esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 3 Diagnosability Evaluation Based on Structural Analysis
### Quantitative Evaluation
#### 3.2.1 Residual Similarity Metrics  
The residual \(r\) in control theory is used to construct the test quantity \(T(z)\). Therefore, \(r\) of the MSO-based design system acts as \(T(z)\), which tests the original hypothesis \(\Phi=assump(MSO)\). Thus, it determines the system's current behavioral pattern. In the diagnosability design, not only should the sensitivity of the residuals generated based on MSO to the fault be judged but the magnitude of the sensitivity of the residuals to the fault should also be fully considered. The residual \(r\) should be zero if the system is free from disturbances and noise and no performance degradation or failure occurs. However, noise, interference, and other influencing factors inevitably appear in the system. Assuming that the noise is Gaussian white noise, the residual \(r_{f_{0}}\sim N(0,\sum_{e})\) obeys a standard normal distribution with mean 0 and variance \(\sum_{e}\) when there is no fault. The distribution of the fault-free residuals \(r_{f_{0}}\) and fault residuals \(r_{f_{i}}\) in the two-dimensional residual space is shown in Figure 4.  
The problem of the similarity of the residuals is translated into an issue of evaluating the similarity of multivariate distributions in mathematical statistics. Furthermore, the difficulty of detecting \(f_{i}\) can be measured by measuring the difference in probability distribution between \(r_{f_{i}}\) when \(f_{i}\) occurs and \(r_{f_{0}}\) when there is no fault. The problem of isolating \(f_{i}\) and \(f_{j}\) can be measured by measuring the difference in probability distribution between \(r_{f_{i}}\) and \(r_{f_{j}}\). As can be seen from Figure 4, the difficulty of detecting fault \(f_{2}\) can be quantified by obtaining the probability distributions of \(r_{f_{0}}\) and \(r_{f_{2}}\) in the residual space, then utilizing the relevant tools to evaluate the difference in probability distributions between \(r_{f_{0}}\) and \(r_{f_{2}}\). Similarly, the isolation difficulty of faults \(f_{1}\) and \(f_{2}\) can be quantified by obtaining the probability distributions of \(r_{f_{1}}\) and \(r_{f_{2}}\) and measuring the difference in probability distribution between them. Figure 4: The fault residual distribution in the case of no interference. The current metrics for measuring the similarity of multivariate probability distributions include Hausdorff distance, Kullback-Leibler (K-L) scatter, Jensen Shannon (JS) scatter, Bhattacharyya distance, modified distance, Fisher information distance, energy distance, Mahalanobis distance, Bayesian error rate, and so on. Table 1 discusses the current state of research on the quantitative evaluation of diagnosability at the present stage. The table analyzes and summarizes the residual probability distribution measurement strategy, method application scenarios, method characteristics, and related literature.
## 3 Case Study  
### Data Description  
A real-world case study based on Beijing rail transit is presented below. There were 17 lines and 236 stations in the Beijing rail transit system in 2014. The AFC data of the Beijing rail transit system for 8 working days in 2014 were collected, namely, 19 February, 26 February, 5 March, 12 March, 19 March, 26 March, 2 April, and 9 April.  
Line 1 is the main rail transit line in the Beijing rail transit system. It crosses the whole central urban area and undertakes important passenger transportation tasks in the morning and evening rush hour. Moreover, line 1 closely links residents living in the east and west areas of Beijing. On 19 February, a signal interruption occurred at Bajiao Amusement Park station on line 1 between 7:27 a.m. and 8:03 a.m. It affected the operation of adjacent stations and the regional networks, which caused 30 trains to be stopped and 5 trains to be turned back halfway. The Beijing rail transit network and the location of the disruption are illustrated in Figure 2. No signal interruption occurred on the other days.  
Figure 1: Flow chart of the proposed method.  
Figure 2: Location of the station where the disruption occurred.  
Before identifying the abnormal stations of the rail transit system and calculating the influence intensity, data cleaning was conducted, that is, eliminating abnormal data and data irrelevant to this study, such as the data on the transaction type, card type, and ticket type. Then, the large amounts of real-time AFC data for the urban rail transit network were processed and counted by MATLAB, and the tap-in passenger flow distribution of each station was obtained at a 15 min granularity.
# Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11 ah
## 6 Performance Evaluation and Discussion
### Static Traffic Patterns
#### 6.2.2 With Hidden Nodes  
In this section, we study the impact of hidden nodes imposed on performance by extending the maximum distance between the AP and the stations to 100 m for the HT and 450 m for the LT scenario. The results are shown in Figure 8. This clearly reveals that when hidden nodes are present, TAROA gains additional advantage over EDCA/DCF, showing its potential to successfully avoid hidden nodes. While TAROA shows stable throughput performance for all traffic loads as a function of the number of stations, EDCA/DCF suffers more heavily. Even for low traffic loads, the throughput when using EDCA/DCF degenerates fast with more than 32 nodes in both the HT scenario and the LT scenario. For HT, EDCA/DCF shows a throughput drop between 32 and 1024 stations of up to 38%. For LT, its throughput even drops over 56%. Correspondingly, hidden nodes significantly increase the packet loss of EDCA/DCF, TAROA get less packet loss for all three of the traffic loads with more than 32 stations. With hidden nodes, collisions result in a very small amount of packet loss for TAROA, up to \(0.003\%\pm 0.002\%\). In the HT scenario, TAROA consistently outperforms EDCA/DCF in terms of latency. For LT, this is not always the case, due to the aforementioned reasons. For traffic load 1.2 Mbps  
\begin{table}
\begin{tabular}{c|c c c c|c c c c|c c c c} \hline \multirow{2}{*}{\(\mathbf{T\) (Mbps)}} & \multicolumn{8}{c|}{**HT Scenario, Without Hidden Nodes**} \\ \cline{2-13}  & \multicolumn{4}{c|}{\(\mathbf{n=128}\)} & \multicolumn{4}{c|}{\(\mathbf{n=512}\)} & \multicolumn{4}{c|}{\(\mathbf{n=512}\)} & \multicolumn{4}{c}{\(\mathbf{n=1204}\)} \\ \cline{2-13}  & **R = 32** & **R = 128** & **EDCA/DCF** & **TAROA** & **R = 32** & **R = 128** & **EDCA/DCF** & **TAROA** & **R = 32** & **R = 128** & **EDCA/DCF** & **TAROA** \\ \hline
0.75 & 0.70 & 0.52 & 0.75 & 0.73 & 0.34 & 0.34 & 0.75 & 0.75 & 0.28 & 0.35 & 0.75 & 0.75 \\
0.85 & 0.75 & 0.60 & 0.85 & 0.79 & 0.78 & 0.75 & 0.68 & 0.84 & 0.56 & 0.74 & 0.64 & 0.83 \\
1.20 & 0.86 & 0.72 & 0.  
with 1024 stations, in which the traffic is overloaded and stations get less transmission opportunity than they require, the results show that better performance is achieved with hidden nodes than without hidden nodes. This is due to overestimation of the transmission interval.
## 5 Experimentation, Results, and Analysis
### Performance Metrics
#### 5.2.5 Diversity Evaluation Metrics  
In addition to traditional metrics like BLEU and METEOR that assess syntactic and semantic correctness, we incorporate diversity-oriented evaluation metrics to better quantify the linguistic variety of the generated captions. Specifically, we employ the following:  
Vocabulary Coverage (VC)  
Vocabulary coverage measures the extent to which the model utilizes a broad vocabulary during caption generation. Let \(V_{gen}\) be the set of unique words in the generated captions and \(V_{ref}\) the vocabulary of the reference dataset:  
\[VC=\frac{|V_{gen}|}{|V_{ref}|} \tag{28}\]  
Repetition Rate (RR)  
Repetition rate quantifies the average repetition of words in a single caption. For a generated caption \(C_{i}\) with length \(n_{i}\) and set of unique words \(U_{i}\), the repetition rate is:  
\[RR=\frac{1}{N}\sum_{i=1}^{N}\biggl{(}1-\frac{|U_{i}|}{n_{i}}\biggr{)} \tag{29}\]  
where \(N\) is the total number of captions. A lower RR indicates better diversity.  
These metrics collectively provide a comprehensive view of the diversity in the generated captions, helping to assess lexical richness and variety.
