esa-sceva/satcom-chunk-collection huggingface dataset:
# Reputation-Driven Dynamic Node Consensus and Reliability Sharding Model in IoT Blockchain
## 3 System Model
### Node Cross Reconfiguration Sharding Scheme (NCRS)
#### 3.2.2 Details of NCRS
At this time, the reputation of S1 is [1; 2; 4, 8], and the reputation of S2 is [3; 5; 6; 7], and the fitness function value after the crossing is calculated to be 2.25. The purposeful node crossing between shardings can help NCRs gradually find the optimal sharding distribution scheme satisfying the fitness function. ```
Input: Number of Iterations Count, Initialization: Random Shard, \(Fit=+\infty\), Current Iterations CL = 0;  While \(CL<Count\):  Then CL++  Crossover the shards, get a new shard distribution newShard;  Calculate the fitness value newRes;  If \(newRes\leq Fit\) then \(Fit\gets newRes\) \(Shard\gets newShard\) End Output Shard
```  
**Algorithm 1**Node cross reconstruction sharding scheme based on reputation. After a reliable sharding scheme is obtained, the sharding is randomly divided into two categories: order sharding and consensus sharding. There is only one order sharding, and all other shardings are consensus sharding. The function of consensus sharding is to achieve consensus in the acquired transactions, package them into prepared blocks and send them to the order shard. The functions of order shards are: (1) to obtain transactions  
Figure 4: Schematic diagram of node crossing between patches. and forward them to consensus shards; (2) package the prepared blocks to generate a complete block. The consensus process between shards is shown in Figure 5.  
After sharding, the order shard communicates with the client and obtains the transactions sent by the client, and sending the transactions of the same user to the same common sharding for consensus according to the signature of the users participating in the transaction, to realize transaction sharding and reduce the communication volume between the shards; The transaction only completes the consensus within the consensus shard. After completion, it is packaged into a prepared block and sent to the order shard. The order shard processes the prepared block into a complete block for transmission and broadcasting (there is no parent hash in the prepared block because the parent block that needs to be linked cannot be determined when the prepared block is generated, and the parent block can be determined only after order block processing); the processing order of the blocks in the ordering sharding shall be subject to the timestamp of the block received by the order sharding. Sharding is dynamic and not fixed. It needs to be rebuilt in the following two cases: (1) when nodes are eliminated; (2) after completing an epoch consensus. Obtain the reputation environment of the current node, build a new sharding through NCRS, and make the sharding unpredictable by dynamic sharding.
Distant supervision, a method of generating labeled data by aligning unannotated text with predefined knowledge bases [8], has been extensively explored for automating data annotation. Despite its potential, this approach often introduces substantial noise due to erroneous assumptions, such as rigid alignment of text to labels. For example, in classroom dialogues, the same utterance might reflect different pedagogical intentions depending on the context, making rigid alignment approaches prone to misclassification. These limitations, coupled with the resource-intensive nature of manual annotation in educational contexts, highlight the need for innovative approaches to dataset construction. To overcome these limitations, this study introduces EduDCM, a novel framework that bridges the gap between automated methods and domain-specific requirements. By leveraging the capabilities of LLMs and combining them with heuristic alignment and semantic disambiguation techniques, EduDCM ensures that annotations are not only accurate but also context-sensitive. This framework represents a significant step forward in addressing the scalability and precision trade-offs inherent in existing methods. For low-resource educational tasks, the lack of robust and diverse training datasets remains a critical bottleneck. While advances in NLP, such as zero-shot learning and multi-task training [9], offer some solutions, they struggle to effectively adapt to task-specific requirements in the educational domain. Few-shot learning methods, particularly those leveraging prompts with LLMs [10], demonstrate potential but are limited in their scalability and performance under noisy data conditions. Moreover, these methods often fail to account for the dynamic and interaction-driven nature of classroom discussions, where context plays a pivotal role in determining the meaning of an utterance. EduDCM overcomes these limitations by combining LLM-driven dialogue classification with advanced heuristic techniques, enhancing the quality of generated data and fostering model adaptability. In particular, EduDCM's ability to integrate domain knowledge into its annotation pipeline allows it to handle ambiguity and variability in educational dialogues effectively. This makes it a versatile tool for generating high-quality datasets tailored to low-resource educational contexts.
