esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Algorithm
### A Heuristic Algorithm for the Assignment of Machines to Operators  
The term \(i\) is an index of the machines \(i=1,2,\ldots,M\) and \(n_{i}\) is the number of the product types that were assigned to machine \(i\) in the previous section.  
**Step 1**: Calculate the value \(n_{i}\times T_{S}\) for each machine. This is the total time that machine \(i\) needs an operator for setup. Since the cycle time is \(1\), \(n_{i}\times T_{S}\) it is also the load caused by machine \(i\) on the operator.  
**Step 2**: Rank the M machines according to \(n_{i}\times T_{S}\) in ascending order. This ranking facilitates assigning more machines to a single operator, assuming a machine that causes a low load is more likely to be adopted by the current operators.  
**Step 3**: Assign the machine that was first ranked to Operator 1. Check if the machine ranked second can also be assigned to Operator 1. If yes, assign this machine to Operator 1; if no, assign this machine to Operator 2.  
**Step 4**: Check if the machine that ranked third can be assigned to Operator 1. If so, assign it to Operator 1 and proceed to assign the next machine. If no, check if it can be assigned to Operator 2; if so, assign it to Operator 2; if not, add an additional operator and assign this machine to a new operator. The procedure for checking whether a single operator can operate a specific group of machines will be presented in the next paragraph.  
**Step 5**: Repeat the process until all the machines have been assigned to an operator. At the end of this step, we get the number of operators and a partition of the M machines among the operators.  
Checking whether a single operator can operate a specific group of machines G (\(G\subset M\)) should be done in the following two steps:  
1. The initial and simplest examination is to assess whether an additional machine can be assigned to group \(G\) by verifying that the necessary condition is met--specifically, that the total load does not exceed 1, \(T_{S}\sum\limits_{i\in G}n_{i}\leq 1\).
2. If the first condition is fulfilled, we propose a mathematical programming formulation (6) to examine whether the addition of another machine to an operator is feasible without setup overlap.  
The machine assignment procedure described in Section 3.4 determined the required number of machines and the internal production schedule for each machine. Based on this assignment, the following additional data can be derived:  
\([i,j]\)--the scheduling of the product that was ranked \([j]\) on machine \(i\).  
\(a_{i,[j]}\)--the start time of the setup of the product that was scheduled \([j]\) on a machine \(i\)
## 5 Case Study (Results (Step 2 to 5 in Figure 3))
### Relationship Between MWL and Potential Safety Risk (Step 3 in Figure 3)  
Figure 5 shows the number of interviewees who answered that their MWL was so high as to feel a potential safety risk of navigation. The horizontal axis shows the average MWL of interviewees in terms of each scenario. The number of interviewees in the vertical axis indicates the level of potential safety risk of navigation. The level of potential safety risk is higher when the number of interviewees in the vertical axis increases.  
The results suggest a strong positive correlation between the two variables. The number of interviewees in the vertical axis increases approximately linearly with the increase in the average of interviewees' MWL in the horizontal axis. This means that the potential safety risk of navigation (vertical axis) increases when MWL (horizontal axis) increases. This can verify the theory on the relationship between the potential safety risk of navigation and MWL in Section 2.1 from one side. When MWL increases and becomes too high, a navigational safety risk emerges. Thus, this case study can identify the relationship between MWL and MASS in the context of minimising the navigational safety risk by decreasing the MWL.  
On the other hand, Section 2.1 also indicates another theory on the relationship between navigation safety risk and MWL. The potential safety risk of navigation emerges when MWL is too low due to careless attitude, etc. If this theory could be verified, the number of experts in the vertical axis would increase when MWL in the horizontal axis was very small (negative correlation). However, the results do not show a negative correlation. Thus, the case study can neither justify the theory nor identify the relationship between MWL and MASS in the context of minimising the navigational safety risk by avoiding a too-low MWL.
