esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparison of Downscaling Model Accuracy
By May, when the average temperatures rose to the point where vegetation was in the optimal growth conditions and overall vegetation growth was vigorous. Geographically, the central part of Shandong Province is mountainous, and the northeastern coastal region has complex topography. Higher altitudes have lower SIF values, because long-wave radiation becomes a heat source instead of direct sunlight and vegetation is less stimulated by light. As the terrain rises, the air is thinner, the temperature rises slowly, and the vegetation has a slower growth period relative to the plains. In the northeastern coastal region, the cooler temperatures \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **R\({}^{2}\)** & **RMSE** & **MAE** \\ \hline CNN & 0.882 & 0.083 & 0.055 \\ Stacking & 0.924 & 0.055 & 0.034 \\ RF & 0.931 & 0.052 & 0.031 \\ GBDT & 0.928 & 0.053 & 0.033 \\ ET & 0.895 & 0.065 & 0.041 \\ ADA & 0.832 & 0.088 & 0.066 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the accuracy evaluation metrics computed in each model for dataset 2. Figure 6: Density fitting map of RF model predictions from October 2019 to May 2020.  
and slower vegetation recovering are influenced by the nearby ocean. The experimental results show that the downscaled 1 km resolution SIF data more accurately reflects local fine-scale changes compared to the eSIF. From October 2019 to May 2020, the same methodology was applied to generate a 1 km resolution SIF product through monthly-scale SIF inversion for Shandong Province. Figures 9 and 10 present the inversion results, showing both the synthesized eSIF and the downscaled 1 km SIF. Compared to the previous year, the SIF trends are the same. By comparing the SIF data products before and after downscaling, the distribution of data is unchanged, and the downscaling model constructed based on the Random Forest (RF) algorithm can accurately invert the 1 km SIF, which can more accurately observe the  
Figure 8: Monthly 1 km SIF from October 2018 to May 2019. Figure 7: Monthly eSIF from October 2018 to May 2019.  
changes in vegetation cover and provide strong support for studying agriculture-related information through SIF changes.
# Computing the Atom Graph of a Graph and the Union Join Graph of a Hypergraph
## 3 Atom Graphs  
Atom graphs were used in [15] and were formally introduced in [16].  
**Definition 3** ([16]).: _The atom graph of a graph \(G\), denoted by \(AG(G)\), is the graph \((\mathcal{A}(G),E^{\prime})\), where \(\mathcal{A}(G)\) is the set of atoms and \(E^{\prime}\) the set of pairs \(\{A,B\}\) of \(\mathcal{A}(G)\), such that \(A\cap B\) is a clique minimal \((A\setminus B)(B\setminus A)\)-separator of \(G\)._  
**Example 2**.: _Figure 2 shows the atom graph of the graph \(G\) from Figure 1._  
In the definition of the atom graph, the word 'clique' can be removed by Property 2, and the word'minimal' can be removed by Property 5, which implies that, for each pairs \(\{A,B\}\) of \(\mathcal{A}(G)\), each one of \(A\setminus B\) and \(B\setminus A\) is a subset of a full component of \(A\cap B\).  
**Characterization 5**.: _Let \(A\) and \(B\) be distinct atoms of a graph \(G\). Then, \(AB\) is an edge of \(AG(G)\) if and only if \(A\cap B\) is an ab-separator of \(G\) for some \(a\in A\setminus B\) and some \(b\in B\setminus A\)._  
The following property immediately follows from Properties 1 and 3.  
Figure 2: The atom graph of \(G\). (the edge labels that are equal to 1 are omitted).  
**Property 9**: _The atom graph of a graph \(G\) is the atom graph of the chordal graph \(G^{+}\) (\(G^{+}\) is defined in Notation 1)._  
Characterizations 6 and 7 below give relationships between the atom graph and the atom trees. They are both proved for chordal graphs in [21] and also apply to any connected graph through the chordal graph \(G^{+}\) by Properties 1, 4, and 9.  
**Characterization 6**: _The atom graph of a connected graph \(G\) is the union of all the atom trees of \(G\)._  
**Characterization 7**: _The atom trees of a connected graph \(G\) are the maximum weight spanning trees of the weighted atom graph of \(G\)._  
To compute the edges of the atom graph from an atom tree, we will use the following characterization from [22] for chordal graphs, which also applies to any connected graph through the chordal graph \(G^{+}\) by Properties 1, 4, and 9.  
