esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 5 Standards towards 5G Automation  
Developing end-to-end automated management of 5G architecture and services and the integration of the mobile edge computing into 5G introduce new requirements. The set of multi-access applications and services at the edge designed to meet these requirements is greatly increasing the complexity of managing the networked system.  
This complexity manifests itself in different aspects of the network and services, such as the provision and operation of services, predictive analysis, real-time monitoring, analytics, or maintenance of thousands of entities, among others, and inexorably forces an end-to-end network and services automation. The application of ML in mobile edge computing and 5G, which will allow self-configuration, self-optimization and self-healing, will also require component standardization.  
Several organizations, including the 3 GPP, ETSI or ITU, have created working groups to address this problem of standardization and complexity, generating the first architectural standards and models for 5G.  
ETSI Industry Specification Groups (ISGs), ENI (Experiential Networked Intelligence), and SAI (Security AI) are working in parallel with ITU-T's Q20/13 and FG ML5G (Focus Group on Machine Learning for Future Networks including 5G), and 3 GPP TR 23.791 to incorporate ML in 5G and future networks, from the edge to the core network.  
In this section, we discuss the most relevant standards, network and services architectures developed by the main standardization organizations and open forums, which enable the application of ML in mobile edge computing within the framework of 5G.
## 5 Experiments and Results
### Discussion  
#### 5.3.1 Further Analysis of the Experiment Results  
According to the analysis of the two experimental results above, the improved MPC-based tracking controller is able to make the biomimetic spherical robot converge to the desired trajectory on the X-Y plane. Table 3 summarizes the mean square errors (MSE) for all experimental cases. In the tracking of the line trajectory, the position errors are slightly larger than in other case. The main reason is a large error in the initial position. The error of the yaw angle in the case of tracking the square trajectory is the maximal. This is because there are three times of yaw angle adjustments. On the one hand, these state errors of the robot mainly originate from the localization algorithm and the sensors. On the other hand, four thrusters are of the same type, but they also have minor difference, which indicates that different thrusts are outputted under the same PWM signal. In general, these errors are acceptable relative to the size of the BSR.  
Figure 23: The tracking result of the robot in square tracking. (**a**) States of the robot at different times. (**b**) Thrusts varying of the four thrusters.  
Figure 22: Image sequences of the BSR tracking a square trajectory.
###### Abstract  
Smart cities use many smart devices to facilitate the well-being of society by different means. However, these smart devices create great challenges, such as energy consumption and carbon emissions. The proposed research lies in communication technologies to deal with big data-driven applications. Aiming at multiple sources of big data in a smart city, we propose a public transport-assisted data-dissemination system to utilize public transport as another communication medium, along with other networks, with the help of software-defined technology. Our main objective is to minimize energy consumption with the maximum delivery of data. A multi-attribute decision-making strategy is adopted for the selction of the best network among wired, wireless, and public transport networks, based upon users' requirements and different services. Once public transport is selected as the best network, the Capacitated Vehicle Routing Problem (CVRP) will be implemented to offload data onto buses as per the maximum capacity of buses. For validation, the case of Auckland Transport is used to offload data onto buses for energy-efficient delay-tolerant data transmission. Experimental results show that buses can be utilized efficiently to deliver data as per their demands and consume 33% less energy in comparison to other networks.  
