esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks
## 8 Conclusions  
Wireless visual sensor networks have allowed for the development of astonishing applications, strongly supporting the rise of new monitoring and control systems. However, this scenario also puts several new problems in evidence, most of them intrinsically associated with the visual nature of the data gathered from these networks. One of these issues is the notion of quality in such networks, which is a very abstract concept, presenting very different definitions. In this article, a literature review was presented regarding quality assessment of WVSN, identifying the most impacting aspects that affect this evaluation.  
We proposed a classification methodology that organizes quality metrics of WVSN into tree categories: (_i_) spatial coverage quality, that considers target, area and barrier coverage, (_ii_) content quality, mainly expressed by sharpness and perspective, and (_iii_) dependable quality, based on the availability and reliability of the networks. For each category, a set of metrics was identified and organized for better comprehension of their scope and applicability.  
Overall, the proposed classification is a valuable tool. In first place, it is helpful to map, unify and standardize the existing knowledge of quality assessment. Secondly, it creates a guide for comparison among different networks or even for different implementations of the same network under the same terms, enabling then a fair comparison.  
This review also identified some open issues that could lead to future research directions. Occlusion should receive more attention in the literature, especially in target and barrier coverage applications. Such applications could also benefit themselves from research efforts related to metrics to assess content and dependable quality. Finally, for area coverage applications, the proper definition of the minimum sensors set has been a challenging task, which demands a deeper discussion about some trade-offs associated with coverage redundancy.  
Conceptualization, T.C.J., P.P., D.G.C. and F.V.; methodology, T.C.J., P.P., D.G.C. and F.V.; validation, T.C.J., P.P., D.G.C. and F.V.; formal analysis, T.C.J., P.P., D.G.C. and F.V.; investigation, T.C.J., P.P., D.G.C. and F.V.; data curation, T.C.J., P.P., D.G.C. and F.V.; writing--original draft preparation, T.C.J., P.P., D.G.C. and F.V.; writing--review and editing, T.C.J., P.P., D.G.C. and F.V.; visualization, T.C.J., P.P., D.G.C. and F.V. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is financially supported by national funds through the FCT/MCTES (PIDDAC), under the project EXPL/EEI-COM/1089/2021. This research was also supported by INEGI-LAETA (FCT project UIDB/50022/2020).  
**Data Availability Statement:** Not Applicable, the study does not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Methodology
#### 2.2.4 Data Treatment  
The PCB 352A24 acceleration sensor having a frequency bandwidth of 1.0 to 8 kHz was used to collect the vibrational responses of the toggle clamping system. To accurately represent frequencies present in the bandwidth of this sensor, the Nyquist theory must be fulfilled. The Nyquist theorem explains that sampling rate must be at least twice as high as the maximum frequency captured by the sensor. Increasing the sampling rate beyond this twofold threshold improves the quality of the acquired signal. For this reason, we sampled the acceleration signal at 50 kHz. This value was sufficiently large to satisfy the Nyquist theorem while not overloading the buffer of the data acquisition system. Prior to performing further data analysis, the acquired signal was subjected to a bandpass filter. This filter passed frequencies from 1 to 8 kHz while attenuating frequencies greater than 8 kHz, essentially keeping only the frequency range that the acceleration sensor accurately captured.
## 1 Introduction
### A Resilience-Oriented Approach Using Graph Invariants for C-RAN Backhaul Design
The main contributions of this paper can be summarized as follows: (i) study of C-RAN backhaul requirements related to resilience; (ii) investigation of graph invariants described in the literature that can capture such requirements; (iii) design of a new graph invariant (Wiener impact) related to backhaul resilience; and (iv) proposal of a method for backhaul design as a graph optimization problem. This paper is organized as follows. In Section 2, we describe our method, map C-RAN resilience requirements into existing graph invariants, and propose the new Wiener impact invariant. Some case studies of the proposed method are presented in Section 3. Section 4 finally draws our conclusions and points at future research directions.