**Characterization 8**: _Let \(G\) be a connected graph, let \(A\) and \(B\) be distinct atoms of \(G\), and let \(T\) be an atom tree of \(G\). Then, \(AB\) is an edge of \(AG(G)\) if and only if there is an edge \(A^{\prime}B^{\prime}\) on the path \(P_{T}(A,B)\) between \(A\) and \(B\) in the tree \(T\), such that \(A\cap B=A^{\prime}\cap B^{\prime}\)._
## 2 Background  
### Wireless Network Data Transmission  
Wi-Fi is a wireless local area network (WLAN) technology that enables multiple devices to transmit data without using physical cables. Wi-Fi networks operate in half-duplex mode, meaning they cannot send and receive data simultaneously. To manage access to the network by multiple devices, Wi-Fi employs the DCF protocol. The core of DCF is the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol, which aims to ensure fairness and efficiency by preventing collisions and allocating transmission opportunities based on contention rules.  
When a device wants to send data, it first listens to the wireless channel to check if it is idle. If another device is transmitting, the device will back off and wait for a random period. This backoff period consists of random slots and a fixed duration, after which the device will attempt to transmit again. If the channel is detected as idle, the device sends its data frames. While the backoff mechanism reduces collisions, simultaneous transmissions and signal attenuation can still cause packet loss. To address this, the receiver sends an acknowledgment frame (ACK) upon successfully receiving a data frame. If the sender does not receive an ACK within a specific time, it will retransmit the data.  
To efficiently manage the wireless channel, DCF defines several time intervals. The Short Inter-Frame Space (SIFS) is the waiting time for high-priority control frames, the Distributed Inter-Frame Space (DIFS) is for data frames and management frames, and the Extended Inter-Frame Space (EIFS) is for error frames. These intervals help prioritize different types of transmissions, ensuring orderly and reliable data transmission within the network.
# Experimental and Numerical Study on UHPC-RC Decks within Hogging Moment Region
## 5 Comparison and Analysis of Results
### UHPC Layer Ratio of 0.6
#### 5.3.2 Load-Displacement Curve  
Figure 13 presents the load-displacement curve for the deck with a UHPC layer ratio of 0.6. For the FE analyses, as the load increases, the model keeps cracking, and the overall stiffness gradually decreases. When the two main cracks near the midspan are formed, the overall stiffness tends to be stable. When the load increases to 330 kN, the stiffness of the specimen decreases rapidly, and finally, failure occurs. The whole process of crack development is similar between the FE and experimental analyses.
# Handover Scheme in LEO Satellite Networks Based on QoE for Streaming Media Services
## 4 Performance Evaluation
### Simulation Results  
The performance of the streaming service in the LEO satellite network is shown in Figure 8a,b. Figure 8a shows the total amount of rebuffering per user at the end of the simulation. From the analysis in Section 3, it can be seen that rebuffering can have a very negative impact on the user's QoE, so in environments with high load pressure, fewer buffers mean a better user experience. As can be seen from Figure 8a, the QoE-based scheme proposed in this paper does not have a rebuffering event; the longest time scheme and load-balancing scheme both have more rebuffering times than the QoE-based scheme.  
Figure 8b displays the MOS score for each user at the end of the simulation, which is illustrated in Section 3 to be reflective of the user's QoE. As can be seen from Figure 8b, for only user number 17, the MOS for the QoE-based scheme is lower than the MOS for the longest time-based scheme, whereas for all other users, the QoE-based scheme has a higher MOS than the other two schemes. This means that the QoE-based scheme will perform better than the load-balancing-based scheme and the longest visible time-based scheme in terms of the QoE affected by the initial buffering and rebuffing of the user.  
From the above three sets of performance comparisons, it can be seen that the QoE-based scheme for streaming media services proposed in this paper performs better than the load-balancing-based scheme in traditional satellite communication. Not only does the QoE-based scheme outperform the other two traditional schemes in the playback stalling event, which affects the user experience the most, the QoE-based scheme also outperforms the other two traditional schemes on the MOS, which measures the overall QoE. This is because the handover and access scheme in traditional satellite communications does not take into account the characteristics of the user's service. For example, the streaming service has a buffer, which is the ability to resist channel instability and network congestion. Using the cache in the buffer to appropriately reduce the required power can reduce the load pressure in the satellite communication environment.
