esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
Although the above work also has high performance in identifying ocean internal waves, it either requires a large number of high-quality datasets as data support, or the network model has room for improvement. However, the challenges associated with image cropping, annotation, and the acquisition of rare remote sensing images can present substantial obstacles for researchers when striving to compile extensive remote sensing image datasets during the data collection phase. Therefore, there is a special need in the field for a method that can greatly increase the data volume of remote sensing datasets, thereby effectively reducing the cost of annotation, and at the same time have a very high recognition rate of ocean internal waves. This paper conducts classification on full-space images acquired through the Moderate Resolution Imaging Spectroradiometer (MODIS), encompassing four distinct categories: ocean scenes, clouds, terrestrial landscapes, and ocean waves. To address the challenge of limited dataset availability, the author employs an enhanced deep convolutional generative adversarial network (DCGAN) to substantially augment the data within each category sample. Additionally, a novel residual network is designed, taking into consideration the channel information of deep features, termed \"WaveNet,\" to enable automated detection of internal ocean waves in MODIS images through an end-to-end approach.
## 3 Strain Gauges and Distributed Optic Fiber Strain Measurements
### 3.2. Berea--Increasing Pore Pressure  
#### 3.2.1. Strain Gauge Readings  
Figure 9 presents the strain gauge readings for the four working channels when the pore pressure is increased to 10 MPa. As with pore pressure increase above, the vertical strains (Ch2 and Ch8) are markedly greater than the hoop strains (Ch5 and Ch7). For example, the hoop strain measured by gauge Ch8 reaches \(\sim\)760 \(\mu\epsilon\), compared to \(\sim\)470 \(\mu\epsilon\) by Ch7, when the pore pressure is increased to 10 MPa. Discrepancy between the channels orientated in the same direction is also noted. It is further observed that the strain readings increased positively (less compressive) with the pore pressure in a largely piecewise manner, indicating successive changes (reduction) in the sandstone bulk modulus when the pore pressure is increased beyond 4-6 MPa.  
Figure 6: The mean value of Rayleigh frequency shift measurements at the 11 censoring points with increasing confining pressure, in comparison with the baseline value (sensor region B), for Berea core.  
Figure 8: The mean value of Brillouin frequency shift measurements at the 11 censoring points with increasing confining pressure; in comparison with the baseline value (sensor region B) for Berea core.  
Figure 7: Berea core Brillouin frequency measurements with increasing confining pressures. The locations of a few selected monitoring points are marked.
# Context-Based Dynamic Meshed Backhaul Construction for 5G Heterogeneous Networks
## 4 Conclusions  
This paper proposed a novel method to control mmWave meshed backhaul for efficient operation of mmWave overlay 5 G HetNet through SDN technology. Our algorithm was featured by two functionalities, i.e., backhauling route multiplexing for overloaded mmWave SC-BSs and dynamic deactivation of SC-BSs for underloaded spot. The effectiveness of the proposed meshed network was confirmed by both numerical analyses and experimental results. Numerical results showed that the proposed algorithm can cope with the locally intensive traffic and reduce energy consumption. Especially, we were first to establish a WiGig device based testbed to demonstrate the proposed method. Preliminary measurement results confirmed the proposed dynamic formation of the meshed network's efficiency.  
Conceptualization, G.K.T. and K.S.; Methodology, H.O. and R.S.; Validation, H.O. and R.S.; Investigation, H.O., R.S. and G.K.T.; Data Curation, M.N.; Writing-Original Draft Preparation, G.K.T.; Writing-Review & Editing, G.K.T.; Visualization, H.O., R.S. and G.K.T.; Supervision, K.S. and A.K.; Project Administration, [PERSON] and [PERSON]; Funding Acquisition, K.S. and A.K.  
