermore, by using Equation (4), the following relationship can be found:  
\[\int\limits_{\Omega_{L}}\frac{\partial C}{\partial t}dV=\frac{1}{\Delta\rho} \cdot\frac{\partial}{\partial t}\int\limits_{\Omega_{L}}\rho dV=\frac{1}{ \Delta\rho}\cdot\frac{\partial M_{L}}{\partial t}, \tag{14}\]  
where \(M_{L}\) is the mass of the lighter fluid. It ought to be noticed that the mass correction term \(q_{2}\) is only applied in the interface layer. By using Equations (12)-(14), the term \(q_{2}\) can be written as:  
\[q_{2}= \begin{cases}\frac{1}{\Delta V}\left(\frac{1}{\Delta\rho}\frac{\partial M _{L}}{\partial t}-\int\limits_{\Omega_{L}}\Gamma\
abla^{2}\mu_{C}dV+\int \limits_{\Omega_{L}}q_{1}dV\right),&0<C<1\\ 0,&\text{elsewhere}\end{cases} , \tag{15}\]  
where \(\Delta V\) is the volume of the interfacial zone, as shown in Figure 1. It should be noted that the term \(\Gamma\
abla^{2}\mu_{C}=0\) is applied in areas away from the interface layer. In the present study, the source term \(q_{2}\) only works to reduce the errors of analyzing the interface layer if \(0.001\leq C\leq 0.999\), and it is finally written as:  
\[q_{2}= \begin{cases}\frac{1}{\Delta V}\cdot\left(\frac{\Delta M}{\Delta\rho \cdot\Delta t}-\sum\limits_{\Omega_{L}}\Gamma\
abla^{2}\mu_{C}\cdot dV+\sum \limits_{\Omega_{L}}q_{1}\cdot dV\right),&\text{if}(0.001\leq C\leq 0.999)\\ 0,&\text{elsewhere}\end{cases}  \tag{16}\]  
where \(\Delta M=M^{n}-M^{0}\), \(\Delta t\) is the time step during the calculation, \(M^{0}\) is the total mass of the lighter fluid for two-phase flows at \(t=t_{0}\), and \(M^{n}\) is the total mass of the lighter fluid at \(t=t_{n}\). \[M^{n}=\int\limits_{C<0.5}\rho dV=\rho_{L}V_{L}^{n}, \tag{17}\]So far, a modified Cahn-Hilliard model with mass conservation and interface correction has been constructed. In this paper, we use the second-order central difference scheme to discretize the term \(\Gamma\
abla^{2}\mu_{C}\), and the second-order Lax-Wendroff scheme to discretize the term \(\
abla\cdot(\mu C)\).
# Energy Efficiency for Green Internet of Things (IoT) Networks: A Survey +
## 3 Literature Review
### Reasons and Solutions for Energy Consumption  
As mentioned above, energy consumption, in general, is one of the most challenges and problems in IoT-based WSNs applications [64]. Sensor nodes are often operated by batteries and thus, these nodes can only run for a limited period of time. There are several reasons that could lead to exhaust the limited battery of the sensor nodes. However, several attempts have been made to minimize the energy usage of the smart objects and thus lengthening the lifetime of the network. Network lifetime is one of the most crucial metrics for the evaluation of sensor nodes but, in the literature, there are different definitions of network lifetime. Generally, it is realized as the length of time that sensor nodes would be fully operational. In other words, network lifetime is the time until the first node dies [68]. Another definition of network lifetime is the time until the first node or group of nodes in the sensing field exhausts runs its energy [69]. A node can only fulfill its mission as long as it is live, so losing a node would damage the network which would lose some of its functionalities. Hence, the main aim of any energy-efficient protocol is to keep the nodes alive for longer and thus prolong the network lifetime.  
Various sources of energy wastage and different solutions have been mentioned in the literature and are demonstrated in the following sub-sections.
## 2 Related Work
### Task Scheduling Strategy
#### 2.3.3 Strategic Optimization Objectives  
[PERSON] et al. [23] used the sum of latency cost and energy consumption of all mobile devices as the algorithm's optimization objective. This is because the offloading decision of mobile devices has an impact on subsequent offloading; thus, reinforcement learning's long-term objective optimization can be a good fit for task offloading. The authors built a reinforcement learning-based task offloading framework on this basis and applied two reinforcement learning algorithms. Experiments showed that an offloading strategy with long-term aims can reduce the device's overall energy consumption and average latency.
