esa-sceva/satcom-chunk-collection huggingface dataset:
# Technologies for the Production of Fertilizable Mammalian Oocytes
## 1 Introduction
### Strategies for Contrasting Infertility in the Female  
According to what has previously been reported, clinicians, biologists and, in general, healthcare professionals facing infertility problems have firstly to take into consideration that this pathology is multifactorial and strongly influenced by clinical condition, treatments, age, environmental factors and lifestyle. Thus, prevention of infertility is the first step to cope with. To prevent infertility, improvement in the lifestyle, early diagnosis and proper treatment of the primary pathology eventually leading to infertility are needed [5; 6; 8]. Taken together, all these items could be collected under the term \"ecofertility\" [24]. Another approach finalized to limit infertility problems in young women is the preservation of fertility. Fertility preservation is an emerging field that offers treatments aimed to protect future reproductive ability for individuals at risk to lose their reproductive potential, particularly useful for oncologic patients subjected to gonadotoxic therapies for cancer treatment. For these individuals, cancer treatment can be lifesaving (up to 80% of survivors) but can permanently affect reproductive capacity [12; 13; 25]. Oncofertility, therefore, is a term currently used to describe the support of fertility preservation to patients with cancer, exerted through storage of gametes, embryos or gonadal tissues for later use. Different options are in fact offered to female patients undergoing cancer therapy, according to their age and partner status. In particular, oocyte or embryo cryopreservation before chemotherapy or radiation are the most frequently adopted strategies in the field of fertility preservation. However, these options cannot be used in prepubertalal patients, or in reproductive age women for which ovarian stimulation, usually performed to obtain a larger number of oocytes to be stored or subjected to IVF,should be avoided because it would interfere with the patient's treatment plan [25]. Other options are offered to these patients, i.e., the cryopreservation of either whole ovary or cortical fragments (strips) of the ovarian tissue followed by transplantation [26]. Frozen-thawed ovarian cortical fragments can be also subjected to in vitro culture, inducing follicular development in vitro (in vitro growth, IVG) and finally achieving oocyte maturation from immature (germinal vesicle stage, GV) to fully mature (metaphase II, MII) stage (in vitro maturation, IVM) [25; 27; 28; 29; 30; 31; 32]. According to such a wide range of therapeutic options today available for approaching the treatment of different forms of infertility, the specific aims of this paper are to review the most recent opportunities to obtain oocytes suitable for fertilization by various in vitro technologies. However, before going through this subject, it is important to remind how mature oocytes are physiologically produced, because the complexity of this process allows understanding how difficult is to obtain them in vitro.
## 2 Materials and Methods
### Research Method
#### 2.3.4 Hurst Index  
The Hurst index(H) reflects the correlation and consistency between the future change trend and the past change trend of the data in the time series. In this paper, the Hurst index is used to characterize the trend of FVC in the time series: 0 < H \(<\) 0.5 means that the vegetation cover has a reverse continuous variation; H = 0.5 means that the vegetation cover varies randomly; and 0.5 < H \(<\) 1 means that the vegetation cover has a positive continuous variation [24]. This study uses the commonly used R/S analysis method (Rescaled Range Analysis Method) to calculate the Hurst index (H), dividing a time series of total length \(T\) into several short series of length \(n\). For each \(t\), calculate its rescaled extreme deviation, which is based on the following principle: assume a time series of {FVC}\({}_{t}\), \(t\) = 1, 2, 3... \(n\), and define the mean series as follows:  
\[\overline{\text{FVC}_{t}}=\frac{1}{T}{\sum\limits_{t=1}^{\tau}{\text{FVC}}} \underset{(\tau)}{\tau}=1,2,\ldots n \tag{6}\]  
Cumulative deviation: calculate the cumulative deviation from the mean of the time series \(\tau\) as follows:  
\[X(t,\tau)=\sum\limits_{t=1}^{\tau}{(\text{FVC}_{(t)}-\text{FVC}_{(\tau)})}\ \ 0<t<\tau \tag{7}\]  
Range: calculate the range of fluctuations within each time series \(\tau\) as follows:  
\[R(\tau)=\underset{1\leq\leq\tau}{\text{max}}X(t,\tau)-\underset{1\leq\leq \tau}{\text{min}}X(t,\tau)(\tau=1,2,\ldots,n) \tag{8}\]  
Standard deviation: calculate the standard deviation of each time series \(\tau\) as follows:  
\[S(\tau)=[\frac{1}{\tau}\frac{\tau}{\tau=1}{(\text{FVC}_{(t)}- \text{FVC}_{(t)})}^{2}]^{\frac{1}{2}}\tau=1,2,\ldots n \tag{9}\] \[\frac{R_{(\tau)}}{S_{(\tau)}}=c\tau^{H}\]\[\log(\frac{R_{(\tau)}}{S_{(\tau)}})=\log(c)+H\log(\tau) \tag{10}\]  
where \(H\) is the Hurst index, \(c\) is a constant which takes the value of the intercept of the regression equation and \(\tau\) is the length of the time series.
