esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Method for Cross-Modal Collaborative Analysis and Evaluation in the Intelligence Era
## 3 Research Method
### Model Architecture
#### 3.2.1 Representation, Normalization, and Alignment of Cross-Modal Learning Behavioral Data  
The data in the learning process includes multiple modalities such as video, text, and audio. In multi-modal data, each modality provides specific information for other modalities, and there is a certain correlation between the modalities. In this paper, the text, audio, and visual in the video of the learner's learning process are used as data for processing, and the two techniques of batch normalization and layer normalization are used to remove the dimension and realize the normalization of the training data. Formula (1) is a specific representation of normalization. For input \(z_{i}^{l}\), after calculating its mean \(\mu\) and variance \(\sigma^{2}\), normalization is performed, where \(\gamma\) and \(\beta\) represent the learnable parameter variables of scaling and translation, respectively. For each neuron, before the data enters the activation function, the mean and variance of each batch are calculated along the channel so that the data maintains a normal distribution with a mean of 0 and a variance of 1 to avoid the disappearance of the gradient.  
\[\tilde{z}_{i}^{l}=\frac{z_{i}^{l}-\mu}{\sqrt{\sigma^{2}+o}}\times\gamma+\beta \tag{1}\]  
The aligned hidden vectors are spliced as the input of the subsequent module; it is the LSTM-Attention (Long Short-Term Memory and Attention Mechanism) combination module. The technical route of characterization, normalization, and alignment of cross-modal streaming media data is shown in Figure 2.
# Inversion of a Thunderstorm Cloud Charging Model Based on a 3D Atmospheric Electric Field
## 3 Experimental Results and Analysis
### Comparative Analysis of Electric Field Data  
According to the lightning activity in Nanjing and surrounding areas, Nanjing Meteorological Bureau has established a monitoring network covering the whole city by using the traditional atmospheric electric field meter, as shown in Figure 7. Point A in the figure is the three-dimensional electric field meter (AEFM), which is located at Nanjing University of Information Science and Technology (118 deg43'00.00'' E, 32 deg11'56.40'' N). Pukou, Liuhe, Nanjing meteorological Bureau, and Kunlun are the observation of several conventional atmospheric electric field meters that are closest to point A. The positive direction of X is the east direction, and the positive direction of Y is the north direction.  
The distance between each station and the three-dimensional electric field meter is known in Figure 7. So the vertical electric field components of each station can be calculated based on the  
Figure 6: The convergence curve of the three-layer charge model.  
Figure 7: Monitoring network of the atmospheric electric field meter in Nanjing.  
structural parameters of thunderstorm cloud retrieved in Table 1. The vertical electric field values obtained by the three-dimensional electric field meter were compared with the official measurements as shown in Table 2.  
It can be seen that the vertical electric field values at the other locations obtained by the three-dimensional electric field meter were consistent with the official measurements.
# A Detailed One-Dimensional Hydrodynamic and Kinetic Model for Sorption Enhanced Gasification
## 4 Sensitivity Analysis of Fuel Feeding Rate
### Performance Diagram for Gasifier Operation  
Based on the results derived from this work, a performance diagram of the bubbling fluidized bed gasifier was created in Figure 12. This relates the selected gasification temperature (based on downstream requirements of the syngas composition) and fuel feeding rate to the power of the syngas. Additionally, the gas velocity ratio from the superficial gas velocity at the fluidized bed surface and the gas velocity ratio for the lowest velocity in the fluidized bed are depicted.  
By combining the gasification chemistry (which lead to the syngas power) and the hydrodynamic information (represented as the gas velocity ratio), it is possible to identify a realistic operational range of the gasifier.  
