esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Method
### Spatial Weight Matrix  
A spatial weight matrix is a tool used in spatial analysis to represent the spatial relationships among different locations. It quantifies how much influence one location has on another, based on factors like distance, connectivity, or other relationships. This matrix is crucial for spatial econometrics, helping to understand and model spatial dependencies and interactions.  
The most commonly used spatial weight matrices in spatial econometrics are the contiguity-based (neighbour) matrices, the inverse distance, and the inverse distance raised to some power or exponential distance decay matrix ([PERSON] et al., 2023). The selection of spatial weight matrices is characterised by a great deal of arbitrariness, which causes a problem in inference. As the spatial weight matrix should also be theoretically grounded, this research produced the metrics based on the three methods to visually analyse and decide on the one that is most suitable. Each matrix is then row-normalised to ensure comparability.  
The contiguity-based (neighbour) method is effective in accounting for provinces in close proximity, emphasising immediate spatial relationships. The inverse distance method, on the other hand, ensures coverage of the entire nation, diminishing the influence of distance but considering all regions. Conversely, the inverse distance-decay method combines the advantages of both the neighbour and inverse distance approaches. While maintaining a focus on nearby provinces, this method also accounts for more distant provinces, thus providing a more comprehensive view of spatial relationships.  
The efficacy of the inverse distance-decay method, particularly in highlighting regional influences, is further corroborated by visual maps in the Appendix A (Figure A2). These maps display the weights of three sample provinces--Bangkok, Chiang Mai, and Phuket--and are shown in the Appendix A. The visualisation reveals that the inverse distance-decay weights are superior in illustrating regional influence, especially in cases like Bangkok, which is surrounded by many other provinces. Therefore, in our analysis, a distance-decay parameter, denoted as \(\alpha\) (alpha), was employed to emphasise regional influences over nationwide effects, setting \(\alpha\) to 0.01%. This value was chosen to balance the focus between local and broader geographical Impacts, aligning with the literature that regional factors are significant in the context of economic interdependencies and regional variation.
# Triple-Attention Mixed-Link Network for Single-Image Super-Resolution
## 4 Experiment
### Study in Attention Modules  
In this section, we study the attention modules we introduced in our network. We define the baseline model as a mixed-link network with 6 blocks. We then conduct an experiment on the effects of these attention modules. We first train three models which only contain one of the three attention modules. Then we train networks with two of the three modules. Finally, we use all these modules for training. There are 7 different combinations and the results for scale factor \(\times\) 2 on Set5, Set14, and BSD100 are shown in Table 3. The first line shows the PSNR score and the second line shows the improvement over the baseline. CA denotes the channel attention, KA means the kernel attention, SA means the spatial attention. There is an obvious increase of performance when adding these attention modules to the baseline network among the experiment results. When using one attention module, there will be at most 0.14 dB gain in Set14, 0.42 dB with two module, and 0.58 dB with all three attention modules.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module** & **Set5** & **Set14** & **BSD100** \\  & **Scale \(\times\) 2** & **Scale \(\times\) 2** & **Scale \(\times\) 2** \\ \hline Baseline & 37.81 & 33.40 & 32.05 \\ \hline CA & 37.89 & 33.45 & 32.08 \\  & (+0.08) & (+0.05) & (+0.03) \\ \hline KA & 37.95 & 33.54 & 32.14 \\  & (+0.14) & (+0.14) & (+0.09) \\ \hline SA & 37.91 & 33.49 & 32.12 \\  & (+0.10) & (+0.09) & (+0.07) \\ \hline CA + KA & 38.19 & 33.82 & 32.27 \\  & (+0.38) & (+0.42) & (+0.22) \\ \hline CA + SA & 38.15 & 33.54 & 32.17 \\  & (+0.34) & (+0.14) & (+0.12) \\ \hline KA + SA & 38.09 & 33.66 & 32.22 \\  & (+0.28) & (+0.26) & (+0.17) \\ \hline CA + KA + SA & 38.30 & 33.98 & 32.37 \\  & (+0.49) & (+0.58) & (+0.32) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of networks with different attention modules.  
Figure 9: Visual comparison of Mix, Dense and Residual network structure.
