esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Classification of Granulation Levels
#### 3.3.2 The Influence of Multi-Source Data Fusion on Multi-Category Classification Models  
Multi-source data fusion can be described as three classifications: data-level fusion, character-level fusion, and decision-making level fusion [57]. In character-level fusion, each data source is pre-processed to extract representative features. For example, visible and near-infrared transmittance spectra were defined as multivariate data. Savitzky-Golay smoothing and normalization were used to preprocess the spectral data, and then the principal components were extracted from the spectral data using PCA. This was followed by character-level fusion and dimensionality reduction of spectral data. However, this will result in decreased accuracy of object identification and decreased predictive performances of the model classifier.  
The rough set can be used to eliminate redundancy and improve efficiency [58]. External characteristics (\(M\) and \(V\)) can complement spectra data fusion [59]. Therefore, data-level fusion was performed based on external characteristics and spectral data, and then external characteristics (\(M\) and \(V\)) and principal components of preprocessing spectra were selected as condition attributes. This was followed by use of a classifier of decision attributes, such as PCA-SVM and PCA-GRNN, to identify important indicators that external features (\(M\) and \(V\)) of samples can be used to improve the predictive performances of models for accurate assessment of different granulation levels. To investigate the advantages of multi-source data fusion, we used this process to compare the predictive performances of PCA-SVM and PCA-GRNN models for samples at five granulation levels.  
The predictive results of the PCA-SVM and PCA-GRNN for the prediction set are shown in Tables 6 and 7. Using multi-source data fusion, the \(ARM\) values of PCA-SVM and PCA-GRNN were 98% and 99%, respectively. As shown in Table 6, the predictive performances (\(CA\), \(CS\), and \(CSP\)) were at least 0.96 for each class in the PCA-SVM. Comparison of the two models reveals that PCA-GRNN performed better than PCA-SVM for \(CA\), \(CS\), and \(CSP\) (Table 7). Both models showed high predictive accuracy, with significant decreased numbers of incorrectly classified samples as analyzed by the confusion matrix. Multi-source fusion could be utilized to further improve predictive performances of nonlinear multi-category models (PCA-SVM and PCA-GRNN); the results of the confusion matrix analysis showed that most of samples were classified as the correct category. However, it was difficult to fully distinguish granulation levels of 0% and 1-25%, and also difficult to completely differentiate granulation levels of 51-75% and more than 75%. Overall, the results showed that the PCA-GRNN model combined with multi-source data fusion was the most efficient method for determination of granulation levels in honey pomelo.  
Figure 11: Discriminant analysis 3D-plot of PCA-GRNN model for predicted samples.
# Effect of Waste Engine Oil and Waste Cooking Oil on Performance Improvement of Aged Asphalt
## 4 Results and Discussion
### Rheological Properties
#### 4.6.2 Complex Modulus (G*)  
Asphalt with large complex modulus has better resistance to flow deformation. Figures 13 and 14 illustrated the effects of WEO, WCO, and different dosages on the complex modulus of rejuvenated asphalts at different temperatures.  
It was well-known that the higher the \(|\)G*\(|\), the higher the resistance to flow deformation. As seen in Figures 15 and 16, the complex modulus of rejuvenated asphalt with WEO and WCO had the similar trend. Complex modulus decreased as the oil content and temperature increased, which appeared much more obvious after the temperature exceeded 76 \({}^{\circ}\)C. With the temperature rising, all the resistance to flow deformation of rejuvenated asphalts kept decreasing; especially when the oil content was larger than 3%. At the same time, the corresponding anti-rutting performance of rejuvenated asphalt was deteriorating. However, when the temperature was above 76 \({}^{\circ}\)C, the rutting factor of rejuvenated asphalt was close to that of the aged asphalt. This indicated that the effect of waste oil would be weakened at high temperature. Therefore, a proper range should be selected to get better high temperature performance for rejuvenated asphalts with WEO or WCO.  
Figure 11: Influences of WEO on phase angel of rejuvenated asphalt (different aging time of VA).  
Figure 12: Influences of WCO on phase angel of rejuvenated asphalt (different aging time of VA).  
In addition, with the aging time increasing, complex modulus of both rejuvenated asphalts decreased notably. A similar trend with rutting resistance factor was observed. Though the WEO and WCO had a similar trend, the WCO-rejuvenated asphalt kept higher complex modulus at the same temperature and aging time, which would show good performance at high temperatures. The conclusions also confirmed the previous analysis of the different regeneration effect on WEO and WCO in different contents.
