esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Acquisition and Processing
### Data Processing and Analysis  
#### 3.2.1 Variable Selection  
Considering the availability of data and the influence of variables, this study employed correlation analysis to identify variables significantly associated with hospital visitation volume, as shown in Figure 3. Variables such as construction area, number of beds, number of medical departments, number of positive comments after diagnosis, number of national key specialties, number of bus routes, number of parking spaces, and outpatient and inpatient volume all exhibited significant correlations (\(p<0.01\)). However, the number of subway lines showed a low correlation with other variables, and its test results were not significant, so it was excluded from further analysis. Additionally, structural equation modeling (SEM) requires the correlation levels between observed variables to range between 0.3 and 0.7. The correlation coefficient for the number of staff was too high, making it ineffective for measuring hospital size as a latent variable; thus, it was also removed. The retained variables generally had correlation coefficients above 0.3, indicating strong interrelations. However, analyzing the factors influencing hospital traffic attraction and visitation volume using individual variables alone could lead to inaccuracies or multicollinearity issues. Therefore, employing factor analysis for variable simplification and dimensionality reduction is a more reasonable approach.
## 3 Conceptual Design of the System  
Laboratory irradiation aims to treat wastewater or ships ballast water with an electron beam of energy 800 keV in order to inactivate biological contaminants. The industrial purification and disinfection system should also consist of a pre-treatment unit dedicated to the special composition of treated wastewater. Additional auxiliary equipment would contain tanks, pumps, piping, and control and a monitoring system. The pre-treatment unit has to be designed ensuring appropriate parameters of the stream treated in the irradiation unit and to form an irradiated layer with a thickness suitable for the penetration of the electrons from the accelerator with defined energy. The general concept of the installation combines the EB accelerator as the first stage process to decompose toxic compounds and make them prone to biodegradation and to eliminate microbial contamination with the following biological treatment. This approach allows for the application of lower irradiation doses and makes the electron accelerator application more cost-effective.  
For an industrial irradiation unit, it is very important to consider the shielding requirements needed for the safe operation of the facility and also for the safety of the workers due to the health hazards involved with radiation. The shielding material must absorb the energy of scattered and induced radiation to prevent incidental leakage of radiation from the irradiation area.  
The radiation sources which must be considered are the primary radiation, which in the presented study is an electron beam of energy 800 keV, and secondary radiation, which is X-ray generated as an effect of electrons interaction with the atoms of irradiated medium. Due to the fact that the generation of X-rays depends on atomic number Z, the under beam system is constructed in a way where the electron beam contour lies on the surface of the water in a vessel or a floating dock (Figure 2), in which two walls of shield are surrounded by water which attenuate radiation as well. The construction of walls of greater thickness is required on the sides of the rooms accessible for service  
Figure 2: Diagram of the projected under beam water stream formation and irradiation chamber: (**A**) side view, (**B**) plan view.  
The design of the wastewater treatment installation, mathematical simulations and experimental measurements with the use of the model under beam system were performed in order to characterize the penetration ability of primary and secondary radiation and the exposure rate for the water and steel in the irradiation room.
# Strip Consistency Tube in the Assessment of Tear Lactoferrin in Non-Obese Diabetic (NOD) Mice
## 1 Introduction  
Sjogren's syndrome (SS) is a multifactorial autoimmune disorder, mainly affecting salivary and lacrimal glands, which is influenced by genetic as well as environmental factors that are not completely understood as yet. Although dry eyes and dry mouth characterize the disease, the expression of clinical spectrum is diverse, extending from a solitary organ-specific autoimmune excornopathy to a systemic disorder affecting several organs. While [PERSON]'s syndrome is one of the most common autoimmune diseases, it has no specific and non-invasive diagnostic tests. Diagnosis is conducted by biopsy of the exocrine glands and blood tests for specific antibodies [1]. Since ethical issues strongly hamper the collection and study of tissue or tear samples in humans in order to investigate pathogenetic mechanisms, animal models of SS or dry eye disease help us to analyze many pathogenetic relations from which conclusions can be drawn for similar human diseases. NOD mice have been reported to have similar ocular surface disease and dry eye features to human ocular surface disease, including decreased tear volume, the loss of corneal barrier integrity (increased staining scores), reduced tear mucins, and increased lacrimal gland inflammatory infiltrates [2], which led us to choose NOD mice to examine tear lactoferrin changes. Indeed, tear lactoferrin has been reported to be a good biomarker of dry eye disease in SS. The severity of ocular surface damage due to SS-related dry eye largely depends on the tear secretory function of the lacrimal gland and that the function of the lacrimal gland can be evaluated by determination of the level of tear lactoferrin [3].  