###### Abstract  
To identify the magnitude and direction of in situ stress in deeply buried tunnels, an inversion method for the stress field was proposed based on a finite number of measurement points of surface strain. Firstly, elastic strain data of finite points on the surface of tunnel surrounding rock were acquired using the borehole stress relief method at the engineering site. Secondly, a finite element model of the tunnel surrounding rock with plastic damage was established, and the parameters of the finite element model were substituted using the SIGINI subroutine. Then, an improved Surrogate Model Accelerated Random Search (SMARS) was developed using genetic algorithm programming on the MATLAB(tm) platform to invert and attain the globally optimal boundary conditions. Finally, the obtained optimal boundary conditions were applied to the numerical model to calculate the stress distribution in the engineering site. The reliability of this method was validated through a three-dimensional example. The method has been successfully applied to the stress-field analysis of deep tunnels in Macheng Iron Mine, Hebei Province, China. The research results show that this method is a low-cost, reliable approach for stress-field inversion in the rock around a tunnel.  
borehole stress relief method; genetic algorithm; SMARS; surface strain; surrounding rock stress field Article
## 3 Results and Discussion
### Effect of Tert-Butanol on Oxidation--A Putative Explanation  
_Tert_-butanol (TBA) is commonly used as a co-solvent in oxidation reactions due to its ability to stabilize reactive oxygen species and prevent undesired radical chain reactions. Herein, TBA appeared as a side product in TBHP-based oxidation reactions. The impact of _tert_-butanol can be analyzed based on the observed trends in conversion efficiency and oxide formation in the TBHP\({}_{\text{aq}}\) and TBHP\({}_{\text{dec}}\) systems [62].  
Reactions using TBHP exhibited different selectivity compared to H\({}_{2}\)O\({}_{2\text{aq}}\). It is well known that polyoxometalates (POMs) undergo partial decomposition or structural transformation in the presence of peroxides such as H\({}_{2}\)O\({}_{2}\). This process often leads to the formation of lower molecular weight oxo or peroxo species, which may exhibit enhanced catalytic activity due to the increased accessibility of the metal centers and the formation of more reactive intermediates. However, the presence of _tert_-butanol in these oxidation reactions can help to stabilize the POM structure under such oxidative conditions. _Tert_-butanol is known to moderate the reactivity of hydrogen peroxide and suppress radical-induced decomposition, thereby supporting the persistence of catalytically active peroxo species while minimizing the structural degradation of the POM framework. Hence, TBHP led to significantly higher oxide formation, particularly with vanadium-substituted catalysts [63]. It is commonly accepted that the reaction mechanism with some oxo-metals and with TBHP accepts peroxide coordination to metal-oxo species (Mo, W, V, Ti) with the formation of tBuOO-M, with such species being less destabilized by the presence of tBuOH. This is in  
Figure 8: \({}^{31}\)P and \({}^{13}\)C MAS NMR spectra of **MRImd@PMo\({}_{\text{11}}\)VO\({}_{\text{40}}\)** before (**a**,**b**) and after the three catalytic runs (**c**,**d**).  
contrast with H\({}_{2}\)O\({}_{2}\) which might evolve to an M-OOH species, transforming into M(O\({}_{2}\)) which is considered to be less reactive [53; 54; 55] (Scheme 4).
## 3 Research Methodology
### Performance Evaluation
#### 3.3.3 Cost Factor Calculation  
Based on the given information about loss of the energy, current energy and other parameters, the optimum cluster head was selected. The dissipation of energy arises because of the data transfer. We calculated total energy loss from the Flower Pollination Algorithm [38].  
Total Energy Loss = Energy Lost by All Cluster Members + Energy Lost by Sensor Nodes. These following energies and losses are calculated:  
**Cluster Member's Energy Loss [39]:**  
\[E_{c-member(i)}\sum_{j=1}^{N}\{Eamp\ x\ n\ x\ K\ x\ D(i,j)\ x\ E(tx)x\ K\}\]  
_Eamp x n x K x D \((i,j)\)_ (\(D\) is the distance of node from sink) \(x\ E(tx)\) are the total number of energies and \(E_{c-member(i)}\) is the cluster member's total energy loss.  
