esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Signal Processing  
Data processing was done within a custom software framework, which allowed semiautomatic offline processing guided by the clinical investigator. The software was implemented in Matlab. The processing pipeline included multiple steps, which are briefly described below.  
ROI definition: A custom graphical user interface allowed manual definition of ROIs. Details of the placement are as stated before. The ROIs were defined in the first frame and were kept in size and shape throughout the respective recording.  
Image processing: To account for small movements of the thoracic region, we used optical flow to adapt the position of predefined ROIs.  
Signal extraction: To derive iPPG signal courses, we averaged the pixels over each ROI using the green channel.  
Signal processing: To account for a slightly inhomogeneous frame rate and eventually missing frames, we first interpolated iPPG signal courses to a sampling frequency of 100 Hz by Matlab's Piecewise Cubic Hermite Interpolating Polynomial. Afterwards, we detrended the signal courses and applied a 250 tap finite-impulse-response high-pass filter with a cutoff frequency of 0.5 Hz in forward and backward direction.  
Feature extraction: We used the signal-to-noise ratio (SNR) as described in [28] to quantify the perfusion. The SNR is calculated in the frequency domain. It relates the power around the heart rate fHR and its first harmonic 2* fHR (we use fHR \(\pm\) 5 ppm and 2* fHR \(\pm\) 5 ppm) to the overall power in the frequency range between 30 and 200 beats per minute. The heart rate fHR was derived from the reference ECG. More details on the calculation of the SNR can be found in our previous works [4,6]. The SNR was calculated from signal excerpts of 10 s. For each subject, a representative SNR of each side of the chest wall was derived by averaging all 10 s signal excerpts of a recording.
# A Deep Learning Model for Estimation of Patients with Undiagnosed Diabetes
## 3 Experimental Results  
### Comparison of Basic Characteristics among NG, IFG, and UDG  
The basic characteristics of each group are shown in Table 1. In the development dataset, the IFG and UDG contained more old, male participants than the NG. Also, the IFG and UDG participants had higher systolic blood pressure (SBP), diastolic blood pressure (DBP), weight, body mass index (BMI), waist circumference (WC), fasting plasma glucose (FPG), total cholesterol (TC), and triglycerides (TGs). Family history of diabetes (FHD), smoke, and hypertension were more frequent in the UDG and IFG groups than in the NG group. High-density lipoprotein (HDL) levels were lower in the IFG and UDG than in the NG. Of these variables, seven NIVs (year, WC, BMI, gender, smoking status, hypertension, and family history of diabetes) were selected as candidate variables for learning.  
Results of the bivariate analysis for evaluating the deep learning method are summarized in Table 2. Age in year (odd ratio (OR): 1.03, 95% confidence interval (CI): 1.03-1.04, \(P\) = 0.01), WC (OR: 1.08, 95% CI: 1.07-1.09, \(P\) = 0.01), BMI (OR: 1.18, 95% CI: 1.15-1.22, \(P\) = 0.01), male gender (OR: 1.74, 95% CI: 1.41-2.15, \(P\) = 0.01), smoking status (OR: 1.77, 95% CI: 1.44-2.19, \(P\) = 0.01), hypertension (OR: 2.77, 95% CI: 2.23-3.47, \(P\) = 0.01), and family history of diabetes (OR: 2.05, 95% CI: 1.64-2.57, \(P\) = 0.01) had significant effects on UDG. They were selected to build machine learning models.
# Approximation Algorithms for Sorting \(\lambda\)-Permutations by \(\lambda\)-Operations
## 2 Preliminaries and Basic Facts
### Strips  
A maximal subsequence \((\pi_{i}\,\pi_{i+1}\,\dots\,\pi_{j})\) without any breakpoints \((\pi_{k},\pi_{k+1})\), for all \(i\leq k<j\), is called a _strip_.  
For the problems of sorting unsigned \(\lambda\)-permutations, if the strip's elements are in ascending (resp. descending) order, then we call it an _increasing_ (resp. _decreasing_) strip. Strips containing only one element are considered to be increasing ones. For example, considering sorting unsigned \(\lambda\)-permutations by both operations and \(\pi=(6\,4\,5\,3\,2\,1)\), we have two increasing strips (6) and \((4\,5)\), and a decreasing strip \((3\,2\,1)\). Note, however, that segment \((3\,2\,1)\) is not a decreasing strip for the problem of Sorting \(\lambda\)-Permutations by \(\lambda\)-Transpositions. This is because, direct from the definition of strips and breakpoints, there are no decreasing strips when this problem is being considered.  