## 1 Introduction
### Subcultures and Safety  
A relevant aspect of our study is related to the existence of subcultures and their potential impact on safety. In fact, the interest in subcultures within high-risk organizations is determined by their potential influence on safety. This leads to the following critical question: How does the existence of different cultural groups within high-reliability organizations impact safety? In other words, can the existence of different subcultures within an organization be a risk factor for safety?  
According to the International Atomic Energy Agency (IAEA) [38] (p. 7), the existence of subcultures is not an element that has a positive or negative impact per se:  
* Is the existence of subcultures likely to be harmful? One viewpoint is that, unless the different subcultures all contain something that results in a common sense of purpose, different priorities and agendas can arise and this can create serious problems. The counterargument is that different subcultures give different perspectives, and this is advantageous.'  
The literature of organizations does not seem to provide a clear answer either. Some theoretical approaches to the study of complex, high-risk organizations acknowledge subcultures and their potential strength for safety as a key element. From the perspective of the man-made disaster theory [39], accidents are caused by a lack of information flow. This perspective considers that within an organization, there are warning signs that, if detected, could prevent accidents. Signs which are most likely known by an organizational subgroup. Therefore, the existence of subcultures is not necessarily negative; in fact, acknowledging and managing them could contribute to preventing'safety blind spots' [40]. [PERSON] and [PERSON] [41] also consider that subcultures can develop within a strong, integrated culture without weakening it and with the aim of strengthening the organization's flexibility to change and adapt to external contingencies. In that sense, organizations with increased cultural variety are better prepared to respond to complex environments. According to [PERSON] [42], cultural differences should be considered key factors of safe evolutions in nuclear facilities, meaning research should focus on how safety is built as a result of cultural interactions.  
On the contrary, other authors defend the need of cultural integration as a way to ensure organizational reliability and safety. [PERSON] et al. [30] consider that the existence of differentiated professional subcultures hampers cooperation and negatively affects safety. In their study, they analyze differences between three groups (maintenance, operations and engineering) of three Swedish nuclear power plants, focusing on key safety climate aspects such as safety management, occupational safety and resources. Similarly, another study in the Norwegian oil and gas industries reveals the existence of different ways to interpret safety rules. Such interpretation varies depending on whether personnel belong to management, engineering or operations, thus impacting differentiated regulatory compliance [43].
## 5 Results
### The Proposed Mechanisms  
We compared the performances of the proposed mechanisms, PIF, ReCIF, and ReCIF+PIF, with both the default NDN forwarding mechanism and LFBL. We considered three metrics to evaluate the mechanisms: the content delivery ratio, the average delay of delivered contents, and the overhead. The first two metrics were explained previously, and overhead stands for the number of interest packets forwarded for successfully received contents. Each consumer requested content based on popularity according to a Zipf-like distribution. We varied the content request rate, the number of consumers, the cache size, and the network size. The following sections discuss the results.
## 3 Microtromor Survey Application
### Data Acquisition  
In this experiment, VIDO micromotion detectors were used for data acquisition. Figure 6 shows a VIDO micromotion detector, and the main performance indicators of the detector are shown in Table 1.  
Figure 5: The schematic of hierarchical frequency fusion.  
9 sets of stations formed the observation array, the stations are numbered from 1 to 9, and the specific arrangement of the observation array is shown in Figure 7. The goal of this survey was to comprehend the geological conditions between 10 and 50 m beneath the site. So, in this experiment, the minimum spacing between adjacent stations was set at 3 m according to the ideal exploration depth of 3 to 5 times station spacing. The arrangement of the array was set as rectangle, which can be paired to generate station pairs of various distances and azimuths. Station pairs with multiple azimuths can attenuate the impact of dominant sources in certain directions. Station pairs with various distances receive signals of various wavelengths, reflecting the medium's information at various depths.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Main Performance Indicators** \\ \hline Frequency bandwidth & 0.2–500 Hz \\ Voltage output sensitivity & 92 \(\pm\) 7.5\% V/m/s \\ Number of data channels & 3 \\ A/D conversion & 24 bits \\ Sampling frequency & 50 Hz, 100 Hz, 200 Hz, 500 Hz \\ Dynamic range & 128 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main performance indicators of VIDO micromotion detector.  
Figure 6: VIDO intelligent micromotion detector.  
Figure 7: Arrangement of the observation array.  
In this experiment, the micromotion detector's sampling frequency was set to 250 Hz, and the microtremor signals were constantly gathered for 35 min. Figure 8 depicts the vertical component of waveforms of microtremor signals in this acquisition.
