esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Applications of GenAI-Driven CV in Smart Agriculture
### Agricultural Drones and Remote Sensing Image Analysis
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Application** & **Methodology Used** & **Sensor Type** & **Outcomes** \\ \hline
[209; 212; 216; 217] & Crop Classification & CNN, transformer, Morphological-LSTM & Hyperspectral, RGB & High-accuracy crop type and variety mapping \\ \hline
[202; 210] & Disease Detection & GANs, YOLOv8, attention-based models & RGB, multispectral & Early disease recognition and precision treatment \\ \hline
[205; 219] & Yield Prediction & Multimodal DL, time-series modelling & Multispectral, satellite & Enhanced yield forecasting from environmental cues \\ \hline
[218] & Soil Moisture Monitoring & SSA-CNN, neural networks & Multispectral & Non-invasive estimation of soil properties \\ \hline
[203; 204; 222] & Phenotyping & Object detection, regression, counting algorithms & RGB, multispectral & High-throughput analysis of seedlings and tillers \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of key remote sensing and UAV-based image analysis applications in agriculture.
# On the Virtual Cell Transmission in Ultra Dense Networks
## 3 Transmission Schemes Based on Virtual Cell Merging  
Since no cooperation among the initial virtual cells will introduce strong ICI, it is natural to let the virtual cells with distinct ICI jointly serve their users in light of coordinated multi-point (CoMP) transmission. This motivates the idea of virtual cell merging. An intuitive solution is to group the initial virtual cells that overlap each other together as done in [11]. However, it is not easy to control the size of the merged virtual cell clusters due to the random network topology; hence, the signal processing complexity is not predictable. Instead, this section proposed an interference graph-based virtual cell merging scheme, where the size of the merged virtual cells can be well scaled by tuning the interference graph threshold. According to information theory [21], linear precoding, such as ZF and MVC-MMSE, can obviously eliminate the intra-cluster and inter-cluster interference with a much lower complexity. Therefore, we consider ZF and MMSE based on the low complexity virtual cell merging to eliminate the strong interference.
# Research on Space Maglev Vibration Isolation Control System Modeling and Simulation
## 2. Maglev Vibration Isolation System Design
### Overall Structural Design  
The overall three-dimensional structure of the actuator is shown in Figure 3. The actuator mainly includes a frame support structure, a magnetic circuit structure, coil components, and a limit structure. The materials for actuators mainly include aluminum, neodymium iron boron, DT-4 electrical pure iron, and titanium alloy. Fasteners are selected from titanium alloy materials, while non-metallic materials include adhesives used for magnetic steel installation.
# Remote and Proximal Sensing Techniques for Site-Specific Irrigation Management in the Olive Orchard
## 2 Materials and Methods
### Experimental Design  
The orchard consisted of 12 plots, each containing 12 trees arranged in three rows of four trees, with the only exception of one plot of two rows [5; 6]. Three plots were fully irrigated (FI), six plots were deficit irrigated (DI), whereas the remaining three did not receive any irrigation (RF, rainffed) (Figure 1).  
The orchard was subdivided into two homogeneous zones (C1 and C2) by k-means clustering, using the Hill-Climbing algorithm [64] of the SAGA-GIS software. The input data to calculate k-means clustering were: ECa\({}_{1}\), ECa\({}_{2}\), TC\({}_{\text{gamma}}\), and NDVI. Such homogeneous zones simulated two prescription areas for site-specific management (in particular precision irrigation) of the olive orchard (Figure 2). The selection of only two clusters was driven by the small surface of the experimental field and by the relatively homogeneous soil.
# Intelligent Computation Offloading Based on Digital Twin-Enabled 6G Industrial IoT
## 2 Related Work
### DT-Enabled Computation Offloading with Edge  
By using the base stations closer to the physical entity as the edge servers, the problem of too wide coverage and heavy transmission burden of cloud computing can be alleviated. Edge computing usually has the advantage of lower costs of transmission, computation, and storage. In [29], the offloading problem was transformed into an equivalent convex problem with tractable solutions, in which users offloaded their tasks to edge servers using grant-free random access. The authors of [30] developed a two-stage incentive mechanism to encourage mobile devices to offload more computation tasks to edge servers when making optimal computational offloading and resource allocation decisions. In [31], a digital twin-assisted edge computing framework for fair perception delay minimization was presented and a low-complexity iterative algorithm was developed using successive convex approximation. Considering the mapping deviation between DTs and actual network states, Ref. [32] formulated the computational offloading problem as an online matching problem in an uncertain bipartite graph. By optimizing the subchannels, Ref. [33] aimed to minimize the total task delay, while accomplishing the interconnection of all IIoT devices, edge correlation, computational capacity allocation, and transmission power allocation.