# Indole Degradation in a Model System and in Poultry Manure by _Acinetobacter_ spp.
## 3 Results  
### Indole Removal Efficiency  
Indole degradation efficacy tests of NTA1-2A, TAT1-6A, and a mix of the two strains at different initial concentrations of 58.58, 117.15, 175.73, 234.30, 292.88, 351.45 mg/L were inoculated with 100 \(\upmu\)L of the strains and incubated for 6 days under aerobic conditions. Samples (2 mL) were collected every 2 d to determine indole concentration by HPLC. Indole degradation was performed by the bacterial strains in the culture media amended with different concentrations. The degradation efficiency (%) was 66.36%, 94.87%, and 96.0% respectively in NTA1-2A, TAT1-6A, and a mix of strains within 6 days at 2.5 mM (292.88 mg/L) concentration (Table 1). When indole concentration was 3 mM (351.45 mg/L), the strains degraded only 26.43% (NTA1-2A), 25.29% (TAT1-6A), and 29.0% (mix).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Indole Removal Efficiency (\%)**} \\ \hline \multirow{2}{*}{**C (mg/L)**} & **Incubation** & \multirow{2}{*}{**NTA1-2A**} & \multirow{2}{*}{**TAT1-6A**} & \multirow{2}{*}{**Mix Strains**} & \multirow{2}{*}{\(p\)**-Value**} \\  & **Time (d)** & & & & \\ \hline
58.58 & 2 & 77.72 \(\pm\) 0.24  & 100 \(\pm\) 0.00  & 100 \(\pm\) 0.00  & 0.000 \\  & 4 & 77.87 \(\pm\) 0.04  & 100 \(\pm\) 0.00  & 100 \(\pm\) 0.00  & 0.000 \\  & 6 & 77.86 \(\pm\) 0.03  & 100 \(\pm\) 0.00  & 100 \(\pm\) 0.00  & 0.000 \\
117.15 & 2 & 88.64 \(\pm\) 0.05  & 96.32 \(\pm\) 6.37  & 98.04 \(\pm\) 3.40  & 0.069 \\  & 4 & 88.60 \(\pm\) 0.04  & 100 \(\pm\) 0.00  & 100 \(\pm\) 0.00  & 0.000 \\  & 6 & 88.61 \(\pm\) 0.14  & 100 \(\pm\) 0.00  & 100 \(\pm\) 0.00  & 0.000 \\
175.73 & 2 & 91.89 \(\pm\) 0.10  & 92.59 \(\pm\) 0.04  & 94.67 \(\pm\) 2.08  & 0.054 \\  & 4 & 91.78 \(\pm\) 0.10  & 92.61 \(\pm\) 0.0  & 95.57 \(\pm\) 2.45  & 0.038 \\  & 6 & 91.42 \(\pm\) 0.26  & 92.61 \(\pm\) 0.0  & 95.67 \(\pm\) 2.31  & 0.012 \\
234.30 & 2 & 70.37 \(\pm\) 1.87  & 91.32 \(\pm\) 3.69  & 93.00 \(\pm\) 2.00  & 0.000 \\  & 4 & 70.37 \(\pm\) 1.86  & 91.32 \(\pm\) 3.68  & 93.00 \(\pm\) 2.00  & 0.005 \\  & 6 & 76.88 \(\pm\) 3.22  & 94.16 \(\pm\) 2.44  & 95.00 \(\pm\) 1.73  & 0.000 \\
292.88 & 2 & 44.33 \(\pm\) 9.19  & 94.23 \(\pm\) 1.31  & 94.67 \(\pm\) 1.15  & 0.002 \\  & 4 & 59.00 \(\pm\) 2.78  & 94.31 \(\pm\) 2.64  & 95.67 \(\pm\) 1.53  & 0.000 \\  & 6 & 66.36 \(\pm\) 25.1  & 94.87 \(\pm\) 1.06  & 96.00 \(\pm\) 1.00  & 0.038 \\
351.45 & 2 & 12.71 \(\pm\) 7.99 & 13.33 \(\pm\) 10.25 & 18.00 \(\pm\) 6.93 & 0.538 \\  & 4 & 17.60 \(\pm\) 4.91 & 18.68 \(\pm\) 3.91 & 22.00 \(\pm\) 3.22 & 0.269 \\  & 6 & 26.43 \(\pm\) 3.95 & 25.29 \(\pm\) 5.03 & 19.00 \(\pm\) 3.52 & 0.482 \\ \hline \hline \end{tabular} a.b: Values in row with different letters are significantly different (\(p<0.05\)).  
