esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Self-Healing Approaches
### Machine-Learning Algorithms
#### 4.3.5 Supervised Knowledge-Based Algorithm  
A supervised knowledge-based algorithm (SKBA) is an ML algorithm that combines expert knowledge with data-driven methods to perform classification or prediction tasks. SKBAs are typically used in situations where the amount of available training data is limited, and expert knowledge can be used to guide the learning process. SKBA is described by [33] as a learning tool that stores the previous fault and restoration data and compiles coincidental solutions. The algorithm is tuned to recognise the cause of fault, develop an imperative plan for restoration and execute restoration operations. The authors of [33] adopted a methodology that uses SKBA to formulate a restoration strategy in an active power grid. SKBAs can be used to detect and diagnose faults in a system and then take action to correct them. The algorithm typically involves a three-step process:  
* Knowledge acquisition: Expert knowledge is collected and formalised in a knowledge base, which typically includes rules or heuristics for diagnosing faults;
* Data acquisition: Data are collected from the system, including sensor data, performance metrics, and other relevant information;* Model training: The SKBA is trained using the knowledge base and the available data. The model is then used to detect and diagnose faults in the system based on the input data. The execution of SKBA on an automated restoration scheme, as noted in [33], requires a suitable understanding of resources that can be deployed to prevent fault events and deterioration of QoS. Automated restoration strategies are widely deployed in active power grid networks, ensuring the serviceability of electricity and constant power supply to consumers. Implementing self-healing functionality using a knowledge-based algorithm in guided sequential strategies provides the power grid operators with reliable knowledge of the network parameters from MATLAB to be able to visualise the origin of network overloading on the network, according to [33]. The proposed restoration algorithm is deployed with suggested strategies, rerouting proceedings, and distributed generator deployment. The proposed approach creates the possibility of network stability and transmission continuity. The SKBA in [33] is illustrated in (Figure 9) and conceptualised based on the following guidelines: 1. Detect the overloaded transmission lines in the power network; 2. Identify the affected buses that have overloaded transmission lines connected; 3. Identify the busbar with the highest reserve capacity factor to serve as a candidate for the restoration strategy; 4. Identify the nearest distributed generator located near the overloaded transmission line; 5. Identify the overloaded line termination; 6. Establish line connectivity using the highest reserve capacity busbar index.  
Figure 9: Knowledge-based supervised learning for power grid.
# Effects of Irrigation with Desalinated Water on Lettuce Grown under Greenhouse in South Korea
## 3 Results and Discussion
### Sodium Accumulation in Lettuce Leaves  
The concentration of Na\({}^{+}\) in the lettuce leaves increased with the salinity level of irrigation water in the first experiment Figure 7a; the highest concentration was found in BS#04, whereas the lowest value was observed in BS#01. Significant differences among treatments were found after both the first and second phases of the experiment (\(p<0.05\)). For all treatments, the Na\({}^{+}\) concentration during the second phase of the experiment increased as compared with the concentration levels after the first phase. The greatest difference in the Na\({}^{+}\) concentration was observed in BS#04 (3185.2 mg kg\({}^{-1}\)), followed by BS#03 (2720.5 mg kg\({}^{-1}\)), BS#02 (1996.3 mg kg\({}^{-1}\)), and BS#01 (1270.3 mg kg\({}^{-1}\)). These results show that the use of saline water increases the concentration of Na\({}^{+}\) in the lettuce leaves and that the continued use of saline water may cause increased Na\({}^{+}\) accumulation in lettuce leaves.  
In the case of the experiment with desalinated irrigation water, a significant difference in the concentration of Na\({}^{+}\) in the lettuce leaves was observed between treatments during the first phase of the experiment, whereas no significant differences were detected during the second phase Figure 7b. As shown in Figure 7, differences between the Na\({}^{+}\) accumulation levels within the desalinated irrigation treatments were remarkably less than those present within the saline irrigation water treatments. This indicates that the use of the desalination system can help prevent Na\({}^{+}\) accumulation in lettuce leaves.  
