esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition
## 3 Methods
### CNN Model
#### 3.3.4 Results Analysis  
After we had found all the best parameters for our model, we no longer had a use for validation data and divided the data into a new 70% (train) and 30% (test) split for optimal testing. After training the model, we tested the model on the unseen test data. Table 5 provides a comprehensive performance report of the model for each sign. The precision, recall, and F1-score metrics, all at 99.9%, illustrate the effectiveness of the model. However, a few signs such as 'V', 'N', and 'W' have shown minor errors.  
Precision is at 1.00 for almost all signs, with a slight dip to 0.99 for 'V'. This suggests a marginally lower accuracy when predicting this sign. Recall also maintains a high score of 1.00 for most signs, with a minor decrease to 0.99 for 'N' and 'W', indicating slightly lower completeness when identifying these signs. The F1-score is at 1.00 for almost all signs, with a slight decrease to 0.99 for 'V', 'N', and 'W', indicating a minor imbalance between precision and recall for these signs. The model had a training time of 22 s, and predictions on the unseen test data took 22 s.  
Figure 13: Displaying model Accuracy vs. Validation accuracy and model loss vs. Training loss.  
To gain further insight into the wrongly predicted signs, we decided to display the incorrectly predicted images. In Figure 14, we loop through the indices of the incorrectly predicted images and display each image along with its actual and predicted label. It also displays the top three predicted probabilities for the image as a bar chart, with the color of each bar indicating the confidence of the model for different labels, the actual label (green), wrongly predicted label (red), or another label (gray). Looking at the images, we can see that in the two occurrences where the model predicted L but it was N, the model almost achieved the correct answer. While two errors are a very small sample size, and it is hard to say anything conclusive, it could indicate that L and N share some features. The same situation is with V and O, where the model twice predicted that V was O. Again, the model is very close to labeling it correctly, which indicates that although the model predicted wrong, it was still very close to the correct label. Again, because the sample size of wrongly predicted images is so small, we found it hard to conclude anything and decided that these errors were acceptable.
# Terahertz Spiral Spatial Filtering Imaging
## 5 Conclusions  
In summary, we have proposed and implemented a THz SSF imaging system, which could be an effective way to achieve high contrast imaging at the THz frequency. For this purpose, we built a QCL-based 4/ imaging system, and an SPP acting as a filter was placed at the Fourier plane. Firstly, the principle of the imaging method was analyzed, and the analytic expression of the high-contrast THz image was derived. Then, we imaged the targets with different image contrasts in the simulations and experiments. The edges of the targets can be extracted because of the enhanced imaging contrasts. This technique can allow us to directly access the outline profiles of objects under low image contrast conditions. As demonstrated, edge contrast enhancement was clearly observed in all the imaging experiments. Considering that there are no mature imaging methods to improve the contrast of the THz image at present, our technique could be a supplementary method to THz imaging.  
The research was performed by the authors as follows: Conceptualization, H.L.; methodology, H.L. and S.W.; software, H.L. and M.Z.; validation, H.L. and C.L.; formal analysis, H.L. and M.Z.; investigation, H.L. and G.F.; resources, X.L. and G.F.; data curation, H.L. and S.W.; writing--original draft preparation, H.L.; writing--review and editing, S.W.; visualization, H.L.; supervision, X.L. and G.F.; project administration, S.W. and C.L.; funding acquisition, G.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China under Grant (2018 YFB2202500, 2018 YFF01013004), the National Natural Science Foundation of China (61971397, 61501424, 61671432, 61731020 and 61988102), and the Beijing Municipal Natural Science Foundation under Grant 4172066.  
Not applicable.  
The authors declare no conflict of interest.
# A Process Decision-Making Method for Planar Machining of Box-Type Components
## 7 Discussion  
This paper presents an ontology-based decision-making method for the planar machining process of box-type parts. A hierarchical information representation model is constructed to describe the various concepts and their interrelationships in the process design of box-type parts. Based on this model, an ontology-based decision-making model for planar machining processes is developed. This model, when combined with SWRL (Semantic Web Rule Language) inference rules, can automatically generate the machining plans and process parameters for box-type parts. This section compares the proposed method with the ontology-based method for automatic generation of dimensional parameters for external cylindrical operations of shaft-type parts, as described in Section 2.  
