esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 QoS Provisioning Techniques for Future FiWi Networks
### FiWi QoS Provisioning Techniques and Mechanisms
\begin{table}
\begin{tabular}{|p{142.3 pt}|p{142.3 pt}|p{142.3 pt}|} \hline
**RPR Traffic Classes** & **WiMAX Scheduling Services** & **Applications** \\ \hline \hline
**A0**: Low latency and jitter, pre-allocated reserved bandwidth & **UGS**: Unsolicited grant service, reserved fixed-size grants & Real-time fixed-size data packets on a periodic basis, without silence suppression (VoIP, T1, and E1 voice service) \\ \hline
**A1**: Low latency and jitter, pre-allocated unreserved realizable bandwidth & **ErtPS**: Extended real-time, efficiency of both UGS and rtPS polling service, dynamic size allocations & Delay-sensitive real-time flows, variable-size data packets on a periodic basis (VoIP with activity detection) \\ \hline
**B-CIR**: Committed information rate, predictable latency and jitter, pre-allocated unreserved reclaimable bandwidth & **rtPS**: Real-time polling service, real-time unicast request opportunities & Real-time flows of variable-size data packets on a periodic basis (streaming audio-video, MPEG) \\ \hline
**B-EIR**: Excess information rate, predictable latency and jitter, unallocated unreserved bandwidth & **nrtPS**: Non real-time polling service, unicast assurance polling on a regular basis & Delay tolerant data services with a minimum required data rate (FTP) \\ \hline
**C**: Unpredictable latency and jitter, unallocated unreserved bandwidth & **BE**: Best-effort service & Delay and jitter tolerant data services (Web browsing, E-mail, data transfer) \\ \hline \end{tabular}
\end{table} 
Table 1: Bidirectional mapping of RPR traffic classes and WiMAX scheduling services. first, until the deficit counter of their traffic classes become smaller than a pre-selected threshold. Subsequently, lower priority packets are scheduled. * **PQ:** Priority queuing (PQ) is applied to distinct traffic classes with various QoS requirements. The straightforward slot allocation of PQ is suitable for higher priority queues. PQ is used at the outputs of the FIFO and DFPQ schedulers in order to provide service differentiation of higher and lower priority SFs. Recall that the CPU (see Fig. 7) monitors and controls all the schedulers directly. Upon reception of UL-requests, the optical-wireless interface node is able to change the reserved bandwidth of traffic class A0 based on the requested bandwidth for UGS SFs. Also, once an RPR node receives A1 and B-CIR packets, the CPU informs the DFPQ scheduler to dynamically adjust its threshold for ertPS and rtPS. This adaptive interaction between optical and wireless segments is crucial to provide end-to-end QoS connectivity for the reserved (UGS/A0) and real-time traffic classes over SuperMAN.
## 4 Comparison  
As Table 1 shows, shorted via technology is employed in [1; 2; 3] to achieve the features of multi-band and broadband. Although the bandwidths of these antennas are all greater than 9%, the cross-polarization ratio below 18 dB and the size exceeding 1.3\(\lambda_{0}\) are not conducive to antenna integration and miniaturization applications. Slotting, shorted vias, and resonant stubs have been used in cross-combination to achieve multi-frequency characteristics. For example, in [10; 11], additional resonant modes are introduced by etching slots on the radiating patch and loading parasitic resonant branches. In [18], a miniaturized antenna with four operating bandwidths is designed by combining slotting and shorted vias techniques. However, the gain of the antenna of below 2.5 dB and the low radiation efficiency are problems that need to be solved. A defected ground structure and coupled resonator network are also investigated in [4; 12], respectively, obtaining tri-band and quadband resonance characteristics. For these two multi-band antennas, the advantages of broadband and high gain can only be realized as separate advantages on the individual antennas.  
Compared with existing antennas, this paper utilizes off-center feeding technology to achieve the characteristics of tri-band resonance on a single-layer patch. The introduction of the circular parasitic patch not only widens the impedance bandwidth, but also improves the gain of the antenna. Benefiting from the diverse current distribution of the two patches, the antenna has directional and omnidirectional diverse radiation patterns. The measured results show that the radiation efficiency of higher than 85.6% and the maximum gain of greater than 9.42 dBi are obtained in a stacked structure. The impedance bandwidth of the antenna designed in this paper at \(f_{1}\) and \(f_{2}\) is too narrow. In conclusion, the design method in this paper is simple, easy to implement, and simultaneously obtains high gain, enhanced bandwidth, and pattern diversity on a single antenna.