Figure 6: Effect of desalinated irrigation water on lettuce shoot and total fresh weights after the (**a**) first and (**b**) second phases of the experiment. Bars represent the mean \(\pm\) standard deviation, \(n=20\). Different letters indicate significant differences among treatments by [PERSON]’s honest significant difference test at \(p<0.05\). AS: after water treatment using the BWD system, i.e., desalinated water irrigation.
# Using Mixed Reality (MR) to Improve On-Site Design Experience in Community Planning
## 3 Results and Analysis
### On-site Design Intuitiveness Using MR  
According to the interviews, we saw that the mean and standard deviation of the score for on-site design intuitiveness using MR were 4.15 and 0.21, respectively (as shown in Figure 10), which mainly included three concrete factor evaluations in our questionnaire.  
For the realness of the physical environments, the mean and standard deviation of the score were 5.00 and 0.00 apart (Figure 10). Almost all the participants expressed that MR had not changed any details of the physical environment of this community public space; thus, they could experience completely real spatial perception even while wearing the MR head-mount display Microsoft HoloLens. Second, regarding the verisimilitude of the virtual design objects, the mean and standard deviation of the score were 3.81 and 0.40, respectively, as Figure 10 shows. Most of the participants stated that MR technology not only presented stereoscopic forms and exact scales for 3D street furniture models but also described life-like details for them. A few participants responded that there were some chromatic aberrations and sawtooth for 3D street furniture models, but this did not affect their perception and judgment. With respect to the immersion of the MR environments, the mean and standard deviation of the score were 3.63 and 0.50, respectively, as shown in Figure 10. Furthermore, most of the participants said that in the MR environments, they  
Figure 9: The questionnaire with the main questions.  
could walk freely about the site to deeply understand the real environmental details and choose a suitable 3D street furniture model for the appropriate position, size, and material using gestures and gazes. Nevertheless, some participants pointed out that the relatively overloaded head-mount display and its narrow field of visualization (FOV) influenced their immersion experience to some extent in the experiment.  
Through the Kruskal-Wallis test (Tables 1 and 2), we also found no significant differences in the scores of on-site design intuitiveness using MR in the different MR usage experience groups (\(p=0.112>0.05\)) or in the different design experience groups (\(p=0.145>0.05\)). We found that the improvements in on-site design intuitiveness originated from the natural technological characteristics of MR, regardless of the user.
###### Abstract  
Natural language understanding (NLU) is an important aspect of achieving human-machine interactions in the automotive application field, consisting of two core subtasks, multiple-intent detection, and slot filling (ID-SF). However, existing joint multiple ID-SF tasks in the Chinese automotive domain face two challenges: (1) There is a limited availability of Chinese multi-intent corpus data for research purposes in the automotive domain; (2) In the current models, the interaction between intent detection and slot filling is often unidirectional, which ultimately leads to inadequate accuracy in intent detection. A novel multi-intent parallel interactive framework based on heterogeneous graphs for the automotive applications field (Auto-HIPIF) was proposed to overcome these issues. Its improvements mainly include three aspects: firstly, the incorporation of the Chinese bidirectional encoder representations from transformers (BERT) language model and Gaussian prior attention mechanism allow each word to acquire more comprehensive contextual information; secondly, the establishment of a heterogeneous graph parallel interactive network efficiently exploits intent and slot information, facilitating mutual guidance; lastly, the application of the cross-entropy loss function to the multi-intent classification task enhances the model's robustness and adaptability. Additionally, a Chinese automotive multi-intent dataset (CADS) comprising 13,100 Chinese utterances, seven types of slots, and thirty types of intents were collected and annotated. The proposed framework model demonstrates significant improvements across various datasets. On the Chinese automotive multi-intent dataset (CADS), the model achieves an overall accuracy of 87.94%, marking a notable 2.07% enhancement over the previous best baseline. Additionally, the model performs commendably on two publicly available datasets. Specifically, it showcases a 3.0% increase in overall accuracy on the MixATIS dataset and a 0.7% improvement on the MixSNIPS dataset. These findings showcase the efficacy and generalizability of the proposed model in tackling the complexity of joint multiple ID-SF tasks within the Chinese automotive domain.  