## 2 Mathematical Formulation  
The decision problem concerns the scheduling of tasks and the allocation of workers to the proper task in a multi-project environment in order to minimize the maximum completion time, i.e., the makespan. In this study, we consider the problem subject to the following assumptions:  
(1) Manual assembly processes are assumed to be carried out by a worker team. A mode represents a task-worker team with a constant duration. (2) During the execution of each task, the assigned mode cannot be changed, i.e., preemption is not allowed during the execution of each task. (3) The precedence relationships of each project forces each task to be scheduled after all precedence tasks. (4) The projects are independent of each other. (5) Each worker cannot be allocated to more than one task at the same time. (6) In the worker team of each task, workers start meanwhile and finish meanwhile until the task is completed. (7) The maximum number of workers in each task is constrained by the work-space. (8) The set-up time for each task is included in the task duration, and the transportation times of workers between the tasks are negligible. (9) Breakdowns are not considered. The notation used in this section can be summarized as follows:  
**Indices**:  
\(I\): Set of projects  
\(J_{i}\): Set of tasks for project \(i\in I\)  
\(Q\): Set of maximum number of tasks for each project that can be executed concurrently due to floor  
space constraints  
\(M_{ij}\): Set of task execution modes in task \(j\in J_{i}\), which correspond to the worker team  
\(K\): Set of hierarchical levels  
\(T\): Set of time periods  
\(W\): Set of workers
**Parameters**:  
\(r_{i}\): release date of project \(i\in I\), i.e., the earliest time that tasks of project \(i\in I\) can start  
\(d_{i}\): due date of project \(i\in I\)  
\(f_{i}\): end time of project \(i\in I\)  
\(pred(j)\): the predecessor set of tasks \(j\), i.e., \(pred(j)=\{j^{\prime}|j^{\prime}\prec j\}\)  
\(w_{ij\max}\): maximum number of workers in task \(j\in J_{i}\)  
\(w_{ij\min}\): minimum number of workers in task \(j\in J_{i}\)  
\(w_{k}\): number of type-\(k\) workers  
\(z_{mk}\): number of type-\(k\) workers in mode \(m\in M_{ij}\)  
\(d_{ijm}\): processing time of task \(j\in J_{i}\) in mode \(m\in M_{ij}\)  
\(Mur_{ij}\): processing time of task \(j\in J_{i}\)  
\(f_{ij}\): end time of task \(j\in J_{i}\)  
**Decision Variables**:  
\(S_{ij}\): start time of task \(j\in J_{i}\)  
\[x_{ijt}=\left\{ \begin{array}{ll}1,&\text{if task $j$ is performed at time $t\in(0,T]$}\\ 0,&\text{otherwise}\end{array} \right.\]  
\[y_{ijm}=\left\{ \begin{array}{ll}1,&\text{if task $j$ is executed in mode $m\in M_{ij}$}\\ 0,&\text{otherwise}\end{array} \right.\]  
In the multi-project environment, the delay of any project will lead to iterations and alterations of related follow-up work, so we assume that the objective is to minimize the completion time of all projects but not a certain project. We should consider the precedence constraints, release date constraint, due date constraint, work-space constraint, and floor space constraint.
## 7 Performance Evaluation and Results
### Performance Metrics  
Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R Squared, Adjusted R Squared, and Mean Absolute Error (MAE) are utilized as evaluation metrics to compare different prediction models. All the evaluation metrics are explained in Equations (2)-(6).  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(X_{i}-Z_{i})^{2}. \tag{2}\]  
where n represents a number of data points, \(X_{i}\) presents the observed values, and \(Z_{i}\) shows the predicted values. MSE measures the average of squared errors between the prediction results and actual values.  
\[RMSE=\sqrt{\frac{1}{n}\Sigma_{i=1}^{n}(X_{i}-Z_{i})^{2}} \tag{3}\]  
where \(Z_{i}\) are the observations, \(X_{i}\) are the predicted values of a variable, and n represents the number of observations. RMSE increases the error rate when the difference between the prediction results and actual values of data is bigger  
\[MAE=\frac{\sum_{i=1}^{n}|y_{i}-x_{i}|}{n} \tag{4}\]  
where \(y_{i}\) represents the predicted value, \(x_{i}\) presents the true value, and n is the total number of data points. The MAE illustrates the difference between the prediction results and the real data.  