With a gasification temperature of 650 \({}^{\circ}\)C and 25 kg/h fuel input, the lowest gas velocity ratio of 20 could be identified. This means that, in any position of the fluidized bed, the superficial gas velocity was 20 times higher than the minimum fluidization velocity. Hence, good mixing of the bed inventory can be assured. At the bed surface, the superficial gas velocity was 26 times higher than the minimum gas velocity and only a little particle extraction can be expected. Increasing the fuel feeding rate to 36 kg/h at 650 \({}^{\circ}\)C, the syngas power increased and reached almost 100 kW. At this point, the velocity at the surface of the fluidized bed increased by a factor of 37 (related to \(u_{m\!f}\)) and by a factor of 27 (related to \(u_{m\!f}\)) for the lowest velocity in the bed. By increasing the gasification temperature and the gasifier feeding rate up to 750 \({}^{\circ}\)C and 36 kg/h, respectively, good mixing in the fluidized bed is guaranteed. However, due to the high velocity at the bed surface (a factor of 47 related to \(u_{m\!f}\)), a high particle extraction has to be accounted for. To avoid this negative effect, the fuel input or the gasification temperature can be modified. In that case, the syngas composition is not very important for downstream applications, an identical syngas power (around 100 kW) can be reached for a gasification temperature of 750 \({}^{\circ}\)C and 30 kg/h fuel input or for an operation with 650 \({}^{\circ}\)C and with a fuel input of 36 kg/h. However, the operational point at 650 \({}^{\circ}\)C reduced particle extraction from the gasifier due to the non-linear behavior of the gas velocity ratio curves.  
Figure 12: Power of syngas for gasification temperatures between 650 and 750 \({}^{\circ}\)C for water-free fuel input of 25 kg/h, 30 kg/h, and 36 kg/h (left side); Corresponding gas velocity ratio (related to \(u_{m\!f}\)) at the top of the fluidized bed and for the position where the lowest velocity in the fluidized bed occurs (right side).
## 6 Applications of VSNs
### Agricultural Monitoring  
Accurate temporal and spatial monitoring can significantly increase agricultural productivity. Due to natural limitations, such as a farmers' inability to check the crop at all times of day or inadequate measurement tools, luck often plays too large a role in the success of harvests. Using a VSN, indicators such as temperature, humidity, soil condition, can be automatically monitored without labor intensive field measurements. For example, VSNs could provide precise information about crops in real time, enabling businesses to reduce water, energy, and pesticide usage and enhancing environmental protection. The sensing data can be used to find optimal environments for the plants. In addition, the data on the planting condition can be saved by sensor tags, which can be used in supply chain management. Let us consider the following scenario for further clarification: in a fruit garden of medium to large size, a number of 50 to 100 sensor nodes are manually deployed in order to provide full signal coverage over the study area. An additional number of 100 to 1,000 leaf nodes with (possibly heterogeneous) specialized sensors (_i.e._, humidity, temperature, soil condition, sunlight) are attached to the local wireless star topologies, periodically reporting measurements to the associated nodes. For example, in a 20-acre fruit garden with eight parcels of land, 10 sensor nodes are placed within each parcel to provide readings on temperature and soil moisture. The sensor nodes are able to support a multi-hop forwarding/routing scheme to enable data forwarding to a sink node at the edge of the fruits garden. Each of the eight parcels contains one data aggregator to collect the sensed data. Ten intermediate nodes are used to connect the sink nodes to the main gateway. This scenario can be better handled by the concept of sensor network virtualization.
# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 2 Basic Principles of FL over Wireless Networks
### Energy Consumption Model
#### 2.3.2 Communication Energy Consumption  
Let \(N_{\text{s}}\) denote the size of the model parameters transmitted during each communication round. The transmission time between the \(k\)-th client and the central server (CFL) or other clients (DFL) is computed as  
\[tom_{k}^{\text{u}}=\frac{N_{\text{s}}}{R_{k}^{\text{u}}}, \tag{8}\]  
where \(R_{k}^{\text{u}}\) is the uplink rate for the \(k\)-th client, derived in (4) or (5). Consequently, the total communication energy consumption for the \(k\)-th client in one round is expressed as  
\[Ecom_{k}^{\text{u}}=E\{|x_{k}|^{2}\}\times tem_{k}^{\text{u}}=\frac{p_{\text{ u}}^{\text{max}}w_{k}^{2}N_{\text{s}}}{R_{k}^{\text{u}}}. \tag{9}\]  
From (9), the communication energy consumption at each client depends on the uplink power and rate. While higher uplink power improves the uplink rate, reducing latency in the FL update process, it also increases energy consumption, which is a concern in resource-constrained environments. Therefore, power control strategies are considered to optimize transmission power, striking a balance between signal quality and energy efficiency. Additionally, bandwidth allocation strategies are also crucial for reducing communication latency and improving uplink efficiency by assigning resources based on client requirements or network conditions, thus ensuring both reduced delays and fairness. Consequently, resource allocation strategies, such as bandwidth and power control, are essential for enhancing communication energy efficiency in FL over wireless networks. These strategies can be efficiently optimized using advanced techniques such as convex optimization or reinforcement learning, as referenced in several studies.