**Energy loss for SN [40]:**  
\[E_{Loss-SN(i)}=\sum_{j=1}^{N}\{(Etx-E(D-aggregation))Eampx\ n\ x\ K\ x\ D(i,sink)\ x\ E(rx)x\ K\}\]  
where \((Etx-E(D-aggregation))\) is the energy used in transmission and \(E(rx)\) is the energy used in reception  
* Sensor node energy \[\mathrm{E}(\mathrm{Total-curr(i)})=\mathrm{ERes(i)+E Harvest(i)}\]
* Required Transmission power \[TP=\frac{SNR}{\alpha}\] where \(SNR\) is signal to noise ratio.
# Reducing Forecast Errors of a Regional Climate Model Using Adaptive Filters
## 4 Proposed Framework for Climate Forecast Error Reduction
### Recursive Least Squares Adaptive Filters  
We employ the Recursive Least Squares (RLS) algorithm for the adaptive filters. The RLS algorithm aims to minimize the MSE (Mean Squared Error) at the filter output. It converges rapidly if the eigenvalues of the input signal correlation matrix are reasonably spread and present good performance when the input signal presents fast amplitude changes [24].  
Let \(\mathbf{R}_{D}(n)\) be the input signal deterministic autocorrelation matrix  
\[\mathbf{R}_{D}(n)=\sum_{m=0}^{n}\lambda^{n-m}\mathbf{x}(m)\mathbf{x}^{T}(m), \tag{8}\]  
and \(\mathbf{p}_{D}(n)\) be the deterministic cross-correlation between the input and the desired signal  
\[\mathbf{p}_{D}(n)=\sum_{m=0}^{n}\lambda^{n-m}\mathbf{x}(m)d(m) \tag{9}\]  
in that \(\mathbf{x}(m)=[x_{m}\,x_{m-1}\,\dots\,x_{m-N}]^{T}\) is the input information vector, \(d(m)\) is the reference signal sample, and \(\lambda\) is the so-called forgetting factor, i.e., an exponential weighting factor that should be chosen between 0 and 1.  
At the \(n\)-th iteration of the RLS, it produces the updated filter coefficients vector \(\mathbf{w}(n)=[w_{n}\,w_{n-1}\,\dots\,w_{n-N}]^{T}\) by means of  
\[\mathbf{w}(n)=\mathbf{R}_{D}^{-1}(n)\mathbf{p}_{D}(n), \tag{10}\]  
Although the computational cost of the matrix inversion \(\mathbf{R}_{D}^{-1}\) is too high, strategies to avoid it leading to viable algorithms exist; for more details, see [24].  
There is one adaptive filter for each cluster resulting from the K-means whose input is obtained from the RCM Eta forecast time series for the cells in the cluster. Meanwhile, the reference signal is composed of the NCEP reanalysis time series of the cells within the cluster.
###### Abstract
Then, the corresponding classification classes are set as one combined class, and the primary classification is performed. Then if the first classification result is the combined class, the second classification is performed among the confused classes using a separate neural network. The main idea is to enhance the accuracy by classifying the classes with high mutual confusion in the second step using a different classifier. There are two problems in the performance evaluation of the proposed method. One is that there is no terrain data set for testing, and the other problem is that there is only one existing method for comparison [22]. To overcome the problems, we set five types of topography classes reflecting the topographical characteristics of the Korean Peninsula and generat a total of 1000 data, 200 for each category based on the known information for the region of data. Using this data, we compare the classification performance through experiments applying various classification methods. In order to compensate for the fact that there is only one reference for performance comparison, we also conducted human classification from the DSM picture. As a result of the experiment, it is confirmed that the accuracy of the proposed two-step NN-based classification method increases by 20%p compared to the existing correlation method in [22], and there is a performance improvement of 11.2%p compared to the human classification from the DSM picture. The contributions of this paper are as follows:  
* We build a new topography dataset for five types of terrain that can be used for automatic topography classification problems. * Using ML, we propose a practical classifier structure that automatically classifies the terrain type of the analysis region. * A geospatial information processing method that can be used when classifying terrain is presented, and feature values with excellent performance are analyzed. * We propose a two-step NN-based classifier, which can significantly reduce the classification error. * In conclusion, we propose the first practical automatic terrain type classification method that can be used for the performance evaluation of new communication systems for airspace in a wide range of simulation regions reflecting the actual environment. The structure of the paper is as follows. Section 2 describes the A2G channel model according to the topography and the construction of a dataset for the automatic topographic classification performance test. Section 3 describes the feature extraction methods for applying ML techniques, and Section 4 describes the proposed ML-based automated channel model selection methods. Section 5 presents the simulation environment and results for comparing the classification performance of the proposed and existing methods, and Section 6 concludes the paper.