# The Current State and Future Potential of Microbial Control of Scarab Pests
## 4 Bacteria  
Although many bacterial species live symbiotically with insects across all developmental stages, at least 100 species of bacteria act as insect pathogens. The majority of these species are found in the Bacillaceae, Pseudomonas, Enterobacteriaceae, Streptococcus, and Micrococcaceae families [18]. Relationships between insects and bacteria, whether beneficial or harmful, can be obligate or facultative, leading to complex ecological interactions. Entomopathogenic bacteria have been used to develop a range of insect control products that utilize spore and/or bacteria-derived toxin formulations. These formulations must be ingested, after which the bacterial spores proliferate in the hemocoel, causing death by starvation and/or septicemia. Bacterial toxins, on the other hand, create perforations in the gut epithelium and typically cause death through secondary infections [19]. Currently, only three bacterial species are available for biocontrol of scarabs in the U.S.; _Paenibacillus poppiliiae_ and _P. lentitimorus_, which make up milky spore disease, and the _Bacillus thuringiensis_ subspecies galleriae (Table 1). In other parts of the world, however, other bacterial species have been isolated from scarabs, such as _Serratia entomphilia_, which is commercially available for biocontrol in New Zealand to control grass grub (_Costelytra zealandica_) and _Yersinia entomphaga_, which shows additional activity against some other scarab species.  
While natural epizootics in field and laboratory populations have been documented for several bacterial strains in lepidopteran populations [20; 21; 22], fewer example are found for scarabs. Milky spore disease (_P. poppiliiae_) and _B. thuringiensis_, however, have been found to persist in the soil for years or decades after application [23], suggesting that they may be capable of producing epizootics under the right conditions. In general, the evolutionary history of these bacteria and their scarab hosts are poorly understood but the identification of new scarab-specific bacterial toxins show strong potential for future development.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Product** & **Species/Strain** & **Estimated Cost/Acre** \\ \hline Bacteria & & \\ Milky Spore Powder (St. Gabriel Organics) & _P. poppiliiae_ & \\ grubGONE! G (Phylom BioProducts) & _Bt_ galleriae & \\ beetleGONE! (Phylom BioProducts) & _Bt_ galleriae & \\ grubHALT! (Phylom BioProducts) & _Bt_ galleriae & \\ \hline \hline \end{tabular}
\end{table}
Table 1: A list of microbial products commercially available in the U.S. for scarab pests and their estimated cost per acre, given recommended concentrations.
# PCONet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems
## 3 The Proposed PCQNet
### Testing of PCQNet  
#### 3.3.1 Baseline1: The Protocol Codebook-Based Precoding Scheme  
The 3 GPP protocol in [15] contains a set of PMIs \(\mathbf{F}_{\text{codebook}}^{(i)},i\in\mathbb{N}\) with corresponding configurations of streams and antennas. The BS firstly calculates the precoding matrix with Algorithm 1 and then feeds back the binary index. The optimal codebook index \(i^{(opt)}\) is acquired with the minimum Euclidean distance by searching the predefined codebooks  
\[i^{(opt)}=\underset{\text{i}}{\text{argmin}}\Big{\|}\mathbf{F}-\mathbf{F}_{ \text{codebook}}^{(i)}\Big{\|}_{F}^{2} \tag{12}\]  
where \(i\) represents the index of the standard precoding matrix defined in the 3 GPP protocol supporting limited scenarios (e.g., \(N_{k,t}=4\), \(N_{k,s}=2\), \(i^{(opt)}\in[0,21]\) ). The number of feedback bits for the protocol codebook scheme under the scenarios with 22 indexes is 5 (\(m=\lceil\log_{2}22\rceil=5\)).  
The protocol codebook-based precoding scheme is labeled as **3 GPP codebook** in our simulation. Since this feedback method of the vector quantization only needs to search the optimal index, it greatly reduces the feedback overhead. However, the scope of the codebook scheme is limited and the precoding matrices retrieved from the codebook indexes inevitably have certain quantization error.
# Map API-Based Evacuation Route Guidance System for Floods
## 4 Implementation and Considerations
### Review  
In modern society, damages caused by floods are increasing every year as forests disappear and extensive modernization is in progress. Flash floods can cause deaths and property damage. In this paper, we propose a route-search system for flash floods.  
The data were obtained through a testbed survey. The requirements of the test district were selected based on the presence or absence of an evacuation route, sufficient walking distance to provide various traffic lines; and valleys, streams, and rivers as auxiliary routes. Based on the walking path search system, the T-Map API was selected and used as an API optimized for walking.  
A map was printed using SHP of the testbed area, public data of the corresponding administrative district, and T-Map API. A building that can be evacuated was selected as a shelter on the printed map, a dataset was created and saved, and markers were displayed on the map. The flood identification model used is also shown on the map. Based on the disaster scenario, it determines the user's current location in the event of a flood and provides a route beyond the flood area to the nearest shelter.  