## 5 Understanding Decentralised Identity Systems
### uPort  
uPort is an open-source solution based on the Ethereum blockchain. Its architecture is based on Ethereum smart contracts, which control identity generation, management, and recovery. These smart contracts guarantee that all identity-related transactions on the Ethereum blockchain are secure, transparent, and irreversible. The uPort mobile app saves the user's private key on their smartphone and serves as a link between the user and the contracts. Users can ensure security and privacy by keeping the private key on their personal device [69].  
When a user generates an identity, two smart contracts are generated on the Ethereum blockchain--\"controller\" and \"proxy\". The proxy contract serves as a unique, persistent identification that communicates with other contracts on the blockchain network. It adds a layer of abstraction between the user's private key and the blockchain, allowing for key recovery while preserving the identity's integrity. The proxy contract collaborates with a controller contract, which maintains access control and allows users to authenticate themselves to the proxy contract. This technique assures that the user's identity is protected even if the private key is lost or compromised [70].  
In the instance of key loss, a Recovery Quorum Contract is used. This is controlled by a group of trustworthy individuals (recovery delegates) who assist in regaining control of the identity. This decentralised recovery mechanism for user identities is a critical element of uPort since it allows users to reclaim their identity without relying on a centralised authority [70].  
uPort uses both on-chain and off-chain data handling. Smart contracts handle essential identification functions on the blockchain and uPort identities can be linked to off-chain data saved on decentralised storage platforms such as IPFS. The data maintained off-chain comprise profile information, attestations, and other identity-related qualities that are cryptographically connected to the uPort identity via a Registry Contract. This way, the owner receives complete control over their data, resulting in a secure and private manner to handle identity [69].  
Limitations  
The main limitation of uPort is the system's dependence on Ethereum, which means that it is subject to the network's scalability and transaction costs. The off-chain storage of sensitive data, while secure and gives users a sense of ownership, still requires trust in the chosen storage solution. The issue of portability and interoperability may arise since it is primarily based on Ethereum. Also, given uPort's reliance on smart contracts, a malicious node could trace and link activities tied to a uPort ID, which may potentially expose users identity and actions, compromising their identity [69; 70].
# A Review of the Impacts of Implant Stiffness on Fracture Healing
## 4 Clinical Applications of Low Young's Modulus Metallic Materials to Accelerate Fracture Healing
The efficacy of fracture fixation is determined not only by Young's modulus of the plate material but also by its length and thickness. While the beneficial effects of the TiNSbSn alloy on bone healing were observed in rabbit tibia studies [11; 58], the optimal dimensions for plates--specifically their length and thickness--will vary for different long bones. It is also critical to carefully consider these dimensions when applying TiNSbSn alloy plates to human patients in clinical settings. Despite this, the successful use of hip prostheses made from TiNSbSn alloy suggests that the principles of design similar to Ti6 Al4V prosthesis could be advantageous for developing fracture treatment plates. The authors consider that the stiffness of implants plays a critical role in fracture healing by optimizing IFM and load transmission to the bone. The TiNSbSn alloy plate emerges as a promising alternative to the Ti6 Al4V alloy plate, particularly in scenarios where a load greater than that which the Ti6 Al4V alloy plate can accommodate is required, allowing the healed bone to bear a larger share of the load. However, the transition of TiNSbSn alloy plates from experimental to clinical use faces several challenges, including cost considerations, indicating that multiple factors must be addressed before these materials can be widely adopted in clinical practice.