For the problems of sorting signed \(\lambda\)-permutations, if the strip's elements are positive, then we call it an _increasing_ strip. Otherwise, the strip is called a _decreasing_ strip. Strips containing only one element are considered to be increasing ones if such an element is positive. Otherwise, they are considered to be decreasing strips. For example, considering sorting signed \(\lambda\)-permutations and \(\pi=(+6\,-5\,-4\,+1\,+2\,-3)\), we have two increasing strips, \((+6)\) and \((+1\,+2)\), and two decreasing strips, \((-5\,-4)\) and \((-3)\). Note that, despite the elements of strip \((-5\,-4)\) are in ascending order, it still is a decreasing strip, according to our definition.  
The number of elements in a strip \(S\) of a \(\lambda\)-permutation \(\pi\) is denoted by \(|S|\). We say that an element \(\pi_{i}\) is out-of-place if \(|\pi_{i}|\
eq i\).  
**Lemma 2**.: _Let \(\pi\) be a \(\lambda\)-permutation, and let \(|\pi_{j}|=i\) be the smallest element out-of-place in \(\pi\) (i.e., \(|\pi_{j}|\) is minimum and \(\pi_{j}\
eq j\)). The strip \(S\) that contains \(\pi_{j}\) is such that \(|S|\leq\lambda-1\)._  
Proof.: First, suppose we have the increasing strip \(S=(\pi_{j}\,\dots\,\pi_{k})\). Let \(R=(\pi_{i}\,\dots\,\pi_{j-1})\) be the segment of elements to the left of \(S\). Note that the absolute value of any element in \(R\) is greater than any element in \(S\). Therefore, \(|\pi_{i}|\geq|\pi_{k}|+1=|\pi_{j}|+k-j+1=i+|S|\), and then \(|S|\leq|\pi_{i}|-i<\lambda\).  
When we have \(\pi_{j}\) in a decreasing strip, the proof is analogous.
## 6 Tracking and Tracing
### Conclusions  
In the tracking and tracing area, there are technologies with different advantages and disadvantages. While Bluetooth is a very well-known and successful standard with constant modernisation, ZigBee has a strong manufacturer-provided mesh structure that allows it to be used as local sensor and tracking network, which is also of interest for the mining sector. These use cases are similar to the LUM, just at a more performant and dedicated local level. The flexibility of this network is important because nodes do not need to be purely stationary and can run at low power consumption.  
Due to the low infrastructure and high approval, tests and installations are comparatively easy for all technologies. It must therefore ultimately be decided for each individual company which network is considered the most profitable for its technical advantages.
## 3 Results and Discussion
### Technical Feasibility  
The technical feasibility was evaluated based on the ability of the heterogeneous photocatalysis to improve the quality of the influent according to the water quality physicochemical parameters and the input biodegradability index (BOD\({}_{5}\)/COD), following the criteria proposed in Table 2[73].  
The selection of a technology for LIW treatment depends on the properties of the pollutants found in the wastewater. For this, the BOD\({}_{5}\)/COD relationship was applied Equation (4) by providing the characteristics of the input wastewater, which can define the performance of Wastewater Treatment Plants (WWTPs), where phenolic compounds are the main contributors to this relationship [74]. If the influent has a complex biodegradability index and persistent pollutants that are difficult to remove by a conventional WWTP, it is feasible from the technical field to implement AOPs.  
\[Bi=\frac{BOD_{5}}{COD} \tag{4}\]  
Figure 12: Study sector located between 29\({}^{\circ}\) 20 and 32\({}^{\circ}\) 15 south latitude.  
where, \(Bi\) is the Biodegradability index, \(BOD_{5}\) in mg/L, and \(COD\) in mg/L. Values higher than 0.6 indicate biodegradable LIW, the range between 0.3 and 0.6 shows moderately biodegradable LIW, and values lower than 0.3 show LIW with low biodegradability [75].  
The physicochemical parameters of BOD\({}_{5}\) and COD of Pisco Endemico's LIW were measured under standardized methods proposed by APHA [76]. The results showed values of 2400 mg/L and 11978 mg/L, respectively (see Table 2). The biodegradability index value is 0.20, indicating that using BF-5 in a photocatalytic system after an effective primary treatment to decrease the values of TSS and Turbidity is technically feasible in this company.