This research was funded by the European Commission (EC) H2020 and the Ministry of Internal affairs and Communications (MIC) in Japan under grant agreements No. 723171 5G-MiEdge in EC and {0159-0077, 0155-0074} in MIC. Parts of this work have been also funded by the Scandinavia-Japan Sasakawa Foundation and the Knowledge Foundation of Sweden through the project SOCRA.  
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.  
The authors declare no conflict of interest.
# Bi-Objective Power Optimization of Radio Stripe Uplink Communications
## 2 Radio Stripes Network Model
### Channel State Information Estimation Phase  
In this phase, the set of mutually orthogonal pilot signals \(\{\mathbf{\Phi}_{t}:\mathbf{\Phi}_{t}\in\mathbb{C}^{\tau_{p}}\wedge t=1, \cdots,\tau_{p}\}\) with power \(||\mathbf{\Phi}_{t}||^{2}=\tau_{p}\) is assigned to each UE and transmitted to the APs to estimate the transmission channel [9; 11]. Assuming that \(t_{k}\) denotes the index of the pilot assigned to UE \(k\) and \(\mathcal{P}_{k}\subset\{1,\cdots,K\}\) is the subset of UE that shares \(t_{k}\), the MMSE channel estimator is [2]  
\[\hat{\mathbf{h}}_{kl}=\sqrt{\tilde{p}_{k}\tau_{p}}\mathbf{R}_{kl}\mathbf{ \Gamma}_{t_{kl}}^{-1}\mathbf{z}_{t_{kl}}, \tag{1}\]  
where \(\tilde{p}_{k}\geq 0\) is the power of the pilot signal allocated to the \(k\)th UE, \(\mathbf{z}_{t_{kl}}\) is the pilot signal received at AP \(l\) after being projected into \(t_{k}\), i.e.  
\[\mathbf{z}_{t_{kl}}=\sqrt{\tilde{p}_{k}\tau_{p}}\mathbf{h}_{kl}+\sum_{i\in \tilde{p}_{k}i\
eq k}\sqrt{\tilde{p}_{i}\tau_{p}}\mathbf{h}_{il}+\mathbf{n}_{ t_{kl}}. \tag{2}\]  
and  
\[\mathbf{\Gamma}_{t_{kl}}=\sum_{i\in\tilde{P}_{k}}\tilde{p}_{i}\tau_{p}\mathbf{ R}_{il}+\mathbf{I}_{N}, \tag{3}\]  
while \(\mathbf{n}_{t_{kl}}\) is the resulting noise. It is worth mentioning that the CSI estimate \(\hat{\mathbf{h}}_{kl}\sim\mathcal{N}_{\mathbb{C}}\left(0,\hat{\mathbf{R}}_{kl}\right)\) and the corresponding error \(\hat{\mathbf{h}}_{kl}=\mathbf{h}_{kl}-\hat{\mathbf{h}}_{kl}\sim\mathcal{N}_{ \mathbb{C}}\left(0,\tilde{\mathbf{R}}_{kl}\right)\) are statistically independent, with correlation matrices given by [2; 11]  
\[\hat{\mathbf{R}}_{kl}=\tilde{p}_{k}\tau_{p}\mathbf{R}_{kl}\mathbf{\Gamma}_{t _{kl}}^{-1}\mathbf{R}_{kl}, \tag{4}\]  
and  
\[\tilde{\mathbf{R}}_{kl}=\mathbf{R}_{kl}-\hat{\mathbf{R}}_{kl}. \tag{5}\]
# A Parameter-Driven Methodology of Wheel Flat Modeling for Wheel-Rail Impact Dynamics
## 2 Three-Dimensional Wheel Flat Model
### New Flat Model  
The 3D wheel flat parameters and coordinate system of three-dimensional views are defined in Figure 2, with the meanings of the parameters listed in Table 2.  
Figure 2: Definition of wheel flat parameters.  
