esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Endmember Extraction and Abundance Estimation  
Several approaches have been developed for the automatic or semiautomatic extraction of spectral endmembers, among which N-FINDR is one of the most powerful. N-FINDR is a geometric algorithm that identifies the simplex of maximum volume that can be inscribed within the data set [23]. The initialization of the algorithm is carried out by selecting a random set of q endmembers \(\left\{\text{E}_{1},\text{E}_{2},\ldots,\text{E}_{\text{q}}\right\}\), with \(\text{q}\leq\text{n}+1\), and n corresponding to the dimension of the feature space. The volume of the simplex is then defined by  
\[\text{V}\left(\text{E}_{1},\text{E}_{2},\ldots,\text{E}_{\text{q}}\right)= \frac{\left|\det\left[\begin{array}{c}1,1,\,\ldots,1\\ \text{E}_{1},\text{E}_{2},\ldots,\text{E}_{\text{q}}\end{array}\right]\right.}{ \left(\text{q}-1\right)!} \tag{5}\]  
To identify the maximum volume of the simplex, this value is recalculated by testing each pixel vector X(i,j) in the first endmembers position:  
\[\begin{split}&\text{V}\left(\text{X}(1,1),\text{E}_{2},\ldots, \text{E}_{\text{q}}\right)\\ &\text{V}\left(\text{X}(1,2),\text{E}_{2},\ldots,\text{E}_{\text{q}} \right)\\ &\ldots\\ &\text{V}\left(\text{X}(\text{r},\text{c}),\text{E}_{2},\ldots,\text{E}_ {\text{q}}\right)\end{split} \tag{6}\]  
where _r and c represent the_ number of rows and columns of the map. When one of the volumes calculated in Equation (6) is greater than the volume obtained with the original endmembers set, the evaluated endmember is replaced with the pixel corresponding to the maximum volume, resulting in a new set of endmembers. This procedure is then iterated by testing the volumes in the other endmember's positions, retaining the combinations corresponding to the maximum volumes. At the end of this process, the resulting volume should represent the maximum volume for the specific data. It is important to note that according to our approach, the obtained endmembers are representative of the feature space, thus to obtain their equivalent in the spectral domain, it will be necessary to reproject them back into the original data space.  
The estimation of the fractional abundances of each endmember within each pixel in the image can be obtained by minimizing the total squared error, under the constraints of non-negativity and/or the sum to one  
\[\begin{split}\min_{\text{x}}\left(\frac{1}{2}\right)\left\|\text {E}\text{a}-\text{S}\right\|_{2}^{2}\\ &\text{x}\geq 0\\ &\text{1}^{\text{T}}\text{x}=1\end{split} \tag{7}\]  
where \(\text{E}\in\Re^{\text{q}}\) refers to the matrix containing the q endmembers, \(\text{a}\in\Re^{\text{k}}\) represents the fractional abundance vector, and \(\text{S}\in\Re^{\text{k}}\) is the observed mixed pixel. The solution of the optimization problemsis then achieved through the SUnSAL algorithm that is based on the Constrained Split Augmented Lagrangian Shrinkage Algorithm (C-SALSA) methodology to effectively solve a large number of constrained least-squares problems sharing the same matrix system in reference [24].
