esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 3 OCR Process
### Postprocessing
In the proposed research, an Arabic text database is prepared, available in three formats, i.e., HTML, PDF, and scanned-document images. The database has 4581 files, and there are about 8994 scanned images. Thus, from the database, 1000 images are used for training by the rule-based method, which reduces the word error rate. [PERSON] and [PERSON] propose an algorithm using Google's online spelling suggestions [101], which helps to improve the accuracy and suggest what should come after each character to make a meaningful word according to the sentence. All of the suggestions given by Google's spelling suggestion algorithm are based on N-gram probability. The authors hybridize this method to make the proposed postprocessing technique. Therefore, the proposed hybrid postprocessing system starts with the generated OCR file (token). The language model checks each token, and if the language model does not find this token, then the error model takes this token and suggests the correct word. This token/word again goes into the language model in an attempt to find matches for the token. If it matches, then the model moves to the next word; otherwise, the error model again suggests the new token provided by Google's spelling suggestion algorithm, as shown in Figure 8.  
The authors of [102] present a corpus-based technique for improving the performance of an Arabic OCR system. The method involves using a large corpus of texts in Arabic to train the OCR system and improve its recognition accuracy. The corpus of texts is preprocessed to ensure that it is suitable for OCR training, and then it is used to train the OCR system. The performance of the OCR system is then evaluated using a set of test images, and the recognition accuracy is compared to that of traditional methods. The study also shows that using a larger corpus of texts leads to better performance of the OCR system. This phase helps to provide better recognition or reduce the word error rate and character error rate. The collection of a large corpus of texts in Arabic is used to train the OCR system  
Figure 8: Hybrid postprocessing technique based on Google’s spelling suggestion algorithm. and then preprocess the corpus to ensure that it is suitable for OCR training. The OCR system is trained using the preprocessed corpus, and the OCR system's performance is evaluated using a set of test images. The research shows that the corpus-based technique improves the recognition accuracy of the OCR system by a significant margin compared to traditional methods. It also shows that using a larger corpus of texts leads to better performance of the OCR system.
# Detection of Unknown DDoS Attacks with Deep Learning and Gaussian Mixture Model
## 3 Proposed Framework--BI-LSTM-GMM
### GMM Module  
It is critical for an ML/DL-based system to know what it does not know. To endow the DDoS attack detection system with the ability to pick up instances not drawing from the distribution model of the training set, we adopt the GMM in our design, which is an extension of the single Gaussian model. With GMM, multiple Gaussian probability density functions are employed to model clustered data, which is a typical scenario in DDoS attack detection. Different types of legitimate traffic or DDoS attacks bear similar characteristics and form individual clusters in the feature space. With unsupervised learning, GMM can establish a model matching the distribution of the training data set. A GMM is a statistic model consisting of \(K\) Gaussian distributions. Each kernel can be specified by its weighting factor \(w_{k}\), mean \(\overrightarrow{\mu}_{k}\), and covariance matrix \(\Sigma_{k}\), as follows  
\[\Lambda=\Big{\{}\lambda_{k}=\Big{(}w_{k},\overrightarrow{\mu}_{k},\Sigma_{k} \Big{)}\Big{\}},\;k=1,\ldots,K \tag{2}\]  
The likelihood of a given instance \(\overrightarrow{x}\) belonging to a GMM can be evaluated according to (3), as follows  
\[p\Big{(}\overrightarrow{x}\Big{|}\Lambda\Big{)}=\sum_{k=1}^{K}w_{k}\mathcal{ N}\Big{(}\overrightarrow{x}\Big{|}\overrightarrow{\mu}_{k},\Sigma_{k}\Big{)} \tag{3}\]  
Figure 4: The BI-LSTM and the classifier.  