big data; delay-tolerant network (DTN); multi-attribute decision making; public transport; energy consumption +
Footnote †: journal: Future Internet
# MM-iTransformer: A Multimodal Approach to Economic Time Series Forecasting with Textual Data
## 3 Proposed Method
### Proposed MM-iTransformer Framework
* \(d_{k},d_{v}\): The dimensionality of the projected query, key, and value vectors within the attention head h. These dimensions control the representation capacity of the attention mechanism, influencing how relationships between input features (both textual and numerical) are modeled and how information is aggregated into output representations. The attention scores are computed using the scaled dot-product attention mechanism proposed in [38], which evaluates the relevance between queries and keys. This mechanism enables the model to weigh the importance of each feature (both textual and numerical) when making predictions, effectively capturing cross-modal interactions between financial news and historical price data. Balanced Contribution of Modalities: To prevent richer textual embeddings from dominating the learning process and overshadowing numerical data, we implement a strategy to balance the contributions of both modalities. * Selective Incorporation of Textual Data: We include only the most recent news articles in the textual embeddings. Given that the latest news tends to have the most significant impact on future market movements, this approach reduces the noise of older articles and focuses the model on the most relevant information. * Dimension Alignment: A linear layer is applied to the textual embeddings, adjusting their feature dimension to align with that of the time series embeddings. This ensures that, although the number of variables may differ between modalities, they can be directly concatenated along the variate dimension without any dimensional mismatch. The combined embeddings are then fed into the MM-iTransformer model, which processes them through multiple layers of attention and feed-forward networks to produce the final forecasts \(\hat{Y}_{L+1:L+H}\). The details are outlined in Algorithm 1. ```
1:Time Series Data: \(Y_{1:L}=\{y^{\prime}_{1},y^{\prime}_{2},\ldots,y^{\prime}_{L}\}_{l=1}^{L}\), TextData: \(\{x_{1},x_{2},\ldots,x_{L}\}\)
2:Future Forecasting H steps: \(\hat{Y}_{L+1:L+H}=\{g^{\prime}_{1},g^{\prime}_{2},\ldots,g^{\prime}_{L}\}_{l=L +1}^{L+H}\)
3:Process Text Data: \(\text{Text}\text{Emb}\leftarrow\text{Finert}.\text{Embed}(x_{L})\text{ last\_hidden\_state}\)
4:\(\text{Text}\text{Emb}\leftarrow\text{Linear}(\text{d},\text{L})\text{(Text}\text{Emb})\)
5:Process Time Series Data:
6:\(\text{For}\ c=1\text{ to }\text{C}\text{ do}\)
7:\(\text{TimeSeries}\text{Emb}[c]\leftarrow\text{Inverted}\text{Embed}(y_{c})\)
8:end for
9:CombineDeadings: \(\text{CombineDeadings}\leftarrow\text{Concatenate}(\text{TimeSeries}\text{ Emb},\text{Text}\text{Emb})\) along the variate dimension
10:ApplyiTransformer Model
11:Load Transformer model with appropriate configurations
12:for\(t=L+1\) to \(L+H\)do
13:\(g^{\prime}\leftarrow\text{iTransformer}.\text{Projection}(\text{CombinedFeatures})\)
14:\(\hat{Y}[t-L]\leftarrow\hat{g}^{\prime}\)
15:endfor
16:return\(\hat{Y}_{L+1:L+H}\)
```  
**Algorithm 1**Modalities fusion under the MM-iTransformer framework.
## 3 Results and Discussion
### Binding Interaction Analysis with PLIP
#### 3.5.3 Interactions with EGFR
Compared to Mut4 alone, new hydrophobic interactions occurred with Lys716A, Leu718A, and Gly719A, while new hydrogen bonding occurred with Val717A, Leu718A, Gly719A, Ser720A, Gly721A, Lys728A, Asp800A, Ala1000A, and Asp1003A. The CSAMut4 conjugate showed shown in Figure 7r and Supplementary Information Table S7r, exhibited eight hydrophobic interactions and twelve hydrogen bonds. New hydrophobic interactions were seen with Gly719A, Phe723A, Ala743A, Thr854A, Glu866A, and Pro877A, while new H-bonds were observed with Ala722A, Phe723A, Arg748A, Arg841A, Thr854A, Gly863A, Val876A, and Arg889A. GLAMut4, on the other hand. displayed only five hydrophobic interactions, but seventeen hydrogen bonds, and one salt bridge as seen in Figure 7s and Supplementary Information Table S7s. A new hydrophobic interaction was seen with Phe723A, while new H-bonds included those with Gly719A, Ser720A, Gly721A, Ala722A, Phe723A, Gly724A, Asp800A, Glu804A, Asn842A, and Asn996A. RMAMut 4, as shown in Figure 7t and Supplementary Information Table S7t, exhibited nine hydrophobic interactions, including those with Leu718A, Ala743A, Asp800A, Arg841A, and Tyr998A. Fourteen hydrogen bonds were observed, including new ones with Gly719A, Ser720A, Gly721A, Phe723A, Val726A, Gln791A, Met973A, Cys797A, Asp800A, Glu804A, and Asn996A. These results once again indicated that conjugates displayed overall larger interactions and there were changes observed as far as the binding region was concerned.
