esa-sceva/satcom-chunk-collection huggingface dataset:
# FAS-Res2 net: An Improved Res2 net-Based Script Identification Method for Natural Scenes
## 4 Experiments
### Dataset  
Table 2 summarizes the SIW-13 and CVSI-2015 datasets. The SIW-13 dataset contains 16,291 images, with 9791 training samples and 6500 testing samples. It includes 13 scripts: Arabic, Cambodian, Chinese, English, Greek, Hebrew, Japanese, Kannada, Korean, Mongolian, Russian, Thai, and Tibetan. Images were captured from natural scene regions and then corrected for horizontal orientation. Script images contain various font styles, complex backgrounds, and may be affected by different lighting conditions during capture. Furthermore, different script categories may share the same or similar subsets of characters, making this dataset very challenging.  
The ICDAR 2015 Video Script Identification Competition released the CVSI-2015 dataset, which includes English, Hindi, Bengali, Oriya, Gujarati, Punjabi, Kannada, Tamil, and ten script categories for Telugu and Arabic. The dataset contains 6412 scene text pictures in the training set, 1069 in the verification set, and 3207 in the test set. Each script class accounts for about 10% of the data, and the dataset is relatively balanced. Compared with the SIW-13 dataset, this dataset is characterized as tidier, more uniform, and clearer. However, due to the limited number of script categories and the small amount of data, deep learning models may encounter learning and convergence difficulties.
## 3 GAs for Multi-Objective Routing Optimization
### Selection, Crossover, and Mutation Operators  
The selection, crossover, and mutation operators are crucial components of the GA-based optimization framework for multi-objective message routing in EnFVs. These operators facilitate the exploration and exploitation of the solution space, leading the algorithm towards optimal or near-optimal solutions [29]. The selection operator is responsible for choosing the fittest individuals from the current population to create the next generation. Various strategies, such as tournament selection and rank-based selection, assign higher probabilities to individuals with better fitness values, promoting the propagation of superior solutions. The crossover operator combines genetic information from two parent individuals to create offspring, mimicking the process of sexual reproduction. Different crossover techniques, like one-point crossover, enable the effective recombination of genetic material and the generation of diverse solutions [7]. The mutation operator introduces random changes in the genetic material of individuals to maintain diversity and explore new regions of the solution space [30]. Commonly used approaches like bit-flip mutation allow the fine-tuning of solutions and prevent convergence to local optima. These operators work together to enable the GA to explore, maintain diversity, and converge towards optimal or near-optimal solutions in multi-objective message routing for EnFVs.
# Cross-View Attention Interaction Fusion Algorithm for Stereo Super-Resolution
## 4 Conclusions  
In this paper, we propose a stereo super-resolution algorithm for cross-view attention interaction fusion, including attention stereo fusion module and enhanced cross-view interaction strategy. The attention stereo fusion module is inserted between the left and right single image super-resolution network branches by using the enhanced cross-view interaction strategy, which combines the advantages of parallax attention and triple attention to improve the consistency of parallax information between stereo image pairs and the cross dimensional interaction between image information. At the same time, an enhanced cross-view interaction strategy is proposed, which includes horizontal dense connection, vertical sparse connection and feature fusion. The three connection methods make full use of the performance of single image super-resolution network branches and further strengthen the constraints between image pair stereo consistency. Experimental results show that the  
Figure 6: Super-resolution visualization comparison on the KITTI 2015 dataset.  
proposed algorithm effectively captures the corresponding relationship between stereo images and improves the ability of recovering details of stereo images. In addition, the proposed algorithm is based on existing pre-trained models, with a focus on considering the impact of different single image super-resolution network models on the reconstruction accuracy of the proposed algorithm. Therefore, in the following research, a model that does not rely on existing single image super-resolution network branches will be further designed to achieve an integrated super-resolution network model for stereo views.  
Conceptualization, Y.Z.; methodology, Y.Z.; software, Y.Z. and Z.Z.; validation, Y.Z., J.L. and T.Z.; formal analysis, Y.Z., J.L. and T.Z.; investigation, Y.Z. and J.L.; resources, T.Z.; data curation, Y.Z. and Z.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, J.L. and Z.Z.; visualization, Y.Z.; supervision, T.Z. and Z.Z.; project administration, T.Z. and Z.Z.; funding acquisition, T.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Research Projects of Anhui Educational Committee (grant No. 2022 AH050830), Scientific Research Foundation for High-level Talents of Anhui University of Science and Technology (grant No. 2021 jrcr37), the Institute of Energy, Hefei Comprehensive National Science Center (grant No. 21 KZS216) and Tianjin Municipal Education Commission Research Program (grant No. 2020 KJ118).  