# Two Design Options for Compact Linear Accelerators for High Flux Neutron Source
## 2 Proton RFQ Accelerator Design
### Design Strategy of RFQ Beam Dynamics  
To start the RFQ beam dynamics designs, we have to determine some key input parameters, for instance, vane voltage, input energy and output energy. For 162.5 MHz and 325 MHz RFQ designs, the main parameters are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **162.5 MHz RFQ** & **325 MHz RFQ** \\ \hline Beam species & H\({}^{+}\) & H\({}^{+}\) \\ Injection energy (keV/u) & 35 & 55 \\ Output energy (MeV/u) & 2.5 & 3.0 \\ Resonant frequency (MHz) & 162.5 & 325 \\ Peak beam current (mA) & 20 & 40 \\ Inter-vane voltage (kV) & 65 & 85 \\ Cavity length (m) & 5.18 & 3.66 \\ Averaged aperture radius \(r_{0}\) (mm) & 5.664 & 3.575 \\ Vane-tip radius \(r_{t}\) (mm) & 4.250 & 2.680 \\ Ratio \(\rho_{t}=r_{0}/r_{t}\) & 0.75 & 0.75 \\ Maximum surface field (MV/m) & 16.85 (1.24 Kilpatrick) & 32.53 (1.82 Kilpatrick) \\ Input \(\epsilon_{t}\) (norm. rms.) (mm-mrad) & 0.200 & 0.200 \\ Output \(\epsilon_{t}\) (norm. rms.) (mm-mrad) & 0.249 & 0.206 \\ Output \(\epsilon_{t}\) (norm. rms.) (mm-mrad) & 0.360 & 0.296 \\ Transmission (\%) & 99.6 & 98.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design parameters of the proton RFQs.  
Figure 2: Collected experimental datum on neutron yields from \({}^{9}\)Be(p, xn) reactions and the fitting curve is Equation (1).
## 2 Theoretical Foundation
### Algorithm  
This algorithm is based on two important assumptions:  
1. The starting point is the endpoint; in this case, the distribution center of _Montemor-O-Velho_;
2. Except for the starting point, each location is visited only once. Before starting the iterative process, it was necessary to define the inputs of this algorithm:  
1. Generate time_matrix \(N\) x \(N\): the calculated distances were searched on Google Maps and imported into arrays in Microsoft Excel;
2. Generate time_windows: the two scenarios with time windows of (0.480) and (0.585);
3. Generate demands from each vaccination center: the vaccine demand values for each district;
4. Generate vehicle_capacities: the constraint of the maximum load of each vehicle;
5. Insert num_vehicles: the number of vehicles needed to transport the required number of vaccines was tested;
6. Insert depot: the starting point considered is the distribution center, _Montemor-O-Velho_;
7. Define allow waiting time: the defined waiting time allowed was 0;
8. Set max time per vehicle: two scenarios (480 min and 585 min) were tested.  
It was necessary to adjust the algorithm code to add a capacity constraint for each vehicle. The software used was Python 3.10.4.  
After defining the inputs, the algorithm returned with a solution for the most optimized routes.  
The algorithm was reproduced on an HP Laptop-15-dw2 xxx with Intel(R) Core (TM) i7-1065G7 CPU @ 1.30 GHz 1.50 GHz,16.0 GB RAM, Windows 11 Home with OS 64-bit, the x64-based processor. The response time of the obtained routes was always between 3 and 12 s. Thus, the computation time is acceptable for real-life applications.  
The analysis consists of using a decision support system that allows for the optimization of the distribution process of the COVID-19 vaccine in Central Portugal. Research demonstrated that there are 89 vaccination centers to consider, and their locations are in Figure 1. This problem requires the use of a decision support method to optimize routes between the vaccination distribution center and the vaccination centers.  
This study began with collecting data about the vaccination process in the Central Zone of Portugal. General Health Directorate (DGS) has identified 89 vaccination centers in this area, and their addresses were added to \(\times\)My Maps\({}^{\Rightarrow}\) as pins.