## 3 Results
### Technical Validation
#### 3.1.3 Data Fusion  
By learning separate representations for each image modality (capture method or sensor) \(A_{i}\), we can perform easy data integration by simply concatenating the representations \(\mathbf{f}_{i}\). In experiments Sections 3.1.1 and 3.1.2 we showed that both MSI and SAR are valuable sources of information for this task, and Figure 10 shows that by further combining them we get a significant improvement in overall accuracy: The combined solution outperforms MSI, which has the higher accuracy of the single-source capturing methods, on average by  
Figure 8: Accuracy for smaller parcels increases using posterior predictive LGPDE classification.  
Figure 9: Confidence of small (**a**) vs. large (**b**) ploughed fields being classified as _ploughed_ from a probabilistic perspective, with higher uncertainty for small fields, as expected.  
approximately 8 percentage points. We show the results on 1500 test parcels for the MLP classifier; the other classifiers followed a similar pattern.  
We also evaluated the final accuracy of the data fusion solution for even larger training data to provide a baseline with ample data. With 6500 training parcels per class we reached an accuracy of 82%, validating that the accuracy can be further improved by utilising more data, as expected. However, the improvement over the 78% accuracy obtained already with 160 parcels per class is only modest. On one hand, this implies that the method can be reliably estimated already from small data and does not require access to thousands of or tens of thousands of training instances. On the other hand, it suggests the problem itself is challenging; as shown in Figure 4 and discussed in the next section, some of the classes are highly similar in appearance, which sets natural upper bounds on classification accuracy.
## 3 Preliminaries  
### Cryptographic Assumptions  
Definition 1: (Decisional Bilinear Diffie-Hellman Problem (DBDH)).  
A multiplicative group of the same large prime order with bilinear maps (\(\hat{e}:\mathcal{G}\times\mathcal{G}\rightarrow\mathbb{G}\)) and an additive group of large prime order (\(q\)) are also considered. Let \(a,b,c,x\in Z_{q}^{*}\) and \(P\) be a generator in \(\mathcal{G}\). The following is the definition of the DBDH problem:  
The next step is to calculate \(\hat{e}(P,P)^{abc}\) consideringan input tuple \((P\in\mathcal{G},aP\in\mathcal{G},bP\in\mathcal{G},cP\in\mathcal{G})\). Ref. [7] defines the advantage (\(Dv\)) of a polynomial time (\(\Phi\)) in resolving the DBDH problem.  
\[Dv_{\Phi}^{DBDH}(\beta)=\begin{vmatrix}Pr[\Phi(\hat{e},P,aP,bP,cP,\hat{e}(P,P) ^{abc}=1)]\\ -Pr[\Phi(\hat{e},P,aP,bP,cP,\hat{e}(P,P)^{x}=1)]\end{vmatrix}\leq\mu \tag{1}\]  
DBDH AssumptionAssume that an adversary (\(\mathbb{A}\)) has an advantage of \(Dv_{\Phi}^{DBDH}(\beta)\leq\mu\), where \(\mu\) is a negligible number and is able to distinguish between \(\hat{e}(P,P)^{abc}\) and \(\hat{e}(P,P)^{x}\). In polynomial time, the DBDH problem is thought to be challenging to solve.  
Definition 2: \(((\mathbf{F},\mathbf{n})-\)Diffie--Hellman Additive \((\mathbf{F},\mathbf{n})-DEA))\). Using bilinear mappings (\(\hat{e}:\mathcal{G}\times\mathcal{G}\rightarrow\mathbb{G}\)), consider groups \(\mathcal{G}\) and \(\mathbb{G}\) of order \(q\). In \(\mathcal{G}\) and \(\delta\in Z_{q}^{*}\), let \(P\) be a generator. The \((\mathbf{F},\mathbf{n})-DEA\) problem is formulated as follows:  
A polynomial function \((\mathbf{F}(x)\in Z_{q}[x])\) of degree \(\mathbf{n^{\prime}}\) is given for \(\mathbf{n^{\prime}}>\mathbf{n}\) and \(P,\delta P,\delta^{2}P,\ldots,\delta^{n}P\), and the \((\mathbf{F},\mathbf{n})-DEA\) problem is used to determine \((\mathbf{F}(x),\mathbf{F}(\delta)P)\).  
\((\mathbf{F},\mathbf{n})-DEA\)_Assumption:_ Suppose that an adversary (\(\mathbb{A}\)) has a larger chance of solving the \((\mathbf{F},\mathbf{n})-DEA\) problem than \(\mu^{\prime}\), where \(\mu^{\prime}\) is an insignificant value. It is thought to be challenging to solve the \((\mathbf{F},\mathbf{n})-DEA\) issue in polynomial time.