# The Protective Effect of Polyunsaturated Fatty Acids against Dry Eye Disease: A Literature Review
## 3 PUFAS Supplementation and DED
### Supplementation of PUFAs for MGD  
An experimental study demonstrated the influence of PUFAs on the quantity and quality of lipids in human meibomian gland epithelial cells, indicating the possible association between PUFAs and meibomian gland dysfunction (MGD) [76]. [PERSON] et al. [77] reported that oral supplementation with LA + GLA and eyelid hygiene synergistically alleviated lid margin inflammation and dry eye symptoms in MGD [77]. The administration of flaxseed oil (contains 55% ALA and 15% LA; 6000 mg per day) for 12 months in patients with obstructive MGD and blebaritis led to improvements in TBUT and dry eye symptoms [78]. The supplementation was also associated with reductions in saturated fatty acids in the meibum and improvements in the meibum score, indicating its direct influence on the meibomian gland [78].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **Design** & **Level** & \begin{tabular}{c} **Sample** \\ **Size** \\ \end{tabular} & **Daily Dose** & **Duration** & \begin{tabular}{c} **Objective and Subjective** \\ **Study Outcomes** \\ \end{tabular} \\ \hline \
\begin{tabular}{c} [PERSON] et al. [64] \\ (2010) \\ [PERSON] \\ et al. [26] (2011) \\ [PERSON]- \\ [PERSON] et al. \\ [13] (2011) \\ [PERSON] et al. \\ [65] (2013). \\ [PERSON] et al. [66] \\ [PERSON] et al. [66] (2016). \\ \end{tabular} & RCT & I & 86 &
\begin{tabular}{c} 200 mg sea buckthorn oil \\ (149 mg \(\omega\)3 + 245 mg \(\omega\)6) \\ RCT \\  
[PERSON] et al. [79] revealed that oral supplementation of EPA and DHA resulted in an improvement in symptoms of DED, and the benefit appeared to be more marked in MGD. Administration of EPA and DHA for 6 months significantly improved the symptoms of DED, the Schirmer and the meibomian gland scores and TBUT in patients with rosacea [22]. [PERSON] et al. [80] showed that \(\omega 3\) PUFA intake improved dry eye symptoms, Schirmer score, tear film stability, meibomian gland expression and lid margin inflammation in patients with MGD [80]. [PERSON] et al. [81] also demonstrated that the supplementation with EPA and DHA resulted in an improvement in ocular surface symptoms, TBUT, tear osmolarity and MMP-9 positivity in patients with MGD. [PERSON] et al. [82] showed that the combination treatment comprising \(\omega 3\) supplements, lipid emulsion eye drops and lid wipes improved meibomian gland function and ocular surface symptoms in patients with evaporative DED (Table 4). [PERSON] et al. [83] reported that diet with high \(\omega 3\) and moderate \(\omega 6\) might be protective against MGD in postmenopausal women, although supplementation with \(\omega 3\) and \(\omega 6\) showed no beneficial effect on DED [83]. A systematic review concluded that a moderate daily dose of \(\omega 3\) may have a protective effect against MGD [84].
## 2 Materials and Methods
### Data Preprocessing
#### 2.3.2 Methods for Calculating Hue Angle  
The CIE 1931 XYZ color system, developed by the International Commission on Illumination (CIE), quantifies color through three spectral tristimulus values: \(X\), \(Y\), and \(Z\). However, these values are not readily interpretable or visually intuitive. To overcome this limitation, the CIE introduced the CIE 1931 xy chromaticity diagram (Figure 4)--a two-dimensional representation that employs chromaticity coordinates, hue angle, and other derived parameters to visually characterize color.  
Firstly, the UAV imagery for RGB bands was transformed to the Commission Internationale de l'Eclairage (CIE) tristimulus values \(X\), \(Y\), and \(Z\) as follows [19; 33]:  
\[X =1.1302R(\text{Blue})+1.7517R(\text{Green})+2.7689R(\text{Red}) \tag{1}\] \[Y =0.0601R(\text{Blue})+4.5907R(\text{Green})+1.0000R(\text{Red})\] (2) \[Z =5.5934R(\text{Blue})+0.0565R(\text{Green})+0.0000R(\text{Red}) \tag{3}\]  
Figure 3: _Suaeda salsa_ biomass experiment: (**a**) experimental unit, (**b**) spatial layout of experimental plots. S1–S6 represent measurement samples 1–6.  
where \(R\)(Blue), \(R\)(Green), and \(R\)(Red) are the reflectance of the blue, green, and red bands, respectively.  