From the perspective of process design, the method proposed in this paper addresses the machining process design for box-type parts. By leveraging the advantages of ontologies, the method successfully enables decision-making for the planar machining process and process parameters for box-type parts, thereby filling the gap in the application of ontology technology in the process design of such parts. Additionally, the method for automatic generation of dimensional parameters for external cylindrical operations of shaft-type parts, proposed in [23], does not consider factors such as blank characteristics, economic accuracy, and geometric tolerance constraints, which could potentially affect the final process design. In contrast, the proposed method in this paper takes into account more factors influencing the process design results in the machining decision-making process for box-type parts. Consequently, the resulting machining plans and process parameters are more aligned with engineering and technical requirements.  
From the perspective of validation through examples, the proposed method demonstrates the effectiveness of using ontology-based reasoning mechanisms to derive machining plans and process parameters for box-type parts. Compared to the research in [23], which focuses on shaft-type parts, the mechanical parts addressed in this paper (box-type parts) are more complex, and the derived process parameters are more comprehensive. For instance, the method takes into account parameters related to blank characteristics, casting processes, and the selection of positioning datums during machining, providing a more thorough and applicable solution for process decision-making.
# Rainfall Similarity Search Based on Deep Learning by Using Precipitation Images
## 4 Experiment and Result Analysis
### Results Analysis  
The NDCG-IPSO was used to conduct image similarity search experiments on the daily precipitation images of the Jialing Basin. \(\gamma_{1}\), \(\gamma_{2}\), and \(\gamma_{3}\) were initialized randomly, and their sum was guaranteed to be 1. The inertia weight \(w\) was set to 0.9, which linearly decreases to 0.4 as the number of iterations increases according to the Formula (13). Individual learning factors \(c_{1}\) and social learning factors \(c_{2}\) were set at 2.5 and 1.25. The particle number and the iteration number of the IPSO were set to 30 and 80 to obtain the optimal parameters of multi-feature distances for the precipitation image, which was shown in Table 1.  
Therefore, we adopted \(\gamma_{1}\)= 0.46, \(\gamma_{2}\)= 0.12, and \(\gamma_{3}\) = 0.42 to calculate the comprehensive distance between precipitation images in subsequent experiments. The experimental results using indicators _NDCG@5_ and _NDCG@10_ were shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Distance** & **71** & **72** & **\(\gamma_{3}\)** \\ \hline coefficient & 0.46 & 0.12 & 0.42 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimal weights of the three feature distances for precipitation image.  
Figure 6: Location of the Jialing Basin.  
As seen in Table 2, the NDCG-IPSO can obtain higher index values on both training and test samples. Particularly, the average accuracy of _NDCG@5_ and _NDCG@10_ of the method on the test samples were 0.978 and 0.964, respectively, which were very close to 1. The experimental results prove the following two points: On the one hand, the three features extracted in this paper can well represent the spatial and temporal characteristics of the precipitation image and meet the needs of the precipitation image analysis, which can be used as indicators for image similarity of daily precipitation images. On the other hand, the NDCG-IPSO has a good effect on fusing feature distances defined in this paper into the comprehensive distance and thus quickly retrieves similar images from precipitation images.  
Figure 7 shows the similarity search results of the precipitation image based on the NDCG-IPSO. For the precipitation image to be queried in the first line shown in Figure 7, lines 2 and 3 display the top 10 images that are very similar in terms of regional precipitation, precipitation distribution, and precipitation center, which can prove the effectiveness of this method and provide technical support for the analysis of similar hydrological processes.
## 3 Design of Smart Terminal Supporting the Proposed DR System  
In order to enable household loads to be incorporated into the proposed DR system conveniently, a plug-and-play smart terminal in the form of a socket was designed. This section mainly describes the key features and hardware design.  
The key features considered in the design of the smart plug are presented in Figure 5a, which correspond to the basic response requirements of the proposed DR system. These requirements and functions are concluded as follows:  
* IoT communication ability. This is the key feature to implement remote monitoring and control.  
Figure 4: The architecture of the customer side.  