# Partial Discharge Wideband Full-Band High-Gain Resonant Cavity UHF Sensor Research
## 2 Principle of Antenna
### The Basic Principle of Microstrip Feeder Transmission Line  
The microstrip feed line is a common way to feed antennas, its basic principle is to achieve the feed by laying metal strip lines on the dielectric substrate. When the electromagnetic wave propagates to the microstrip feed line, a surface wave is generated between the microstrip line and the ground plane, and this surface wave propagates along the microstrip line and eventually reaches the antenna radiation element. The microstrip transmission line width \(w\) and the microstrip transmission line characteristic impedance \(z\) satisfies:  
\[z=\frac{120\pi}{\sqrt{\epsilon_{\mathit{eff}}\left[\frac{w}{h}+2.42-0.44\frac {h}{w}+\left(1-\frac{h}{w}\right)^{6}\right]}} \tag{5}\]  
where \(h\) is the thickness of the dielectric substrate. The desired impedance, dielectric substrate thickness, and equivalent dielectric constant are brought into the equation to calculate the microstrip line width for good impedance matching.
## 3 Results
### Quantification of Phenolic Compounds  
A total of 23 phenolic compounds, including derivatives, were identified by using UHPLC (Table 2 and Figure 1).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Extract**} & \multicolumn{6}{c}{**TPC (mg/g)**} & \multicolumn{6}{c}{**TFC (mg/g)**} \\ \cline{2-9}  & **Leaf** & **Fruit** & **Root** & **Total** & **Leaf** & **Fruit** & **Root** & **Total** \\ \hline Methanol & 39.21 \(\pm\) 4.32  & 95.21 \(\pm\) 8.21  & 30.21 \(\pm\) 2.21  & 164.63 & 24.32 \(\pm\) 3.58  & 20.25 \(\pm\) 3.58  & 72.46 \(\pm\) 4.21  & 28.22 \(\pm\) 3.21  & 41.33 \(\pm\) 4.54  & 82.08 \\ Ethyl acetate & 25.55 \(\pm\) 2.55 [FOOTNO  
As already shown by the TPC and TFC, this analysis also revealed the presence of a high concentration of phenolic constituents in _P. ginseng_ fruit (2327.52 \(\upmu\)g/g) followed by leaf (567.73 \(\upmu\)g/g) and root (254.04 \(\upmu\)g/g). The most abundant phenol was identified as chlorogenic acid (1002.2 \(\upmu\)g/g). Gentisic acid was the second-most abundant, with the highest value in the fruit (854.21 \(\upmu\)g/g). These were followed by _m_-coumaric acid--185.32 \(\upmu\)g/g (leaf); rutin--165.32 \(\upmu\)g/g (fruit); _p_-coumaric acid--125.24 \(\upmu\)g/g (leaf); chlorogenic acid--90.2 \(\upmu\)g/g (root); _p_-coumaric acid--65.3 \(\upmu\)g/g (fruit); gentisic acid--62.30 \(\upmu\)g/g (leaf); syringic acid--75.3 \(\upmu\)g/g (fruit); and _m_-coumaric acid--45.2 \(\upmu\)g/g (fruit). Only trace amounts of flavonoids were found, whereas hydroxycinnamic acid, hydroxybenzoic acid and the total phenolic compounds were found present in a significant amount.  
Figure 1: Chemical structure of major phenolic compounds in _P. ginseng_ extract: (**a**) _p_-coumaric acid; (**b**) _m_-coumaric acid; (**c**) chlorogenic acid; (**d**) protocatechuic acid; (**e**) syringic acid; (**f**) gentisic acid; (**g**) _p_-hydroxybenzoic acid; and (**h**) rutin.