## 3 Experimental Results
### Evaluation of Vehicle Type and Color Classification Accuracy
During the experiment, there were instances in which vehicles were misidentified because of overlap. In addition, at certain intersections, the front of a vehicle approaching from the opposite direction was also detected as a vehicle, resulting in slightly lower detection performance. Figure 12: Results of precision confusion metrics for vehicle type and color classifiers, (**a**) vehicle type classifier precision, (**b**) vehicle color classifier precision. Table 4 shows the accuracy results for vehicle detection and color classification, comparing the proposed method to existing research methodologies [19; 21; 23]. Therefore, to compare the existing studies with the proposed method, [19; 23] is a vehicle color classification study, and [21] is a vehicle detection study. To compare the proposed method with existing methods, the vehicle color classification step proposed in [21] was applied. The experiment was conducted on highway with a higher proportion of trucks and buses than passenger sedans. Experimental results showed an average improvement of 2.8% in the accuracy of vehicle type and color classification using the proposed method. However, the proposed method derives the final result from three frames, and the pre-processing step consumes an additional 0.3 s of processing time on average. Performance comparison experiments between YOLO v3 and YOLO v5s were performed for the vehicle detector. YOLO v5s was built in Python and used a deep learning network in Github. By comparing the YOLO v3 and YOLO v5s deep learning networks for vehicle detection, the vehicle detection accuracy of the YOLO v3 and YOLO v5s model were 12% and 1.8% higher, respectively. However, the YOLO v5s model has shorter learning \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & [19] **Method** & [21] **Method** & [23] **Method** & **Our Method** \\ \hline
1 & 89 & 90 & 91 & 93 \\
2 & 86 & 89 & 88 & 91 \\
3 & 87 & 90 & 87 & 90 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of vehicle type and color classification accuracy of the proposed method. Figure 13: Results of vehicle detection and type and color classification in road images, (**a**) successful results, (**b**) false results. time and lower memory usage compared with YOLO v4. Future research will involve conducting vehicle detection experiments using the newest YOLO deep learning network under low-light conditions.
# Fault Detection, Diagnosis, and Prognosis of a Process Operating under Time-Varying Conditions
## 5 Discussion
Real-time monitoring based on CVA ensures reflectively better protection, and it allows for prompt identification of any errors in the determination of the prognosis model and unexpected failure. As far as the contribution plots are concerned, they had a dual role. As described in the previous sections of this paper, they allowed isolation of the variables that were most representative of the fault trends, making it possible to obtain even more efficient and powerful models for the prediction of the machinery's residual useful life. Moreover, as far as emergency maintenance was concerned, i.e., the application of CVA for fault detection, the contribution plots ensured an even higher reactivity during fault identification. They allowed a quick demarcation of the variable(s) that caused the alert state. Consequently, by linking the variable(s) obtained from the contribution plots to one or more system components, it is possible to target maintenance work. In conclusion, the combination of CVA and contribution plots have shown invaluable application for prognosis analysis.
## 3 Human-Machine Interfaces for Data Acquisition and User Feedback
### Feedback
#### 3.2.2 Feedback Conveyors  
Several tools are available to deliver audio-feedback (see Table 6). In some cases, the instructions can be transmitted directly by smartphone, a method quite incompatible with user privacy. The use of headsets tends to be privileged by researchers: traditional ones are cheap and easily accessible, but often disrupt the sense of hearing on which BVIP rely heavily to understand their environment. Another solution is bone-conducting earphones that allow the delivery of instructions without covering the ears, therefore not impairing sound perception and offering the possibility to communicate with other people.  
The devices for the other types of feedback are as follows: motors on a cane [33], vibrating belt and Braille interface [22], two smartwatches [46], and smartphone vibrations [16; 42].  
The users can also interact with the interface, either to choose a mode (wayfinding or scene description), or just to switch the system on or off. In most papers, this is done  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Audio-Feedback Devices** & **\% of Papers** & **Ref. to Papers** \\ \hline earphones/headsets & 46.7 & [20; 21; 23; 26; 28; 29; 37; 38; 43; 44; 47] \\ bone-conducting earphones & 16.7 & [16; 24; 25; 27; 39] \\ \hline phone/tablet & 13.3 & [33; 36; 40; 41] \\ \hline \hline \end{tabular}
\end{table}
Table 6: Devices for audio feedback.  
via the smartphone, thanks to built-in voice controllers (iOS' Voice Control or Voice Over, Android's Voice Access or TalkBack) to set up the system. In [22], instructions are given through a Braille tablet, or with smartwatches in [46].