automotive applications; spoken language understanding; multi-intent detection; parallel interactive framework; heterogeneous graph +
Footnote †: journal: Applied
# Fuzzy-Logic Dijkstra-Based Energy-Efficient Algorithm for Data Transmission in WSNs
###### Abstract  
In wireless sensor networks, clustering routing algorithms have been widely used owing to their high energy-efficiency and scalability. In clustering schemes, the nodes are organized in the form of clusters, and each cluster is governed by a cluster head. Once the cluster heads are selected, they form a backbone network to periodically collect, aggregate, and forward data to the base station using minimum energy (cost) routing. This approach significantly improves the network lifetime. Therefore, a new cluster head selection method that uses a weighted sum method to calculate the weight of each node in the cluster and compare it with the standard weight of that particular cluster is proposed in this paper. The node with a weight closest to the standard cluster weight becomes the cluster head. This technique balances the load distribution and selects the nodes with highest residual energy in the network. Additionally, a data routing scheme is proposed to determine an energy-efficient path from the source to the destination node. This algorithm assigns a weight function to each link on the basis of a fuzzy membership function and intra-cluster communication cost within a cluster. As a result, a minimum weight path is selected using [PERSON]'s algorithm that improves the energy efficiency of the overall system. The experimental results show that the proposed algorithm shows better performance than some existing representative methods in the aspects of energy consumption, network lifetime, and system throughput.  
energy-efficiency; weighted sum method; [PERSON]'s algorithm; fuzzy membership function; intra-cluster communication cost
## 3 Materials and Methods
### Gas Chromatography-Olfactometry (GC-O)  
GC-O was performed on an Agilent 7890 GC coupled to an olfactory detection port Gerstel ODP-2. GC effluent was split 1:1 between the flame ionization detector (FID) and sniffing port. Samples were separated on both a HP-Innowax analytical fused silica capillary column (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m; Agilent, Santa Clara, CA, USA) and a DB-5 analytical fused silica capillary column (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent, Santa Clara, CA, USA). The flow rate of carrier gas (hydrogen) was 2 mL/min. The oven temperature was first increased from 40 \({}^{\circ}\)C (6 min), at 3 \({}^{\circ}\)C /min, to 100 \({}^{\circ}\)C and then ramped at 5 \({}^{\circ}\)C min\({}^{-1}\) to 230 \({}^{\circ}\)C (20 min); the injector and FID temperatures were set at 250 \({}^{\circ}\)C and 280 \({}^{\circ}\)C, respectively [26]. The temperature of the sniffing port was set to 250 \({}^{\circ}\)C, and the length between the Y-splitter and the sniffing port was 107 cm. Moist air was pumped into the sniffing port at 50 mL min\({}^{-1}\) to provide comfort to the panelists.  
A panel of fifteen trained panelists was recruited to perform GC-O analysis. The panelists were trained for 3 months in GC-O, using at least 30 odor-active reference compounds in a concentration 10 times above their odor thresholds in air. During a GC run described above, a panelist placed his/her nose close to the sniffing port, responded to the aroma intensity of the stimulus, and recorded the aroma descriptor and intensity value as well as retention time. The sniffing time of each run was not more than 30 min. The first panelist sniffed for 30 min and then the next panelist sniffed for 30 min. In this way, olfactory fatigue was avoided, and no other odors were generated in the interim. The aroma descriptors were determined by an evaluation of the odor quality of reference odorants previously. A six-point scale ranging from 0 to 5 was used for aroma intensity (AI) judgment: 0 = none, 1 = very weak, 2 = weak, 3 = moderate, 4 = strong, and 5 = very strong. The AI was an average result of the fifteen panelists [28].