## 3 Methods
### System User Study
#### 3.5.6 Data Collection and Analysis  
We gathered quantitative and qualitative data using self-reported questionnaires, interview feedback, and participant-generated content from the Story Fork tool and Co-Relay system. Initially, observational notes were taken on user behavior (e.g., active hours, interaction frequency) and system outputs (e.g., content volume, story element diversity). Subsequently, questionnaires were administered, incorporating 7-point Likert scales to assess engagement, immersion, creativity, and social needs. In detail, adopting the methodologies of [PERSON] [28] and [PERSON] et al. [29], we employed a 7-point Likert scale to measure participants' engagement and comprehension during story creation. Additionally, we applied the System Usability Scale (SUS) [30] and the NASA-TLX scale [31] to evaluate system usability and task workload, encompassing six dimensions: mental demand, physical demand, temporal demand, performance, effort, and frustration. Semi-structured interviews were conducted as well. Analysis was performed on 12 story drafts (6 from each group) to compare content outputs, collaborative network metrics, and sentiment analysis. Furthermore, group cooperation was evaluated by two raters using the Consensus Assessment Technique (CAT) [32] via video segmentation, with confirmed inter-rater reliability (ICC: 0.82 for Story Fork, 0.80 for Co-Relay). Qualitative insights from interviews delved into participants' subjective experiences during the storytelling process.
# Survey of Smart Parking Systems
## 4 Vehicle Detection Techniques (VDT) Implemented in SPS
### Roadside Units  
Another type of VDT which implements vehicle-to-roadside (V2R) and vehicle-to-vehicle (V2V) communications are RSUs [158]. V2R communications allow vehicles to connect through their on-board units (OBUs) to RSUs belonging to one or more service providers and to download (or upload) various types of data related to SPS or other applications [83]. These data refer to the vehicle's position and, by triangulating this position information, it is possible to determine the availability of parking slots and communicate this information to the vehicle. This is possible because the location of each parking space is stored and compared to the location of each vehicle thanks to the information obtained by the system [285].  
This type of infrastructure is costly to implement and should usually be implemented through a service provider. These implementations occur in oversized car parks, thus allowing more precise control of vehicle positions and parking slot locations. In addition, each vehicle must have an OBU to operate the system, which is another disadvantage [270; 286].
# SCM-IoT: An Approach for Internet of Things Services Integration and Coordination
## 2 Related Work
### ECA Rules  
An event-condition-action (ECA) rule is a computational model for event-driven architectures [80]. An ECA rule is structured in three parts:  
* The event part specifies an event expression that binds the values observed in the change of the computational context to the variables occurring in the expression;
* The condition part specifies a logical expression that restricts the values passed from the event part;
* The action part specifies the changes exerted on the computational context but only for those values that satisfy the condition part.  
The computational context comprises all the objects that respond to actions of the action part by exhibiting changes in their structure or data content. ECA rules use varied forms of shared data storage. Active databases [81] are extended database systems that react to the CRUD (create, read, update, delete) operations performed on tables. Production systems (PS) [82] are collaborative systems for agents that react to the rising data patterns observed in working memory after modifying its content. Besides, the abstract computational model of CPN extends the single working memory unit of PS to a set of partitioned possible shared working memory units corresponding to the set of places in CPN. Under this interpretation of CPN, ECA rules provide an operational reading for them, where transitions are instances of rules, enabled transitions are selectable rules for execution, firing transitions correspond to rule execution, and marking to event detection. Depending on the level of abstraction of the ECA rule language used, CPNs can be understood as the operational interpretation of ECA rules, whereas ECA rules correspond to their declarative counterpart.  
The IoT system development methodology uses this declarative-operational interpretation of ECA rules and CPNs. This approach paved the way in system development for transiting from more abstract description models to more detailed ones.
## 3 Results  
### Preprocessing of Original Spectra  
The near-infrared spectra data of rapeseed plant seed samples underwent various preprocessing methods, encompassing MC, FD, SNV, MSC, SG, and combined SG smoothing. Subsequently, SVR prediction models for seed germination rate were established using the preprocessed spectra data. Figure 2 showcases the spectra data after application of distinct preprocessing methods, whereas Table 3 depicts the outcomes of the SVR modeling.  