And secondly, the normalized chromaticity coordinates \(x\) and \(y\) were calculated from CIE tristimulus values \(X\), \(Y\), and \(Z\) as follows:  
\[x=X/(X+Y+Z) \tag{4}\]  
\[y=Y/(X+Y+Z) \tag{5}\]  
Finally, the hue angle \(\alpha\) can be found from the chromaticity coordinates \((x,y)\) by using the bivariate arctangent function (arctan2) and the following equation:  
\[\alpha=\frac{\arctan 2(x\ -\ 1/3,y\ -\ 1/3)\cdot 180}{\pi}+180 \tag{6}\]
# On the Optimal Calculation of the Rice Coding Parameter
## 4 Performance on Different Datasets Using Single-Parameter Rice Coding
### Influence of the Chosen Batch Size  
The compression factor \(\tau_{m}(\mathbf{n})\) of an encoding method \(m\) for an input dataset \(\mathbf{n}\) of length \(N\) is defined as follows [33]:  
\[\tau_{m}(\mathbf{n})=\frac{N\cdot\max_{i}L(n_{i})}{f_{m}(\mathbf{n})},\text{ where} \tag{28}\]  
\(L(n_{i})\) is the number of bits needed to represent the sample \(n_{i}\) in binary code and \(f_{m}(\mathbf{n})\) is the output bit-length size of the encoding method \(m\) when used over \(\mathbf{n}\).  
In Figure 6, the normalised compression factor for each data type is shown as the batch size varies. The normalised compression factor is calculated as:  
Figure 5: Number of minimal bit-length solutions found by approximating the Rice parameter as given in (20). Labels “floor”, “ceiling”, and “ceiling + 1” should be understood as \(\lfloor S\rfloor\), \(\lceil S\rceil\), and \(\lceil S\rceil+1\), respectively. Each column represents the number of minimal bit-length solutions obtained by using the specified approximations, e.g., there were 144,216 minimal bit-length solutions found by approximating the parameter as \(\lfloor S\rfloor\) and 189,589 minimal bit-length solutions found by approximating the parameter either as \(\lfloor S\rfloor\) or \(\lceil S\rceil\) (both are equal-cost solutions).  
As can be observed for most datasets, the compression ratio of Rice coding may decrease for some datasets as the batch size increases, yielding longer codes on average. This is because a single Rice coding parameter cannot fit well to a large sequence of measurements.  
For the total rain and interval rain datasets, the compression ratio fluctuates as the batch size varies, indicating that the sequence of measurements largely varies over time.
## 3 Results and Discussion
### Antimicrobial Activity
First, there is an ion exchange between the silver ions in the zeolite material and the sodium and calcium ions in the wound fluid. Second, silver ions can be chelated by protein molecules. Third, AlphaSan particles can also be attached to the surface of algin fibres and detach from the fibres and enter the soft tissues [29; 46]. In these studies, a zeolite containing silver ions was effective against _Pseudomonas aeruginosa_ and _Escherichia coli_. However, this study requires confirmation in the case of alginate impression material. In recent years, more and more emphasis has been placed on the possible undesirable effects of silver ions on the human body. The answer to this question can be found in [PERSON]'s work, as silver is inert when deposited in human tissues [29]. It is normally present in mammalian tissue at concentrations <10 mg/dL. Furthermore, for serum, silver concentrations of up to 20 mg/dL are considered nontoxic. In the case of alginate materials, the contact of the alginate material with the patient's mucosa is very short, a maximum of 3 min, which has been tested in these studies. The silver accumulated in the organs can usually be cleared after 8 weeks [45]. Figure 10: Size of the growth inhibition zone of _S. mutans_ on agar in contact with the alginate impression materials studied.
## 2 Material and Methods
### Microbial Diversity Analysis with High-Throughput Pyrosequencing  
After 700 days of incubation, sediment and electrode samples under different treatments were sampled for microbial diversity analysis. The DNA of bulk sediment samples was extracted directly without any pretreatment. However, the electrodes required treatment before DNA extraction. At first, sterile water was used to rinse electrodes, so as to remove large adherent sediment on each electrode surface. Then small pieces of the anode and cathode were randomly obtained using sterile scissors for subsequent DNA extraction. Genomic DNA for 454 pyrosequencing tests was extracted with a DNA Isolation Kit (MOBIO Laboratories, CA, USA) according to the manufacturer's instructions. The extracted DNA was detected with a Nanodrop 2000 instrument (NanoDrop Technologies, Wilmington, USA) and a highly pure genomic DNA (260/280 nm \(\approx\) 1.8) was used for pyrosequencing [22]. Then a PCR-based pyrosequencing technology was used to study the diversity of the bacterial communities. Bacterial 165 rRNA gene fragments were amplified with the two primers 341F (5'-CCTACGGGGAGGCAG-3') and 1073R (5'-CGAGCTGACGACARCCATG-3'). Bacterial 165 rRNA amplification was performed in a PCR system (ABI GeneAmp(@)9700), and the detailed steps were shown as follows: an initial denaturation step at 95 \({}^{\circ}\)C for 2 min, then 25 cycles of 30 s at 95 \({}^{\circ}\)C, 30 s at 55 \({}^{\circ}\)C, 30 s at 72 \({}^{\circ}\)C and a final extension at 72 \({}^{\circ}\)C for 5 min.  