\end{table}
Table 1: Indole degradation ability of NTA1-2A, TAT1-6A, and a mix incubated for 6 days. Values are the mean \(\pm\) SD of triplicate assays.
## 2 A Brief Review of the Shapiro Delay Formulae and the Aims of This Paper
### Standard Shapiro Delay Formulae, Derived from the Null Cone Equation
#### 2.2.7 Fourth Aim of the Paper: Finding New Analytical Methods for Expressing the
The Kepler parametrization of the space coordinates \(x=x(a,e,E)\) and \(y=y(a,e,E)\) for the plane elliptical satellite orbit is (4) \(x=a(\cos E-e)\quad,\quad y=a\sqrt{1-e^{2}}\sin E\), where \(a\) is the semi-major axis of the orbit, \(e\) is the eccentricity and \(E\) is the eccentric anomaly angle, related to the motion of the satellite along the plane elliptical orbit. If the right-hand side of Equation (6) is divided and multiplied by \((dt)^{2}\), where \(t=t_{ct}\) is the celestial time from the Kepler equation \(E-e\sin E=n(t_{ct}-t_{p})\) (\(t_{p}\) is the time of perigee passage), one can obtain  
\[(c^{2}+2V)(dT)^{2}=(1-\frac{2V}{c^{2}})v^{2}, \tag{22}\]  
where \(v^{2}\) is the square of the satellite velocity along the orbit :  
\[v^{2}=v_{x}^{2}+v_{y}^{2}=\bigg{(}\frac{dx}{dt}\bigg{)}^{2}+\bigg{(}\frac{dy}{ dt}\bigg{)}^{2}. \tag{23}\]  
Taking into account the Kepler parametrization (4), the above expression for the velocity for the case of plane motion can be rewritten as  
\[J_{n}^{(3)}(x)=\int\frac{x^{n}dx}{\sqrt{ax^{3}+bx^{2}+cx+d}}. \tag{17}\]0where \(n=\sqrt{\frac{C_{n}M_{\oplus}}{a^{3}}}\) defines the mean motion. Expressing \(dT\) from (22), finding the celestial time \(t_{cel}\) in terms of \(E\) from the Kepler equation and performing the integration over the eccentric anomaly angle \(E\), one can obtain the expression for the propagation time (for the case of no restrictions imposed):  
\[J_{n}^{(3)}(x)=\int\frac{x^{n}dx}{\sqrt{ax^{3}+bx^{2}+cx+d}}. \tag{17}\]1  
\[J_{n}^{(3)}(x)=\int\frac{x^{n}dx}{\sqrt{ax^{3}+bx^{2}+cx+d}}. \tag{17}\]2  
The constant \(\beta\) is determined as  
\[J_{n}^{(3)}(x)=\int\frac{x^{n}dx}{\sqrt{ax^{3}+bx^{2}+cx+d}}. \tag{17}\]3  
The other constant \(C\) can be determined as a result of the integration of the null cone equation from some initial condition--for example, the propagation time \(T\) should be equal to zero, when \(E=0\) (if \(E_{init}=0\)). Further, making the substitution  
\[J_{n}^{(3)}(x)=\int\frac{x^{n}dx}{\sqrt{ax^{3}+bx^{2}+cx+d}}. \tag{17}\]4  
in the integral (25) and denoting \(\overline{\beta}=\frac{\beta}{a}\), one can represent (25) as  
\[J_{n}^{(3)}(x)=\int\frac{x^{n}dx}{\sqrt{ax^{3}+bx^{2}+cx+d}}. \tag{17}\]5  
This integral is of the form  
\[J_{n}^{(3)}(x)=\int\frac{x^{n}dx}{\sqrt{ax^{3}+bx^{2}+cx+d}}. \tag{17}\]6  
where the numerical constants \(a_{1}\), \(a_{2}\), \(a_{3}\), \(b_{1}\), \(b_{2}\), \(b_{3}\), \(b_{4}\) can easily be calculated. Their analytical expressions are not given here, because the above expression will not be used further.