where  
\[\left\{\begin{array}{c}N\left(\overrightarrow{x}\middle|\overrightarrow{\mu}_{k^ {\prime}}\Sigma_{k}\right)=\frac{1}{\sqrt{(2\pi)^{k}|\Sigma_{k}|}}\exp\left(- \frac{1}{2}\left(\overrightarrow{x}-\overrightarrow{\mu}_{k}\right)^{\text{ T}}\Sigma_{k}-1\left(\overrightarrow{x}-\overrightarrow{\mu}_{k}\right)\right)\\ \sum\limits_{k=1}^{K}w_{k}=1\end{array}\right. \tag{4}\]  
Given a training data set and \(K\), the expectation-maximization algorithm is the technique most commonly used to estimate the mixture model's parameters. Once the model is constructed, the GMM can be used to reject unseen instances. According to common practice in statistics, an instance falling within three times of the standard deviation, i.e., \(\overrightarrow{\mu}-3\overrightarrow{\sigma}\leq\overrightarrow{x}\leq \overrightarrow{\mu}+3\overrightarrow{\sigma}\), is considered an instance drawing from the training data distribution. In this case, the instance will be accepted, and the output of the BI-LSTM will be enabled. Otherwise, the instance will be rejected, and the BI-LSTM will be disabled. A rejected instance will be logged and subject to identification and labeling by data engineers. The control flow of the GMM in the proposed framework is presented in Figure 5.
## 4 SDN-NFV Based QoS Management
### SDN-NFV-Based Dynamic Network Scaling Algorithm  
In an SDN-NFV network, virtual switches are considered as VNF in NFV architecture [21]. In this study, a dynamic network resource scaling method based on SDN-NFV is proposed in case of congestion in the 5G transport network, as shown in Figure 2. The activity diagram of the developed SDN-NFV algorithm is shown in Figure 9.  
In the developed algorithm, the detection of congestion in the network is performed by the SDN controller. First, network topology information is obtained by sending an HTTP GET request to the northbound interface of the SDN controller. The Dijkstra algorithm is used to find the shortest path between the source and destination using the topology information obtained. Then, by sending HTTP POST requests to the SDN controller, flow rules are created on the virtual switches. This ensures that traffic follows the shortest path. The SDN controller then measures the current bandwidth value on the ports of the virtual switches at 2 s intervals. If the measured bandwidth value reaches the specified threshold, the congested virtual switch is detected. If there is no congestion, traffic continues to flow along the existing path.  
After the network congestion problem is detected, a request is sent to the VNF manager to create a new virtual switch to reduce the load on the congested switch. If there are sufficient resources in the system, the VNF manager will create a new virtual switch. If there are not enough resources, an alert is sent to the alert manager. When a new virtual switch is added to the topology, the flow rules required to create a new traffic path are created on the virtual switches. Figure 10 shows the network topology after scaling.  
Figure 10: New SDN–NFV network topology.  
Figure 9: Developed SDN–NFV algorithm activity diagram.
# Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator
## 4 Experimental Results
### Experiment with Proximity-Based Adaptive Algorithm
#### 4.3.1 Proximity Threshold  
For this session, experiments are conducted to determine the optimal threshold \(T_{p}\), to identify situations where a device is sufficiently close to an AP in PAP algorithms. If \(T_{p}\) for the PAP algorithm is set to too large of a value, the RSSI value may be unstable and exceed \(T_{p}\) even if it is not close to the AP, so the MP technique is applied. This could lead to a degradation in the positioning accuracy of regions that are relatively stable in the existing positioning system, which solely relies on trilateration. Conversely, when \(T_{p}\) is set to an excessively low value, instances may occur where MP should be applied due to proximity to AP, but trilateration is performed due to failure to exceed \(T_{p}\). This does not address the issues of position unavailability in weak-signal regions and degradation in positioning accuracy, which are inherent to trilateration-based positioning. Therefore, it is necessary to determine the optimal \(T_{p}\) for PAP algorithms through experimentation. \(T_{p}\) is set to 2 m, 3 m, 4 m, and 5 m, and 1000 localizations are conducted at each localization point. Table 5 evaluates the localization accuracy performance by calculating the error between the actual and localized positions at each localization point using the Euclidean distance and presents the localization accuracy according to the localization point type based on \(T_{p}\).  
For the case of 5 m, the largest \(T_{p}\) in the experiment, \(T_{p}\) is frequently exceeded at the center position and other position, resulting in increased errors when positioning is carried out using the MP technique. For the 2 m case, where \(T_{p}\) is the smallest, there are numerous instances where \(T_{p}\) is not surpassed at the side position, leading to the non-application of the MP technique and predominantly resorting to trilateration for positioning, consequently confirming an escalation in positioning errors. With a \(T_{p}\) of 3 m, positioning errors are found to be lower at the center position compared to the 2 m, yet the highest degree of positioning accuracy is observed at the side position and other position. Thus, with a \(T_{p}\) set at 3 m, it is validated that the PAP algorithm accurately identifies cases near an AP and enhances positioning accuracy through the utilization of the MP technique when in proximity. This paper adopts a \(T_{p}\) value of 3 m for positioning through the PAP algorithm.