## 2 KGs Extraction
### Advanced Entity and Relation Extraction
#### 2.2.2 Multimodal and Domain-Specific Extraction  
Multimodal and domain-specific approaches are designed to handle various data types--not only text but also images, audio, and sensor data--thus enabling the high-precision extraction of entities and relations in specific industries. For instance, the GridOnto system integrates fault events from power grids into a KG, demonstrating its effective application in the energy sector [40]. In the security domain, LLM-TIKG utilizes large language models to construct a threat intelligence KG that enhances the extraction of security-related events [41]. Additionally, causal relation extraction methods focus on deriving deep causal relationships from long texts, thereby enriching the KG with explicit engineering and domain-specific facts [42]. Knowledge Guided Attention focuses on chemical-disease relationships by combining attention mechanisms with GCN to reduce noise [43]. Methods that use distant supervision to incorporate the global context and suppress errors have also been proposed [44]. The Experiencer-Driven Graph approach captures deep semantic information in emotion-cause relationships [45], and dual-fusion models that combine text with equipment sensor data have been developed to simultaneously analyze logs and documents [46]. An automated clinical knowledge graph framework that integrates clinical records and literature to support evidence-based medicine (EBM) is also included [47].  
Additional domain-specific improvement techniques include progressive entity type assignment methods to effectively learn domain-specific ontologies [48] and the CG-JKNN model, which integrates image and graph data to form medical relations for tuberculosis diagnosis [49]. In the industrial and healthcare sectors, systems such as the Novel Rational Medicine Use System [50], semantic-spatial aware data integration for place KGs [51], relation labeling in product KGs for e-commerce [52], pipelines for extracting relations using transportation authority data [53], and task-centric KG construction based on multimodal representation learning for industrial maintenance automation [54] have been proposed. For geological data, approaches include deep learning-based methods that combine text and images to extract entities [55], methods that fuse entity descriptions with type information to enhance the completeness of a medical KG [56], joint extraction approaches for geological reports [57], BDCore, which uses bidirectional decoding and joint graph representations [58], prompt learning for biomedical relation extraction using GCN [59], and NER for equipment fault diagnosis that combines RoBERTa-wwm-ext with deep learning [60].
## 1 Introduction
A second distinct site between the \(\beta^{\prime}\) subunit and the DksA transcription factor has been shown to be bound by (p)ppGpp as well [20]. This interaction has been shown to enhance the transcriptional effects of DksA on the RNA polymerase, suggesting synergistic effects of DksA and (p)ppGpp together [20]. The RelA/SpoT homolog proteins are responsible for (p)ppGpp homeostasis. In _E. coli_, there are two enzymes of the Rsh family, RelA and SpoT [21]. SpoT contains a synthetase domain, a hydrolase domain, and two C-terminal regulatory domains; thus, this enzyme can both catalyze the production and the degradation of (p)ppGpp, respectively. RelA contains similar domains, however the functionality of the hydrolase domain of RelA has been lost during evolution, leading to a monofunctional enzyme that can only synthesize the alarmone [22]. In most alphaproteobacteria, including _B. abortus_, the production and the degradation of (p)ppGpp depends on one enzyme named Rsh (for RelA SpoT homolog) [10; 21]. In the present study, we analyzed the impact of alterations in (p)ppGpp synthesis or degradation on the growth, the cell cycle and the infection process of _B. abortus_. We show that mutants either unable to produce (p)ppGpp or producing a (p)ppGpp hydrolase are impaired for the infection process. In addition, our results show that expression of a constitutive (p)ppGpp synthetase negatively impacts growth and DNA replication of _B. abortus_, and also leads to a strong proliferation defect during infection of RAW 264.7 macrophages. We also observed that a _B. abortus dksA_ null mutant was able to proliferate inside host cells as the wild type (WT) strain, suggesting that DksA is not crucially involved in the (p)ppGpp-dependent phenotypes observed during infection. These results suggest that adjustment of (p)ppGpp levels are crucial for the infection process in _B. abortus_.