###### Abstract  
The application of the most recent advances of the Internet-of-Things (IoT) technology to the automation of buildings is emerging as a promising solution to achieve greater efficiencies in energy consumption, and to allow the realization of sustainable models. The application of IoT has been demonstrated as effective in many fields, such as confirmed, for instance, by the Industry 4.0 concepts, which are revolutionizing modern production chains. By following this approach, the use of distributed control architectures and of IoT technologies (both wired and wireless) would result in effective solutions for the management of smart environments composed of groups of buildings, such as campuses. In this case, heterogeneous IoT solutions are typically adopted to satisfy the requirements of the very diverse possible scenarios (e.g., indoor versus outdoor coverage, mobile versus fixed nodes, just to mention a few), making their large-scale integration cumbersome. To cope with this issue, this paper presents an IoT architecture able to transparently manage different communication protocols in smart environments, and investigates its possible application for the monitoring and control of distributed energy resources in a smart campus. In particular, a use-case focused on the integration of the Long Range Wide Area Network (LoRaWAN) technology is considered to cope with heterogeneous indoor and outdoor communication scenarios. The feasibility analysis of the proposed solution is carried out by computing the scalability limits of the approach, based on the proposed smart campus data model. The results of the study showed that the proposed solution would be able to manage more than 10,000 nodes. An experimental validation of the LoRaWAN technology confirms its suitability in terms of coverage and latency, with a minimum LoRaWAN cell coverage range of 250 m, and a communication latency of about 400 ms. Finally, the advantages of the proposed solution in the supervision and management of a PV system are highlighted in a real-world scenario.  
IoT; Energy Management System; Low Power Wide Area Network; LoRaWAN; Building and Home Automation; Smart Campus Data Model +
Footnote †: journal: Applied Sciences
## 2 Research Method
### Downtime Categorization and Categorized Unavailability Coefficient  
In this paper, the division of vehicle downtime caused by maintenance or repair for 6 categories was adopted, depending on the reasons for their unavailability. This division is used in practice in the examined enterprise. The following categories of downtime were taken into account.  
* NB--current repair;
* ON--awaiting repair by an external service (mainly for warranty repairs);
* BCZ--awaiting spare parts;
* NW--post-accident repair;
* WS--engine replacement;
* INN--downtime due to other reasons not mentioned above.  
In accordance with the adopted categorization of downtime, the concept of \"categorized unavailability\" of the vehicle was introduced. It was defined as the operational state of non-availability (standstill) corresponding to the downtime of a specific category. As an indicator of the quantitative assessment of the impact of a given category of downtime on the availability of buses, the \"categorized unavailability coefficient\" of the \(UT_{k}\), is defined by the following formula:  
\[UT_{k}=\frac{\sum_{i=1}^{n}UT_{ik}}{\sum_{i=1}^{n}UT_{i}}*100\% \tag{5}\]  
where \(UT_{ik}\)--the downtime of the tested sample of vehicles in the \(i\)-th observation interval due to the \(k\)-category downtime (Unavailability Time) expressed in vehicle-days.  
The \(UT_{k}\) coefficient was interpreted as an indicator of the frequency of a given downtime category in the total downtime period in the \(i\)-th interval.
## 5 Discussion and Future Research Directions
### Signal Processing and Data Preparation
#### 5.2.2 Wavelet Transform and Time-Frequency Analysis  
Wavelet-based approaches (e.g., DWT, CWT, and wavelet packet transform) have been widely adopted to capture transient or impact-type events, which are common in bearing faults, cavitation phenomena, and gearbox defects. Compared to FFT, wavelets are more effective for non-stationary signals, providing multi-resolution detail. However, parameters such as the choice of mother wavelet, the number of decomposition levels, and thresholding strategies significantly influence detection accuracy and computational load. These trade-offs are especially critical for real-time applications on edge devices, where memory and processing capabilities may be limited. Recent extensions such as variational mode decomposition (VMD) and complete ensemble EMD (CEEMDAN) aim to adaptively decompose signals into intrinsic mode functions under varying speed or load conditions, although their higher complexity and parameter tuning may reduce feasibility in online scenarios.