# A Monarch Butterfly Optimization for the Dynamic Vehicle Routing Problem
## 4 Experimental Results
### Comparison with the Literature for DVRP
Figure 5 shows, for each data set, the dispersion diagrams of the solutions found by our approach. Note that the solution values are presented as the relative error from the BKS. In most cases, our MBO gets a relative error of no greater than 10% from the BKS, and central values tend to be between 0 and 5%. It is interesting to note that the quality and stability of the solutions are not size dependent. In particular, f134, c75, c100, c150, c199, and t100d have instances with less than 5% relative error. This means those instances are better than the BKS. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **MBO** & **BKS** & **Algorithm** & **RE (\%)** & **TMBO (s)** \\ \hline c50 & 590.73 & **570.89** & AAC-2 OPT & 3.48 & 44.20 \\ c75 & **909.56** & 1013.45 & TS 2007 & \(-\)10.25 & 84.50 \\ c100 & **930.38** & 987.59 & GA 2007 & \(-\)5.79 & 82.43 \\ c100b & **839.93** & 841.44 & AAC-2 OPT & \(-\)0.18 & 103.90 \\ c120 & **1112.58** & 1153.29 & AAC-2 OPT & \(-\)3.53 & 126.08 \\ c150 & **1135.85** & 1386.93 & GA 2007 & \(-\)18.10 & 262.19 \\ c199 & **1414.40** & 1758.51 & AAC-2 OPT & \(-\)19.57 & 492.80 \\ f71 & **277.00** & 306.33 & TS 2007 & \(-\)9.57 & 52.48 \\ f134 & **11853.29** & 15528.81 & AAC-2 OPT & \(-\)23.87 & 215.09 \\ tai75a & 1799.15 & **1782.91** & AAC-2 OPT & 0.91 & 83.63 \\ tai75b & **1385.45** & 1452.26 & AAC-2 OPT & \(-\)4.60 & 93.61 \\ tai75c & 1483.41 & **1441.91** & AAC-2 OPT & 2.88 & 81.09 \\ tai75d & 1435.62 & **1422.27** & AAC-2 OPT & 0.94 & 86.42 \\ tai100a & **2212.08** & 2232.71 & AAC-2 OPT & \(-\)0.92 & 116.53 \\ tai100b & **2105.15** & 2182.61 & AAC-2 OPT & \(-\)3.55 & 111.98 \\ tai100c & **1474.32** & 1541.25 & GA 2014 & \(-\)4.04 & 137.05 \\ tai100d & **1722.88** & 1912.43 & AAC-2 OPT & \(-\)10.78 & 123.70 \\ tai150a & 3539.61 & **3185.727** & AAC-2 OPT & 8.78 & 375.45 \\ tai150b & 3038.38 & **2880.57** & AAC-2 OPT & 5.48 & 365.32 \\ tai150c & **2641.62** & 2743.55 & AAC-2 OPT & -3.72 & 377.15 \\ tai150d & 3047.30 & **3045.16** & GA 2007 & 0.07 & 322.49 \\ \hline Average & **2140.41** & 2350.98 & & \(-\)9.38 & 178.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the average MBO results with the hitherto best known solutions.