## 4 Discussion
### Limitations and Implications  
In this study, we found that corn showed a more prone upward trend in the ratio of GPP to \(SIF_{t}\) with rising PRI than soybean (Figures 5 and 8). The SIF-GPP relationship's slope varies depending on the biome or plant functional type (PFT), according to a number of recent research [50; 51; 3; 52; 53; 54]. The photosynthesis of C3 and C4 plants, for instance, showed different patterns in response to environmental conditions. Because C4 plants have evolved from the C4 route to adapt to high light intensity, high temperature, and dryness through C4 photosynthesis, they have improved resource-use efficiency and potential productivity. C4 crops are more suited to warm climates than C3 crops are, and they can withstand greater temperatures and stronger light [9]. The SIF-GPP relationship was highly dependent on the PFT. In addition, as different varieties may respond differently to environmental factors, it would be beneficial to consider the effects of the specific cultivars or genotypes [55; 56; 57]. Overall, it suggests that different PFTs may perform differently when utilizing PRI to enhance the \(SIF_{t}\)-GPP relationship. In addition, although this study has demonstrated that PRI can be used to improve the \(SIF_{t}\)-based GPP estimation, the results were only applicable to two crops, one is corn and the other is soybean. The separation of sunlit/shaded and diffuse/direct beam radiation among the multilayer canopy structure is important for understanding the relationships of GPP to SIF and PRI [7; 35]. To comprehend and forecast the complicated dynamics of SIF emission, more efforts will be required.  
In this study, we used CWSI and VPD to assess the effects of stress conditions on the GPP estimation based on \(SIF_{t}\) and PRI. The results show that the GPP-\(SIF_{t}\) and GPP-PRI relationships are influenced by environmental stress (Figure 6; Table 2). However, the influences of the environmental stress on the GPP estimation based on PRI and \(SIF_{t}\) should be further investigated from leaf to canopy scales [18; 43]. Therefore, in order to develop a more accurate GPP estimation model, more leaf and canopy observation experiments should be carried out in the future.
# Trusted Data Access Control Based on Logistics Business Collaboration Semantics
## 5 Semantic-Based Trusted Data Access Control Process
### Architectural Components of AC_Ontop  
The architecture of the proposed semantic-based trusted data access control consists of 6 modules as shown in Figure 6.  
* Input Layer: Manages database connection, generates SQL queries from SPARQL requests and transforms data for knowledge graph construction and querying.
* Ontop Core Layer: Includes modules for parsing and optimizing SPARQL queries, generating SQL queries and managing access control policies with an Access Control Agent.
* Api Layer: Provides interfaces for user and application interactions with the Knowledge Graph system.
* Application Layer: Focus on development and implementation of practical applications suing knowledge graphs.
* Authorizing Identity Layer and Verification Layer: We employ blockchain technology for identity verification and authorization, introducing a layer that offers standardized interfaces and unified specifications to integrate identity authentication management modules across diverse transportation mode business information systems into a consolidated distributed digital identity authentication system. This approach addresses challenges posed by inconsistent identity identification structures among systems, complexities in securing cross-platform identity information flows, and intricate cross-domain identity authentication procedures. The identity verification and authorization process is structured into five distinct modules, encompassing eight meticulous steps, as shown in Figure 7.  
Figure 5: Rule file.  
Initially, the applicant submits application information through the registration portal to initiate the registration process (step 1). Then send the identity registration request to the administrator to verify the user's identity. When the verification process is successfully completed, issue the registration key to the user (steps 2 and 3). After receiving the registration key, the registration site initiates a request to the certificate authority (CA) server of the blockchain. The CA server performs the registration and activation process of smart contract 1, where identity information will be recorded in the blockchain. In addition, the CA server returns the public key, private key, and other related details to the user (steps 4 and 5). The registration portal stores the user's public key in IPFS to simplify the management and retrieval of public keys. IPFS returns a unique CID for the public key (steps 6 and 7). In the final step, users obtain their private keys and are responsible for their secure storage (step 8).  