The strip meniscometry tube (SMT) has been defined as a non-invasive tool for measuring tear quantity in human tear meniscus [4]. It has been shown to have a strong correlation with the Schirmer test, tear film break-up time (BUT), and ocular surface vital staining scores [5]. This new method of testing has not only been applied to the diagnosis of dry eye disease [6,7] but also for the assessment of the efficacy of punctal occlusion [8, 9, 10]. Recently,it has been shown to be a very useful tool in evaluating epiphora and related symptoms in humans [11].The applicability of SMT in tear volume measurements has also been shown in cats, dogs, mice, and rabbits [12; 13].  
This new method has not been employed for the evaluation of tear protein levels in humans and in mice until now.  
In this study, we examined the usefulness of SMT in tear lactoferrin concentration measurement in NOD and WT mice.
# Removal of Methylene Blue by Metal Oxides Supported by Oily Sludge Pyrolysis Residues
## 3 Results and Discussion
### Influence of Preparation Conditions
#### 3.1.1 Impregnation Time  
As shown in Figure 1, when the impregnation time of CuO-, ZnO-, and TiO\({}_{2}\)-supported pyrolysis residue was 70, 50, and 50 min, respectively, the removal effect of methylene blue was at its best.  
When impregnation time was short, impregnation of pyrolysis residue was insufficient, and metal oxides were mainly distributed at the pore edges of the pyrolysis residue. After that, with the prolongation of impregnation time, the metal oxide gradually diffused into the pores of the pyrolysis residue carrier, which improved the removal efficiency ofmethylene blue [27]. However, with further increase in impregnation time, the removal effect of the composite material on methylene blue decreased. This may be due to the fact that a too-long immersion time can lead to the clogging of micropores, which reduces the removal rate of methylene blue [28].
# Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6 TiSCH Environments
## 7 Performance Evaluation
### Experimental Setup  
To evaluate the effectiveness of our proposed K-means clustering approach, we compared it with other clustering methods, including GRID-based K-means (GBK) [13], ECRP clustering [17], and the default MSF [27]. All procedures were implemented on the same network layer in TSCH using the standard 6 TiSCH simulator, with each node having a battery level of 2821.5 mAh on the same minimal scheduling function. The evaluation involved randomly placing nodes in an area for four scenarios involving 100, 150, 200, and 300 nodes, with each node positioned at specific coordinates. The number of nodes was chosen based on related works to ensure consistency. The optimal value for each parameter  
Figure 8: Scheme of the proposed clustering for cell allocation.  
\(k\) was determined using the Silhouette Score and elbow method. Within the controlled environment of the 6 TiSCH simulator, we carefully assessed the performance using the RPL. The assessment process included uniformly adjusting the packet transmission intervals up to 0.1 percent. Each network node produced 120 packets with 80 bytes of data, leading to a standardized transmission packet size of 1016 bits. This experiment was conducted using two different routing protocols. The first protocol was OF0 [32], which prioritizes paths with the highest packet delivery ratio among potential next-hop neighbors on the route to the root or parent node. The second routing protocol was MRHOF [33], which integrates metrics such as rank, which indicates link quality, and incorporates hysteresis to prevent excessive path changes, thereby minimizing latency and overhead.  
\[\text{Throughput (kbps)}=\frac{\text{Received packet }\times\text{Packet size}}{\text{Simulation time allocation}} \tag{4}\]  
Our work illustrates how a node-based clustering strategy can improve the 6 TiSCH network's scalability and performance. Network performance was measured using reliability indicators such as PDR, joining time, end-to-end latency, and throughput. PDR measures the percentage of successfully delivered packets among all transmitted packets to determine how reliable the network is at providing data. Measuring the time required for a packet to travel from its source to its destination provides the end-to-end latency, indicating how responsive the network is. The joining time indicates how quickly a new node integrates into the network. Throughput, as assessed by Equation (4), measures the data rate successfully sent from one node to another within a certain period or the number of packets sent from each source node to the destination node while considering the packet size, and is measured in kilobits per second (kbps). Table 1 displays the parameter simulations along with our suggested approach.
# Optical Camera Communication as an Enabling Technology for Microalgae Cultivation +
## 2 Proposed Architecture  
The proposed architecture follows a many-to-one unidirectional network topology where photobioreactor nodes transmit sensor-related data to a receiver camera node (Figure 1).  
The photobioreactor nodes comprise a uniform-radiance LED flat panel attached to a glass container that holds the microalgae culture. The receiver node consists of a rolling-shutter (RS) camera connected to the communication endpoint that exposes a control interface and a standard wired communication bus. The transmitter nodes use two different signals to establish a link: a well-known beacon signal that uniquely identifies the node and data packets composed of Manchester-encoded pulses. The receiver uses the beacon signal to perform three separate routines: establishing the communication link, estimating the channel response to complete the decoder training, and finally, sensing the culture by estimating the biomass concentration and growth phase of the microalgae species. Therefore, this system performs two functions. It establishes a monitoring link and directly senses essential culture parameters.  