## 2 Materials and Methods
### Samples and Extraction Procedures
#### 2.2.1 Volatile and Semi-Volatile Compounds Extraction Methods  
For the analysis of potential volatiles, 5 g of each sample, previously cut into small pieces, was extracted using a Teledyne Tekmar Stratum Purge and Trap (P&T) system (Teledyne Tekmar, Mason, OH, USA) controlled with the VOC TekLink 3.2 software. The experimental conditions of the P&T were as follows: VocorbTM 3000 trap, sample temperature of 90 \({}^{\circ}\)C, purge flow of 40 mL/min, purge time of 30 min, desorb time of 2 min, desorb temperature of 250 \({}^{\circ}\)C, and desorb flow of 400 mL/min.  
For volatile and semi-volatile analysis, different extraction solvents were tested, including ACN, EtOH, and MeOH. A known surface of each coated tinplate sheet (1 dm\({}^{2}\)) was extracted by immersion with 10 mL of solvent at 70 \({}^{\circ}\)C in an oven for 24 h. Then, the whole extract was concentrated under a nitrogen stream (RapidVap Vertex Evaporator, Labconco) to 0.5 mL, vortexed, and filtered through a polytetrafluoroethylene (PTFE) 0.22 \(\upmu\)m filter (Advantec, Tokyo, Japan) for GC analysis.  
For the HS-SPME technique, 1 g of each sample, previously cut into small pieces, was weighted into headspace vials with PTFE septa (20 mL; Agilent Technologies) and duplicated. HS-SPME injection was carried out with a SPME fiber assembly divinylbenzene/carbosen/polyddimethylsiloxane (DVB/CAR/PDMS) from Supelco (Bellefonte, PA, USA). Prior to use, the fiber was conditioned for 1 h at 270 \({}^{\circ}\)C following the supplier's instructions. The volatile compounds were exposed to the fiber at 80 \({}^{\circ}\)C for 30 min. Then,the fiber was inserted into the injector port for thermal desorption for 10 min at 200 \({}^{\circ}\)C, and the compounds were separated by GC and were identified using the MS detector operating in the conditions described below. Then, the fiber was baked at 270 \({}^{\circ}\)C for 5 min.
# Frequency-Based Adaptive Interpolation Filter in Intra Prediction
## 5 Conclusions  
This paper proposes the Adaptive Filter Method to generate fractional reference samples for directional VVC intra prediction. With the _high_freq_ratio_ derived from the 1-D scaled DCT, the 8-tap DCT-IFs and 8-tap SIFs in addition to the 4-tap DCT-IFs and the 4-tap SIFs are proposed to increase the precision of fractional reference samples. Depending on _high_freq_ratio_ with respect to the block size, interpolation filters are applied to the reference samples. We conclude that in cases where the correlation between samples is high, 8-tap interpolation filters with strong HPF or strong LPF characteristics marginally affect the BD rate gain, but in cases where the correlation between samples is low, 8-tap interpolation filters with strong HPF or strong LPF characteristics affect the BD rate enhancement. Figure 5 shows the correlation graph for video resolutions and block size. Since high resolution videos show higher correlation value than low resolution videos, the sequences with low resolution achieve more coding gains than those with high resolution. For the proposed Adaptive Filter Method based on _high_freq_ratio_, overall BD rate gains of \(-\)0.16%, \(-\)0.13%, and \(-\)0.09% are observed for the Y, Cb, and Cr components, respectively, compared with the VVC anchor. We believe that the method of searching for high-frequency terms in the frequency domain helps video coding modules requiring strong/weak HPF and strong/weak LPF for next-generation video coding standards.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] derived mathematical interpolation equations; [PERSON] implemented software and performed the experiments; [PERSON] supervised the algorithm; and [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was in part supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (NRF-2018R1D1A1B07045156). This research was in part supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF-2018R1D1A1B07045156).