# Integrating Merkle Trees with Transformer Networks for Secure Financial Computation
## 3 Materials and Method
### Evaluation Index  
In our research, a series of quantitative evaluation metrics were employed to comprehensively assess model performance, reflecting various aspects of model functionality, particularly in prediction accuracy and data integrity protection. Precision is used to measure the proportion of true positives among all samples predicted as positive by the model. High precision directly relates to the credibility of the model in practical applications. The formula for precision is as follows:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{13}\]  
where \(TP\) (true positive) represents the number of samples correctly predicted as positive, and \(FP\) (false positive) denotes the number of samples incorrectly predicted as positive.  
Recall measures the proportion of actual positive samples identified by the model, reflecting the model's ability to capture important events. Recall is particularly crucial in the field of financial security, as missing any real fraudulent event could lead to significant economic losses. The formula for recall is as follows:  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{14}\]  
where \(FN\) (false negative) refers to the number of actual positive samples incorrectly predicted as negative.  
Accuracy is an indicator of the overall correctness of the model's predictions, reflecting the proportion of correctly predicted samples in the total sample set. This metric provides an intuitive overview of model performance and is calculated as follows:  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{15}\]  
where \(TN\) (true negative) represents the number of samples correctly predicted as negative.  
For regression tasks like stock price prediction, the coefficient of determination \(R^{2}\) is used to assess the quality of the model's predictions. \(R^{2}\) measures the degree of correlation between the model's predictions and actual values, with values closer to 1, indicating stronger predictive ability. The formula for \(R^{2}\) is as follows:  
\[R^{2}=1-\frac{\sum_{i}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i}(y_{i}-\bar{y})^{2}} \tag{16}\]  
where \(y_{i}\) represents the actual value of the \(i\)th sample, \(\hat{y}_{i}\) is the model's prediction for the \(i\)th sample, and \(\bar{y}\) is the average of all actual values.  
These evaluation metrics comprehensively assess the model's performance from multiple dimensions. Precision and recall are particularly applicable to classification tasks, such as financial fraud detection, assisting in evaluating the model's ability to identify genuine risk events. Accuracy provides an overall performance evaluation, aiding in understanding the model's performance across the entire dataset. The coefficient of determination \(R^{2}\), as a regression task metric, reflects the degree of fit between the model's predictions and actual outcomes, which is crucial for financial time series analysis like stock price prediction.
# A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
## 9 Future Directions and Development Trends
### Interpretability and Explainability  
An LLM's ability to explain its decisions and predictions is crucial to promote trust, accountability, and widespread acceptance. The current research targets methods that can explain the model's decisionmaking process and inner workings in a format understandable to humans. The approaches we discuss below originated in the machine learning domain. They need to evolve to serve the LLMs context.  
Some architectures are inherently interpretable. For example, decision trees, rule-based models, or sparse models facilitate understanding a model's decision in a transparent and human-understandable format. More research in this area is critical for advancing LLM applications. Using the LLM's attention mechanism, we can highlight important parts of the input data that contributed to the model's predictions. Attention weights indicate the model's focus and thus serve as a means for interpretability.  
Another approach involves extracting human-readable rules or generating post hoc explanations that explain model predictions in natural language or other more straightforward representations. Creating simpler proxy or surrogate models that approximate the behavior of complex original models is another approach to improving interpretability. LIME (local interpretable model-agnostic explanations) or SHAP (SHapley Additive explanations) are approaches for developing feature importance and attribution. This helps to attribute the model's predictions to specific input features. Salience maps and heatmaps (i.e., gradient-based visualization) also help to highlight essential regions in the input data that influence predictions. Another approach involves extracting human-readable rules or generating post hoc explanations. Investigating methods to provide certified or verified explanations guarantees the reliability of explanations.  