## 3 Product Gene Theory
### Encoding Method of Product Gene
As shown in Table 2, the code of one element consists of three segments split by \"#.\" The first segment of the element describes its code, the second segment describes its related values of attributes, and the third segment describes the type of each value. For instance, in the \"4-1#ZG270-500#S\", the \"4\" indicates that the code belongs to \(I\), the \"4-1\" represents the element of material, the \"ZG270-500\" is the material, and the \"S\" indicates that the type of \"ZG270-500\" is a string. In the \"4-11#(3, C6125, 12, N1)#(C, S, C, O)\", \"4-11\" represents the rough turning, the \"(3, C6125, 12, N1)\" indicates that the capability of worker is \"3\", the machine type is \"C6125\", the age of the machine is 12 years, and the inspection method of rough turning is the conventional method, and the \"(C, S, C, O)\" describes the types of values of the \"(3, C6125, 12, N1)\". \begin{table}
\begin{tabular}{c c c} \hline \hline
\begin{tabular}{c} **Information** \\ **Set** \\ \end{tabular} & **Code** & **Explanation** \\ \hline \multirow{4}{*}{\(L\)} & 1-1\#23\#C & The unique code of \(GU\) (1) is “23” and the type of its value is continuity. \\ \cline{2-3}  & 1-2\#M5018A\#S & The code of the equipment is “M5018A”, and the type of value is string. \\ \cline{2-3}  & 1-3\#1F2\#S & The code of the part is “1F2” and the type of value is string. \\ \cline{2-3}  & 1-4\#C6\#S & The code of the type of surface is “C6” and the type of value is string. \\ \hline \multirow{4}{*}{\(C\)} & 2-1\#(905.06, 905, & The actual diameter is 905.06 mm, the design diameter is 905 mm, the lower deviation is \(-0.09\) mm, the upper deviation is 0 mm. \\ \cline{2-3}  & 2-2\#(3.2, 1.6)\#C & The actual surface roughness is Ra3.2, the design surface roughness is \\ \cline{2-3}  & 2-3\#(0.03, & The actual concentricity and design concentricity are both 0.03 mm, and the type of value is continuity. \\ \cline{2-3}  & 2-4\#(50.2, 50, & The actual width is 50.2 mm, the design width is 50 mm, the lower deviation is -0.3 mm, the upper deviation is 0.3 mm. The types of values are all continuity. \\ \cline{2-3}  & 2-5\#(42, 40)\#C & The actual hardness is HRC42, the design hardness is HRC40. The types of values are both continuity. \\ \hline \multirow{4}{*}{\(P\)} & 3-11\#(5, & The allowance of one side is 5 mm; the processing mode of rough turning is \"C1\". The types of values are continuity and string, respectively. \\ \cline{2-3}  & 3-12\#(2, & The allowance of one side is 2 mm, the processing mode of semi-finish \\ \cline{2-3}  & 2)\#(C, S) & turning is “C2”. The types of values are continuity and string, respectively. \\ \cline{2-3}  & 3-71\#NA1\#S & The mode of natural aging is “NA1” and the type of the value is string. \\ \cline{2-3}  & 3-13\#C3\#S & The processing mode of finish turning is “C3” and the type of the value is string. \\ \cline{2-3}  & 3-53\#Q1\#S & The mode of quenching is “Q1” and the type of the value is string. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Meanings of codes in \(GU\) (1).
# A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive
## 2 MTD Techniques
### Discussion
#### 2.3.3 Combination with New Techniques  
How to maintain the vitality of MTD is our concern, and we believe that as a defense framework concept rather than a defense mechanism that needs to be built from scratch, MTD can be very compatible with emerging techniques. We attach great importance to the combination of MTD with new techniques, which is also why we conducted this survey: we are concerned about how MTD is progressing further down the road of new techniques, especially machine learning.  
Undoubtedly, the future of MTD is not just about machine learning. We have noticed that MTD has been combined with many other types of emerging techniques to achieve better active defense effects, such as:  
* SDN-based MTD  
MTD tends to change the existing network configuration and, therefore, usually causes the degradation of network service availability. For example, while IP address hopping can interfere with attackers' scanning and intrusion to some extent, it may cause the failure of the entire network communication, whereas a software-defined network (SDN) can fundamentally change the network structure, giving the central controller the ability to regulate the entire network globally [49]. Therefore, IP address hopping in SDN [50] could minimize the impact of moving target defense techniques on the entire network.  
* MTD-applied cloud computing  
Cloud computing has been widely adopted to process massive traffic data. Many large data centers have utilized cloud computing to provide convenient services due to highly centralized data and services, which is precisely the reason why cloud services are in demand of high-level defense mechanisms to protect these highly centralized data and services. Favorably, the combination of MTD with cloud computing outstandingly improves the proactive defense capability of cloud servers [51] and ensures the security of cloud services [52].