# Heterogeneity of Ex Vivo and In Vivo Properties along the Length of the Abdominal Aortic Aneurysm
## 3 Results  
A total of 12 patients referred to elective AAA open repair surgery gave consent to be enrolled in the study (mean age 65 \(\pm\) 6 years, 92% males). Hypertension was reported in 7 patients in the population (58%). Four patients (33%) presented dyslipidemia, with one patient also presenting coronary artery disease, while two patients (17%) in the population presented peripheral vascular disease.  
The average maximum diameter for the population was 4.94 \(\pm\) 0.85 cm, with 4 patients (33%) selected for surgery because they presented critical complications (e.g., penetrating atherosclerotic ulcer, acute ischemic leg, rapid aortic growth, peripheral vascular disease) despite having a maximum aortic diameter lower than 5 cm (i.e., current clinical threshold used for surgical decision). The study protocol included open repair procedure with complete aortic resection resulting in the aorta not being opened during surgery with consequent minimal blood loss for the patients. The procedure led to an average length of stay of 7.7 days for the patients, consistently with the average length of stay reported for cohorts receiving open repair surgery without complete aortic resection.  
The shape of the AAA varied across patients. Position 1 was associated with the neck for all patients and exhibited no dilatation; this region was considered a non-dilated tissue control in the longitudinal heterogeneity analysis. Position 2 exhibited no dilatation in 5/12 patients with the remaining patients having some dilatation beginning in this area. Position 3, 4, and 5 were the central regions of the aneurysm and exhibited the most growth with the largest dilatation being present in position 3 for 6/12 patients, position 4 for 4/12 patients, and position 5 for 4/12 patients. In some cases where the maximum dilatation spanned multiple regions, both positions were accounted for. For position 6, 6/12 patients exhibited a decrease in dilatation while for the rest of the patients, this area remained largely dilated. A double aneurysm, where two separate dilatations are present along the abdominal aorta, was seen in 3/12 patients, with the first generally present around position 3 and another area of growth at position 5 and 6.  
The results reported below concern the changes of selected ex vivo and in vivo properties along the length and circumference of the aorta. Only statistically significant results are reported and discussed.
# Rhythm-Based Attention Analysis: A Comprehensive Model for Music Hierarchy
## 3 Proposed Methods
### Recursive Detection Module
#### 3.3.1 Data Structure and the Definition of Recursive Detection Task  
We employ a tree structure to represent the hierarchical organization of music. Each node corresponds to a musical unit, such as a note, phrase, or sentence. The root node represents the entire composition, while the leaf nodes are the fundamental musical units (notes). The edges signify the relationships between musical units and their sub-units, transitioning from segments to phrases and, ultimately, to notes.  
Node attributes include a list of musical notes and the cumulative quantized attention score for each note, as previously introduced. Edge attributes encompass the rhythmic  
Figure 5: Process flow of basic rhythmic grouping calculations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Basic Rhythmic Grouping** & **Kernel Value** \\ \hline lamb & \([-1,1]\) \\ Anapest & \([-0.5,-0.5,1]\) \\ Trochee & \([1,-1]\) \\ Dactyl & \([1,-0.5,-0.5]\) \\ Amphibatch & \([-0.5,1,-0.5]\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Kernel value for basic rhythmic grouping.  
grouping type, identified through the basic rhythmic grouping operators proposed in this chapter.  
Below is the definition of the recursive detection task. The recursive detection task begins with an input of a complete list of notes from a musical piece.  