Figure 2: Preprocessing of near-infrared spectra for rapeseed plant seeds. (**a**) MC. (**b**) SNV. (**c**) MSC. (**d**) FD. (**e**) SG convolutional smoothing. (**f**) SG convolutional smoothing + MC. (**g**) SG convolutional smoothing + SNV. (**h**) SG convolutional smoothing + MSC. (**i**) SG convolutional smoothing + MC.  
Upon examining the outcomes of diverse preprocessing models, it was evident that the SG convolution smoothing method facilitated improved modeling results compared to the no-preprocessing strategy. The determination coefficients (R2c and R2p) for the training and prediction sets were more pronounced, suggesting a superior fit of the model to the data. Moreover, the RMSEC diminished post the application of SG smoothing, signaling a reduction in the discrepancy between the predicted and factual values. The closer values of RMSEC and RMSEP indicated augmented model stability and refined prediction accuracy. Based on these observations, the SG convolution smoothing method was chosen as the preprocessing method for the SVR modeling of rapeseed plant seed germination rates. The prediction outcomes employing this preprocessing method are demonstrated in Figure 3, where the predicted values show a closer alignment with the actual values. Nonetheless, despite the enhancements in data quality and model performance via preprocessing, the modeling outcomes still did not fully satisfy the sought-after prediction requirements. Consequently, SG convolution smoothing was utilized as input for subsequent data processing techniques to further enhance the model's performance.
# Multifrequency Impedance Method Based on Neural Network for Root Canal Length Measurement
## 3 Results
### Pre-Verification
#### 3.1.1 Impedance Verification  
The impedance of the root canal is related to the tooth type, input signal frequency, and the distance from file to apical foramen. We took these three factors in turn as independent variables in order to observe their effects on the dependent variable of impedance.  
First, some different tooth samples were selected to observe the root canal impedance.  
Figure 7 illustrates the impedances of four kinds of selected teeth with several input signals. The selected teeth differed in terms of type and age, but the impedance trends of each tooth type were similar. The impedance of all the teeth decreased with the increase of frequency, thus guaranteeing that the impedance ratio method is reasonable.  
For these four selected teeth, the relationships between the impedance of different teeth and the position of file are presented in Figure 8. The frequency was fixed.  
It was found that the impedance changed smoothly when the file was far away from the apical foramen, while the impedance decreased rapidly when the file was close to the apical foramen. In particular, the impedance varied the most within 1 mm of the file from the apical foramen. Moreover, when the distance from apex was 0 mm (i.e., the file was at the position of the apical foramen), there was not much difference among the impedances of different tooth samples. The results in Figure 8 provide a theoretical basis for the multifrequency impedance ratio method for root canal length measurement.  
In the next step, the tooth type was kept constant. The impedance of the file at various positions from the apical foramen varying with frequency was explored.  
Figure 7: The impedance of different input signal frequencies varying with the tooth type.  
As shown in Figure 9, the impedance decreased as the frequency increased, regardless of the distance between the file and apical foramen. These phenomena verified the correctness of the early methods for root canal length measurement, as well as the reliability of the proposed measurement system.  
When the frequency increased to approximately 20 kHz, the impedance almost stopped increasing. Therefore, setting the highest frequency as 20 kHz barely affects the subsequent analysis of experimental results when the impedance ratios of different frequency combinations were calculated. It was considered sufficient to conclude the experiments with the frequency ranging up to 20 kHz.
## 3 Methodology
### Data Partitioning  
After the data cleansing activity, data partitioning was performed. Relevant data were extracted and combined for further analysis, as shown in Table 1.  