Not applicable.  
The authors declare no conflict of interest.
# Power Control and Link Selection for Wireless Relay Networks with Hybrid Energy Sources
## 2 System Model
### Optimizing Objective  
Most previous studies on two-hop green wireless relay networks concentrated on minimizing the outage probability or transmission time with fixed bits at each slot [10; 21; 22]. However, when the network is busy and needs to provide services for multiple users, there will be a continuous stream of data bits to be transmitted. In this case, throughput can show the carrying capacity of the network, which is attractive to us. In addition, the source node powered by the grid energy can also be applied to some wireless networks. For example, macro base stations powered by the grid energy can maintain basic coverage in heterogeneous wireless networks. To take above aspects into consideration, we set grid energy consumption and throughput as optimization objectives. However, according to Equations (2) and (3), improving the throughput requires increasing the transmission power of the source node, which will lead to higher grid energy consumption. Therefore, increasing throughput and reducing grid energy consumption are conflicting objectives, which can be formulated as:  
\[obj=\left\{\begin{array}{c}\max\sum\limits_{i=1}^{N}I_{SD}^{i}R_{SD}^{i}+I_ {SRD}^{i}R_{RD}^{i}\\ \min\sum\limits_{i=1}^{N}I_{SD}^{i}Tp_{SD}^{i}+I_{SRD}^{i}\underline{T}p_{SR}^ {i}\end{array}\right.. \tag{8}\]  
There are many ways to solve multi-objective problems, and linear weighted summation is an effective method of them [23]. We use the weighted summation method to transform the conflicting multi-objectives into a single objective. Thus, the conflict objectives can be denoted as:  
\[TR=\sum\limits_{i=1}^{N}{[\omega_{t}(I_{SD}^{i}R_{SD}^{i}+I_{SRD}^{i}R_{RD}^{i })-\omega_{g}(I_{SD}^{i}Tp_{SD}^{i}+I_{SRD}^{i}\frac{T}{2}p_{SR}^{i})]}, \tag{9}\]  
where \(\omega_{t}\) and \(\omega_{g}\) are weight coefficients of throughput and grid energy consumption, respectively. \(\omega_{t}(I_{SD}^{i}R_{SD}^{i}+I_{SRD}^{i}R_{RD}^{i})\) and \(-\omega_{g}(I_{SD}^{i}Tp_{SD}^{i}+I_{SRD}^{i}\frac{T}{2}p_{SR}^{i})\) are the weighted throughput reward and grid energy reward in the \(i\)-th slot.
# Evolution of Permeability and Sensitivity Analysis of Gas-Bearing Coal under Cyclic Dynamic Loading
## 4 Results and Discussion
### The Influence of the Cyclic Loading Amplitude on the Permeability  
The variation in coal permeability with the amplitude of cyclic dynamic loading is illustrated in Figure 4. It is evident from the figure that the permeability of coal is positively correlated with the amplitude of cyclic dynamic loads. At amplitudes of 2.5 MPa, 3.75 MPa, 5 MPa, and 6.25 MPa, the permeabilities are recorded to be 0.0144 um\({}^{2}\), 0.0154 um\({}^{2}\), 0.017 um\({}^{2}\), and 0.0193 um\({}^{2}\), respectively, with growth rates of 7.2%, 14.3%, 26.1%, and 43%. It is evident that the amplitude of dynamic loading exerts a more pronounced effect on the increase in permeability. The reasons for the increase in permeability are fundamentally consistent with those for different frequencies. The sharp 43% increase in permeability at an amplitude of 6.25 MPa is due to the fact that, at this stage, the cyclic dynamic loading is in the yield phase of the coal body stress-strain curve. Consequently, the degree of damage is greater, and the development of fractures is more pronounced. For coal seams from which it is difficult to extract gas, one method employed is to enhance the amplitude of vibrational loading.  
Figure 4: Variation in permeability with cyclic dynamic load amplitude.  
Figure 3: Variation law of permeability with cyclic dynamic load frequency.