The process involves the following steps:  
1. Node Initialization: Each note is first treated as an individual node.
2. Merging Process: Recursively iterate through all nodes. During this step: * Apply basic rhythmic grouping operators to calculate scores for patterns such as Iamb, Anapest, Trochee, Dactyl, and Amphibatch. * Traverse nodes from left to right, and according to patterns' scores, the model will decide whether to merge the consecutive 2 or 3 nodes into a new node. All nodes that are not merged will also become independent new nodes.
3. Tree Structure Formation: Complete the traversal to form the upper layers of the tree structure. Continue the process until the tree is fully constructed, culminating in the root node.
4. Output Extraction: After the tree construction process is completed, the entire tree is passed into a downstream post-processing module: the musical phrase extraction module. This module estimates the boundaries of each phrase based on data density and segment length and aligns these with the level-order traversal results of the tree. Once the computation is complete, the layer that most closely matches the estimated results is selected to derive the output of musical sections and phrases.  
Figure 6 shows the tree structure for recursive detection. This tree-based approach effectively captures the hierarchical nature of music, allowing for dynamic adjustments based on rhythmic patterns. By recursively merging nodes based on rhythmic grouping scores, the structure supports nuanced interpretations and outputs that align with initial musical segmentations. This method provides a robust framework for analyzing and processing complex musical compositions.
## 5 Conclusions  
A four-step procedure of image analysis is proposed in this paper. The original IR image is filtered and converted to grayscale image in the first step. The image is then converted to 3-D color to magnify the temperature difference in the second step. A  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Stage**} & \multirow{2}{*}{**Experiments Group**} & **Threshold** & **Accumulation** & \multirow{2}{*}{**Failure (\%)**} & \multirow{2}{*}{**Number of Cell Failures**} \\  & & **Temperature (\({}^{\circ}\)C)** & **Up to 50\({}^{\circ}\)C (\%)** & & \\ \hline \multirow{4}{*}{First stage} & 1 & 50.1 & 98.5 & 0 & 0 \\  & 2.1 & 50.0 & 96.9 & 1.6 & 1 \\  & 2.2 & 50.0 & 96.5 & 2.0 & 1–2 \\  & 2.3 & 50.0 & 94.5 & 4.0\({}^{1}\) & - \\ \hline \multirow{4}{*}{Second stage} & 1.1 & 50.0 & 96.7 & 1.8 & 1 \\  & 1.2 & 50.0 & 97.2 & 1.3 & 1 \\ \cline{1-1}  & 1.3 & 50.0 & 96.8 & 1.7 & 1 \\ \cline{1-1}  & 2 & 50.0 & 46.2 & 52.2 & 31 \\ \cline{1-1}  & 3.1 & 50.0 & 98.4 & 0 & 0 \\ \cline{1-1}  & 3.2 & 50.0 & 97.0 & 1.5 \({}^{2}\) & - \\ \hline \hline \end{tabular} \({}^{1}\) The case is determined as a contamination in the IR image. \({}^{2}\) Misjudgment of defects caused by reflection.  
\end{table}
Table 3: Case integration table.  
histogram of thermal temperature distribution is then built in the third step to find out the percentage of high temperature area. A cumulative chart of the temperature distribution is finally built in the last step to determine the number of cells that may be abnormal or defective. A total of ten cases have been examined by this procedure. According to results of analysis, not only the inspection time can be greatly shortened, but also the defect position of the IR image can be analyzed in real time. This is especially convenient for maintenance people because the cumulative charts may determine not only the area of the defect region, but may also tell the number of defective cells. The procedure can possibly prevent erroneous inspections caused by reflection, and thus obtain the best result.  
In this paper, experiments were conducted not only on the solar farm field but also on the solar modules mounted on top of high rise buildings. It was found that this procedure applies to small scale solar facilities in the metropolitan area as well. It would be very helpful for the development of diversified solar power in crowded area.  
[PERSON]-H.L. Contributed to the guidance and revision of the paper. [PERSON]-[PERSON] devised the experimental strategy and carried out this experiment and wrote the manuscript and contributed to the revisions. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Data Availability StatementData is contained within the article.  
The authors declare no conflict of interest.