## 6 Function Genes
### Stress-Related Genes  
Plants, as sessile organisms, have evolved a complex mechanism to adapt to various environmental stresses, such as drought, mechanical damage, and high and low temperatures [178; 179]. Overexpression of _CsCOR1_ (encoding cold-regulated proteins) and _CsHSP1.2_ (encoding heat shock protein) genes in transgenic tobacco and _Arabidopsis_ enhanced drought tolerance, salt tolerance, and thermotolerance, respectively [180; 181]. The expression level of _HXKs_ (hexokinase-encoding genes), _FRKs_ (fructokinase-encoding genes), _CsGolS1_ (gactinol-synthase-encoding genes), and two _CsF3 Hs_ (flavonoid 3-hydroxylase-encoding genes) was increased after abiotic stresses [182; 183; 184]. Recent studies have reported that _UGT91Q2_ (glucosyltransferase-encoding gene), _CsWRX726_, and _CsbZIP18_ from _C. sinensis_ were involved in the modulation of cold stress tolerance, drought tolerance, and negatively regulate freezing tolerance, respectively [185; 186; 187]. In total, three _CsCAD_genes (_CsCAD1-3_), encoding cinnamyl alcohol dehydrogenases, were upregulated after mechanical damage and insect attack [188].  
Insect and disease are damaging stressors that threaten the cultivation of tea plants. The expression level of _CsHPL_ (hydroperoxide lyase-encoding gene) and _CsGolS_ genes were significantly increased after insect attack and tea inchworms eating the crop [183; 189]. The genes encoding lipoxygenase (LOX) and phenylalanine ammonia lyase (PAL) were isolated from tea plants as pathogen-induced genes [190]. Overexpression of _CsTLPs_ from _C. sinensis_ encoding thuamatin-like proteins in transgenic potato (_Solanum tuberosum_) displayed greater fungal resistance by unregulating the transcripts of _StPAL_ and _StLOX_ genes [191; 192]. [PERSON] et al. (2021) reported that transcription factor WRKY14 mediates the resistance of tea plants to blister blight [193]. Environmental factors significantly affect plant growth and the biosynthesis of secondary metabolites. Mining more stress-specific genes is an effective way to enhance resistance using modern biotechnologies.
# Fast and Accurate Visual Tracking with Group Convolution and Pixel-Level Correlation
## 3 Proposed Method
### Feature Fusion  
In order to make full use of the features extracted from the backbone network and the advantages of deep networks, features from different layers are used in our feature fusion, and at the same time, in order to speed up the inference, a group convolution [13] is used to first reduce the feature dimensions to simplify the number of parameters and then aggregate the features via a pointwise convolution.  
Group convolutions [13] have been widely applied as efficient convolution methods. Their specific process is shown in Figure 4. \(C_{1}\times H\times W\) is used as the input, and the output is \(C_{2}\times H\times W\), which represents the channel, height, and width of the convolution. The input is divided into g groups, and each group uses a convolution with a kernel size of \(k\times k\) and \(C_{1}/g\) channels. Compared with the number of parameters of an ordinary convolution, i.e., \(k\times k\times C_{1}\times C_{2}\), the number of parameters of the group convolution is \(k\times k\times C_{1}\times C_{2}/g\), which is \(1/g\) of an ordinary convolution, greatly reducing the parameter redundancy. A group convolution is equivalent to decomposing the input and processing the data in parallel, which can speed up the operation. The number of parameters and FLOPs is calculated using Equations (6) and (7):  
\[Params_{normal}\colon k\times k\times C_{1}\times C_{2},\quad FLOPs_{normal} \colon k\times k\times C_{1}\times C_{2}\times H\times W \tag{6}\]  
\[Params_{group}:\frac{k\times k\times C_{1}\times C_{2}}{g},\quad FLOPs_{group }:\frac{k\times k\times C_{1}\times C_{2}\times H\times W}{g}. \tag{7}\]  
Figure 4: Feature fusion model with a group convolution and pointwise convolution: (**a**) denotes input features, (**b**) denotes group convolution, (**c**) denotes pointwise convolution, and (**d**) denotes output.  