## 4 Discussion
### Interpretation of Parameter Values with Biggish Dispersion Degree  
There are three main reasons to explain the high dispersion of each mentioned fly ash coefficient. Firstly, there are many influences from other admixtures, such as slag and limestone flour. Secondly, fly ash is a collected industrial by-product of calcining coal powder in power stations; therefore, the quality of fly ash fluctuates greatly. Thus, the fluctuation in the quality of the fly ash is mainly reflected in the content of vitreous body, particle characteristics, particle size distribution, as well as the presence of \(M_{g}O\) and other substances. Consequently, there are periodic changes in the operation of the power station and differences in the fly ash produced by different power stations. Moreover, the coal used in the same power plant may not be homogeneous. Thirdly, fly ash contributes more to the concrete than it does to the mortar because of the physical action of fly ash in the concrete (except from the chemical action). The main physical action is that fly ash can be filled with coarse aggregate particles while there is no such physical effect in mortar mixed with fly ash.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **LF** & **FA** & **SL** & **C** \\ \hline
25 th percentile & 1.516 & 1.310 & 1.423 & 0.241 \\ median & 1.598 & 1.502 & 1.551 & 0.307 \\
75 th percentile & 1.847 & 1.635 & 1.616 & 0.384 \\ \hline \end{tabular}
\end{table}
Table 12: The 25 th percentile, median, and 75 th percentile of the impact index of each cementitious material.  
Besides, there is a high dispersion of \(ss_{SL}\) in Figure 5. There are also three interpretations. Firstly, the fineness of cement in each engineering project and experiment varies; and the finer the cement is, the faster the activity excitation speed of slag is. Secondly, in the middle and later stage of hydration, the hydration activity of slag is related to its fineness. The finer the slag is, the more active the activity is. At last, the experiment environment and background vary greatly, such as curing condition and specimen size.
## 4 Discussion
### PQ Monitor Allocation through the Prism of Grid Reliability  
PQ monitoring is important not only for compliance verification or physics investigation, but also for power system development, its investment planning, and its economic analysis (in particular, cost savings due to the diminished number of events and avoided outages). Let us explain the Lithuanian DSO's investment planning scheme by defining the distribution grid segment as a radial branch (consisting of both MV and LV levels) connected to its HV/MV transformer. It is noteworthy that another approach to the term could be based on the administrative division. The segment is selected for monitoring if its SAIFI and SAIDI are higher than the values in the DSO's 10-year investment plan. If planned investments can enable the achievement of these values within 3 years, monitors are not installed. The projects can be divided into primary and secondary. Primary projects are the prevention of undesirable dangerous events (see Table 16). Secondary projects are focused on the installation of PQ mitigation devices. Discussion on the topic is continued in Sections 4.2.1 and 4.2.2.
###### Abstract  
The research conducted in the paper highlights the importance of pressure or holding pressure in the mold cavity as a critical production parameter in the high-pressure die casting process for Al-Si alloys. The experiments revealed a direct correlation between the pressure or holding pressure in the mold cavity and the mechanical properties of the castings, including ultimate tensile strength, percentage share of porosity, and the structure of the alloys. The results of the experiments showed that increasing the pressure or holding pressure in the mold cavity led to an increase in ultimate tensile strength and a reduction in the porosity of the castings. The higher pressure or holding pressure also resulted in the elimination of pores in the casting, which further improved its mechanical properties. The increase in ultimate tensile strength and reduction in porosity can be attributed to the better filling of the mold cavity, leading to reduced air entrapment and porosity in the castings. Overall, this paper emphasizes the need for optimizing the technological parameters of the die casting process to ensure the high-quality and efficient production of castings with reduced defects. The results of this study suggest that controlling the pressure or holding pressure in the mold cavity can significantly improve the mechanical properties of the castings, which is essential for achieving the desired quality standards.  
die casting; production parameters; molding pressure; porosity; quality Article
# Consistency Models of NoSQL Databases
## 3 Consistency Models
### Eventual Consistency  
Eventual Consistency strengths the Weak Consistency model. Replicas tend to converge to the same data state. While this convergence process runs, it is possible for read operations to retrieve an older version instead of the latest one. The _inconsistency window_ will depend on communication delays between replicas and its sources, the load on the system and the number of replicas involved [16].  
This model is half-way a strong consistency model and a weak consistency model. Eventual Consistency is a popular feature offered by many NoSQL databases. Cassandra is one of them, and it can offer availability and network partition on such a level that it does not compromise the usability of the most accessed websites in the world that uses Cassandra. One of them is Facebook, the company that initially developed Cassandra.