The latitude and longitude values appearing in the JSON file of the route provided in this study were the same as those of the actual GPS. Therefore, the search time proposed in this study for floods affects the search for the nearest shelter in the evacuation route system and depends on whether the evacuation route is provided. However, an evacuation route was provided by the proposed system, which prevented secondary accidents.
# A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode
## 4 Establishment of Performance Indicator Evaluation Model  
The determination of performance indicator weights is based on the system's operational principles and task execution. These weights reflect the significance and prioritization of performance indicators within the comprehensive evaluation framework. As a result, during the performance evaluation process, adopting an appropriate method to establish indicator weights is of utmost importance.  
According to the classification of the influence of human factors in the weighting process, indicator weighting methods can be divided into subjective weighting methods and objective weighting methods [22]. The subjective weighting methods rely on the subjective judgment of the decision maker, while the indicator weights usually assigned by the objective weighting method change dynamically with the change of the data set. In this paper, based on the satellite telemetry data to assess the effectiveness of the satellite attitude control system, the determination of the weights depends entirely on the characteristics of the actual data, and it is more appropriate to use the objective weighting method.  
Principal component analysis (PCA) is a commonly used objective weighting method, which is more suitable for solving the weights of indicators in tracking mode. However, PCA can be affected by the correlation of the indicators, which will make the obtained indicator weights inaccurate. Compared with the traditional principal component analysis method, the improved principal component analysis method based on generalized grey number is proposed in this section. The method offers greater adaptability and flexibility, and has advantages in dealing with uncertain data, adapting to different data types, and retaining more information.
## 2 Network Model and Threat Model
### Network Model  
Generally speaking, the AMI is a network architecture used to measure, collect, store and analyze the power usage information of consumers and to support the two-way communication between consumers and utilities, leading to an efficient balance between energy demand and supply. Figure 1 illustrates an example of AMI. As we can see, a mesh network is established by smart meters (considered as smart measurement devices), which measure the electricity usage of consumers. The information measured by smart meters will be transmitted to data aggregators/concentrators through the mesh network. The data aggregators then transmit the collected data to utilities through a backbone network (e.g., a wired high-speed network). After that, utilities will perform operations (e.g., billing, demand response and others), leading to effective interactions between consumers and utilities.  
We denote each smart meter as a node in the network, and all nodes establish a wireless mesh network, which will be used to transmit data, as shown in Figure 2. There are four types of nodes in the wireless mesh network [23]: (i) the master gateway station (MGS); this represents the AMI head-end connected to the wide area network (WAN), such as the Internet; (ii) DAP station (data aggregation point); this represents the gateway of each subnetwork; (iii) the mesh relay station (MRS); this represents the relay node between the MGS and the DAP station; and (iv) the mesh-station with access point MSAP); this represents a residential smart meter. To obtain accurate data, the DAP-station will verify the memory of all nodes in the network. If the injected malicious code on the remote device is found, the remote code update mechanism will be activated to reset the remote device.
## 1 Introduction
Several path selection works have been proposed but they focus on the shortest distance [12], link latency [14; 15], link bandwidth [16] as routing metrics for route path selection. These types of metrics are insufficient and not appropriate to meet the challenging Service-Level Agreement (SLA) of delay-sensitive applications. Such flows have a stringent requirement for rerouting, for example, the routing of flows is required to be completed within 25 ms [13]. Moreover, the existing routing schemes overlook a set of critical switches during route path selection decisions. Such a set of switches have many flow table entries more often, which further augment the convergence time, and may cause update operation to yield a non-optimum path. Furthermore, frequent network changes with flows variations cause undesirable network performances in term of longer path stretch which lead to higher path setup latency, and longer switch flow table space consumption. This paper proposed an optimized route path selection scheme by incorporating new routing metrics considering link and switch parameters. The contributions of this paper can be summarized as follows. * Devising a model to estimate the link quality based on link latency and link delivery ratio. The restoration approach is embedded with this model to help in the selection of a route for every newly arrived flow. * Proposing a scheme to detect critical switches among a set of candidate paths. Link quality value and a minimum number of critical switches are used in the computation of an optimized route. The rest of the paper is organized as follows: Section 2 presents the various route path selection solutions. The design of the proposed solution is described in Section 3. Experimental setup and performance evaluation are explained in Section 4. Finally, Section 5 concludes the paper and suggests future works.