## 5 Simulation Results and Analysis
Summarizing the performance of the RL models, TQC emerged as the best overall, matching or surpassing the GA, PSO, and RRT* in terms of its average success rate, feasibility-constrained distance, average computation time, average direct path percentage, and feasibility-constrained mission time. One notable advantage is TQC's tendency to select a higher forward speed, which reduces the average mission time relative to that of the aforementioned classical methods. Nevertheless, the GA and PSO remain highly viable if the mission environment is relatively static and offline computation is acceptable. Meanwhile, algorithms such as the APF and RRT* may either be too locally trapped (APF) or prone to generating winding routes (RRT*), which become quite long when executed by the dynamic AUV model. Overall, these observations highlight that the final choice of planning approach depends on whether one prioritizes offline or online execution, how critical short mission times are, and to what extent the full dynamic model can or should be embedded into the planner's decision-making process.
## 3 Results and Discussion  
### Performance Criteria  
Various performance criteria were utilized to gauge the performance of the models established in the present study. These metrics included squared correlation (\(R^{2}\)), root mean square error (_RMSE_), mean absolute error (_MAE_) and mean absolute percentage error (_MAPE_). The formulations of these criteria are presented by Equations (14)-(17). These  
Figure 4: 10-fold cross validation schematic view.  
performance indices have been used by some other researchers as well in predictive and classification studies [32; 33; 34; 35; 36; 37].  
\[R^{2}=1-\frac{\sum_{k=1}^{N}\left(z_{i}-\overline{b}\right)^{2}}{\sum_{k=1}^{N} \left(b_{i}-\overline{b}\right)^{2}} \tag{14}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{h=1}^{N}\left(z_{h}-b_{h}\right)^{2}} \tag{15}\]  
\[MAE=\frac{1}{N}\sum_{h=1}^{N}\left|z_{h}-b_{h}\right| \tag{16}\]  
\[MAPE=\frac{1}{N}\sum_{h=1}^{N}\left|\frac{z_{h}-b_{h}}{b_{h}}\right|\times 1 00\% \tag{17}\]  
where \(N\) is the total quantity of samples; \(z_{h}\) and \(b_{h}\) signify predicted and real values; \(\overline{b}\) is the mean value of \(b\).
## 4 Proposed Architecture  
The proposed framework architecture consisted of a set of services distributed across different computing sites. Each application was made of separate independent deployable components/containers, with all possible functionalities hiding their implementation details. The provision of those separate modular services enabled the services to be characteristically small and loosely coupled. This improved both testability and management, using containerization packages configured in each end device node. Those configurations provided minimum environmental capabilities for the application to run properly.  
Different layers of the proposed architecture are shown in Figure 1, describing each layer with its computational resources. The device layer known as the edge layer held all capabilities of sensing and communicating with the surrounding environment. The sensor nodes were grouped to allow connection and disconnection management.  
The gateways worked cooperatively to ensure that the sent data were well cached and processed to reduce system response times. Additionally, the middle layer orchestrated communication between sensing nodes by providing a single-entry point to the local systems. The manager containers worked hand-in-hand with the device groups to maintain system updates of the resources. The manager containers were hosted in the cloud layer to allow multiple local domains to share data and resource status and make all resource-intensive applications run concurrently.  
This proposed scheme was designed such that, in highly dynamic distributed systems, the management of multiple deployed sensor nodes and their resources could be achieved efficiently. The role of the manager (Docker Swarm), shown in Figure 2, was to ensure the most efficient resource allocation possible by keeping track of service deployment. It was able to manage sensor node groups and allow system scale-up, and updated as many containers as possible in clusters of machines.  
The communication of sensitive data generated by multiple sources occurred in high velocity at high volumes. It was managed by messaging hub techniques provided by the MQTT (message queuing telemetry transport) protocol. The aforementioned multiple layering entry allowed a manager to define communication channels for applications. It reduced the intra-service network overhead. The message broker module was included at each layer in the Docker container.