Developing interactive tools that enable users to interact with the model at various levels of granularity can be used to provide user-centric explanations. This is akin to the _drill-down_ and _roll-up_ features of online analytical processing (OLAP) in data warehousing applications. Disclosing a model's capabilities, biases, and potential errors to users is a required step toward emphasizing the importance of ethical considerations and transparency. Lastly, educating users about model capabilities and limitations and providing guidance on interpreting model outputs is mandatory for advancing LLMs.
# Mobility Support 5G Architecture with Real-Time Routing for Sustainable Smart Cities
## 4 Simulations  
This section presents the simulation setting and experimental results in detail. To simulate the real-time scenarios, we used NS-2 with default parameters that are utilized in various simulator systems [56; 57]. In the experiments, we set the parameters as follows: The observing area was 10 \(\times\) 10 m\({}^{2}\). The area was split into various clusters and the transmission range of each medical node was set to 3 m. The number of medical nodes that can sense and transmit the data continuously to the sink node was set to 20. The simulation was executed for 1000 to 5000 rounds. The initial energy for each node was 2 J and the size of the carrying data packets per node was set to 64 bits. The cache memory for each was set at 15 Mb. In addition, a few nodes were assumed to be malicious entities to identify security threats and evaluate the effectiveness of the proposed architecture. Table 2 demonstrates the simulation environment of the set of experiments.
## Materials and Methods  
### Bacterial/Fungal Strains and Chemicals  
The enterocin producer of _E. faecalis_ Gr17, which was isolated from a traditional Chinese low-salt fermented whole-fish product, was incubated in Man, Rogosa and Sharpe (MRS) broth (LuQiao, Beijing, China) at 37 \({}^{\circ}\)C. _Staphylococcus aureus_ (ATCC 6538), _Bacillus cereus_ (ATCC 11778), _Listeria monocytogenes_ (ATCC 35152), _Salmonella enterica_ (CGMCC 1.10603), _Escherichia coli_ (CGMCC 1.90), _Pseudomonas aeruginosa_ (ATCC 9027), _Pseudomonas fluorescens_ (ATCC 14150), _Shevanelle putefaciens_ (ATCC 8071), _Canidia albicans_ (ATCC10231), _Aspergillus flavus_ (CGMCC 3.15726), _Penicillium expansum_ (CGMCC 15686), and _Alternaria alternata_ (CFCC 87705) were used as the indicator strains for the antimicrobial activity measurements. _S. aureus_, _B. cereus_, _L. monocytogenes_, _S. enterica_, _E. coli_, _P. aeruginosa_, _P. fluorescens_, and _S. putefaciens_ were grown in trypticase soy broth (TSB), and _C. albicans_, _A. flavus_, _P. expansum_, and _A. alternata_ were grown in potato dextrose broth (PDB). TSB and PDB were purchased from LuQiao Institute (Beijing, China).  
The essential oils (EOs) (purity: 98.0%), including eugenol (ECO), thymol (TEO), carvacrol (CAEO), cinnamaldehyde (CEO), menthone (MEO), and linalool (LEO), were purchased from Macklin Institute (Shanghai, China). The emulsifiers (food-grade), including the soybean protein isolate (SPI, \(>\)90% protein), whey protein isolate (WPI, 97.7% protein dry basis), sodium caseinate (SCN, purity: 90.0%), hydroxypropyl methylcellulose (HPMC, viscosity 4000-6500 mPas, 2% in distilled water at 20 \({}^{\circ}\)C), konjac gum (KGM, viscosity \(\geq\) 15,000 mPas, 1% in distilled water at 20 \({}^{\circ}\)C), and xanthan gum (XGM, viscosity \(\geq\) 1200 cps, 1% in KC1 at 20 \({}^{\circ}\)C), were purchased from Sigma-Aldrich Company (St Louis, MO, USA). The 2,2-diphenyl-1-picrylhydrazyl (DPPH) measurement kits, the thiobarbituric acid reactive substance (TBARS) assay kits, and the total volatile basic nitrogen (TVB-N) assay kits were purchased from the Jiancheng Bioengineering Institute (Nanjing, China). Fresh salmon was bought from a local market in Beijing, China.