## 3 Results and Discussion
### Nondestructive Evaluation of Porosity
With increasing porosity, the reflectance peak became smoother and displayed a lower peak intensity at specific frequencies, indicating a weakened characteristic of strong reflection. The correlation heatmap was employed to visually depict the relationships among multiple terahertz feature parameters by representing the correlation coefficients between corresponding variables. The color intensity in each cell indicated the magnitude of the correlation coefficient, with darker colors indicating stronger correlations and lighter colors indicating weaker correlations. The elongation of the elliptical shape denoted higher correlation strength, with leftward tilting indicating negative correlation and rightward tilting indicating positive correlation. Additionally, the significance of the relationships between feature parameters was assessed using \(p\)-value tests: \(p<0.05\) indicated a probability of occurrence of the feature parameter samples of less than 5%, signifying a significant statistical difference; \(p<0.01\) indicated a highly significant difference; \(p<0.001\) indicated an extremely significant difference, as shown in Figure 16.  
Figure 15: Feature extraction of reflectance spectrum: (**a**) average reflectance; (**b**) reflectance standard deviation; (**c**) maximum reflectance; (**d**) minimum reflectance; (**e**) reflectance peaks. According to the outcomes of the correlation analysis, noticeable correlations were found among multiple feature parameters, and these correlations demonstrated significant statistical differences. The results of the correlation analysis demonstrated the feasibility of utilizing multiple feature parameters to describe and characterize variations in porosity, and differences were not attributable to random factors. Furthermore, these correlated feature parameters provided effective input features for subsequent machine learning prediction models, resulting in more accurate porosity predictions. Additionally, the correlation analysis served as a foundation for exploring the complex relationships between the feature parameters, allowing for further in-depth investigations into the mechanisms of their interactions.
# Data Security-Based Routing in MANETs Using Key Management Mechanism
## 2 Proposed Model
### Trust Management  
It is possible to predict the trust value by comparing the packet sequence ID of the nodes using log reports. AODV is a receptive routing protocol that organizes routes whenever the destination sequence numbers are necessary for the most recent route to acquire. This functionality allows AODV to operate an updated route to the destination. However, the projected destination nodes are less reliable due to malicious behavior. Thus, the trust value is computed using the cryptographic function, hybrid energy assessment, packet-delivery success rate, mobility, and location key calculation.  
Initially, nodes with the largest trust values are selected for packet transfer. These evaluations make the identified route credible and secure with a high confidence value. Figure 3 shows the flow diagram of the proposed technique.  
Figure 3: Flow diagram of the proposed technique.  
Figure 2: Reverse-path formation.  
Additionally, a Received Signal Strength Indicator (RSSI) is used to determine whether the selected trust node lies within the communication range, as nodes can travel in any direction and operate as both hosts and routers [27].
# A Network Traffic Prediction Method for AIOps Based on TDA and Attention GRU
## 3 System Model  
### Topological Data Analysis (TDA)  
TDA is an emerging field in complex data analysis, which studies some special geometric properties, which can remain unchanged after the shape of the graph is continuously changed and are labelled \"topological properties\" [19]. Through calculations using algebraic tools, the topological properties of the data in each spatial dimension can be strictly defined. For example, the two-dimensional space mainly includes the number of points and the degree of connection between the points, whereas the three-dimensional space mainly includes the number of hollow spheres and the degree of connection between the spheres.
## 7 Conclusions  
One major and very important result of the experiment is that the overhead introduced by using homomorphic encryption (either partially or fully homomorphic) only makes up for a fraction of the overall runtime, compared to the runtime of our plain text simulation. As can be seen with, e.g., our runtime comparison for 10 cars, fully homomorphic encryption and plain text are merely identical. For 30 cars, fully homomorphic encryption performs worst of all three approaches. However, the overhead is only 1.2%, thus, given the benefits this offers, it can be neglected for our application.  
For our future applications, these results are very valuable. For practical applications of homomorphic encryption in the context of the VICINITY project, the number or simulated cars are realistic and feasible. Hence, we expect no penalties in terms of runtime, due to the use of homomorphic encryption. However, this new approach will greatly improve privacy of the car owners and thus greatly boost the acceptance of VICINITY to them.  
Figure 10: Runtime of the simulation with and without (fully, partial) homomorphic encryption.  
Another result of the experiment is that we proved the applicability of hardware-in-the-loop- and software-in-the-loop-simulation within the proposed simulation framework. While the used realtime scheduler for the simulation definitely had an impact on the overall simulation runtime (which has obviously to be expected, since it needs to communicate with real hardware and has to slow down simulation time), it fits perfectly in the approach of dynamically interchangeable models at runtime, which can contain wrapped simulators and depict simulation time in finer grained and therefore more detailed steps.