## 2 Materials and Methods
### ChIP-qPCR Assay  
The ChIP-seq assay was performed against the NF-\(\kappa\)B p65 (p65) protein using SNU719 cells (3 \(\times\) 10\({}^{7}\) cells/sample) treated with 100 nM DHT for 0, 0.5, and 1 h, followed by formaldehyde crosslinking. The resultant SNU719 cell lysates were sonicated to obtain DNA fragments of approximately 100-500 bp length. Immunoprecipitation was performed using 2.5 ug of either rabbit anti-p65 antibody (Ab) (Cell Signaling Technology, Danvers, MA, USA) or control rabbit IgG Ab (GeneTex, Irvine, CA, USA), followed by overnight incubation with Ab-coated Dynabeads protein A/G (Invitrogen, Carlsbad, CA, USA). The incubated beads were washed five times with ChIP-seq wash buffer (50 mM HEPES [pH 7.5], 500 mM LiCl, 1 mM EDTA, 1% NP-40, 0.7% Na deoxycholate, and 1\(\times\) protease inhibitors) and then washed once with 50 mM NaCl in TE buffer. Immunoprecipitated DNA was eluted using ChIP-seq elution buffer (50 mM Tris-HCl [pH 8], 10 mM EDTA, and 1% sodium dodecyl sulfate [SDS]), reverse-crosslinked at 65 \({}^{\circ}\)C, treated with RNase A (0.2 mg/mL) and proteinase K (0.2 mg/mL), purified with phenol and chloroform, and subjected to quantitative polymerase chain reaction (qPCR) validation using the ChIP assay. The ChIP samples were used to determine the p65 enrichment through a qPCR assay. The p65 enrichment was investigated for the \(AR\) exon 1 region (chrX: 67,545,300-67,545,350 bp) and _MICA_ promoter region (Chr6: 31,394,900-31,385,050 bp), respectively. Information on the primer sequences used in the ChIP assay was to be submitted upon request.
## 5 Discussion
### Objective Function
#### 5.3.4 Global Criterion Method  
* Advantages: Targets the identification of a solution that optimizes all objectives concurrently, potentially yielding more balanced and universally acceptable outcomes. Moreover, concentrating on a singular, global criterion enhances efficiency in discovering a solution that moderately satisfies all objectives, diminishing the necessity for iterative or repeated optimization processes.
* Disadvantages: Developing a global criterion that accurately encapsulates the significance of all objectives poses a challenge, particularly in scenarios where the objectives significantly conflict or are difficult to quantify. Furthermore, a singular focus on a global criterion may result in missing some Pareto optimal solutions, especially those poorly represented by the selected global criterion.
## 4 Experimental Results and Security Analysis  
In this section, we simulate the proposed chaotic stream cipher based on the improved coupled map lattices by Matlab, and measure the linear complexity, as well as the randomness tests under the FIPS PUB 140-1 and NIST SP800-22. We also analyze the resistance for some known attacks.  
From the results presented in Section 3, we find that the proposed improved CML schemes have overcome the weakness of the original CML for \(\varepsilon=0.3,\,0.4,\,0.5,\,0.7\) and \(0.8\). For simplicity, in the rest of the experiments of this section, we set \(\varepsilon=0.7\) for our simulation.  
Figure 14: The structure of the improved compound seven stage CML, combining logistic map and tent map in alternate stage.  
Figure 15: The simulation result of the improved compound CML, combining the logistic map and tent map in an alternate stage.
## 2 Literature Review
### Application of Mixture of Experts  
In response to the above-mentioned systematic contradictions, [PERSON] et al. [28] proposed the MoE model, providing an innovative solution for constructing an intelligent software engineering system. Its core mechanism consists of two key components: the expert routing decision layer based on the gating network and the expert sub-model cluster oriented to domain features. The sparsely gated MoE architecture proposed by Google [29], which employs a sparse activation mechanism, lays the foundation for large-scale engineering applications.  
The MoE method has high computational efficiency and scalability. MoE enables dynamic resource allocation via conditional computation, achieving exponential parameter scaling (e.g., trillion-parameter models like Switch Transformer [30]). Furthermore, MoE has the capabilities of domain adaptation and task decoupling. It uses multidimensional features to compute expert weights and map tasks to experts. In software engineering, this allows the creation of diverse expert clusters: language-specific compilers, phase-aware controllers for workflows (requirements analysis, code generation, test verification), and cross-role behavior modelers (developer intent recognition, tester pattern mining, requirement tracing). [PERSON] et al. [31] proposed that the dynamic routing mechanism could solve the capacity bottleneck of the traditional single model architecture in complex task scenarios.  