# An Application of Elastic-Net Regularized Linear Inverse Problem in Seismic Data Inversion
###### Abstract  
In exploration geophysics, seismic impedance is a physical characteristic parameter of underground formations. It can mark rock characteristics and help stratigraphic analysis. Hence, seismic data inversion for impedance is a key technology in oil and gas reservoir prediction. To invert impedance from seismic data, one can perform reflectivity series inversion first. Then, under a simple exponential integration transformation, the inverted reflectivity series can give the final inverted impedance. The quality of the inverted reflectivity series directly affects the quality of impedance. Sparse-spike inversion is the most common method to obtain reflectivity series with high resolution. It adopts a sparse regularization to impose sparsity on the inverted reflectivity series. However, the high resolution of sparse-spike-like reflectivity series is obtained at the cost of sacrificing small reflectivity. This is the inherent problem of sparse regularization. In fact, the reflectivity series from the actual impedance well log is not strictly sparse. It contains not only the sparse major large reflectivity, but also small reflectivity between major reflectivity. That is to say, the large reflectivity is sparse, but the small reflectivity is dense. To combat this issue, we adopt elastic-net regularization to replace sparse regularization in seismic impedance inversion. The elastic net is a hybrid regularization that combines sparse regularization and dense regularization. The proposed inversion method was performed on a synthetic seismic trace, which is created from an actual well log. Then, a real seismic data profile was used to test the practice application. The inversion results showed that it provides an effective new alternative method to invert impedance.  
Keywords:seismic inversion; impedance; elastic-net regularization; reservoir prediction +
Footnote †: journal: Applied Sciences
# Optical Study on Temperature-Dependent Absorption Edge of \(\gamma\)-InSe-Layered Semiconductor
## 2 Experimental Techniques  
For the growth of single-crystalline InSe, high-purity 99.999% In and Se compounds were purchased from Sigma Aldrich. The stoichiometry mixture (1:1 of In and Se) was mixed in a quartz ampoule and evacuated to 10\({}^{-4}\) Pa. The homogenization of the InSe powders was conducted in a horizontal furnace at 550 \({}^{\circ}\)C for two days. The homogenized InSe powders were then transferred into a vertical Bridgman instrument for the growth of the high-quality InSe single crystal. During the Bridgman process, the ampoules containing the InSe mixture were melted in a high-temperature region at 850 \({}^{\circ}\)C for a duration of 24 h, and after that, the ampoule was slowly lowered to a low-temperature zone (1 \({}^{\circ}\)C at a rate of 0.1 mm/h). The transitions from higher to lower temperatures facilitated the nucleation of large-size InSe single crystals with typical dimensions of 5 cm in length and 1 cm in diameter. The InSe devices were fabricated on a SiO\({}_{2}\) (300 nm)/n\({}^{+}\)-Si substrate. The electrical contacts on the InSe bulk crystals were provided by silver (Ag) paste. The prepared InSe sample was stored in an evacuated container to avoid surface oxidation effects, and the sample was also placed in the vacuum chamber during the optical experiment.  
The X-ray diffraction (XRD) analysis of InSe crystals was performed using a Bruker D2 Phaser diffractometer (Bremen, Germany) with Cu-K\(\alpha\) radiation under Bragg-Brentano geometry. The Raman scattering experiment was performed using a Micro-Raman spectrometer (HORIBA-iHR550, Kyoto, Japan). In the PC measurement, a monochromatic light source was provided by the Oriel 1000 W (Alameda, CA, USA) halogen light source and the normalized PC signals were collected by a lock-in amplifier and recorded by a computer. The PC measurement was set up to obtain the associated \(\alpha\), which may be difficult to obtain using the conventional techniques, especially in the low-energy region below the band edge [11, 12]. It should be noted that in order to obtain a good description of \(\alpha\) through PC measurement, some well-defined conditions, as stipulated by [PERSON] et al., should be satisfied. The conditions of applicability can be facilitated by a proper normalization process [12] of the PC signals. The match had been shown to be quite exact at the near and above band edge regions. Below the band edge in the low energy region, the obtained \(\alpha\) derived from PC measurement may be regarded as a lower bound to \(\alpha\). The uncertainty is most likely due to changes in the recombination process of the photoexcited carriers at low energies. Nevertheless, the presently studied energy range of \(\sim\)1.0 to 1.4 eV (the below-band edge range is \(\sim\)1.0 to 1.2 eV) falls within a small photon energy range so that a fairly good estimate of \(\alpha\) can be provided by the properly normalized PC data.