Figure 6: Semantic-based trusted data access control architecture.  
Figure 7: Identity authorization and verification process.
## 2 Methodology  
In the study area, the mine experience analogy method can maintain the stability of drifts during the shallow mining period. After mining into the deep, drift support can encounter many challenges. In this paper, a systematic approach of drift support design is proposed for deep hard-rock mines: (1) We collected basic information including engineering geology investigation, laboratory data, and in situ stress conditions. After the drift was excavated, a survey line was laid along the face of the drift, the geometrical parameters of the structural surfaces intersecting the survey line were measured, and the geological features were described individually. Rock samples were collected in the field for laboratory testing to determine the physical and mechanical properties of intact rocks. In situ stress conditions in the study area were obtained using the hydraulic fracturing method and stress-relief method. Field observations were conducted to determine which kind of failure occurs in the rock mass. (2) The rock masses were classified based on Q, RMR, and GSI and the collected basic data, and the discontinuities of the drift rock masses were analyzed using Dips software. (3) The rock mass parameters required for the numerical simulation and the support pressure required for the support design were calculated based on the rock mass classification results. (4) Preliminary support system design and the wedges that caused structural damage in the drift were analyzed using Unwedge software. The plastic zone and stress state around the drift were analyzed using RS2 software. (5) The factor of safety was used to evaluate the reliability of the drift. (6) If both the numerical simulation results and the factor of safety indicated that the support system was effective, field support installation and displacement monitoring were carried out to further evaluate the reliability of the support system. Figure 1 shows the process of drift support design.  
Figure 1: Flow chart for drift support design in the Sanshandao Gold Mine.
## 3 Results
### S10 Induced ROS Accumulation and Callose Deposition  
The DAB staining results indicated that wheat seedlings inoculated with _F. graminearum_ only (Fg) showed lighter coloration, whereas wheat seedlings in the S10 + Fg group exhibited darker staining at 24 hpi (Figure 4A). The S10 + Fg treatment group showed considerably more significant darker coloration than the Fg group at 48 hpi (Figure 4A), indicating that S10 induced ROS accumulation in wheat seedlings upon _F. graminearum_ infection. Consistently, the H\({}_{2}\)O\({}_{2}\) content in the S10 + Fg treatment group was 1.5- and 1.68-fold higher than in the Fg treatment group at 24 (_p_ < 0.0001) and 48 hpi (_p_ < 0.0001), respectively (Figure 4B). Callose deposition is ROS triggered defense for cell wall reinforcement. Callose deposition was noticeably enhanced in S10-treated together with _F. graminearum_-inoculated plants at 24 hpi and 48 hpi (Figure 4A). The S10 pretreatment resulted in more than 2-fold increases in the fluorescent intensities of callose deposition at 48 hpi (_p_ < 0.0001), in comparison with the Fg treatment group (Figure 4C). Noteworthily, the S10 treatment group (S10) exhibited light ROS accumulation and callose deposition, indicating that they were triggered by _F. graminearum_ infection instead of S10 treatment. Thus, S10 induced plant resistance upon pathogen attack and led to a rapidly activated cellular defense response.  
Figure 4: Effects of _S. pratensis S10_ on ROS accumulation and callose deposition in wheat seedlings. (**A**) ROS accumulation and callose deposition in wheat seedlings in different treatment groups at 24 and 48 days post inoculation (dpi), respectively. Bars = 50 \(\upmu\)m. (**B**) The H\({}_{2}\)O\({}_{2}\) content in different treatment groups. Data are presented as mean \(\pm\) SE of three independent experiments consisting of three biological replicates (_p_ < 0.05, ANOVA, Duncan’s test). (**C**) The relative fluorescent intensity of callose deposition was evaluated by the ImageJ software. Data are presented as mean \(\pm\) SE of three independent experiments consisting of three biological replicates (_p_ < 0.05, ANOVA, [PERSON]’s test). Lowercase letters on bars indicate significant differences. SDW represents wheat plants treated with sterile distilled water used as a control; S10 means the wheat seedlings were treated with _S. pratensis_ S10 only; Fg indicates the wheat seedlings that were inoculated with _F. graminearum_ only; and S10 + Fg represents the wheat seedlings that were pretreated with S10 24 h prior and then inoculated with _F. graminearum_.