# Children's Functional Clothing: Design Challenges and Opportunities
## 4 Functional Textiles
### Functional Textiles for Improved Protection and Wellbeing
#### 4.1.2 Ultraviolet Protective Textiles  
In addition to the antimicrobial properties, the study of UV radiation protection is also an important theme explored in the textile area. UV protective textiles are designed to improve protection against the harmful effects of UV radiation, addressing risks to both textile and human health, especially the skin [35]. Engineered to block or absorb UV rays, these textiles become crucial in mitigating the potential risks of prolonged or excessive sun exposure. Unchecked UV radiation can lead to various skin issues, including pigmentation, sunburn effects, premature ageing, skin cancer, and DNA damage [35; 36]. The efficacy of UV protection in textiles is influenced by several factors, such as the choice of fibre (some fibres, like polyester and nylon, offer inherently better shielding against UV radiation), the material's structure (greater weave/knit tightness or thicker fabrics), the colour of the textile (darker colours tend to absorb more UV radiation than lighter colours), applied treatments, the types of textile dyes used and conditions of dyeing processes [36; 43].  
Despite the effectiveness of numerous synthetic finishing agents in creating UV-protected textiles, their environmental impact raises concerns when released as effluents, posing risks to the ecosystem [42]. Therefore, there is a growing exploration of environmentally friendly materials [35]. Natural dyes, in addition to antimicrobial properties, can also exhibit commendable protection against UV radiation [44].  
Clothing with proven UV protection, beyond what conventional textiles offer, has compelling evidence in safeguarding children's skin. A study by [PERSON] et al. [45] found that consistent use of ultraviolet protection factor (UPF) 30-50+ clothing, covering at least half of children's bodies, significantly reduces pigmented mole development. Wearing this clothing for 3.5 years prevents almost one-quarter of moles compared to children without such protection, implying a reduced risk of future melanoma in these children.
# Suppression of Harmonic Current in Magnetic Bearing-Rotor System with Redundant Structure
## 5 Conclusions  
For a rotor suspended by a heteropolar magnetic bearing with redundant structures, there is disturbance from the mass unbalance and sensor runout in the systems, which causes harmonic currents in the coil. To address this problem, this paper first analyzed the stiffness properties of the magnetic bearing with redundant structures and established the corresponding equivalent closed-loop model. Then, on the basis of the equivalent closed-loop model and the requirements of system stability, a repetitive controller was designed and applied in the magnetic bearing-rotor system with redundant structures. Lastly, the corresponding simulation was carried out, and the numerical results demonstrated that, compared to the case with no repetitive controller in the system, the proposed repetitive controller could effectively suppress the harmonic current of the coils and improve the anti-interference ability of the system. More importantly, by introducing repetitive controllers in the magnetic bearing-rotor system with redundant structures, the rotor suppression accuracy was greatly improved. Additionally, inserting the repetitive controller not only improved the robustness of the magnetically levitated rotor but also reduced the power consumption of the system. However, there are two shortcomings of the repetitive controller proposed in this paper. On the one hand, the response time is long, exceeding 1 s. On the other hand, the repetitive controller requires the exact knowledge of the period of the external signals, and the period is required to be constant. To address these drawbacks of the repetitive controller, we will design improved repetitive control to improve the robustness of the system in future work. Furthermore, when the repetitive control law is applied to the DSP platform for a magnetic bearing, some aspects should be considered. The sampling time must be accurate, and filters should be used to reduce the noise of signal. In particular, the approximate approach can be adopted to realize the phase lead link \(z\) of repetitive control.  
Validation, formal analysis, and writing--original draft preparation, B.C.; methodology, X.C.; visualization, S.S.; supervision, R.Z.; visualization, S.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Shenzhen Science and Technology Research and Development under Grant JCYJ20190809150603586, the National Key Program of China under Grant No. 2018 YFB2000103, and the Fundamental Research Funds for the Central Universities under Grant 2020-YB-022.  
Not applicable.  
The authors declare no conflict of interest.
# Data-Driven Robust Control Using Reinforcement Learning
## 2 Materials and Methods
### Robust Control Using Reinforcement Learning  
In this paper, a class of dynamical systems is considered, which can be described by the following linear state-space equation:  
\[\dot{x}(t)=Ax(t)+Bu(t), \tag{1}\]  
where \(x\in\mathbb{R}^{n}\) is the vector of \(n\) state variables, \(u\in\mathbb{R}^{m}\) is the vector of \(m\) control inputs, \(A\in\mathbb{R}^{n\times n}\) is the state matrix, and \(B\in\mathbb{R}^{n\times m}\) is the input matrix. It is assumed that matrix \(A\) is a squared \(n\times n\) unknown matrix and the system (\(A\) and \(B\)) is stabilized. Our target is to derive a control algorithm \(u(t)\) that can regulate the state variables contained in \(x(t)\) based on input and output data without knowing matrix \(A\).  