# Characterizing Ionospheric Effects on GNSS Reflectometry at Grazing Angles from Space
###### Abstract
The structure of this paper is outlined as follows: Section 2 presents the GNSS-R data descriptions, reflection events, and ray point settings for the simulations. Section 3 illustrates the methodologies utilized for the determination of parameters such as sTEC, relative ionospheric delay, Doppler shift, and ionospheric piercing points. Subsequently, Section 4 presents the results and analysis of the parameters explained in Section 3. Finally, in Section 5, a discussion of the findings is presented along with the conclusions in Section 6.
## 3 Results and Discussion  
### Experimental Validation  
To ensure the reliability of the numerical simulation outcomes, efficiency calculations at varying flow rates were carried out and subsequently compared against experimental data. Figure 4 illustrates the experimental setup, which comprises pipes, a ventilation fan, control systems, data acquisition systems, and a venturi tube. Pressure measurements were taken using an intelligent pressure transmitter EJA110E-DMS5J-912 EA (Yokogawa Sichuan Instrument Co., Ltd., Sichuan, China); the testing accuracy reaches \(\pm 0.055\%\), the response time is 90 ms, and the maximum working pressure is 16 MPa), while the flow rate was calculated by measuring the pressure difference across both sides of the venturi tube. Shaft power was derived from the motor's input power and its efficiency. All instruments underwent rigorous calibration to guarantee the precision of the experimental findings. During the experiments, the atmospheric pressure and temperature were 100,460 Pa and 30.1 \({}^{\circ}\)C, respectively.  
Figure 4: Experimental platform.  
Figure 5 illustrates a comparison of the fan efficiency between numerical simulations and experimental results. A remarkable agreement in the trend of efficiency variation is evident between the two approaches. The efficiency's relative error remains 5% at 1.0 \(Q_{\text{BEP}}\). As the flow rate increases, while the relative error also rises, it is essentially maintained within a 10% margin. Thus, the results of numerical simulation are reliable and accurate.
## 3 Results and Discussion
### Influence of NO\({}_{x}\) and RH on SOA (Experiments: Tol-1 to Tol-4)
#### 3.2.3 Influence of NOx and RH on Light Scattering
Generally, the SSA values decreased as [NO\({}_{\rm X}\)]/[AHC] increased for both NP and NR SOA (see Figure S7 in Supplementary Materials) at constant RH. However, in the NR case there was a clear change in the SSA trend at an [NO\({}_{\rm x}\)]/[AHC] ratio of 0.15. Like the MAC, the SSA was insensitive to RH in the NP case but not in the NR case, for which the SSA was lowest at 20% RH (see Figure S8 in the Supplementary Materials). This indicates that the relative contribution of absorption to the total light extinction by SOA was enhanced under drier conditions in the NR regime. These results suggest that water mediated toluene-NO\({}_{\rm x}\)-OH chemistry increases the scattering potential of toluene SOA in the NR regime by substantially changing its composition, presumably by promoting the formation of more or stronger chromophores.
# Time Slotted Channel Hopping and ContikiMAC for IPv6 Multicast-Enabled Wireless Sensor Networks
## 3 Background
### TSCH and the Orchestra Scheduler
The configuration that is activated by default for Orchestra consists of a sender-based slotframe for enhanced beacon (EB) transmissions, a receiver-based slotframe for LL unicast transmissions, and a common-shared slotframe for any other traffic, mostly LL broadcast. Some of the problems experienced with ContikiMAC are due to its single channel communication. Multichannel communication might offer a solution, and its potential can be exploited in wireless networks for simultaneous transmission on orthogonal channels. Multiple channel communication can allow for a more efficient use of the available bandwidth. Another mechanism employed by TSCH is channel hopping, which ensures that a timeslot switches frequency in a next slotframe. Channel hopping can reduce the influence of external interference [17].
###### Abstract  
Reducing energy consumption in a MEC (Multi-Access Edge Computing) system is a critical goal, both for lowering operational expenses and promoting environmental sustainability. In this paper, we focus on the problem of managing the sleep state of MEC servers (MECSs) to decrease the overall energy consumption of a MEC system while providing users acceptable service delays. The proposed method achieves this objective through dynamic orchestration of MECS activation states based on systematic analysis of workload distribution patterns. To facilitate this optimization, we formulate the MECS sleep control mechanism as a constrained combinatorial optimization problem. To resolve the formulated problem, we take a deep-learning approach. We develop a task arrival rate predictor using a spatio-temporal graph convolution network (STGCN). We then integrate this predicted information with the queue length distribution to form the input state for our deep reinforcement learning (DRL) agent. To verify the effectiveness of our proposed framework, we conduct comprehensive simulation studies incorporating real-world operational datasets, with comparative evaluation against established metaheuristic optimization techniques. The results indicate that our method demonstrates robust performance in MECS state optimization, maintaining operational efficiency despite prediction uncertainties. Accordingly, the proposed approach yields substantial improvements in system performance metrics, including enhanced energy utilization efficiency, decreased service delay violation rate, and reduced computational latency in operational state determination.  