# A Practical Model for the Evaluation of High School Student Performance Based on Machine Learning
## 2 Related Works
### Evaluation of Student Performance  
To evaluate the performance of students in geographic lessons and classify students, data which consisted of 15 attributes such as grades in homework assignments, oral assessments, short tests, semester exams, etc., were obtained from 307 students and fed into Naive Bayesian (NB), Random Forest (RF), Sequential minimal optimization (SMO), multilayer perceptron (MLP) and Bayes net (BN) algorithms. The accuracy of the mentioned models was 68.99, 67.04, 65.38, 59.81 and 65.67, respectively. Data have been collected concerning the \"geography\" module [34]. Similar to the previous reference in ref [15], the authors employed linear regression (accuracy = 64.25), decision tree (accuracy = 59.8), and naive Bayes (accuracy = 71) algorithms in data analysis with 303 input features including student demographics, the salaries of educators, and assessment data. Moreover, in ref [12], logistic regression (accuracy = 0.839), elastic net (accuracy = 0.839), decision tree (accuracy = 0.767), random forest (accuracy = 0.828) and neural network (accuracy = 0.833) algorithms were utilized to evaluate student performance in English lessons. Their database was NA-PLAN which is a set of standardized literacy and numeracy tests sat by all students in Australia and the tests cover five learning areas known as \"test domains\" (reading, writing, spelling, grammar and punctuation, and numeracy), alongside student background information which is collected by schools from students' parents via enrolment forms. Overall, 2,235,804 rows of student information were used in the analysis. For monitoring student performance at a Brazilian technical high school, authors in ref [35] developed an ML system based on NB, SVM, tree-based method (Simple CART), and rule-based method (OneR) algorithms. Their dataset contained information about course name, age, sex, birthplace, course duration, the identification of each discipline studied, number of faults in the first to fourth bimester, and student status at the end of the course. In this research, the usefulness of ML was highlighted for monitoring student performance. In ref [18], student performance was predicted using multi-class support vector classification. Real data from 395 secondary students in Portugal were employed, which was collected by a questionnaire method and school reports. The data contained grades assigned and student info, and each instance is categorized in five levels as A to F. This research indicates that by predicting the performance of the students, they can provide appropriate extra tasks to improve students' education. In ref [36], the answers of 702 students in the 10 th grade were analyzed in chemistry lab classes. The researchers used the k-mean clustering method for segment answers, then a DL algorithm was employed for the development of explanatory models of the segmentation. Their results highlight that one key factor for chemistry learning is the attention and involvement of students in classes.
## 3 Design of Main Working Parts
### Film-Covering Device  
Figure 15 shows the structure of the film-covering device, which is mainly composed of a film-hanging rod, connecting frame of the film-hanging rod, film-spreading roll, adjusting rod of the film-spreading roll, connecting rod of the film-spreading roll, etc. The mulch extends from the hanging rod and is pressed onto the V-shaped seeded by the spreading roll, Both sides of the mulch are guided onto the seeded by the spreading roller and are fitted against the soil. In order to prevent film damage in the mulching process, the two sides of the spreading roll are designed to have a circular arc, which can not only mulch the film but also blanket the edge of the film. At the same time, the distance between the head of the seedling and film edge ranges from 0-40 mm, meeting the agronomic requirements of crop cultivation. The film height of the film-spreading roll can be adjusted by changing the vertical position of the adjusting rod of the film-spreading roll. Compared with the existing film-covering device, the film-spreading roll designed in this paper can realize V-shaped film coating and edge pressing under the premise of ensuring no damage to the film and improving the film coating quality of the V-shaped seedbed.  
Figure 14: Movement process of soil particles on the surface of \(x_{2}o_{2}z_{2}\).  
According to agronomic requirements, the seedlings must outcrop on the edge of the film in the process of film mulching, so the selection of mulching film needs to meet:  
\[L\leq L_{2}+2L_{1}-2X \tag{29}\]  
where \(L_{1}\) is the linear distance from the head of the seedling to the furrow (250 mm);  
\(L_{2}\) is the width of the furrow (200 mm);  
\(L\) is the width of the film (mm);  
\(X\) is the distance between the outcrop of seedling and film edge, which is set to be 40 mm.  
Taking all the factors into consideration, the width of the film is set to be 620 mm.
## 3 Experimental Results
### 2 \(\times\) 2 Array Performance
#### 3.2.1 Coupling  
It is well known that a smaller spacing between elements is desirable for a compact array because it helps to suppress the grating lobes when scanning. Although a half-wavelength spacing minimizes the grating lobes, due to multiple factors, it might not be possible to achieve this integration density. For example, the size of the beamformer  
Figure 6: Smith chart of the return loss and isolation results for a single radiating element in the Ka band frequencies from 27.5 to 31 GHz.  
chips that must be placed on the opposite face of the PCB limits the density of radiating elements, because it is very challenging to bring the chips nearer and still have room for the RF distribution or DC biasing. Furthermore, a smaller spacing results in higher coupling between antenna elements. In Figure 9, mutual coupling outcomes for the antenna array are plotted.  
To keep a low active reflection coefficient and acceptable axial ratio of the beam scanning array, mutual coupling levels should be lower than \(-20\) dB [31]. Looking at Figure 5, it is clear that almost all the S-parameters are able to meet this condition in the frequency of interest, showing a worst case of \(-18\) dB in a small frequency band from 26.5 to 28 GHz. Besides, it is verified that the mutual coupling is reduced when the frequency increases because the electrical distance between the radiating elements also increases.  