Table 2: _Cont._parameters, phagocytosis, lymphocyte proliferation, and antibody responses were studied at different study points (6, 18, and 28 days). OEO and _B. subtilis_ supplementation significantly improved growth performance, as evidenced by increased weight gain and a reduced feed conversion ratio. OEO and _B. subtilis_-supplemented groups notably enhanced hematological parameters and phagocytic activity. OEO supplementation significantly increased lymphocyte proliferation. OEO and _B. subtilis_ supplementation boosted serum antibody levels at 18 and 28 days, respectively. Histological examination revealed improvements in intestinal morphology. Overall, supplementation with _B. subtilis_ and OEO showed beneficial effects on growth and immunity, suggesting potential alternatives to antibiotics in poultry production [98]. Holstein dairy cows supplemented with Digestarom(tm) Diary(r), an essential oil blend (3 g/day), showed improvement in their immune status and energy metabolism during the transition period without affecting the health of the newborn calves [99]. [PERSON] laying hens were supplemented with different concentrations (100, 300, and 500 mg/kg) of microencapsulated essential oils (MEO) for 56 days (supplements for one week on and one week off). MEO supplementation (300 and 500 mg/kg) improved egg production and feed conversion ratios compared to the control group and decreased the egg-breaking ratio. Additionally, shell thickness and Haugh unit values increased significantly. Immunoglobulin and cytokine levels in serum and parameters related to intestinal morphometry were higher in the MEO300 and MEO500 groups than in the control. Furthermore, supplementation with MEO enhanced the plasma antioxidant capacity. Overall, intermittent feeding of MEO improved egg production, antioxidative processes, immune functions, and intestinal morphology, enhancing egg quality in laying hens. The study suggests feeding 300 mg/kg of MEO can significantly benefit animal health and egg quality [100].
## 3 Results and Discussion
### Preliminary Study to Evaluate the Amount of SSA to Incorporate in the Asphalt Mixtures  
Considering that SSA is more porous and rougher than granite aggregates, the workability of the corresponding asphalt mixtures might be lower. Thus, two SSA percentages were used in this preliminary study to verify the maximum SSA incorporation rate that can result in asphalt mixtures with adequate volumetric characteristics and durability.
## 5 Construction
* Proxy Key Generation: \(CR.token(SK_{CR_{r}},\;t)\longmapsto(tk_{CR_{r}})\) After \(CN_{r}\) verifies the authenticity of the message source, \(CR_{r}\) generates a proxy key \(tk_{CR_{r}}\) to facilitate secure decryption. \(CR_{r}\) selects a random number \(t\in\mathbb{Z}_{p}\), ensuring the uniqueness and confidentiality of the proxy key. The composition of \(tk_{CR_{r}}\) is as follows: \[tk_{CR_{r}}=\Big{(}D^{t}=g^{(a+r_{t})\beta t},\;\forall l\in\mathbb{U}_{l}:\;D _{l}{}^{t}=\big{(}g^{r_{t}}H(l)^{\eta}\big{)}^{t},\;D_{l}{}^{t}=\big{(}g^{r_{t }}\big{)}^{t}\Big{)}\] Finally, \(tk_{CR_{r}}\) is sent to \(CR_{r}\) to perform the first stage of decryption. * First Stage of Decryption: \(CN.Decryption(CT_{2},\;tk_{CR_{r}})\longmapsto(IDM)\) Using the proxy key \(tk_{CR_{r}}\), which corresponds to the attribute set \(S\), \(CN_{r}\) executes this algorithm to decrypt \(CT_{2}\). The decryption process is designed as a recursive algorithm. * If a node \(x\) is a leaf node, its attribute is assigned as \(i=att(x)\). If \(i\in S\), meaning that the attribute satisfies access policy for that leaf node, the decryption is computed as follows:  
Finally, \(CR_{r}\) verifies the correctness of the decryption result using the following operation:  
\[HMAC\bigg{(}key,\ \widetilde{C}\bigg{|}\bigg{|}timestamp\bigg{)}\dot{=}\,\sigma.\]