For any point \(A(x,y)\) on the wheel flat, which has depth \(F_{\rm I}(x,y)\):  
\[F_{\rm I}(x,y)=c\sqrt{1-\frac{x^{2}}{a^{2}}-\frac{y^{2}}{b^{2}}}+d_{0}\quad(x \in[-a,a],y\in[-b,b]) \tag{1}\]  
In Equation (1), \(a=L_{0}/2\), \(b=L_{w}/2\), and \(c=d-d_{0}\).  
The length of the new flat is determined by Equation (2):  
\[L_{0}=2\sqrt{2R_{w}d_{0}-{d_{0}}^{2}} \tag{2}\]  
where \(d_{0}=d/\alpha\).  
In the direction of the wheel flat width, considering the cutting effect of the rail head on the wheel tread, the width of the flat is determined by Equation (3).  
\[L_{w}=2\sqrt{2R_{r}d-d^{2}} \tag{3}\]  
According to Equations (2) and (3), the new flat aspect ratio can be written as Equation (4).  
\[\frac{L_{0}}{L_{w}}=\sqrt{\frac{d_{0}(2R_{w}-d_{0})}{d(2R_{r}-d)}} \tag{4}\]  
Because \(R_{w}>>d_{0}\), \(R_{r}>>d\), Equation (4) is simplified to Equation (5).  
\[\frac{L_{0}}{L_{w}}=\sqrt{\frac{R_{w}}{\alpha R_{r}}} \tag{5}\]  
Based on Equation (5), the aspect ratio of the new wheel flat is related to the radius of curvature of the wheel-rail contact point at the abrasion.
## 3 Results
### Meta-Analyses
#### 3.6.1 Meta-Analysis on the Percentage of Newly Formed Bone  
The graph obtained from the first meta-analysis is represented in Figure 6.  
The global meta-analytical value suggests that the AMD graft resulted in an average newly formed bone percentage of 43.8%, with a 95% confidence interval ranging from 36.6% to 50.8%.  
Both [PERSON] et al. [48] and [PERSON] et al. [46] showed results bellow the meta-analytic mean but within the confidence interval (37.6% and 38.4%, respectively). [PERSON] et al. [47] and [PERSON] et al. [23] showed results above the meta-analytic mean (56.5% and 47.3%, respectively), with [PERSON] et al. [47] being the most discordant study regarding the mean in this first meta-analysis. The heterogeneity was 77.4%, indicating a high degree of variability between the studies (Cochran's Q test, \(p\) = 0.004).  
Meta-regressions were conducted to evaluate covariables that could have influenced the results. The covariables chosen were mean age and sex ratio.  
The first meta-regression studied the effect of the mean age, shown in Figure 7. The second meta-regression studied the effect of the sex ratio, shown in Figure 8.  
The meta-regression shown in Figure 7 indicates that, with an increase in mean age, there is a mean increase tendency in the percentage of newly formed bone (Wald test, \(p\) = 0.003).  
As shown in Figure 8, this meta-regression reveals that as the sample female ratio decreases, there seems to decrease the percentage of newly formed bone. However, there is not sufficient evidence to statistically support this relationship (Wald test, \(p\) = 0.194).  
Figure 6: Forest plot graph of newly formed bone (%). I\({}^{2}\) represents the heterogeneity index. The dashed red line represents the meta-analytic mean [23; 46; 47; 48].  
Figure 7: Meta-regression of the effect of mean age on the percentage of newly formed bone.  
It is clear from Figure 8 that even when one study is removed, the 95% confidence interval still includes the overall result, suggesting that no single study drastically changes the overall effect estimate.  
A leave-one-out meta-analysis was also performed, as shown in Figure 9.