Figure 7: Antenna element normalized radiation patterns at \(\varphi=0^{\circ}\) for RHCP (**above**) and LHCP (**below**).
## 5 What Is C-RAN?
### Architecture of C-RAN  
The C-RAN architecture divides the baseband processing into the DU and CU to meet more agile deployment opportunities. Hence, a number of functional placement scenarios exist, taking into account the time-criticalness of particularly the functions in the DU. The functional placement scenarios vary from having only the RU on the cell site to having all virtualized baseband processing on the cell site additionally for the benefit of extreme low-latency scenarios. The Next Generation Mobile Networks Alliance (NGMN) has defined six functional placement scenarios, where the baseband processing is handled on either the cell site, aggregation site, or distributed site [114]. These functional placement scenarios are illustrated in Figure 15.  
Due to the various functional placement scenarios, deployment of C-RAN becomes very agile, and the specific deployment can adapt to solve a specific problem in a specific area. One thing that needs to be considered is the transport network in-between the fronthaul connecting the RU to the DU and the midhaul connecting the DU to the CU [111]. Hence, when deploying C-RAN, the use of the capacity-demanding fronthaul network must be determined by the availability or opportunity for deployment of a high-capacity link between the RU and the DU. Hence, if such a link is not existing or possible to deploy, the DU, and maybe also the CU, must be left on the cell site.  
C-RAN is currently the architecture suggested in specifications by Open Radio Access Network (O-RAN) and ITU-T [115], IEEE's Next Generation Fronthaul Interface [116] and Small Cell Forum [117]. Hence, it is a widely discussed architecture for future mobile network deployments.
## 6 Sensitivity Analysis
* Variation in the subcontracting cost: It was noted that an increase in the subcontracting cost \(C_{sub}\) (case x) had the logical effect of the major maintenance being performed with more frequency to rejuvenate the system and limit the presence of shortages. Likewise, the increase in the subcontractor's cost \(C_{sub}\) caused that the system to increase the inventory level \(Z^{*}\) because it was more profitable to opt for internal production than to use the subcontractor. Furthermore, it was obvious that by increasing \(C_{sub}\), the critical age of subcontracting \(A^{*}_{sub}\) increased due to the high cost of external supplies. Reducing the cost of subcontracting had the opposite effect of increasing \(C_{sub}\) (case ix). * Variation in the minimum repair cost: The reduction in the minimum repair cost \(C_{R}\) (case xi) triggered the conduction of more major maintenance since this action kept the machine in a better condition, decreasing the presence of defects; this is the reason that explained the decrease in the critical age for major maintenance \(A^{*}_{o}\). Furthermore, it is shown in Table 7 that with the decrease of \(A^{*}_{o}\), the unit was functional for less time; therefore, less inventory accumulated, reducing \(Z^{*}\). On the other hand, the critical age of subcontracting \(A^{*}_{sub}\) was reduced to guarantee the product availability due to the reduction in the inventory level \(Z^{*}\). By decreasing the minimum repair cost, the opposite effects were observed (case xi). * Variation in the cost of major maintenance: From the results, it was noted that the increase in the major maintenance cost \(C_{M}\) (case xiv) led to observing that this action was performed less frequently; therefore, the age \(A^{*}_{o}\) increased. As \(C_{M}\) increased, the system reached greater levels of deterioration to account for the high cost of major maintenance. The objective of postponing this maintenance action was to let the system operate for longer as an attempt to increase the inventory level, and thus, increase the capacity to respond to customer demand; this increased the inventory level \(Z^{*}\). Moreover, as the machine was operational for longer periods, it was obvious that less subcontracting was required; therefore, the age \(A^{*}_{sub}\) increased because it was not economical for the company to have the subcontractor supply units in advance. Reducing the cost of major maintenance had the opposite effects (case xiii). Based on the results discussed in Table 8, it was validated that the proposed control policy worked efficiently and that the logic of the control strategy was consistent throughout the analysis.