At the level of engineering practice, MoE technology has shown significant application value. For example, Deepseek-v3 [32], GPT-4 [33] and other large models have adopted MoE architecture. This approach can be applied throughout the software development lifecycle: activating requirements experts for specifications in the analysis phase, domain-specific models (e.g., SQL optimization or concurrent programming experts) in the development phase, and quality assurance experts in testing. This dynamic scheduling breaks traditional tool-chain constraints and creates a knowledge enhancement loop through continuous expert experience accumulation.
## 5 Experimental Results and Discussion
### Horizontal Positioning Error Analysis by Static Data  
This subsection discusses the horizontal positioning error, which contains both the lateral and longitudinal positioning error of the proposed method. We use static data in this discussion because it is difficult to obtain a perfect ground truth of dynamic pedestrian trajectory. Figure 17 shows the static positioning results in both the middle and deep urban canyon environments. In the case of the middle urban canyon, it is obvious that the positioning results of the conventional WLS using only LOS are located on wrong side of the street. Instead, the proposed 3D map method can estimate the result with correct side of the street. To compare the result of the 3D map method before and after the RAIM satellite selection methods, the red points are denser than the yellow points, not only in lateral but also in  
Figure 14: Demonstration of the bins of the histogram of the lateral positioning error. The red line indicates the pedestrian walking trajectory.  
longitudinal directions. In Table 5, the RAIM satellite selection method slightly improves the 3D map method in the middle urban scenario; however, it improves the 3D map method greatly in the deep urban canyon case. The means of positioning error are about 11.6 and 3.82 m before and after using the RAIM, respectively. Comparing Tables 4 and 5, the improvements using the RAIM method are about 4.9 m in the lateral error and 7.8 m in horizontal error. Therefore, we can conclude that the proposed RAIM method can reduce not only lateral but also longitudinal positioning error.  
Figure 16: Histogram of the lateral positioning error of the proposed 3D map method before and after using the RAIM satellite selection method in the deep urban canyon.  
Figure 15: Histogram of the lateral positioning error of the proposed 3D map method before and after using the RAIM satellite selection method in the middle urban canyon.
# Encapsulated Probiotics: Potential Techniques and Coating Materials for Non-Dairy Food Applications
## 4 Biomaterials Utilized for Probiotics Encapsulation
### Carbohydrate Polymers
#### 4.1.7 Carrageenanans  
Carrageenanans are natural hydrophilic polymers extracted from red seaweeds (Rhodophyta). Among the three commercially available carrageenanans (\(\kappa\)-, \(\
u\), and \(\lambda\)-carrageenanan), \(\kappa\)-carrageenanan is the most widely used in the encapsulation of probiotics [1]. This is due to the thermosensitive and thermoreversible characteristics of \(\kappa\)-carrageenanan, making it a suitable material to deliver probiotics as the release can be controlled with temperature [79]. In general, encapsulation using \(\kappa\)-carrageenanan involves the addition of probiotics to melted (at 80-90 \({}^{\circ}\)C) \(\kappa\)-carrageenan during the cooling period (at 40-50 \({}^{\circ}\)C). The encapsulation is completed when gelation occurs, i.e., when the reaction mixture is cooled to ambient temperature [1]. Nevertheless, the gels produced using \(\kappa\)-carrageenanan are brittle and weak [41]. The properties of the formed gel can be enhanced by combining it with \(\iota\)-carrageenanan, locust bean gum, alginate, and carboxymethyl cellulose [41,79]. \(\kappa\)-carrageenanan hydrogels have been used to deliver probiotics. It is observed to increase the viability of probiotics under gastrointestinal conditions and storage at refrigeration conditions (4 \({}^{\circ}\)C) and room temperature at 22 \({}^{\circ}\)C [79]. However, the rate of probiotic release from carrageenanan-based hydrogels is much slower than from alginate-based hydrogels [41]. Carrageenanan (\(\kappa\)-carrageenanan) has also been used to enhance the viability of _B. bifidum_ incorporated in grape juice [7]. The viability of _B. bifidum_ encapsulated with carrageenanan (7.09 log CFU/mL) was higher than the free probiotics (6.58 log CFU/mL) after being stored for 35 days.