## 5 Methodology  
To effectively use the model proposed in the conversation initiation stage, we devised a two-stage strategy, as shown in Figure 2. Stage 1 is the stage of classifying whether to maintain or change the topic through the model by analyzing the child's utterances. In the case of a topic change, another predetermined topic is returned by the conversation collection protocol. In case of topic maintenance, Stage 2 is executed. Stage 2 finds a segmentation point in the sentence and generates a questioning-like sentence. For example, if the utterance \"I want to quit.\" is input in the Stage 1 model, it is classified as a topic change and the next topic is returned based on the conversation collection protocol. If an utterance \"We make volcanoes and dinosaurs\" is input in the Stage 1 model, it is classified as topic maintenance and the utterance is transmitted to Stage 2. In Stage 2, it generates a questioning-like sentence by finding the segmentation point and returns the sentence \"Do you make dinosaurs?\".  
\begin{table}
\begin{tabular}{c c c c} \hline
\begin{tabular}{c} Conversation initiation \\ (topic maintenance) \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline
**Children Utterance** & **Expert Utterance** & **Label** \\ \hline gajog-ilang yeohaeng-eul gass-eoyo. & gajog-ilang \textless{}S\textgreater{}yehaeng-eul gass-eoyo. & Topic maintenance \\ \hline gajog-ilang jimingag gagagigo & gajog-ilang jimingag gagajigo \textless{}S\textgreater{} & Topic maintenance \\ lolleokoseuotee tass-eoyo. & lolleokoseuotee tass-eoyo. & Topic maintenance \\ \hline ne ollagassda naeyleogassda haneun & ne \textless{}S\textgreater{} ollagassda naeyleogassda haneun & Topic maintenance \\ geosdo tassgo & geosdo tassgo & Topic change \\ \hline deo eobs-eoyo. & - & Topic change \\ \hline geuman hallaeyo. & - & Topic change \\ \hline \end{tabular}
\end{table}
Table 2: Data sample with structured data for training the model.  
We compared machine learning approaches (LightGBM and SVM) and deep learning approaches to determine an appropriate analytical model when developing a topic maintenance or change model and a sentence generation model.
## 3 Materials and Methods
### The EduDCM Framework
#### 3.1.1 Knowledge Base Construction for Educational Dialogue Classification  
The knowledge base is fundamental to the EduDCM framework, serving as a structured reference for annotating unlabelled educational dialogues. It is constructed using the TalkMoves dataset, which defines three pedagogical categories, such as \"learning community\", \"rigorous thinking\", and \"content knowledge\". Each category is enriched with representative examples derived from annotated datasets and expert-curated dialogues, reflecting diverse classroom interaction styles and contexts. The knowledge base is further expanded to support multilingual adaptability by including equivalent utterances in different languages. These enhancements ensure that the knowledge base comprehensively represents the pedagogical landscape, enabling robust alignment and classification of unannotated dialogues.
# Generating Activity-Based Mobility Plans from Trip-Based Models and Mobility Surveys
###### Abstract  
Manifold applications in transportation system engineering rely on accurate modeling of human mobility demand. This demand is often represented by so-called mobility plans. Distinguished by their levels of aggregation, activity-based and trip-based models are the most prominent types of demand models in the literature. Macroscopic trip-based models are widely available but do not model mobility at the person level. In contrast, activity-based approaches simulate mobility microscopically but are complex and thus rarely available. The goal of this article is to present, apply, and validate an approach to generate activity-based mobility plans which microscopically reproduce real-world mobility demand but circumvent the complexity of activity-based approaches. To achieve this, existing trip-based models and mobility surveys are employed. Application results for car mobility in the city of Munich show that the obtained mobility plans are realistic on both a microscopic and a macroscopic level with regard to time, space, and activities. The presented approach can thus be considered appropriate for generating activity-based mobility plans whenever the development of a full-scale activity-based demand model is infeasible.  
mobility modeling; activity-based; trip-based; demand; mobility plans Article
# IAACaaS: IoT Application-Scoped Access Control as a Service +
## 6 Conclusions and Future Work  
AC is an essential topic when dealing with online services. However, when IoT devices are considered to be interacting with those services, some extra considerations have to be borne in mind, because not every AC approach is suitable for these constrained devices.  