## 2 Design of LPU Reliability Assessment Algorithm for Flash-Memory Systems
### Cosine Similarity-Based LPL Reliability Assessment
Moreover, given that the entropy feature matrix also incorporates binary elements, it follows that the elements of any two such vectors, \(\mathbf{f}_{\text{efv}}\) and \(\mathbf{c}_{\text{cmp}}\), are non-negative. Consequently, their dot product, \(\mathbf{f}_{\text{efv}}\cdot\mathbf{c}_{\text{cmp}}\), is inherently non-negative, which restrictively bounds the range of their cosine similarity (\(CS(\mathbf{f}_{\text{efv}},\mathbf{c}_{\text{cmp}})\)) to [0, 1]. To illustrate this, consider vectors \(\mathbf{f}_{\text{efv}}=[0,1,0,0]\) and \(\mathbf{c}_{\text{cmp}}=[1,0,1,0]\), where the cosine similarity is computed as follows:  
\[\text{CS}(\mathbf{f}_{\text{efv}},\mathbf{c}_{\text{cmp}})=0.\]  
For \(\mathbf{c}_{\text{cmp}}=[0,1,1,0]\), the cosine similarity is  
\[\text{CS}(\mathbf{f}_{\text{efv}},\mathbf{c}_{\text{cmp}})=\frac{1}{\sqrt{2}}.\]  
Further, considering the definition of \(\mathbf{f}_{\text{efv}}\), it is clear that non-zero bits generally indicate unreliability in the prior information of the associated VNs. In cases where \(CS(\mathbf{f}_{\text{efv}},\mathbf{c}_{\text{cmp}})\) is zero, it signifies that no unreliable node information influences the update of the corresponding CN. Conversely, when \(CS(\mathbf{f}_{\text{efv}},\mathbf{c}_{\text{cmp}})\) is non-zero, it implies the involvement of unreliable verification nodes in the update process. Essentially, a higher \(CS(\mathbf{f}_{\text{efv}},\mathbf{c}_{\text{cmp}})\) indicates an increase in participation by unreliable VNs, thus escalating the likelihood of violating verification constraints. Therefore, we can evaluate the reliability of the LPU output information by calculating the \(CS(\mathbf{f}_{\text{efv}},\mathbf{c}_{\text{cmp}})\). The higher the \(CS(\mathbf{f}_{\text{efv}},\mathbf{c}_{\text{cmp}})\), the higher the unreliability of the output information. Subsequently, we can adjust the execution strategy of the LPU during the decoding process by using the \(CS(\mathbf{f}_{\text{efv}},\mathbf{c}_{\text{cmp}})\), thereby reducing the decoding delay. The proposed algorithms can be extended to support multi-level soft quantization, such as 3-level or 4-level quantization voltages. For higher-level quantization, the primary modification lies in the definition of the entropy feature matrix. In the current 2-level case, an entropy value of 1 is assigned to unreliable voltage regions, while in 3-level or 4-level quantization, additional non-zero values are assigned to different unreliable levels. The reliable voltage regions would still be assigned an entropy value of 0. Since the LDPC matrix is binary, the calculation of the cosine similarity between \(\mathbf{f}_{\text{efv}}\) and \(\mathbf{c}_{\text{cmp}}\) is only affected by the non-zero elements. This extension enables the proposed algorithms to handle increased noise sensitivity while maintaining their effectiveness.
## 7 Conclusions  
In this paper, by adding the ICS procedure to research the service delay in IEEE 802.11 networks, it is found that our delay evaluation results are significantly better than the traditional evaluation results. The average service delay of the Nav model in all the scenarios is larger than that of the analysis model due to missing the ICS procedure in the Nav model. Since a DIFS duration is generally much shorter than a random backoff duration, our model saves the bandwidth and improves transmission efficiency. Moreover, a comprehensive statistical characterization of the service delay is one of the most useful tools for dealing with Quality of Service (QoS) issues, especially when the network is loaded with numerous emergency traffic. Therefore, our model also applies to 802.11 e networks based on prioritization and will also lay a foundation for subsequent research work, such as dealing with emergency events in public safety networks or traffic flow information in the Internet of Vehicles.  
**Author Contributions:** [PERSON] researched the literature, conceived the study concepts, designed the algorithm, and took charge of the original draft preparation; [PERSON] improved the mathematical models, provided the systematic research and analysis methodology, edited the manuscript, and the refinement of the paper was completed under the supervision of [PERSON]; [PERSON] completed and checked the English grammar; [PERSON] checked the numerical simulations and formula deducing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded in part by the National Natural Science Foundation of China under Grant 61663024, and in part by the Hongliu First Class Discipline Development Project of Lanzhou University of Technology under Grant 225305.  
Figure 7: The standard deviation of the service delay in all scenarios.