\[R^{2}=1-\left[\sum_{i=1}^{n}\frac{(y_{i}-x_{i})^{2}}{(y_{i}-x_{i})^{2}}\right] \tag{5}\]  
R-squared or (\(R^{2}\)) is used to define the percentage of variance for the predicted value (Y) from the independent variable (X). The output values range from 0 to 1, which mean that they range from 0% to 100%.  
\[Adjusted\ R^{2}=1-\left(\frac{(1-R^{2})(N-1)}{N-p-1}\right) \tag{6}\]  
Adjusted \(R^{2}\) shows how a regression model is impacted by using useless variables. If the regression model used an unrelated variable, the output value will decrease. Where (N) represents the number of samples in a data set, and (P) is the number of features.
## 4 The Proposed Development Model: Holistic Security and Accessibility Layered Architecture (HSALA)
### Theoretical Validation of HSALA
#### 5.3.2 Compliance with Accessibility Standards  
In Table 10, we see how HSALA addresses key accessibility guidelines and legal requirements.  
HSALA's dedicated focus on accessibility ensures that applications not only meet technical standards but also comply with legal requirements, fostering an inclusive digital environment.
# Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model
## 3 SCADA Data Processing for for xLSTM
### Hyperparameter Tuning for xLSTM  
Hyperparameter optimization for the xLSTM architecture constitutes a critical determinant of model performance when applied to SCADA data analytics for wind power forecasting. The multifaceted architectural components of xLSTM, including sLSTM and mLSTM with their respective exponential gating mechanisms and matrix memory structures, necessitate systematic calibration of numerous interdependent parameters to achieve optimal predictive accuracy. This calibration process extends beyond conventional neural network parameterization to encompass specific considerations for xLSTM, e.g., head dimensionality, the number of heads per layer, block-specific projection factors, forget gate initialization strategies, and the optimal ratio of mLSTM to sLSTM blocks within the composite architecture. Given the temporal complexity and non-stationary characteristics inherent in wind turbine SCADA data, including diurnal variations, seasonal patterns, and stochastic atmospheric phenomena, a methodical hyperparameter optimization framework was implemented to maximize the xLSTM model's capacity to capture both short-term fluctuations and longer-term dependencies in the multidimensional parameter space of wind power generation.
# Survey on Security Threats in Agricultural IoT and Smart Farming
## 5 Conclusions  
The goal of this study was to conduct a thorough literature research on the benefits and the security threats imposed by the introduction of novel ICT technologies in the agricultural sector nowadays as well as on the associated mitigation measures and strategies. Research began with the advantages of ICT technologies and their importance in the agri-sector and then focused on the threats emerging alongside the evolution of smart agriculture or Agriculture 4.0. Also, research conducted in the context of this paper showed that, despite the rapid evolution in technology, stakeholders throughout the entire agricultural sector need to exercise caution on how they adopt and exploit new technologies and tools. The agri-sector tends to be even more vulnerable compared to other sectors that engage digital tools, so the mitigation of security threats is and will be an ever-open research subject.  
The new era in agriculture employs concepts and assets from Industry 4.0 that facilitate the transition to Agriculture 4.0 and the smart farming era. The increasing complexity of methods used for cultivation and production of agri-products leads to even more potential threats on various facets. By making the agriculture ecosystem smart and connected, multiple \"backdoors\" have opened with stakeholders and security specialists urged work together to block them entirely, in order to guarantee the safety and efficiency of all the new possibilities offered to farmers.  
To conclude, in order to characterize a new method or system as successful, it should be able to: (i) reduce costs; (ii) save time; (iii) increase trust; (iv) reduce risk. Stakeholders in agriculture should be willing to adopt new ways of working only when they are convinced that the newly proposed method or system is secure, safe and usable, increases productivity and brings added value. Taking the above into account, it becomes clear that the consolidation of new technologies in the sensitive sector of agriculture is an enormous challenge which should be carried out step by step, and only by efficiently engaging the directly affected stakeholders across the supply chain in security-preserving activities and investments.  
This research received no external funding.  
The authors declare no conflict of interest.