# Towards Improved Inertial Navigation by Reducing Errors Using Deep Learning Methodology
## 4 Discussion  
By comparing our results with prior research, we found that a higher error-reduction improvement was obtained for the position calculation compared with PF [23], ANN [40], or RL [59] approaches (PF: 29% [23], ANN: 15% [40], and RL: 32.64% [59]). We achieved relatively closed error improvement compared with one RF [39] LSTM [52] approach (RF: 40.08% [39], LSTM: 40% [52]). However, the RF approach from [PERSON] et al. [39] works for only accelerometers, and the LSTM approach from [PERSON] et al. [52] was applied to low-grade IMUs with a lower data-sampling frequency (10 Hz) over a short range (2 m). Compared to EKF [22], SVM [42], RNN [50], and GRU [56], our error reduction was lower for the position (38.69%) than the improvements achieved with it (EKF: 65% [22], SVM: 96% [42], RNN: 64.8% [50], and GRU: 72.0% [56]). The EKF approach from [PERSON] et al. [22] achieved up to 65% error reduction for the position, but the system needs external measuring sources such as GPS, which is not suitable for GPS-denied situations. The SVM approach from [PERSON] et al. [42] achieved up to 96% error reduction, but their system also mostly relies on GPS. The RNN approach from [PERSON] et al. [50] achieved up to 64.8% error reduction. However, their system requires aid from external cameras. The GRU approach from [PERSON] et al. [56] achieved up to 72.0% error improvement, but their system focuses on gyroscopes' denoising only. These results show that the proposed CNN methodology is a useful tool for the error-reduction method for MEMS IMUs. The CNN-training and -test results show the useful capability of data regression and classification.
## 3 Results
### Image Contrast  
The image contrast was also directly assessed on the three 5-mm-diameter anechoic holes of Ph2. Figure 7b shows an image of Ph2 for the plane y = 0. The holes are identified as well as the speckled background area used for the calculation of C0. The holes 1, 2, and 3 have similar positions to the threads 1, 2, and 3 of Ph3, respectively.  
Table 3 presents the contrast C0 obtained for the four rotate-translate scans presented in Table 1 and for a cyst area defined by a disk of radius 0.8 mm centered on each hole. First, we noted that the cylindrical anechoic holes can all be detected with a contrast higher than 20 dB compared to the speckle background. This result is in agreement with the iCyR values determined in Section 3.3, because the radius of the holes was 2.5 mm and the largest median iCyR was 1.5 mm. The C0 values of Table 3 are similar for the three holes. The contrast can then be considered as spatially homogeneous in the DSCA. However, C0 decreases with the decreasing angular period \(\Delta\alpha\) for the straight transmit. Moreover, the contrast was stronger for the steered transmit acquisition at \(\Delta\alpha_{2}\) than for the straight transmit acquisition at \(\Delta\alpha_{3}\). For these two acquisitions, the iCyRs were both on the order of 1 mm. We can note that the disk of the cyst area was at a distance larger than 1 mm from the edges of the hole. As a consequence, the influence of the LSF on C0 might be considered low, and the lower value of C0 for the steered transmit at \(\Delta\alpha_{2}\) was most likely linked to the higher number of tomographic positions in this mode (Table 1) and the resulting increased spatial averaging of the electronic noise during the reconstruction process. The C0 values of Table 3 can be considered as a measure of the contrast to noise of the different acquisition parameters. They all decreased with the number of tomographic positions.  
The contrast was evaluated for non-overlapping concentric rings of the same area as the central disk. Figure 9a displays \(C0\) as a function of the external radius of each ring for hole 1. As expected, \(C0\) reached \(0\) dB for rings located outside of the hole. Inside and in the vicinity of the scattering edges of holes, \(C0_{dB}\) was influenced by the spread of the LSF. As for \(Cc\) shown in Figure 8a, the contrast \(C0\) decreased with the decreasing \(\Delta\alpha\) for the straight transmit. However, whereas the \(Cc\) values were similar for the straight transmit at \(\Delta\alpha_{3}\) and the steered transmit at \(\Delta\alpha_{2}\), \(C0\) was lower for the steered transmit acquisition from a distance of about \(1\) mm from the edges.  
Figure 9b-e illustrates the visibility of the anechoic hole 1 for the different rotate-translate acquisitions. For straight transmits, the center of the hole was indeed darker when \(\Delta\alpha\) decreased. Additionally, the hole appeared larger in the lateral direction (along the x-axis). This result corresponds to the anisotropy of the LSF, identified by the aCyR (Figure 8b).