Generally speaking, during the tracking process, there may be problems such as illumination changes and scale variation, which require the tracking task to use as much feature information as possible. It is usually considered that in the shallow layer of a network, the network extracts the fine-grained information [26] of the object, such as its color and shape, to help locate the object's position, and as the network deepens, the network extracts the semantic information of the object. Fusing these features from different deep and shallow layers helps to track the target. After correlation, the features of the three stages are concatenated together, and the fusion of the features is implemented using a pointwise convolution [27], which achieves the fusion of cross-channel information quickly and efficiently.
# Hybrid Zoning Algorithm to Optimize Overhead in Smart Mobile Communication
## 7 Conclusions  
With the proliferation of localization techniques, many studies have been carried out to reduce the side effects of mobile routing protocols, allowing several new protocols to see the day. They have proven that using geographic information can help further improve routing protocols. In this study, we used two strategies to optimize the overhead in mobile communication with location information of nodes. The first strategy, the Z-MPR technique, divides locally the neighborhood into zones and elects dominant nodes in different zones to separate them from each other in order to reduce the collision. The second strategy, the GFR technique, divides the whole network into propagation zones and avoids useless transmissions between them. Both techniques coexist together and operate in the same routing algorithm in a distributed manner that we call Z-GFR. We implemented our solution in the context of OLSR and showed by simulations that our technique Z-GFR outperforms the OLSR protocol and two other zoning techniques. Z-GFR has reduced considerably the traffic overhead of OLSR and has increased the throughput, making the network more reliable and suitable for real-time application such as voice over ip or online gaming.  
Conceptualization, M.S.; Formal analysis, M.S.; Software, H.B.; Supervision, A.H.; Writing--original draft, M.S.; and Writing--review and editing, M.S.  
This research received no external funding.  
The authors declare no conflict of interest.
# Active Optics and Aberration Correction Technology for Sparse Aperture Segmented Mirrors
## 4 Simulation Verification
### SiC Material Segmented Mirror Active Optical Control  
Based on the segmented mirror support system developed in Section 4.1, the segment material is replaced with SiC. Adjust the segment material to SiC, and adjust the segment thickness according to the principle of equal stiffness until the mirror surface shape accuracy is equal for Zerodur and the SiC segment in Y and Z conditions.  
This section is about the sparse aperture segmented mirror error of the SiC segment introduced by Y and Z working conditions. Schemes 1 and 2, similar to Section 4.2, are utilized to implement the co-phase correction for the segmented mirrors under the Y and Z conditions. The correction steps are the same. The residual deformation of the segmented mirrors after the co-phase correction of the two schemes is shown in Figure 8.  
Comparing Scheme 1 and Scheme 2 in Figure 8, we can get that because of the smaller thickness and lighter mass of the SiC segment, the rigid body displacement along the X and Y directions under gravity load is smaller. It reflects the advantage of SiC material in the thin mirror technology and the adaptability of the support structure. Comparing the results of the analysis of the Zerodur material segmented mirrors in Section 4.2, it can be found that the residual deformation of the SiC material segmented mirrors is smaller after correction, thus confirming the excellent adaptability of the SiC material segmented mirrors to gravity loads.  
Figure 6: Residual deformation of the mirror surface after aberration correction for Scheme 1 (**top**) and Scheme 2 (**bottom**), (**a**) Deformation of mirror surface in Y condition, (**b**) Deformation of mirror surface in Z condition, and (**c**) Deformation of mirror surface in T condition.  
Figure 7: Comparison of residual aberration before and after correction of two schemes, (**a**) Comparison of mirror aberration coefficients in the Y condition, (**b**) Comparison of mirror aberration coefficients in the Z condition, and (**c**) Comparison of mirror aberration coefficients in the T condition.