# Formation Mechanisms and Kinetic Modeling of Key Aroma Compounds During Qidan Tea Roasting
###### Abstract
Strecker aldehydes, as important products of the Maillard reaction, can be synthesized through various pathways, such as amino acid-assisted decarboxylation via the \(\alpha\)-dicarbonyl group, direct degradation through the Amadori rearrangement, oxidative decarboxylation, and thermal reactions [12]. The field of dynamics, which focuses on the study of rates of change in physical, chemical, or biological systems, offers a powerful tool for understanding the dynamic changes in flavor during the roasting process. Kinetics, a branch of dynamics, has been used to study the rates of change in various systems. Kinetic modeling and gas chromatography-mass spectrometry (GC-MS) can be employed to predict changes in the quality and flavor of kiwifruit during storage [13]. [PERSON] [14] reviewed the development of kinetic models for flavor thermogenesis, especially the kinetic changes during the Maillard reaction, and also mentioned the corresponding kinetic models developed in real food systems that can predict the formation of individual flavor compounds or groups of flavor compounds. It has been demonstrated that 2-furfuryl mercaptan, methyl mercaptan, and 2-ethyl-3-methylpyrazine serve as reliable indicators for predicting the shelf life of roasted coffee products. This prediction is based on the Sulfur/Roast Evolution Index at various storage temperatures, utilizing a first-order kinetic reaction model and the Arrhenius equation to develop predictive modeling results [15]. We have screened the most significant aroma-active compounds produced during the Maillard reaction of Qidan tea, specifically 2/3-methylbutylraldehyde, 2-methylpyrazine, and benzaldehyde, and developed corresponding kinetic equations [16]. Building on this research foundation, this study will: (1) conduct a descriptive sensory assessment of the aroma characteristics of chitin tea at various roasting times; (2) utilize CAMOLA to determine the skeletal origins of these key aroma-active compounds and elucidate the pathways of their production; and (3) establish a corresponding Maillard reaction system using theanine and glucose to verify the accuracy and generalizability of the kinetic equations. The goal of this study is to establish a robust theoretical framework for the roasting process of Qidan tea. This will assist the tea-making industry in reducing its reliance on experiential knowledge, enhancing roasting efficiency, and offering scientific guidance for practical production.
## 2 Materials and Methods
### LMO Trait Identification and Event-Specific PCR Analysis  
Samples were analyzed using immunochemical strip kits for CP4 EPSPS, Cry1A, Cry 1F, Cry2A, PAT/bar, 2 mEPSPS, DMO, and AAD-12 proteins (Envirologix Inc., Portland, ME, USA) to identify the presence of transgenic traits (herbicide or insect resistance), thus determining whether the cotton was LMO or non-LMO. Cotton leaves were ground in a 1.5 mL tube using a pestle, to which 0.5 mL of extraction buffer (Envirologix Inc.) was added until the sample was finely ground. Test strips were dipped in the mixture and bands appeared on the strips after approximately 10 min if kit-specific proteins were present. Each sample was classified as non-LMO (one band), LMO (two or more bands), herbicide-resistant LMO, and/or insect-resistant LMO.  
Event-specific PCR analysis was performed to detect the presence of introduced LMO genes in the collected samples. Genomic DNA from cotton tissues was extracted using the Exgene Plant SV mini kit (GeneAll, Seoul, Korea), following the manufacturer's recommendations. One oligonucleotide primer set for the _adhC_ gene was used to confirm the stability of the PCR reactions. Specific PCR analysis using primer sets for 9 cotton events (T304-40, LLCOTTON25, MON1445, MON531, MON15985, MON88913, GHB1614, GHB119, and 281-3006) was conducted to characterize the collected suspicious samples, as previously described (Table 1) [21; 22; 23; 24]. The certified reference materials that served as controls in the PCR reactions were purchased from the American Oil Chemist's Society(AOSC; Urbana, IL, USA) and the Institute for Reference Materials and Measurements (IRMM; Geel, Belgium). The PCR reaction was prepared using the 2X EF-Taq PCR Pre-Mix (Solgent, Daejeon, Korea) in a reaction mixture of 30 \(\upmu\)L total volume, containing 50 ng genomic DNA, and 1 \(\upmu\)L per primer (10 pmol/\(\upmu\)L). PCR amplification was performed using the ProFlex PCR System (Applied Biosystems, Waltham, MA, USA) under the following conditions: initial denaturation at 95 \({}^{\circ}\)C for 5 min; 33 cycles of denaturation at 95 \({}^{\circ}\)C for 30 s, annealing at 59 \({}^{\circ}\)C for 30 s, extension at 72 \({}^{\circ}\)C for 30 s, and a final extension at 72 \({}^{\circ}\)C for 7 min. The amplified products were observed by electrophoresis on 2.5% agarose gel with 1X TAE buffer for 25 min at 135 V. Bands were identified using the ChemiDoc XRS+ Imaging System (Bio-Rad, Hercules, CA, USA). The PCR-amplified products were then subjected to nucleotide sequencing by Biofact Inc. (Daejeon, Korea).