# Optimal Design of a Hybrid Solar PV/BG-Powered Heterogeneous Network
## 3 System Description
### Opportunities and Potential of Renewable Energy in Bangladesh
#### 3.1.2 Biomass Energy  
Bangladesh has a lot of biomass resources that might be used to generate electricity. Agricultural residue, animal dung, chicken droppings, and other biomass sources are the most readily available in the country. Agricultural residue is the most available resource for biomass in this agricultural country, with rice husk playing a key role. As per Bangladesh's 'Rice Mills Owners Association', the country has roughly 540 rice mills, each with a capacity of 30 tons per day and the potential to produce 171 MW per day [47; 48]. In fiscal year (FY) 2012-2013, roughly 90.21 million tons of biomass were accessible in Bangladesh, with a 1344.99 PJ energy potential, which is equivalent to 373.71 TWh of electrical energy [49]. Reference [50] forecasts that Bangladesh will be capable of generating 7682 GWh of energy from rice husk in 2030.
# Novel Cuckoo Search-Based Metaheuristic Approach for Deep Learning Prediction of Depression
###### Abstract  
Depression is a common illness worldwide with doubtless severe implications. Due to the absence of early identification and treatment for depression, millions of individuals worldwide suffer from mental illnesses. It might be difficult to identify those who are experiencing mental health illnesses and to provide them with the early help that they need. Additionally, depression may be associated with thoughts of suicide. Currently, there are no clinically specific diagnostic biomarkers that can identify the severity and type of depression. In this research paper, the novel particle swarm-cuckoo search (PS-CS) optimization algorithm is proposed instead of the traditional backpropagation algorithm for training deep neural networks. The backpropagation algorithm is widely used for supervised learning in deep neural networks, but it has limitations in terms of convergence speed and the possibility of getting trapped in local optima. These problems were addressed by using a deep neural network architecture for depression detection tasks along with the PS-CS optimization technique. The PS-CS algorithm combines the strengths of both particle swarm optimization and cuckoo search algorithms, which allows for a more efficient and effective optimization of the network parameters. We also evaluated how well the suggested methods performed against the most widely used classification models, including (K-nearest neighbor) KNN, (support vector regression) SVR, and decision trees, as well as the most widely used deep learning models, including residual neural network (ResNet), visual geometry group (VGG), and simple neural network (LeNet). The findings show that the suggested method, PS-CS, in conjunction with the CNN model, outperformed all other models, achieving the maximum accuracy of 99.5%. Other models, such as the KNN, decision trees, and logistic regression, achieved lower accuracies ranging from 69% to 97%.  
particle swarm-cuckoo search (PS-CS); convolutional neural network (CNN); visual geometry group (VGG); residual neural network (ResNet); simple neural network (LeNet)  
68T07; 68T20; 68W50
# Investigation of Nanocutting Characteristics of Off-Axis 4H-SiC Substrate by Molecular Dynamics  
[PERSON]  
1 School of Mechanical Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, Hubei, China; [EMAIL_ADDRESS] (M.W.); [EMAIL_ADDRESS] (Y.X.); [PERSON]_ADDRESS] (S.L.)  
[PERSON]  
1 School of Mechanical Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, Hubei, China; [EMAIL_ADDRESS] (M.W.); [EMAIL_ADDRESS] (Y.X.); [EMAIL_ADDRESS] (S.L.)  
[PERSON]  
1 School of Mechanical Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, Hubei, China; [EMAIL_ADDRESS] (M.W.); [EMAIL_ADDRESS] (Y.X.); [EMAIL_ADDRESS] (S.L.)  
[PERSON]  
the cutting of monocrystalline SiC. Ductile-mode machining was observed at a cutting depth smaller than 60 nm. It was shown that the damage-free machining of SiC is possible by surface modification to an amorphous structure. Since the thinning process takes place at the nanoscale, it is reasonable to investigate the nanomachining with molecular dynamics (MD). [PERSON] et al. [8] studied the nanometric machining of 3C-SiC, 4H-SiC, and 6H-SiC through MD simulations. They found that 4H-SiC shows the minimum subsurface lattice damage depth in contrast to 6H-SiC. [PERSON] et al. [9] revealed the deformation mechanisms of 6H-SiC through nanocutting MD simulations. They found that the plastic deformation of 6H-SiC can be realized by phase transformation and dislocations. [PERSON] et al. [10] found that a larger cutting depth and lower cutting speed induced larger cutting force and deeper surface damage through the MD simulations of the 3C-SiC scratching process. These studies concentrated on the nanomachining parameters and mechanisms of on-axis SiC. However, off-axis SiC nanomachining has not been thoroughly investigated.  