## 3 Processing Methods
### Structure of MDISE Model
#### 3.2.2 1D Inception Module  
To enhance the richness of feature information and fully extract and utilize effective data, the 1D Inception module is employed to further extract multi-scale deep features. The 1D Inception module proposed in this paper is shown in Figure 6b. A \(1\times 1\) convolution kernel is used to reduce the dimensionality of the input features, thereby decreasing the number of parameters. Additionally, two series-connected \(3\times 1\) convolutional layers are used to replace a \(5\times 1\) convolutional layer, which not only preserves the original receptive field but also further reduces the number of parameters. A BN layer is introduced after each convolutional layer. Furthermore, a ReLU activation function is added after the BN layer between every two series-connected convolutional layers. Similarly, the number of convolution kernels in each branch is set to 32, and the same padding strategy is applied to maintain consistency between the input and output feature dimensions. Finally, the number of channels for the output data is 128.
## 3 Survey of Techniques for Updating
### Dissemination
#### 3.1.5 Multihop Over-the-Air Programming (MOAP)  
MOAP [7] is a multi-hop, over-the-air code distribution mechanism specifically targeted at MICA2 motes running TinyOS. It uses store-and-forward, providing a _ripple_ pattern of updates; lost segments are identified by the receiver using a sliding window, and are re-requested using a unicast message to prevent duplication. A keep-alive timer is used to recover from unanswered unicast retransmission requests: when it expires a broadcast request is sent. The base-station broadcasts publish messages advertising the version number of the new code. Receiving nodes check this against their own version number, and can request the update with subscribe messages. A link-statistics mechanism is used to try to avoid unreliable links. After waiting a period to receive all subscriptions, the sender then starts the data transfer. Missing segments are requested directly from the sender, which prioritises these over further data transmissions. Once a node has received an entire image, it becomes a sender in turn. If a sender receives no subscribe messages, it transfers the new image to program memory from EEPROM, and reboots with the new code.  
MOAP provides dissemination and activation of updates, with some performance optimization, but no additional significant features to support autonomous updates.
## 2 System Model and Problem Formulation  
In this section, we first establish a system model and then develop an optimal time allocation problem and optimal power allocation problem for energy efficiency maximization of the wireless power communication networks, namely, FD dual-antenna energy harvesting and single-antenna energy harvesting.
## 1 Introduction
The contributions of this paper can be summarized as follows:  
* First, we take advantage of the unique features of VANET [19; 20], e.g., high dynamics, hybrid architecture, and vehicle-to-infrastructure (V-2-I) and vehicle-to-vehicle (V-2-V) communications, to propose our 3 VSR scheme. Specifically, the high dynamics ensure the real-time update of feedback. The hybrid architecture, i.e., vehicles, roadside units (RSUs), server, and trust authority (TA), enables the storage of feedback and the computation of trust scores. * A secure routing is proposed using sensing logic as trust model to enhance security of VANET, as efficient multi-hop trust assessment technique. This trust model is capable of assessing trust between multi path, arbitrary and bridge topologies. * In our 3 VSR, nodes perform trusted routing behavior mainly according to the trust relationship between them, a node that will behave malicious eventually denied from the network. Also, system performance is improved by means of 3 VSR as anonymous routing, that avoid verifying and requesting certificate exchange at each step. This also helps in minimizing computation and routing overhead. * In this article, we have proposed sensing logic-based trust model, that despite highlighting posterior uncertainty during trust propagation also capable of correctly assess arbitrary topologies. The rest of the paper sectioned as follows: In Section 2, we define the problem by formalizing the system model, adversary model and goals. In Section 3, we briefly highlight the AODV routing and Dirichlet distribution, which have been applied in trust model. In Section 4, we define the framework of the proposed scheme also with trust assumptions. In Section 5 trust model based on Sensing logic fundamentals are discussed in detail. In Section 6, trusted routing operation is revealed in detail. Experimental setup and results are explored in Sections 7 and 8. Finally, related work is highlighted in Section 9, with conclusions in Section 10.