## 2 Materials and Methods
### Data Processing
#### Calculation of Vegetation Indices and Photosynthetic Observations at the Canopy Scale
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vegetation Index** & **Computation Equation** & **Reference** \\ \hline Normalized difference vegetation index & \(NDVI=\frac{R_{nat}-R_{nat}}{R_{nr}+R_{nt}}\) & [32] \\ Near-infrared reflectance of terrestrial vegetation & \(N1 Rv=NDVI*R_{nir}\) & [33] \\ Fluorescence correction vegetation index & \(FCVI=R_{nir}-R_{nl}^{-}\) & [20] \\ Red-edge NDVI & \(NDVI_{Iroddeg}=\frac{R_{nr}-R_{nr}}{R_{nir}+R_{nr}}\) & [34] \\ \hline \hline \end{tabular} \(R_{nir}\) is the reflectance in the near-infrared band, \(R_{nir}\) is the reflectance in the red band, \(R_{nr}\) is the reflectance in the red-edge band, and \(R_{nl}^{-}\) is the average reflectance in the broad 400–700 nm visible band.  
\end{table} 
Table 3: Details on the vegetation indices used in this study. where PAR is the integral of E over the 400-700 nm wavelength range. To evaluate the ability for monitoring water stress in maize via diurnal variations in maize conditions, we calculated the NMR and AMR of the canopy indices (CIs) based on the continuous canopy spectrum measurement data. We adopted the NMR expression proposed by [PERSON] et al. [12], which is as follows:  
\[\mathrm{NMR_{CI}}=\frac{\overline{\mathrm{CI}}_{12:00-13:00}}{\overline{ \mathrm{CI}}_{8:00-9:00}} \tag{10}\]  
where \(\overline{\mathrm{CI}}_{12:00-13:00}\) denotes the mean measured CI value between 12:00 and 13:00, and \(\overline{\mathrm{CI}}_{8:00-9:00}\) denotes the corresponding mean measured CI value between 8:00 and 9:00. The CIs adopted in this study include the NDVI, SIFy, \(\Phi_{\mathrm{F}}^{\mathrm{FCVI}}\), and \(\Phi_{\mathrm{F}}^{\mathrm{NIRv}}\). Owing to the typically high values of SIFy under water stress from 8:00 to 9:00, followed by a rapid decline, we selected 8:00 to 9:00 as the time window for the NMR to better reflect the healthy physiological state in the morning. Based on the analysis of meteorological data and the diurnal variation of different CIs, maize are most affected by water stress between 12:00 and 13:00, which is why this time window was selected for noon. The AMR can be calculated via the equation proposed by [PERSON] et al. [23]:  
\[\mathrm{AMR_{CI}}=\frac{\overline{\mathrm{CI}}_{14:30-17:00}}{\overline{ \mathrm{CI}}_{8:00-11:00}} \tag{11}\]  
where \(\overline{\mathrm{CI}}_{14:30-17:00}\) denotes the mean measured CI value between 14:30 and 17:00, and \(\overline{\mathrm{CI}}_{8:00-11:00}\) denotes the mean measured CI value between 8:00 and 11:00. To compare with the AMRs of other vegetation types, our AMR time window is based on the study by [PERSON] et al. [23].