## 3 Results and Discussion
### Burned Area by Land-Use/Land Cover Class  
The studied fire affected five dominant land use/land cover classes, and the results are illustrated in Figure 5. A large burned area occurred within the forested area (53.4%), most of which were natural forests (35.4%) and 18.0% comprising commercial planted forest. This was followed by the grasses and shrubs category with 24.4%, while cultivated land represents 21.1% of the burned area. The smallest class is that of wetlands with 1.1%.  
Figure 4: Spatial distribution of burned area by severity class.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Severity Level** & **Burned Area (ha)** & **Percentage** \\ \hline High & 54,583 & 37.4 \\ Moderate-high & 35,968 & 24.7 \\ Moderate-low & 23,299 & 15.9 \\ Low & 32,023 & 21.9 \\ Total & 145,873 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Proportion of burned areas by severity class.  
Figure 5: Burned area by land-use/land cover class.  
Different terrain conditions are displayed in Figure 6. Figure 6a,b show the smoke over planted forests during the fire incident, which claimed eight lives in a forest village and destroying several structures, including the Sawmill (Figure 6d) [26].
## 2 Materials and Methods
### The Measurements of Environmental and Growth Parameters  
Environmental parameters, including solar radiation (SWR, W \(\cdot\) m\({}^{-2}\)), air temperature (Ta, \({}^{\circ}\)C), relative humid (Rh, recorded as a fraction), and soil moisture (SM, m\({}^{3}\)/m\({}^{3}\)), were continuously collected using commercial devices. A data logger (HOBO U30, Onset Computer Corporation, Bourne, MA, USA) in the micro-meteorological station nearby our experimental plots was used to record the real-time values of SWR (300-1100 nm, S-LIB-M003, Onset Computer Corporation), as well as Ta and Rh (S-THB-M002, for Ta and Rh collection, Onset Computer Corporation), with a time interval of 5 min (Figure 1b,c). VPD was further calculated using Ta and Rh according to Equation (1). The values of SM in the soil layer at 20 cm were also collected by soil moisture sensors (S-SMC-M005, Onset Computer Corporation). To obtain high quality measurements of SM, two sensors were equipped in each plot (Figure 1b). Daily means of SM in each plot were averaged from the records of two sensors.  
\[\mathrm{VPD}=0.611\times e^{\frac{17.27\times T_{a}}{14.2^{\circ}}}\times(1- \mathrm{Rh}) \tag{1}\]  
Weekly measurements of growth parameters were conducted over four plots of wheat and maize to analyze their influence on the variation of seasonal and diurnal SIF. These parameters included leaf area index (LAI), mean leaf inclination angle (MTA), chlorophyll content (Chl), and relative leaf water content (RWC). Both LAI and MTA were measured using the LAI-2200c Plant Canopy Analyzer (LI-COR Inc., Lincoln, NE, USA) under low solar irradiance (about 17:00-18:30 for each measurement). Daily LAI (MTA) in each plot was calculated as the average values of three continuous LAI (MTA) measurements over the same plot. Chl values were indicated as SPAD readings, which were collected at noon (12:00-12:30) using a hand-held absorbance-based dualwavelength chlorophyll meter (SPAD-502, Minolta Corporation, Ltd., Osaka, Japan). About 20 leaves were selected as samples for each plot and the averaged value was saved to represent the concentration of Chl. The daily mean of RWC for each plot was averaged from six leaf samples excised at midday (12:30 to 13:30) every time. The excised leaf was weighed immediately and recorded as the fresh weight (FW). Then, the fresh leaf was soaked in distilled water in a test tube for eight hours in darkness. After that, the turgid weight (TW) was obtained by weighting the water-saturated leaves. The leaves were then dried overnight at 80 \({}^{\circ}\)C and re-weighed as the dry weight (DW). RWC was calculated according to Equation (2) [37; 38]:  
\[\mathrm{RWC}=\frac{\mathrm{F}-\mathrm{DW}}{\mathrm{TW}-\mathrm{DW}} \tag{2}\]