Figure 3: The cycle of applying educational data mining in research.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No** & **Category** & **Feature** & **Values** & **Description** \\ \hline
1 & & Applicant & Text + Numeric & Student ID for identification and mapping \\
2 & & Cumulative Grade Point Average (CCPA) & * 0.00-4.00 & Student overall CPA \\
3 & & Attempt Count & Low (1), Medium (2) and High (\(>\)2) & Status of the student attempts in the module \\
4 & & High Risk & Yes/No & A student having a high failure rate in the same module \\
5 & & Term Exoced at Risk & Yes/No & Shows the students’ progression in the degree plan \\
6 & Student Academic & At Risk & Yes/No & A student failed two or more modules previously \\
7 & Information & Student Success Centre (SSC) & Yes/No & Student referred to SSC for any assistance \\
8 & & Other Modules & Low (1), Medium (2) and High (\(>\)2) & Student register for other modules \\
9 & & Flagisprint Count & * Numeric Value & Students have been accused of any plagiarism \\
10 & & Coursework 1 (CW1) & * 1-100 & Marks obtained in CW1 \\
11 & & Coursework 2 (CW2) & * 1-100 & Marks obtained in CW2 \\
12 & & End Semester Examination (ESE) & * 1-100 & Marks obtained in ESE \\ \hline
13 & & Activity on Campus & * Time spent in minutes & Student Moodle activity on campus \\
14 & & Activity Off Campus & * Time spent in minutes & Student Moodle activity off campus \\ \hline
15 & & Played & * No. of times video played & eDiffy access and played video \\
16 & Student Video & Paused & * No. of times video passed & eDiffy access and passed the video \\
17 & Interactions & Likes/Dislikes & Yes/No & eDiffy access and either liked or disliked video \\
18 & & Segment & * No. of segments rewind & eDiffy access and most-watched segment mapped with ESE \\ \hline
19 & & Result (Target Variable) & * Numeric value & Overall marks obtained in the module \\ \hline \end{tabular}
\end{table}
Table 1: List of extracted features and description. CPA: grade point average.
## 3 Results and Discussion
### Nutrient Composition of Propolis Residue
#### 3.4.2 Micronutrient Profile  
The mineral analysis highlighted a good spectrum of essential minerals within the propolis residue, explained in Table 4 and Figure 2. The mineral profile of propolis residue  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Nutrients** & & \\ \hline Moisture & g/100 g & 37.08 \(\pm\) 1.07 1  \\ Protein (Nx6,25) & g/100 g & 12.56 \(\pm\) 0.75  \\ Total fatty compounds & g/100 g & 16.0 \(\pm\) 1.3  \\ Dietary fiber & g/100 g & 24.2 \(\pm\) 3.9  \\ Ash & g/100 g & 2.95 \(\pm\) 0.20  \\ Carbohydrates & g/100 g & 7.21 \(\pm\) 4.32  \\ Fructose & g/100 g & 0.299 \(\pm\) 0.06  \\ Glucose & g/100 g & 0.183 \(\pm\) 0.04  \\ Sucrose & g/100 g & n.d. 1  \\ Maltose & g/100 g & n.d. \\ Lactose & g/100 g & n.d. \\
**Total Sugars** & g/100 g & 0.482 \(\pm\) 0.07 4  \\ \hline
**Energy value** & kCal/100 g & 271 \(\pm\) 12  \\  & kJ/100 g & 1122 \(\pm\) 48  \\ \hline
**Minerals** & & \\ \hline Calcium & mg/kg & 2100 \(\pm\) 290  \\ Iron & mg/kg & 92 \(\pm\) 18  \\ Phosphorus & mg/kg & 2520 \(\pm\) 470  \\ Magnesium & mg/kg & 1210 \(\pm\) 230  \\ Manganese & mg/kg & 50 \(\pm\) 11  \\ Potassium & mg/kg & 9800 \(\pm\) 1900  \\ Copper & mg/kg & 5.1 \(\pm\) 1.1  \\ Selenium & mg/kg & n.d. \\ Sodium & mg/kg & n.d. \\ Zinc & mg/kg & 32.5 \(\pm\) 6.3  \\ \hline \hline \end{tabular}
\end{table}
Table 4: Nutrient (%) and mineral profile (mg/kg) of propolis residue (per 100 g).  