In this paper, we have presented an architectural model that enables AC in IoT contexts by using the OAuth 2.0 protocol. On the one hand, OAuth 2.0 ensures that the model fits low-capable devices, because tokens provide all the required information to perform the authentication process, thus making it extremely light. On the other hand, OAuth 2.0 makes this model interoperable with other RESTful services on the rest of the Internet and also with any other client apart from IoT devices.  
Besides, our externalized approach enables an as a service authorization service that can be used by application developers as an additional layer to their applications in a seamless way. Moreover, the fact that the AC system is completely external to services facilitates the configuration of authorization policies, since they are all managed from a central, single component.  
Furthermore, this design has been implemented and deployed in the scope of FIWARE, which lets us conclude that the proposed model satisfies the special requirements of IoT devices in terms of performance, scalability and interoperability.  
As a future research line, some improvements could be considered. In cases when storing the OAuth 2.0 credentials in the IoT device constitutes an issue (e.g., access to the device could be compromised), an approach like using a different OAuth 2.0 grant could be considered. Moreover, the whole process could be made even lighter by supporting the CoAP protocol in the interactions in which the IoT devices take part. On the other hand, the performance of the proposed solution in large IoT deployments should be evaluated. Then, auto-scalable scenarios that adapt the available resources to the system demand can be also explored.  
The authors would like to thank the FIWARE European Project and its partners. Thanks to FIWARE Lab, we have achieved the deployment and testing of the proposed ideas in real scenarios.  
[PERSON] conceived of and designed the proposed model. [PERSON] implemented and tested the use case scenario. [PERSON] studied the related work, and [PERSON] reviewed the contribution.  
The authors declare no conflict of interest.
## 2 Study Area, Data and Methods
### A Trend and Correlation Analysis  
The Mann-Kendall (MK) trend test, as a non-parametric test, is suitable for handling non-normally distributed data and has been extensively used to examine long-term trends for meteorological, hydrological, and land surface vegetation parameters ([PERSON], 2016; [PERSON], 2016). The MK test does not require normality when processing time series data and is insensitive to outliers. With the MK trend test and a time series with \(n\) observed data ordered by time sequence (\(x_{1}\),..., \(x_{n}\)), the null hypothesis H0 states that the series does not show a monotonic trend, and the alternative hypothesis H1 indicates there is an obvious monotonic trend. For all \(k,j\leq n\) (\(k\
eq j\)), the key statistical variable \(S\) (Equation (4)) is tested.  
\[S=\sum_{k-1}^{n-1}\sum_{j-k+1}^{n}\text{sgn}(x_{j}-x_{k}) \tag{4}\]  
\(S>0\) indicates that the latter observation tends to be greater than the former one, and \(S<0\) denotes the opposite. The variance \(VAR(S)\) is calculated (Equation (5)), where \(g\) is the number of groups, and \(tp\) is the number of observed values in the \(p\)-th group.  
\[VAR(S)=\frac{1}{18}[n(n-1)(2n+5)-\sum_{p-1}^{S}t_{p}(t_{p}-1)(2t_{p}+5) \tag{5}\]  
Finally, the Mann-Kendall statistical value (\(m_{k}\) tau) is obtained, which is represented by \(Z_{mk}\) (Equation (5)). The Mann-Kendall tau coefficient ranges between \(-1\) and \(+1\). A positive value implies an upward temporal trend, whereas a negative value implies a decreasing trend.  
\[\begin{split} Z_{mk}=\left|\frac{S-1}{\sqrt{Var(s)}}\right|,S>0\\ Z_{mk}=0,S=0\\ Z_{mk}=\left|\frac{S+1}{\sqrt{Var(s)}}\right|,S<0\end{split} \tag{6}\]  
Correlations between the time series of plant asymmetric indices and climatic data are assessed using Spearman rank-order correlation coefficients, a nonparametric method used to evaluate the monotonic relationship between two continuous or ordinal variables. The concrete process for calculating Spearman correlation coefficients is further described elsewhere [38; 39].