In the following sections, both nodes are presented, highlighting the influence of the microalgae cultivation needs and requirements in their design.  
Figure 1: Proposed architecture based on flat-panel photobioreactors. RS, rolling-shutter.
## 2 Dataset Description and Processing
### Data Processing
#### 2.3.1 Scaling Layer  
Measurements are filtered with moving average, fixed window, and three-order one-dimensional median filtering [16]. Median filtering helps to effectively remove impulsive noise from vibration signals without distorting the underlying data. It is particularly useful in applications where preserving sharp edges and details as signal descriptors is important to maintain the general shape of this signal. The scaling layer also involves another step of fixed-time-window averaging based on the time-domain smoothing of signal amplitudes [17]. This further helps reduce noise and smooth signal data and improves overall data quality while achieving more accurate and reliable results. Afterwards, a final min-max normalization slice is included to scale each channel record in the range [0, 1], allowing for the better tuning of learning systems on different channels with different scales [18]. It is important to use this layer in different places and not just at the beginning of the data processing phase. For example, the extraction layer and the denoising layer can change the feature scales, and it is better to rescale them again to ensure the importance of the data after such steps.
# A Miniaturized and Fast System for Thin Film Thickness Measurement
## 1 Introduction
With no need for reference sample correction, the film thickness measurement system had the performance of a measurement range of 1-75 \(\upmu\)m, measurement uncertainty of 0.1 \(\upmu\)m, and measurement speed of 10 ms. The efficient and stable film thickness calculation of the standard films with micron-grade thickness, the film layer on the PCB chip, and the Germanium-based lens verified the feasibility of this method.
## 5 Methods for Identification and Quantification of Coumarins
### Analysis
#### 5.2.1 Thin-Layer Chromatography  
A simple procedure used for the separation of coumarins in spirit beverages is the thin-layer chromatography (TLC)--Table 4. It is a cost-effective, fast and widely used technique. It uses a thin layer of stationary phase (typically silica gel or alumina) placed on a flat support such as glass or plastic. For quantification purposes, TLC must be combined with other analytical techniques [162, 168, 169].  
In general terms, TLC includes the following four steps: sample application, separation, development and visualisation. Visualisation techniques include ultraviolet (UV) light, chemical staining, and exposure to additional detection techniques. The capacity to examine and identify coumarins on the TLC plate depends on the detection methods used, as well as the parallel elution of a standard for comparative purposes. Staining or chemical derivatization may also be used to detect some compounds that do not have natural fluorescence.  
Although more advanced chromatographic techniques, such as HPLC or GC, are preferred for separation and subsequent quantitative and high-resolution analyses, allowing the automation of some steps, TLC remains a valuable tool, particularly when performing rapid qualitative analyses [168].
# CAL: Core-Aware Lock for the big.LITTLE Multicore Architecture
## 6 Conclusions  
In this paper, we analyze the throughout decline and long tail latency issue induced by skewed locking assignment among big core and little core, due to the inherent performance diversity in Big.LITTLE multicore architecture. Targeting on this architecture, we design a core-aware lock named CAL, which reorder the lock request based on the fairness computational model. CAL operates through three main stages: core-aware queue insertion, fairness-aware node reordering, and lock request selection. These stages ensure that both big cores and little cores have equal opportunities to access critical sections in programs. Evaluations on benchmarks and real-world application LevelDB show that CAL achieves the fairness goal for the big.LITTLE multicore architecture. Compared with the competition-based lock algorithm, the CAL's fairness has increased by up to 67%; and Its throughput is 26% higher than FIFO-based locks and 53% higher than competition-based locks, respectively.  
**Author Contributions:** Conceptualization, S.N.; methodology, S.N.; software, Y.L.; validation, S.N.; formal analysis, Y.L.; investigation, Y.L.; resources, W.W.; data curation, J.N.; writing--original draft preparation, Y.L.; writing--review and editing, S.N.; visualization, J.N.; supervision, W.W.; project administration, W.W.; funding acquisition, W.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the open project of Satellite Internet Key Laboratory in 2023 (Project 3: Research on Mixed reality deduction technology for mega constellation operation and maintenance) funded by Shanghai Key Laboratory of Satellite Network and Shanghai Satellite Network Research Institute Co., Ltd.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Acknowledgments:** The authors would like to thank the reviewers for their thoughtful comments and efforts toward improving our manuscript.  