* Parameter acquisition. The real-time acquisition of the underlying load operation status is the basis for potential assessment, load aggregation, DR decisions, and so on.
* Expansible load control ability. In addition to a circuit switch, other control blocks like an infrared block are allowed to be added to the smart terminal.  
As for the programs running on the smart terminal, there need to be functions for local parameter checks and action, in addition to ordinary functions. This is because IoT transmission rates are far slower than utilities expect during urgent periods, which means direct load control is not practical for quick DR tasks. A feasible approach to prevent the influence of delay is completing control locally, which is followed in this paper. The utility should distribute quick DR response requirements, which may be in the form of frequency or voltage thresholds, through LAs to the HIG, and finally to smart terminals. Then, the smart terminals will automatically act when the parameters collected exceed the threshold. A more detailed description is provided in Section 5.2.1.  
The chip choices for the smart terminal were as follows: stm32f0 was chosen as the processor of the smart terminal with the corresponding Lora terminal communication chip for the Lora HIG. A professional energy metering chip was also applied in the design with the concern of local fault detection and action. A relay was taken as the switch device coupled with zero-voltage and zero-current switching, allowing the terminal to maintain 10-kW loads.
## 3 Materials and Methods
### The Structure of the Proposed Model  
The architecture of CPO-BiTCN-BiLSTM-SA comprises two main components (as illustrated in Figure 2): the model building section and algorithm optimization section. It can be specifically described as detailed below.  
(1) BiTCN is introduced to address the limitation of traditional TCNs, which typically extract only forward information in network traffic. BiTCN is detailed in Section 3.2.  
(2) The BiLSTM network is introduced to further augment the model's capability to learn from long-term network traffic sequences and extract bidirectional information. BiLSTM is elaborated upon in Section 3.3.  
(3) The SA layer is introduced to assign weights to the traffic sequences output from the BiLSTM layer, thus enhancing the network's focus on important features. The SA mechanism is discussed in Section 3.4.  
(4) The aim in introducing CPO is to obtain optimal hyperparameters for the BiTCN-BiLSTM-SA model, thereby reducing the need for manual parameter tuning. Details regarding the utilization of CPO are provided in Section 3.5.  
(5) The performance of the CPO-BiTCN-BiLSTM-SA network traffic prediction model is compared with seven benchmark models to illustrate its advantages. Specific experimental results are presented in Section 4.
## 2 Materials and Methods
### Methods
#### 2.3.2 Vegetation Dynamic Monitoring  
The Sen+Mann-Kendall method has been widely used in vegetation dynamic monitoring in recent years. Compared with the conventional linear regression method, it can better avoid the interference of measurement error or abnormal values [44]. The detailed calculation formula of the trend of the time series (\(\beta\)) can be found in the literature [44] as can the equations to test whether there is an increasing or decreasing trend in the statistic \(S\)[45].  
Figure 2: Spatial distribution of topographic factors.  
In this study, \(n>10\), and we used the test statistic \(Z\) for trend testing. The formula is as follows:  
\[ZZ=\left\{\begin{array}{cc}\frac{S-1}{\sqrt{VAR\ (S)}},&S>0\\ 0,&S=0\\ \frac{S+1}{\sqrt{VAR\ (S)}},&S<0\end{array}\right. \tag{2}\]  
\[VAR(S)=\frac{\left[n(n-1)(2n+5)-\sum_{i=1}^{m}t_{i}(t_{i}-1)(2t_{i}+5)\right]} {18} \tag{3}\]  
where \(n\) is the number of sequential data, \(m\) is the number of knots (repeated data) in the sequence, and \(t_{i}\) represents the width of the knot (the number of repeated data in the \(i\)th set of repeated data). With the bilateral trend test for \(Z\), the critical value \(Z_{1-\alpha/2}\) is found in the normal distribution table at the given significance level \(\alpha\). When \(|Z|\leq Z_{1-\alpha/2}\), the sequence trend is considered insignificant; if \(|Z|>Z_{1-\alpha/2}\), the sequence trend is considered significant.  