The sequence data were analyzed using QIIME 1.9.0 [23]. OTUs were generated through UCLUST based on a 97% similarity level, and singletons were removed during the process. UCHIME was used to remove chimeras of 16S and ITS data [24]. The greengenes database ([[http://greengenes.lbl.gov](http://greengenes.lbl.gov)]([http://greengenes.lbl.gov](http://greengenes.lbl.gov))) was used to assign the taxonomic identity of phylotype of bacteria [25]. The assignment method is BLAST using assign_taxonomy.py script with default parameters in QIIME.
## 6 Accident Case Development and Implementation for the Accident Response--A Case Study
### Case Database  
The metro accident cases in a metro company in Wuhan used to develop the accident case database mainly came from the following sources:  
1. Checking records of metro operations, which included both the contents and results of operational checks.
2. Safety production monthly reports of metro operations, which kept a record of all accidents that have occurred over the previous month and their corresponding details, including the accident process description, relevant accident response measures, accident impact, cause analysis, and corrective measures.  
For example, a turnout failure accident saved in the safe production monthly report of the metro operations was introduced as follows, which consists of the accident characteristics, accident response, and post-accident assessment. At 6:12 a.m. on 14 July 2016, the indication of the 16 th and 18 th turnouts were lost, thereby affecting the metro operation and subsequently resulting in train delays. At 6:15 a.m., the operational company decided to change the train route. At 6:47 a.m., the fault was fixed, and the train route was changed back to the original line. The cause of the accident and the corrective measures as determined after the investigation. The turnout failure was attributed to the omission of overhaul work on the 16 th and 18 th turnouts due to bad weather. However, it is worthy to note that the accident records, such as the one shown in Figure 6, were unstructured and expressed in natural language. This means that they were difficult to process digitally. These documents would be processed into structured data using NLP techniques.  
Figure 6: The annotated metro accident cases.  
A total of 120 accidents were extracted from the safety monthly reports on metro operations. With the support of NLP techniques, these accidents were analyzed and automatically annotated. The classes, properties, and individuals of the abovementioned ontological model were used to assist the annotation of metro accidents, as shown in Figure 6, such as the \"train number,\" \"station,\" and \"fault characteristics.\" These annotations belong to different attributes, which can be used in the similarity calculations. These cases were stored in CSV files [49].  
Based on the statistical analysis of the metro operating accidents, more than 80% of accidents were caused by the poor state of equipment and were represented by equipment failures, such as signal system failures and metro vehicle failures. Thus, in this study, the \"signal system failure\" accidents were selected as the concrete example to test the proposed method. As shown in Figure 7, the classes and individuals of the \"signal system failure\" were developed using Protege 4.3.
###### Abstract  
The dynamic prediction of large spacecraft is a time-consuming process. The impulse-based substructuring (IBS) method is an efficient and accurate method for the dynamic analysis of large-scale structures. The ordinary IBS method couples the substructure impulse response functions (IRFs) with coincident interface nodes. However, the interface nodes in a practical structure are usually mismatched. This paper extends the ordinary IBS method and presents a method for coupling multi-domain substructures, including IBS, FEM and modal substructures, with non-matching interfaces. Both displacement compatibility and force equilibrium are satisfied on the non-matching interfaces of substructures by introducing the normalized interpolation technique into the framework of IBS. To evaluate the performance of the proposed method, numerical models and a practical application with a lunar lander and a rover are presented. The main bodies of the lander and the rover are coupled by non-matching interfaces, and the coupled system is excited by a buffer load. With the employment of this proposed method, the computing time of the landing simulation of the lunar lander is dramatically reduced, and the results show strong agreement with references. It is demonstrated that the proposed method is accurate and efficient in the transient dynamic analysis of engineering structures.  
impulse-based substructure; structural dynamic analysis; lunar lander; non-matching interfaces +
Footnote †: journal: Applied