In this paper, the molecular-dynamics method was adopted to study the nanocutting characteristics of off-axis 4H-SiC. A 4\({}^{\circ}\) off-axis 4H-SiC MD model was built, and an on-axis one was also built as a comparison. Different off-axis models were applied to investigate the influence of lattice orientation. The effects of cutting depth and cutting speed were also investigated. Cutting force, temperature, and coordinate number were extracted and analyzed.
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 3 Results
### System Budgets
#### 3.6.3 Link Budget  
A link budget analysis was performed, taking into account UOA's GCS and the TTC components of the S/C. This resulted in the UHF link at 403 MHz to close when the elevation is \(5^{\circ}\) with a surplus of 20 dB for uplink and 16 dB for downlink. Regarding the S-Band TTC link, it is able to close with the 5 dB required margin at an elevation \(25^{\circ}\) in the case of uplink at 2030 MHz and at an elevation \(30^{\circ}\) in the case of downlink at 2210 MHz.  
Moreover, the PNT signal power at ground level was calculated, which is also assumed to be transmitted at 2210 MHz, and it resulted in \(-157.19\) dBW at \(5^{\circ}\) of elevation and \(-143.89\) dBW at the elevation of \(90^{\circ}\). As there is not currently an existing standardized user segment for LEO PNT applications, no analysis was conducted on compatibility with the user segment, and it is not considered a part of this paper.
## 4 Skin Biology, Vitamin D Synthesis, and Its Impacting Factors  
### General Structure and Function of Skin  
The largest space-consuming organ in the human body is the skin, which has an average surface area of 2 m\({}^{2}\) in humans and is the body's main line of defense against harmful substances, ultraviolet light, microbes, and physical trauma. It acts as a complex barrier and interfaces between the internal and external environment of the body. In addition, it serves as an excretory, sensory, and immunologically active organ and partakes in the regulation of the body's temperature [112]. The epidermis, dermis, and hypodermis are namely the three main layers that make up the skin [113]. The outermost layer, known as the epidermis, is made up of a stratified squamous epithelium that is mostly composed of keratinocytes [114]. Layers of keratinocytes move from the stratum basale to the stratum corneum, where they mature into corneocytes and eventually shed to form the epidermis that performs various barrier functions [115; 113]. Corneocytes in the stratum corneum then are enclosed in a lipid matrix that resists shearing forces and permits water loss, functioning as a \"bricks and mortar\" system. In the stratum corneum, lipids produced by lamellar bodies constitute a vital barrier, controlled by calcium concentrations [112]. At this point, it must be referred to that fibroblasts inside a collagen-elastin matrix, blood arteries, lymphatic vessels, nerve terminals, and skin appendages like hair follicles and glands are all located in the dermis, a connective tissue layer that lies underneath the epidermis. Adipose tissue, which fabricates the hypodermis, serves as insulation, a cushioning agent, and the starting point for blood arteries that continue into the dermis [114].  
Many vital tasks necessary for preserving health and homeostasis are carried out by the skin [116]. In addition to reducing water loss and preserving hydration, skin serves as a barrier of defense, protecting internal organs from environmental danger such as infections, toxins, and physical damage [117; 118; 119]. By producing perspiration and modifying blood flow, the skin plays a crucial part in thermoregulation [120]. Being a sensitive organ, it has receptors for pain, pressure, touch, and temperature changes, all of which enable one's body to react to outside stimuli. Another essential role of the skin involves, among many other immune responses, the one due to Langerhans cells that constitute the immunological defense, deciphering skin's ability to recognize and counteract infections. Furthermore, exposure to UV\({}_{\text{B}}\) rays stimulates the skin to synthesize vitamin D, necessary for bone health and calcium absorption, while the skin aids in the excretion of body wastes such as salts and urea through its appendages, the sweat glands. Such distinct roles demonstrate how important the skin is as a dynamic, multipurpose organ for defense, control, and general physiological homeostasis [115].