In this study, the trends of the FVC time-series data were classified into the following four categories:  
\[\beta\left\{\begin{array}{cc}&\beta>0\left\{\begin{array}{cc}&|Z|>1.96,& \text{Significant improvement}\\ &|Z|\leq 1.96,&\text{Slight improvement}\end{array}\right.\\ &\beta<0\left\{\begin{array}{cc}&|Z|>1.96,&\text{Significant degradation}\\ &|Z|\leq 1.96,&\text{Slight improvement}\end{array}\right.\end{array}\right. \tag{4}\]
# Detection of Helminthosporium Leaf Blotch Disease Based on UAV Imagery
## 2 Materials and Methods
### Algorithms in Comparison
#### 2.4.4 Support Vector Machine  
In support vector machine (SVM) solutions, a large margin strategy and kernel mapping technique are used for tasks of classification. In the past years, it has been proven that SVM solutions have outperformed many existing methods on several classification and nonlinear function estimation problems [33]. Especially in the small-size training dataset, SVM performs well with good generalization capability [34]. Since the training dataset size was small in this study, the SVM method was applied for classification based on the features extracted in Sections 2.4.1-2.4.3.
## 4 Advancements in Industry 4.0 and Pandemic Management
### Comparative Analysis of AI, IoT, and Cloud Computing in Industry 4.0
#### 4.7.1 Artificial Intelligence (AI)  
AI's impact on Industry 4.0 is predominantly seen in its ability to facilitate complex decision-making processes and predictive maintenance. Through machine learning algorithms and neural networks, AI processes large volumes of data for failure prediction, quality control, and production optimization. However, the challenges lie in the need for high-quality data, the complexity of model interpretation, and the ongoing requirement for human oversight to contextualize AI outcomes.
# Co-Simulation of Multiple Vehicle Routing Problem Models
## 2 Related Work
### Mathematical Formulation of the Problem  
This section deals with the mathematical formulation of the problem. In the combinatorial optimization field, VRP is one of the most challenging research problems. The issue was studied more than 40 years ago. It involves defining the best sequence of routes for a fleet of vehicles to provide the service to a specific set of clients. The fleet of vehicles is situated at a central depot, every vehicle has certain capacity and each client has a given demand. The aim is to optimize the total traveled distance to serve a geographically dispersed group of clients [25]. First, we have the following data:  
1. One depot;
2. A set of commands;
3. A fleet of vehicles.  
The objective is to reduce the total traveled distance from a depot to serve clients, using a similar fleet of vehicles. The issue of solving the VRP faces several design challenges and focuses on the assignment of clients to the routes. This process involves the determination of several routes for each depot by (1) assigning each client to a unique route without exceeding the capacity of the vehicle, and (2) determining a series of clients on every vehicle route. Hence, the mathematical formulation of the vehicle routing problem can be described as follows:  
* \(R\) denotes the set of nodes, \(R=\{r_{0},r_{1},r_{2},\ldots,r_{n}\}\), with \(r_{0}\) is the depot and \(r_{i},i\
eq 0\) is the client;
* \(V\) denotes the sets of vehicles;
* \(N\) is the number of all clients;
* \(M\) is the number of vehicles;
* \(Q(k)\) is the capacity of vehicle \(k\);
* \(E\) is the edge set between nodes defined as: \(E=\{(r_{i},r_{j})|i\
eq j\wedge r_{i},r_{j}\in R\}\);
* \(b_{i}\) is the demand of client \(i\);
* \(c_{ij}\) is the cost of transporting one unit from node \(i\) to node \(j\);
* \(x_{ij}^{k}\) is 1, if vehicle \(k\) travels from node \(i\) to node \(j\); 0, otherwise,
* \(y_{ik}\) is 1, if vehicle \(k\) offers service for client \(i\); 0, otherwise.  