Figure 2: Nutrient and mineral profile of Brazilian propolis residue. This figure presents the nutrient and mineral composition of propolis residue, showcasing high levels of essential elements such as phosphorus (2520 mg/kg), potassium (9800 mg/kg), magnesium (1210 mg/kg), and calcium (2100 mg/kg). The graph emphasizes the propolis residue’s value as a nutritional supplement and its potential in health applications.  
is particularly rich, with high levels of essential minerals such as phosphorus (2520 mg/kg). Specifically, potassium was notably high at 9800 mg/kg, indicating the propolis residue's potential in supporting cardiovascular health and muscle function. Furthermore, the richness in magnesium (1210 mg/kg) and calcium (2100 mg/kg) can support its application in bone health and metabolic processes. These values highlight the potential utility of propolis residue in mineral supplementation, as it can provide substantial amounts of essential minerals. For instance, the calcium content (2100 mg/kg) is comparable to many dairy products, which typically contain between 400 and 1200 mg per 100 g (equivalent to 4000-12,000 mg/kg). The magnesium content (1210 mg/kg) and phosphorus content (2520 mg/kg) are also significant when compared to typical cereal contents of 150-350 mg/kg and 250-350 mg/kg, respectively [67, 68]. Notably, the potassium content (9800 mg/kg) is much higher than that found in bananas, which contain approximately 358 mg per 100 g (equivalent to 3580 mg/kg) [69].
# A Deep Learning Approach with Feature Derivation and Selection for Overdue Repayment Forecasting
## 3 Proposed System  
We introduce multiple deep learning models to predict future overdue repayment behavior in online lending based on previous repayment behaviors. The structure of our proposed AD-BLSTMapproach is illustrated in Figure 1. AD-BLSTM integrates DNN, LSTM and the attention mechanism for the purpose of appropriately representing different parts of the input repayment behavior record and improving prediction performance and interpretability. At the same time, we propose an improved feature derivation method that can generate extensive fully-combined new features and select an arbitrary number of the most significant features based on a scorecard model.  
The purpose of the task is to classify future repayment behavior into two types, overdue repayment behavior (positive) and normal repayment behavior (negative), according to the past repayment logs. Therefore, the problem is simplified into a binary classification task.  
As illustrated in Figure 1, the structure of our system can be divided into five modules: feature derivation and selection, a multi-BiLSTM layer, a DNN layer the attention mechanism and an output layer. We first divide the input data into dynamic and static features. The last repayment label serves as the target and all the previous time-dependent features are set as dynamic features that are fed into the multi-BiLSTM layer. Produced by feature derivation and the selection module, static features are fed into the DNN layer. We simplify the task to a binary classification task, and so the objective is to minimize the cross-entropy loss:  
\[\mathcal{L}=-\sum_{i}[\,\hat{y}^{(i)}\log(y^{(i)}_{o})+(1-\hat{y}^{(i)})\log (1-y^{(i)}_{o})\,] \tag{1}\]  
where \(\hat{y}^{(i)}\) is the ground-truth and \(y^{(i)}_{o}\) is the output probability by the system.
## 8 Discussion
### Results Compared with Hot Spot Stress Method
Figure 14 illustrates the results based on equivalent structural stress and hot spot stress. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**FAT**} & \multirow{2}{*}{**Knee Point Stress Range \(\Delta\sigma_{kp}\)**} & \multicolumn{2}{c}{\(N_{hs}\leq 10^{7}\)} & \multicolumn{2}{c}{\(N_{hs}>10^{7}\)} \\ \cline{3-6}  & & \(m^{\prime}\) & \(C_{hs}\) & \(m^{\prime}\) & \(C_{hs}\) \\ \hline
90 & 52.7 MPa & 3 & \(1.458\times 10^{12}\) & 5 & \(4.065\times 10^{15}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters for FAT90 curve. Figure 13: FAT90 curve. According to Table 5 and Figure 14, it can be found that the discrepancy of results obtained by hot spot stress and equivalent structural stress are mostly around 15%, which meets engineering demands. The discrepancy maybe mainly stems from the complicated stress state involved in large-scale structures, which are usually multi-axial stress state, while in small-scale welded joint test specimens the stress state is relatively simple, which is usually single-axial stress state, so the fatigue assessment using different methods agrees perfectly with each other. It can be found that the fatigue damage calculated by equivalent structural stress is higher than that calculated by hot spot stress, which means the fatigue assessment by equivalent structural stress method is more conservative and tends to be safer when engineering structures are designed. Actually, compared with hot spot stress method, the two greatest advantages of equivalent structural stress method lie in the mesh-insensitive quality, which is very important when large-scale engineering structures are dealt with, and the capability of unifying different curves into one master S-N curve, which can avoid the confusion when the desired curve needs to be selected.