# A Time-Frequency Image Quality Evaluation Method Based on Improved LIME
## 3 Experiment and Analysis
### Experimental Result  
A sample is randomly selected, and its original vibration signal is shown in Figure 7. The time-frequency image generated by five time-frequency transform methods and then grayscale processing is shown in Figure 8. As can be seen from the figure, the STFT image shows that the four frequency bands are relatively complete and the sidebands of each channel are also relatively rich. The wavelet transform image has better performance for the middle two frequency bands, but the other two frequency bands are fuzzy. The Winger-Ville transfer image method is clearer, but the aliasing phenomenon is severe. The image of the signal decomposed by EMD ignores the lowest frequency component. The frequency band of the SST image is the clearest, but the corresponding loss of most of the sideband components is a major issue.  
The same network structure is trained with five input sets generated by five time-frequency methods, respectively. The design of network hyperparameters is shown in Table 2.  
Figure 8: Five time–frequency images of one sample.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Mini batch size & 64 \\ Epoch & 8 \\ Initial learning rate & 0.001 \\ Decay & 0.1 \\ Momentum & 0.9 \\ Parameter & Value \\ \hline \hline \end{tabular}
\end{table}
Table 2: The network hyperparameters.  
Figure 7: The original vibration signal of one sample. The acquisition time of the signal is 3 s.  
The improved LIME method is used to explain the trained network, and the interpretation results are shown in Figure 9. The Y-axis coordinate in the figure is the feature name, and the X-axis coordinate is the coefficient value of the trained model g. This value reflects how much the network depends on the corresponding feature when making a decision. The larger the value, the more attention the network pays to that feature when making a decision. It can be found that when the time-frequency images with rich texture features formed by STFT and wavelet transform are input into the network, the main basis of network classification is related to texture features. When the clearer time-frequency image formed by SST is used as the network input, the main basis of network classification is more inclined toward the irrelevant feature of image brightness.  
Figure 9: _Cont._  
The quality scores of the five time-frequency images are calculated, as shown in Table 3. It can be seen that the classification accuracy of the network is highest when the time-frequency images obtained by STFT and wavelet transform are input, but the consistency of the latter is weaker than that of the former. Although the accuracy of the Winger-Ville transfer is relatively low, the final quality score is higher than that of the wavelet transform because of the high consistency. The accuracy of EMD is much lower compared to other methods because the frequency component is ignored. The accuracy of SST is high, but the final quality score is very low because of the low consistency.
# Performance Analysis of the Multi-Stage Dew-Point Indirect Evaporative Air Cooler
## 3 Results and Discussion
The outdoor air parameters \(t_{1i}\) = 30 \({}^{\circ}\)C and RH\({}_{1i}\) = 45% are assumed to be representative for temperate European climate conditions [22]. Regarding these parameters, the temperature obtained by DPIEC 1, DPIEC 2, and DPIEC 3 is 20.8, 19.0, and 19.7 \({}^{\circ}\)C, respectively. Multi-stage configuration DPIEC 2 achieves the lowest temperature of outlet air for the parameters considered, while the highest cooling capacity is obtained for DPIEC 1. The lowest cooling capacity is reported for regenerative DPIEC 3 (6.2 kW). The proposed configuration of multi-stage cooler (\(W_{3}/W_{1}\) = 0.3) obtains lower outlet air temperatures and higher cooling capacities, as shown in Table 4. The differences in outlet temperatures between coolers are the highest for the low outlet temperature (5.6% lower than regenerative cooler) and cooling capacity (10.2% higher than regenerative cooler). The development of evaporative exchangers is directed at decreasing air outlet temperatures. The efficiency of the exchangers strongly depends on the inlet air parameters that are related to the system location. The average outdoor air parameters for the hot season were assumed for eight cities. The performance of two dew-point indirect evaporative coolers DPIEC 2 and DPIEC 3 was simulated for each set of outdoor air parameters. The simulation results are presented in Table 5.  
The lowest air temperature (15.6 \({}^{\circ}\)C) was achieved for Denver by the proposed variant of DPIEC 2. On the other hand, for the average parameters occurring in Izmir, the outlet air temperature equaled 25.9 \({}^{\circ}\)C. In general, the indirect evaporative cooling potential is higher in dry climates and lower in moderate and humid climates. For each city analyzed, the multi-stage unit (DPIEC 2) achieves lower supply air temperatures and higher dew-point effectiveness in comparison to DPIEC 3. The highest difference between effectiveness is observed for the humid (20.0 g/kg) Izmir air parameters and the lowest for the dry air (7.9 g/kg) parameters of Denver.