From these definitions the objective function problem can be formulated as follows:  
\[min\ \sum_{i\in R}\sum_{j\in R}c_{ij}\sum_{k\in V}x_{ij}^{k} \tag{1}\]  
where Equation (1) represents the objective function that aims to reduce the total travel cost and the constraints formulations are:  
\[\sum_{j\in R}x_{0j}^{k}=1,\forall k\in V \tag{2}\]  
\[\sum_{j\in R\
eq i}\sum_{k\in V}x_{ij}^{k}=1,\forall i\in R \tag{3}\]  
\[\sum_{i\in R}b_{i}y_{ik}\leq Q(k),\forall k\in V \tag{4}\]  
\[\sum_{i\in R}x_{il}^{k}=\sum_{j\in R}x_{lj}^{k},\forall l\in R,\forall k\in V \tag{5}\]  
where Equation (2) aims to ensure that every client is allocated in the single route and Equation (3) is the capacity constraint set for vehicles. Whereas Equation (4) is defined to assure that each client can be served only once and Equation (5) ensures that all delivery vehicles must return to the original depot after finishing their task.
## 3 Design of Fixed-Time Consensus Protocol  
This section presents a cooperative control scheme for the coordinated speed control of multi-PMSMs based on an undirected topology graph. A fixed-time consensus protocol \(u_{i}\) is designed to replace the traditional speed loop controller. Communication within the established topology structure between the PSMSs ensures that the state variables \(\omega_{r,i}\) in Equation (1) converge within the fixed time. At the same time, a TISMO to estimate disturbances and compensate for the disturbance \(f_{i}\) in the control protocol. The main block diagram is illustrated in Figure 2.  
Figure 1: Schematic diagram of the traditional DCC method for multi-PMSMs.
# Improvement of Fast Simplified Successive-Cancellation Decoder for Polar Codes
###### Abstract  
This paper presents a new latency reduction method for successive-cancellation (SC) decoding of polar codes that performs a frozen-bit checking on the rate-other (R-other) nodes of the Fast Simplified SC (Fast-SSC) pruning tree. The proposed method integrates the Fast-SSC algorithm and the Improved SSC method (frozen-bit checking of the R-other nodes). We apply a recognition-based method to search for as many constituent codes as possible in the decoding tree offline. During decoding, the current node can be decoded directly, if it is a special constituent code; otherwise, the frozen-bit check is executed. If the frozen-bit check condition is satisfied, the operation of the R-other node is the same as that of the rate-one node. In this paper, we prove that the frame error rate (FER) performance of the proposed algorithm is consistent with that of the original SC algorithm. Simulation results show that the proportion of R-other nodes that satisfy the frozen-bit check condition increases with the signal-to-noise-ratio (SNR). Importantly, our proposed method yields a significant reduction in latency compared to those given by existing latency reduction methods. The proposed method solves the problem of high latency for the Improved-SSC method at a high code rate and low SNR, simultaneously.  
Keywords:polar codes; successive cancellation decoding; latency reduction; constituent code; frozen-bit check +
Footnote †: journal: Information Information
## 7 Conclusions and Future Works  
In this paper, we presented our model on RF energy harvesting and information transmission in IoT relay systems based on time switching, power splitting and NOMA. Considering the energy constrained nature of the IoT nodes, here a power constrained IoT relay node first harvests the energy from the source node RF signal to power up themselves. The IoT relay node can harvests the energy using either time switching relaying or power splitting relaying protocol. Then in the next subsequent stage, IoT relay node transmits the source node information along with its information data using NOMA protocol. We have mathematically derived the outage probability, throughput and sum-throughput for our proposed system based on TS, PS and NOMA. Furthermore, we verified our derived analysis with the simulation results and some representative performance comparisons were presented. We showed that our analytical results for TS and PS relaying protocol exactly matched with the simulation results. We also found out the optimal time switching factor \(\alpha^{*}\) and optimal power splitting factor \(\varepsilon^{*}\) that maximizes the sum-throughput of the proposed system through the formulated Golden section search algorithm as shown in Algorithm 1.  
For future work, we would like to investigate the ergodic capacity of the proposed system and derive the exact-forms of outage probability and sum-throughput for the proposed system. We would also like to study the performance of our proposed system by introducing interference from other nodes.  
[PERSON] and [PERSON] conceived the idea; [PERSON] designed the experiments, performed the simulation experiments and analyzed the data; O.N.O. contributed to developing some mathematical analysis part; [PERSON] wrote the paper; [PERSON] and O.N.O. critically reviewed the paper.  
This research is supported through KD funding from Norwegian Ministry of Education.  
The authors declare no conflict of interest.