**Conflicts of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Nonlinear Vibration Isolation via a NiTiNOL Wire Rope
## 3 Methods
### Stiffness and Damping Ratio Analysis  
For a comprehensive understanding of the stiffness and damping nonlinearities of the isolator, the generalized equivalent stiffness and a generalized equivalent damping ratio were defined. According to the characteristics of the Bouc-Wen hysteresis, the area of the hysteretic loop is related to \(\beta/\gamma\), and there is no hysteresis with \(\beta=0\). Therefore, an anhysteretic form of Equation (8) is defined as  
\[\dot{z}_{\text{an}}=\left[1-\xi\text{e}^{-\frac{y^{2}}{y_{\text{b}}}}-(\gamma+ \beta)|z_{\text{an}}|^{n_{\text{b}}}\right]\dot{y} \tag{18}\]  
The anhysteretic force \(z_{\text{an}}\) can be calculated through numerical integration of Equation (18), and it is a single-valued function characterizing the stiffness nonlinearity induced by both the Bouc-Wen hysteresis and the pinching effect. The total anhysteretic restoring force of the isolator is  
\[f_{\text{an}}=k_{1}y+k_{3}y^{3}+\delta z_{\text{an}} \tag{19}\]  
The generalized equivalent stiffness is  
\[k_{\text{e}}=\frac{\text{d}f_{\text{an}}}{\text{d}y}=k_{1}+3k_{3}y^{2}+\delta \frac{\text{d}z_{\text{an}}}{\text{d}y} \tag{20}\]  
The first two terms on the right-hand side indicate the traditional equivalent stiffness due to elastic deformations of the wire rope and the linear spring. The third term indicates the influences of the inner frictions and the phase transitions of the wire rope on the stiffness. Thus, the stiffness is defined as the generalized equivalent stiffness. The dimensional generalized equivalent stiffness is \(K_{\text{e}}=k_{\text{e}}K_{\text{c}}\).  
The generalized equivalent damping ratio is defined by the ratio of the dissipated energy and stored energy. The dimensionless restoring force is expressed as  
\[f_{\text{r}}=k_{1}y+k_{3}y^{3}+\delta z \tag{21}\]  
and the dimensional restoring force is \(F_{\text{r}}=f_{\text{r}}K_{\text{c}}/A_{\text{b}}\). The dissipated energy in one period is  
\[E_{\text{d}}=\int_{-a}^{a}f_{\text{r}}\text{d}y-\int_{a}^{-a}f_{\text{r}} \text{d}y \tag{22}\]  
The stored energy is derived from the anhysteretic restoring force, and the maximum stored energy in a period is  
\[E_{\text{s}}=\int_{0}^{a}f_{\text{an}}\text{d}y \tag{23}\]  
The generalized equivalent damping ratio is given as  
\[\zeta_{\text{e}}=\frac{E_{\text{d}}}{4\pi E_{\text{s}}} \tag{24}\]  
It should be noted that the generalized equivalent damping ratio is different from the traditional damping ratio in two aspects. Firstly, the stored energy is calculated from the anhysteretic restoring force. Thus, it not only contains the elastic stored energy, but it is also influenced by the inner frictions and the phase transitions. Secondly, the damping effects induced by the inner frictions and the phase transitions are independent of the frequency. Therefore, there is no \(\eta\) in the denominator of Equation (24).
## 6 Illustrative Results
Figure 8: Results obtained by simulating the TRNSYS model of room 3 with radiators for one day, 5 December. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Previous Day**} & \multicolumn{3}{c}{**Current Day**} \\ \hline
**Room** & **Step Case [kg/h]** & **Ramp Case [kg/h]** & **Saving [\%]** & **Step Case [kg/h]** & **Ramp Case [kg/h]** & **Saving [\%]** \\ \hline
1 & 2958 & 2701 & 9.51 & 3198 & 2932 & 9.04 \\
2 & 1791 & 1652 & 8.44 & 1951 & 1797 & 8.56 \\
3 & 1751 & 1682 & 4.1 & 1901 & 1833 & 3.68 \\
4 & 2548 & 1997 & 27.6 & 2711 & 2152 & 26 \\
5 & 1853 & 1695 & 9.23 & 2007 & 1845 & 8.8 \\
6 & 11,154 & 10,467 & 6.57 & 11,772 & 11,108 & 5.98 \\
7 & 6817 & 6201 & 9.94 & 6968 & 6369 & 9.4 \\ Total (J) & 28,872 & 26,395 & 9.4 & 30,507 & 28,036 & 8.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance index value (4) for radiators in both step and ramp case evaluated by applying the optimal set-point signals with the TRNSYS model using weather conditions of the previous days (4 December, i.e., those used by the genetic algorithm) and of the current day (5 December). Figure 9: Results obtained by simulating the TRNSYS model of room 3 with fan-coil units for one day, 5 December.