As an RL framework, the proposed robust control algorithm consists of an agent that takes actions and learns the consequences of its actions in an unknown environment. The environment is defined by a state vector \(x(t)\) that describes its states at time \(t\). The action at time \(t\) is represented by \(u(t)\). As a consequence of the action, a cost \(r(t)\) is incurred and accumulated. The cost function \(r(t)\) is assumed to be known and predefined as a function of the current state and action. The objective of the learning process is to minimize the total cost accumulation in the future.  
At each decision time point, the agent receives information about the state of the environment and chooses an action. The environment reacts to this action and transitions to a new state, which determines whether the agent receives a positive or negative reinforcement. Current RL techniques propose optimal actions by minimizing the predicted cost accumulation. However, uncertainties due to noises in the data or inaccurate estimation of the cost accumulation can lead to suboptimal actions and even unstable responses. Our target is to provide the agent with a robust and safe action that can guarantee the reduction of the future cost accumulation in the presence of uncertainties. The action calculated by the proposed algorithm may not be the optimal action that reduces the cost in the fastest way, but it can always guarantee the stability of the system, which is imperative in many critical applications.
# Numerical Modeling for Tunnel Lining Optimization
## 2 Case Study
### Tunnel Geometry and Excavation Process  
The tunnel geometry is shown in Figure 3. It was projected to have an external width of 10.60 m and an external height of 8.50 m. The construction sequence considers three partial excavations: (1) upper-middle section, (2) central bench, and (3) lateral benches alternately. The primary lining, PL, comprises a 20 cm thick layer of steel fiber-reinforced shotcrete (SFRS), f'c = 25 MPa. The secondary lining, SL, consisted of a 40 cm thick reinforced concrete support with design strength after 28 days of f'c, 30 MPa.  
A schematic representation of the tunnel excavation process is depicted in Figure 4, consisting of four main stages: (1) excavation of the upper-middle section with excavation phase lengths of 1.0 m stabilizing the ground through the primary lining, leaving a central pillar unexcavated to reduce the risk of tunnel face failure; (2) once an excavation length of the upper-middle section of 35 m has been reached, the excavation of the central bench begins (stage 2); first, a 9 m long work ramp is built and behind it, the central bench is completed; (3) when a distance of 35 m of excavation of the central bench is reached, measured from the beginning of the ramp, the alternate excavation of the lateral banks with advances of 3.0 m is started, stabilizing them by employing a 20 cm thick layer of shotcrete reinforced with steel fibers; and (4) reinforcement and casting of the footings and sidewalls of the secondary tunnel lining, having an advance of 20 m; the secondary lining of reinforced concrete is implemented in the vault. Simultaneously, the reinforcement and casting of the bottom slab are carried out to close the tunnel ring. Figure 5 shows the excavation of the upper half of the section complete with the central core, highlighting the adverse ground conditions; poor soil quality and the presence of water seepage are observed.  
Figure 3: Tunnel cross-section and primary lining configuration used.
# Air Conditioning Load Forecasting and Optimal Operation of Water Systems
## 4 Conclusions  
This paper takes the central air conditioning system of a hotel as the research object, establishes the load-forecasting model based on SVR through MATLAB, and establishes the equipment energy consumption model based on the principle of each piece of equipment in the water system. On this basis, the objective function of energy consumption optimization of the hotel's water system is established, and the dynamic optimization of operating parameters of the water system at different load rates is realized. The main conclusions are as follows:  
1. The air conditioning load-forecasting model based on the SVR principle has similar prediction results in the test set and the training set, indicating that the model generalization performance is good. Moreover, whether PCA is used for dimensionality reduction or not, the load-forecasting model has good generalization performance. The advantage of PCA is that it can significantly reduce the calculation cost of the SVR model, but it reduces the accuracy of load forecasting. The reason for this may be the information loss caused by extracting principal components from high-dimensionality data.
2. Based on each equipment model of the water system, the objective function of energy consumption optimization of the water system is established. The operating conditions of each piece of equipment and the energy conservation of the heat-exchange process are taken as the constraint conditions, and the predicted air conditioning load is taken as the disturbance variable. SQP is used to solve the energy consumption optimization problem of the hotel air conditioning water system with controllable variables and disturbance variables and realize the optimization of operating parameters of sewage system at different load rates. After optimization, the average energy-saving rate of the water system with different load rates is 12.40%, which shows that the water system optimization based on load forecasting proposed in this paper has a good energy-saving effect.  
Data curation, K.W. and X.C.; Investigation, K.W. and B.Z.; Writing--original draft, X.C.; Writing--review & editing, Z.H. All authors have read and agreed to the published version of the manuscript.  
This study was funded by National Natural Science Foundation of China (no. 51608001), and Anhui Province Undergraduate Innovation and Entrepreneurship Training Program (no. S202010360231).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.