# A Novel Radio Network Information Service (RNIS) to MEC Framework in B5G Networks
## 3 Our Proposed RNIS as a Service to MEC Framework in B5G Networks
### RNIS as a Service Implementation
```
1#Resource:SubscriptionPOST
[EMAIL_ADDRESS]('/<appRoot>/rmi/v2/subscriptions',methods=['POST'])
3 defregister_application(appRoot):
4
5 try:
6 NotificationSubscription=request.json.get('NotificationSubscription')
7 callback_uri=request.json.get('callback_uri')
8
9 ifnotNotificationSubscription:
10 returnjsonify({'error':'ClientNotificationSubscriptionnotprovided'}),400
11
12 if'nok'==subscription_verification(NotificationSubscription)
13 returnjsonify({'error':'InvalidNotificationSubscription'})
14
15 ifnotcallback_uri:
16 returnjsonify({'error':'Clientcallback_urinotprovided'})
17,400
18 id=insert_application(appRoot,NotificationSubscription,callback_uri)
19
20 returnjsonify({'NotificationSubscription':NotificationSubscription,'callback':callback_uri,'id_register':id}),200
21
22 exceptExceptionase:
23 returnjsonify({'error':str(e)}),500
```  
Listing 5: Register_application. ```
1 defreceive_and_send_messages(iteracao,current_directory,notification,callback_url):
2 try:
3 credentials=pika.PlainCredentials(username='****',password='****')
4 parameters=pika.ConnectionParameters(host='localhost',credentials=credentials)
5 connection=pika.BlockingConnection(parameters)
6
7 channel=connection.channel()
8
9 channel.exchange_declare(exchange=notification,exchange_type='topic')
10 result=channel.queue_declare(queue=',exclusive=True)
11 queue_name=result.method.queue
12
13 routing_key='my_topic'
14 channel.queue_bind(exchange=notification,queue=queue_name,routing_key=routing_key)
15
16 defcallback(ch,method,properties,body):
17 message_with_time=body.decode()
18 try:20 requests.post(callback_url, json=message_with_time )
21 except requests.exceptions.RequestExceptionas e:
22 print(f\"Erroroenviar ocallback ({notification}) : {e}\")
23
24 channel.basic_consume(queue=queue_name, \
25 on_message_callback=callback, auto_ack=True)
26 channel.start_consuming()
27 exceptExceptionas e:
28 print(f\"Error receivingmessages ({notification}): {str(e) }\")
```  
The RNIS implementation works with the logic of multiple threads, where different parts of the program can run simultaneously. Each part of the program that is executed simultaneously is called a \"thread\". A thread is the smallest unit of execution in a process, and a process can have several threads. To process and forward notifications, we have simultaneous threads, while initially we used 1 thread for each type of information, working to notify consumers.
## 3 Push-Pull Converter Sizing and Parameterisation
### Sizing and Parameterisation of the Switching Devices  
The main criterion used for the sizing of the switching devices is based on determining the rated operating voltage and current values according to the topology and case under study. It will also be necessary to consider an appropriate switching frequency to minimise the switching losses.  
Thus, for both the power transistor and the rectifier diode, the rated voltage value will be determined by the maximum blocking voltage during turn-off. For the current, two limit values must be defined, the average and the peak current, which define the thermal and physical operating limits of the switching device, respectively. Generally, sizing switching devices according to the average current criterion also ensures compliance with the peak current criterion since in power switching devices the peak pulsating current is usually much higher than the steady-state current (on the order of two to three times higher) [42].  
Thus, based on the push-pull converter operation, the transistor blocking voltage (\(V_{B_{Q}}\)) is determined by twice the input voltage (26). The average current (\(I_{Q_{sw}}\)) can be calculated from the quotient of the rated design power and the rated input voltage (27).  