## 4 Experimentation
### Results and Discussion  
This section describes and explains the experimentation of the proposed facial expression recognition system (FERS). The proposed FERS was implemented using Python 3.7.9 version, Tensorflow 2.3.1 version, Keras 2.4.3 version, CUDA version 11.2, and NVIDIA 460.79 Driver Version in Windows 10 Pro 64-bit, Intel(R) Core(TM)-i7-9700 CPU, 3.30 GHz(8 CPU) Processor, and in a 8 GB NVIDIA GeForce RTX 2070 SUPER XLA GPU device with 16 GB RAM. During experimentation, we employed both gray-scaled and RGB-colored images as some databases have RGB images while others only have gray-scaled images. During image preprocessing, from each input image \(\mathcal{I}\), we detected the face region by applying the methods discussed in Section 3.1. Furthermore, the detected face region \(\mathcal{F}\) was normalized to a fixed size image \(\mathcal{F}\in\mathbb{R}^{200\times 200}\). For recognizing the expression classes on the human face, in this work, we employed deep learning-based approaches where three convolutional neural network (CNN) architectures (Figures 4-6) were designed. These CNN architectures were trained in such a way that they would perform both feature computation and expression classification tasks. For better understanding the functionality of these architectures, at first, we started the experiment using \(CNN_{1}\) architecture (Figure 4), where the input to this system is an image \(\mathcal{F}\in\mathbb{R}^{n_{1}\times n_{1}}\), \(n_{1}=48\), i.e., the training \(\mathcal{F}_{48\times 48}\) samples were used to train \(CNN_{1}\) architecture while the performance of the trained \(CNN_{1}\) model was evaluated using the remaining testing samples. Learning the parameters in any CNN architecture is a very important task and depends on two factors, i.e., epochs and batches. Both these factors affect the learning capabilities of the architecture during the training of samples in the network. Thus, a trade-off between epochs and batches was established, which improved the performance of FERS using \(CNN_{1}\) architecture (Figure 4). The demonstration of the performance with the trade-off between epochs and batches is shown in Figure 11 and from this figure, it is observed that the performance gradually improved with increasing epochs (best performance at nearly 700 epochs) while keeping 16 batches fixed.  
Inspired by the experiment shown in Figure 11, another experiment was conducted using \(CNN_{1}\) architecture while keeping the fixed batch = 16 with varying epochs with respect to the KDEF, CK+, and SFEW database; the performance is shown in Figure 12. From this figure, we can observe that during epochs between 700 and 800, the performance for each database was good.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Database** & **Class** & **Training** & **Testing** \\ \hline KDEF & 7 & 1210 & 1213 \\ \hline GENKI & 2 & 2000 & 2000 \\ \hline CK+ & 6 & 663 & 146 \\ \hline SFEW & 7 & 346 & 354 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Presentation of the employed facial expression databases.
# Application of Generalized S-Transform in the Measurement of Dynamic Elastic Modulus
###### Abstract  
Resonance is commonly used for in situ measurement of the dynamic elastic modulus to evaluate the strength of concrete samples. Many researchers are also exploring the application of this convenient measurement technology for safety monitoring. Nevertheless, the presence of cracks and variations in curing conditions within samples can impact the resonance frequency range, potentially leading to potential inaccuracies in measurements. In order to improve the measurement accuracy of resonance frequency, this study introduces the Generalized S-Transform (GST) algorithm for measuring the dynamic elastic modulus, which utilizes its high time-frequency resolution to scan the power peak-point in non-stationary and transient excitation signals to determine the resonance frequency. For concrete materials with lower consistency, the experimental results verify the high accuracy of this algorithm in measuring resonance frequency compared with Fast Fourier Transform (FFT). This provides a reference for using the algorithm to measure the dynamic elastic modulus in civil engineering applications, such as buildings and bridges.  
dynamic elastic modulus; resonance frequency; generalized S-transform; time-frequency analysis; concrete +
Footnote †: journal: Applied Sciences
## 2 Experimental Setup
### Temperature Controlling System  
To measure the acoustic activity of melting ice, not only an AE measurement system but also a stable thermostatic setup is essential and this was achieved by an in-house-built device that monitors and controls the temperature in a closed volume. To reach a stable temperature in the range of \(-15\) to \(+45\)\({}^{\circ}\)C, two Peltier cooler modules (Adaptive APH-199-14-15-E) are driven by a controller of Meerstetter Engineering GmbH controller (Meerstetter TEC-1090-HV) and the accompanying software (TEC Service V 3.00).  
A \(10\,\mathrm{k}\Omega\) NTC temperature sensor measures the air temperature inside the box and provides the feedback for the software to adjust the cooling power of the Peltier elements. To guarantee an equally distributed temperature inside the box with an inner volume of ca. \(4\times 4\times 10\) cm\({}^{3}\), a ventilator is constantly circulating the air.  
The explained setup provides, depending on the experiment, a constant temperature or a linear temperature change until reaching a target temperature. This exactly enables the freezing and melting of a water under controlled environmental conditions.