multi-access edge system; MEC server status decision; workload prediction; STGCN; DQN +
Footnote †: journal:  
0
Footnote 0: _Future Internet_**2024**, _16_, 441. [[https://doi.org/10.3390/fi16210441](https://doi.org/10.3390/fi16210441)]([https://doi.org/10.3390/fi16210441](https://doi.org/10.3390/fi16210441))
# Ambiguity-Resolved Model Tests for Carrier-Phase GNSS
## 2 The Mixed-Integer GNSS Model
### The Mixed-Integer Model Test  
We now introduce our mixed-integer model (MIM) test for testing the hypotheses of (1). Its statistic is designed to measure the \(\mathcal{H}_{0}\)-\(\mathcal{H}_{a}\)_change_ in model fit, while at the same time doing justice to the integer estimation of the ambiguities under both hypotheses.  
**Definition 1** (**Mixed-Integer Model (MIM) Test**).: _Let the change in \(\mathcal{H}_{0}\)-\(\mathcal{H}_{a}\) model fit be captured by the statistic_  
\[\underline{\mathbb{I}}(a_{0},a)=||\hat{\varrho}_{0}(a_{0})||_{Q_{yy}}^{2}-|| \hat{\varrho}_{a}(a)||_{Q_{yy}}^{2} \tag{4}\]  
_and let \(\underline{\hat{\varrho}}_{0}\) and \(\underline{\hat{\varrho}}\) be admissible integer estimators of \(a\in\mathbb{Z}^{n}\) under \(\mathcal{H}_{0}\) and \(\mathcal{H}_{a}\) (cf. (1)), respectively. Then, the \(\alpha\)-level MIM test for rejecting \(\mathcal{H}_{0}\) in favor of \(\mathcal{H}_{a}\) is defined as_  
\[T(\underline{\hat{\varrho}}_{0},\underline{\hat{\varrho}})>\tau_{\alpha} \tag{5}\]  
_with the critical value \(\tau_{\alpha}\) satisfying \(\mathsf{P}[\underline{\mathbb{I}}(\underline{\hat{\varrho}}_{0},\underline{ \hat{\varrho}})>\tau_{\alpha}\ |\ \mathcal{H}_{0}]=\alpha\)._  
Note that this MIM test requires the integer estimation of the ambiguities under both hypotheses, \(\mathcal{H}_{0}\) and \(\mathcal{H}_{a}\). However, the user still has the freedom in choosing which member from the class of admissible integer estimators to select. An integer estimator \(\underline{\hat{a}}=\mathcal{I}(\underline{\hat{a}})\), with \(\mathcal{I}:\mathbb{R}^{n}\mapsto\mathbb{Z}^{n}\), is said to be admissible when its _pull-in regions_\(\mathcal{P}_{z}=\{x\in\mathbb{R}^{n}\mid\mathcal{I}(x)=z\}\), \(z\in\mathbb{Z}^{n}\), cover \(\mathbb{R}^{n}\), while being disjoint and integer translational invariant [17]. Popular choices for \(\mathcal{I}(.)\) are integer rounding (IR), integer bootstrapping (IB), integer least-squares (ILS), or vectorial combinations (VIB) of them. The above MIM test becomes equivalent to the LR test if both \(\hat{a}_{0}\) and \(\hat{a}\) are taken as the ILS solutions. ILS is the most complex, but has the advantage of providing the largest possible ambiguity success rate, i.e., the largest probability of correct integer estimation. IR and IB are both simple to execute, whereby IB, especially after ambiguity decorrelation, often obtains a close-to-optimal ambiguity success rate. The freedom in choice of integer estimator implies that the integer estimator choice for \(\mathcal{H}_{0}\) need not be the same as the one for \(\mathcal{H}_{a}\). For example, the ILS principle may be chosen to obtain \(\underline{\hat{a}}\) from \(\underline{\hat{a}}\), while the IB principle is chosen to obtain \(\underline{\hat{a}}_{0}\) from \(\underline{\hat{a}}_{0}\). Therefore, such choices should be taken in dependence of the required success rates.