Finally, the peak current is determined by the maximum instantaneous input current of the converter, which is highly dependent on the current ripple imposed by the primary leakage and magnetisation inductances of the transformer:  
\[V_{B_{Q}}=2\cdot V_{in} \tag{26}\]  
\[I_{Q_{Adv}}=\frac{P_{O}}{V_{in}} \tag{27}\]  
In the case of the rectifier diode, the blocking voltage (\(V_{B_{D}}\)) is determined by twice the input voltage multiplied by the transformation ratio (28). On the other hand, the average current (\(I_{D_{Adv}}\)) can be calculated from the quotient between the rated design power and the output voltage, considering the current sharing between the branches of the full-wave rectifier (29). Finally, the peak current is determined by the maximum instantaneous output current of the converter, which is highly dependent on the current ripple imposed by the low-pass filter inductance:  
\[V_{B_{D}}=2\cdot V_{in}.\frac{N_{S}}{N_{P}} \tag{28}\]  
\[I_{D_{Adv}}=\frac{P_{O}}{2\cdot V_{in}} \tag{29}\]  
Finally, considering the non-ideal model of the switching devices, the main parameters (see Figure 2), \(R_{DS}\) and \(C_{OSS}\) for the transistors and \(V_{\gamma}\) and \(R_{D}\) for the rectifier diodes, can be obtained directly from their datasheets for the expected operating voltage and current conditions.
# A Practical and Efficient Node Blind SignCryption Scheme for the IoT Device Network
## 3 The Proposed Scheme
### Preliminary  
In this subsection, we provide some definitions used in our proposed scheme as follows:  
* \(n\): A large prime number, which it computes from two large primes \(p_{1}\) and \(p_{2}\) such that \(n=p_{1}\cdot p_{2}\), where \(p_{1}\equiv p_{2}\equiv 3\pmod{4}\).
* \(l\): The total number of all Internet of Things (IoT for short) nodes.
* \(\hat{n}\): A large prime number, which it computes from two large prime \(p_{3}\) and \(p_{4}\) such that \(\hat{n}=p_{3}\cdot p_{4}\), where \(p_{3}\equiv p_{4}\equiv 3\pmod{4}\).  
\begin{table}
\begin{tabular}{c l l l l} \hline  & **Sign-Cryption** & **Unsign-Cryption** & **Totally** & **Approx.** \\ \hline [1] & \(2 Mu+1 Pa\) & \(3 Pa+1 Ad+1\oplus\) & \(4 Pa+2 Mu+1 Ad+1\oplus\) & \(327 Mu+1\oplus\) \\ [2] & \(4 Mu+1 Ex+2 Ha+1\oplus\) & \(1 Ex+2 Pa+2 Mu+2 Ha\) & \(2 Ex+2 Pa+6 Mu+4 Ha+1\oplus\) & \(647 Mu+1\oplus\) \\ [3] & \(4 Ha+1 Ex+2\oplus\) & \(3 Ha+1 Pa+2\oplus\) & \(1 Ex+1 Pa+7 Ha+4\oplus\) & \(3228 Mu+4\oplus\) \\ [4] & \(1 Ex+2 Mu+2 Ha+1\oplus\) & \(2 Pa+3 Ha+1 Ad+1 Ex+2 Pa+2 Mu+1 Ad+5 Ha+1\oplus\) & \(409 Mu+1\oplus\) \\ Ours & \(4 Ha+29 Mu+1\oplus 1E\) & \(1 SD+2 Ha+1\oplus\) & \(33 Mu+1 SE+1 SD+6 Ha+2\oplus\) & \(36.2 Mu+2\oplus\) \\ \hline \end{tabular}
\end{table}
Table 1: Performance comparison.  
* \(DS_{i}\): An IoT data sender, which is a sensor node that forwards collected data to the receiver \(R\), where \(i=1\sim l\) and \(l\) is the number of all sensor nodes.
* \(BS\): A base station, which helps to collect data sent from a sensor node \(DS_{i}\), where \(i=1\sim l\).
* \(R\): An IoT data receiver, which receives data from the sender \(DS_{i}\).
* \(\oplus\): An exclusive-or operation for symmetric encryption/decryption usage.
* \(H_{1}\), \(H_{2}\): Two secure hash functions that each of them maps \(Z_{n}^{*}\rightarrow\{0,1\}^{n}\) with collision-resistance and outputs the same \(n\)-bits hash strings.
* \(E_{pk_{j}}\): A symmetric key encryption function for the party \(j\) with the public key \(pk_{j}\), where \(j\in\{DS_{j},R\}\), where \(j=1\sim l\).
* \(D_{sk_{j}}\): A symmetric key decryption function for the party \(j\) with the private key \(sk_{j}\), where \(j\in\{DS_{j},R\}\), where \(j=1\sim l\).