## 4 Conclusions  
The study mainly focused on the feasibility of using LIBS technology combined with chemometrics to identify different penicillin manufacturers. We analyzed the LIBS spectra of different PPT, AC and ACP manufacturers and used the decrease in the Gini index of RF to evaluate the important weight of each spectral line for the manufacturer identification of three types of penicillin. In the identification of four different PPT manufacturers, the importance weight of Na ranked second. In the identification of four different AC and ACPT manufacturers, the importance weight of Na ranked first. This indicated that Na played an important role in the identification of penicillin manufacturers. We used three classification algorithms--LDA, SVM and ANN--to identify different manufacturers of PPT, AC and ACPT, respectively. The RF-ANN model achieved 100% classification accuracy for all three test sets, successfully accomplishing penicillin manufacturer identification. The results show that LIBS technology combined with chemometrics could be used to identify different penicillin manufacturers. In future work, we will test the robustness and generalization of these classification models in the application of drug detection.  
Supervision, Q.W.; methodology, G.T.; software, X.X.; formal analysis, Z.Z.; validation, G.C.; writing--review and editing, K.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (No. 62075011).  
Not applicable.  
The data in this study can be requested from the corresponding author.  
The authors declare no conflict of interest.
# Profile Design of the Grooved Die and Rolling Force Prediction in the Cold Pilger Rolling Process
## 3 Die Groove Shape Design in the Cold Pilger Rolling Process
### Pilger Die Groove Shape Design  
The shape of the mandrel follows the shape of the roll groove, which determines the _R.A._ of the tube and the load applied on the roller. The pilger die groove shape design method is described as follows. As shown in Figure 13, three coordinates are needed. The mandrel and the roll inner groove shape are determined by the initial tube outer diameter, the final product outer diameter, and the product outer diameter at the principal deformation zone. The equation for the roll shape design is  
\[y=e^{ax+b}+c \tag{10}\]  
Here, \(y\) represents the outer shape of the roll. The variable (\(a\), \(b\), \(c\)) are important factors for determining the roll shape. The coordinates of each point are represented in Figure 13,  
Figure 12: Roll groove shape with parabolic curve.  
Figure 13: Coordinates of key points for die shape design in cold pilger rolling.  
and these are represented with Point 1 (0, \(d_{o1}\)), Point 2 (\(L\), \(d_{o2}\)), Point 3(\(PDZ\), \(d\)), respectively. These values are substituted into Equation (11) to obtain the variables \(a\), \(b\), and \(c\).  
\[\begin{array}{l}d_{o1}=e^{b}+c\\ d_{o2}=e^{La+b}+c\\ d=e^{PDZa+b}+c\end{array} \tag{11}\]  
Here, the constant \(c\) is:  
\[c=d_{o1}-e^{b} \tag{12}\]  
And \(a\) can be obtained by following the below-mentioned process. By substituting Equation (12) into Equation (11), we get  
\[\begin{array}{l}d=e^{PDZa+b}+d_{o1}-e^{b}\\ d-d_{o1}=e^{PDZa+b}-e^{b}\\ d-d_{o1}=e^{b}\big{(}e^{PDZa}-1\big{)}\end{array} \tag{13}\]  
When Equation (12) is substituted to Equation (11), we obtain  
\[\begin{array}{l}d_{o2}=e^{La+b}+d_{i1}-e^{b}\\ d_{o2}-d_{o1}=e^{La+b}-e^{b}\\ d_{o2}-d_{o1}=e^{b}\big{(}e^{La}-1\big{)}\end{array} \tag{14}\]  
Here a can be obtained using Equations (13) and (14).  
\[\begin{array}{l}\frac{d_{o2}-d_{o1}}{d-d_{o1}}=\frac{e^{La}-1}{e^{PDZa}-a} \end{array} \tag{15}\]  
Using the values obtained from the numerical analysis, \(b\) and \(c\) can be calculated.  
\[\begin{array}{l}d-d_{o}=e^{b}\big{(}e^{PDZa}-1\big{)}\\ \ln(d-d_{o})=b+\ln\big{(}e^{PDZa}-1\big{)}\\ b=\ln\Big{(}\frac{d-d_{o}}{e^{PDZa}-1}\Big{)}\end{array} \tag{16}\]  
The mandrel shape is designed in the same way as the roll shape design. Figure 12 depicts a variable roll, and the mandrel is designed using Equation (10). The exponential values that are employed for the roll design are 0.0045, 0.4628, and 6.3515, respectively. The values employed for the mandrel design are 0.0045, 0.3832, and 4.0130, respectively.