## 2 Materials and Methods
### Methods
#### 2.3.2 Modelling Methods  
1. Partial Least Squares Regression (PLSR)  
PLSR is a multivariate regression modelling method that can modify data in the presence of a strong correlation between variable components and solve the problem of complex data structures more effectively than standard classical regression analysis methods. The method combines the features of several regression analysis methods, including typical correlation analysis, multiple linear regression analysis, and principal component analysis, to provide a more comprehensive representation of information based on a suitable regression model [37]. In addition, the PLSR approach may also efficiently address the noise issue, minimize the spectral dimension, and emphasize the contributing reasons for variations in soil texture content [38].  
(2) Convolutional Neural Network (CNN)  
CNN is a class of feedforward neural networks that includes convolutional computation, has a deep structure, and is one of the representative algorithms for deep learning. CNN has a powerful reinforcement feature capability, network parallel learning, and local weight sharing, reduces network complexity, and avoids the complexity of explicit feature extraction and data reconstruction, resulting in a higher-quality classification. CNN is well suited to the needs of soil monitoring and assessment since it can accurately and flexibly extract different soil properties from remote sensing images in spatial mode [25].  
CNN is based on a hierarchical organization of neurons by learning to layer, with neurons between layers linked by deviations and weights. The network structure includes an initial layer, a middle layer, and an output layer. The initial layer is the input layer, which is used to input remote sensing data and sample point data. The middle layer is the hidden layer, which contains a convolutional layer, activation function, pooling layer, and fully connected layer, which is used to transform the output feature space and extract feature information. Finally, the output layer is used for predicted soil texture classification attributes [39].  
The forward-propagation and backward-propagation phases comprise the two primary steps of the CNN training process. Forward-propagation feeds the input data into the network, and layer-by-layer propagation determines the output results. By determining the gradient of the loss function, backward-propagation adjusts the network parameters and brings the network output closer to the correct value [40].  
The CNN model in this study was trained with an initial learning rate of 0.001 and an Adam optimizer. The network model used a minimum batch size of 30 and a maximum of epochs of 800. Dropout, early stopping, and reduction in the learning rate were used as a regularization technique, and to prevent overfitting of the network, the learning rate of the network model was set to be reduced to half of the initial learning rate at 400 epochs, and a dropout layer was added to the network model with a dropout rate of 0.30.
## 3 Problem Formulation
### Service Model  
For any service \(r_{k}(\forall r_{k}\in R)\) in the network connection request sets, we represent it via a triple \(r_{k}=(s_{k},d_{k},B_{k})\), where \(s_{k}\) denotes the source node, \(d_{k}\) denotes the destination node, and \(B_{k}\) denotes the bandwidth required by the service, respectively. Also, for the service \(r_{k}\), we define the path as \(P_{r_{k}}=\left\{e_{ij}\middle|i,j\in 1,N_{v}\right\})\left(P_{r_{k}}\in P\right)\), where \(P\) is composed of K pre-computed paths \(P_{r_{k}}\). In addition, we assume that the service is transmitted from the source node to the destination node with the intermediate nodes, only considering the forwarding method based on slot crossing.  
Each service in the FlexE is an Ethernet traffic flow based on the MAC data rate, and all services are represented as a set \(B=\left\{B_{k}|k=1,2,\ldots,n\right\}\). In order to accurately predict the traffic demand, our goal is to calculate the most likely data matrix volume at time slot  
Figure 1: The architecture of Flexible Ethernet.  
based on the historical data of the previous \(T_{s}\) slots. The problem formulation is shown as follows:  
\[\widetilde{B_{k,i}}=\underset{B_{k,i}}{\text{argmax}}(B_{k,i}\big{|}B_{k,i-T_{s} },\ldots,B_{k,i-1}) \tag{1}\]  
where \(B_{k,i-T_{s}},\ldots,B_{k,i-1}\) are the historical observations of traffic flow at previous \(T_{s}\) time slots.