## 2 PA Model and Design Set
### Maplet(tm) Implementation of the Analytical PA Model and Its Design Set  
This section briefly describes the Maplet(tm) code used for implementing the analytical model and the design set of the Class-E PA with FDI presented in the previous subsections. This model is composed of the Equations (4)-(9) and the variables \(\varphi\), \(p\), \(C_{1}/V_{DD}\), \(C_{2}/V_{DD}\), and \(g_{x}\) expressed as explicit functions of the \(q\) and \(D\) variables. These last functions were found through the algebraic solution of the equations proposed in [17; 19], and then they were coding in the Maplet(tm) script presented in Appendix A.1. The amplifier design set was coded based on the implemented model, the solution of the Equation (26) (coded as an intermediate function \(KX2\)), and the functional operator feature of Maplet(tm). The resulting script is described in Appendix A.2. As a result of this implementation  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **[PERSON]**[7] \({}^{a}\) & **kazimierczuk**[31] & **[PERSON]**[30] & **[PERSON]**[23] & **[PERSON]**[17] \({}^{b}\) \\ \hline \(K_{L}\) & \(\infty\) & \(\infty\) & 0.732 & 3.534 & 0.732 \\ \(K_{C}\) & 0.184 & 0.184 & 0.685 & 0.071 & 0.685 \\ \(K_{P}\) & 0.577 & 0.577 & 1.365 & 0.056 & 1.365 \\ \(K_{X}\) & 0.637 & 1.152 & 0 & -4.903 & 0 \\ \hline \(R_{on}\) switch & finite & zero & zero & zero & zero \\ \(L_{SH}\) & infinite & infinite & finite & finite & finite \\ \(C_{SH}\) & linear & linear & linear & linear & linear \\ \(D\) & arbitrary & 0.5 & 0.5 & 0.5 & arbitrary \\ \hline \hline \end{tabular} \({}^{a}\) Calculated for \(D=0.5\) and zero on resistance. \({}^{b}\) Calculated for \(D=0.5\) and \(q=1.412\).  
\end{table}
Table 1: Design Sets of different Class-E PA models.  
Figure 3: Design set relations.  
approach, the amplifier model and its design set were coded as functions of the variables \(D\) and \(q\). Finally, we code Equations (4)-(9) to estimate the main waveforms predicted by the model, the resulting script was summarized in Appendix A.3.  
The scripts summarized in Appendix A could be used at least for two purposes. The first one is to plot the estimated waveforms under ZVS and ZVDS operation of the analytical model while using the \(D\), \(L_{SH}\), \(C_{SH}\), \(R_{L}\), and \(V_{CC}\) values. The second one is to calculate the design set using the \(q\) and \(D\) values.
# Adaptive Deterministic Vibration Control of a Piezo-Actuated Active-Passive Isolation Structure
## 3 Design of Adaptive Active Vibration Controller
### Test Results of \(H_{\infty}\) Control
Figure 13 illustrates the system time-domain response for the case of a residual vibration with frequency [35-70-105] Hz, and Figure 14 illustrates the corresponding PSD estimate. The residual vibration with three frequencies is suppressed by more than 20 dB on average, and the relevant quantitative results can be found in Table 1. The parameter \(\hat{\theta}\) of the experimental results  
Figure 12: The frequency response of the primary path with (dashed line) and without (solid line) inner-loop LQG controller. of the adaptive closed-loop control system is shown in Figure 15. The results in these three figures clearly illustrate the capability of the regulator in dealing with multifrequency deterministic residual vibration. **Case two:** deterministic disturbance of step changes in frequencies  
The time-domain result of the adaptive active vibration control system for the deterministic residual vibration of step changes in frequencies is shown in Figure 16. The sequence of frequencies is as follows: [60; 61; 62; 63; 64; 65] Hz [40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60] Hz, and each pair of disturbances is applied for a period of 5 s. The parameter \(\hat{\theta}\) of the experimental results of the adaptive closed-loop control system is shown in Figure 17. When the deterministic vibration disturbance changes at 5 s and 10 s, the parameter \(\hat{\theta}\) is tuned online and converges to the new target parameter according to the new deterministic residual vibration model. The results in Figures 16 and 17 indicate that the adaptive algorithm can suppress unknown or time-varying deterministic residual vibration, even if the properties of the residual vibration change suddenly over time. The corresponding quantitative results of Figure 16 can be found in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Controller State**} & **Case One** & \multicolumn{3}{c}{**Case Two**} \\  & **[35-70-105] Hz** & **[60–65] Hz** & **[40–55] Hz** & **[55–50] Hz** & **[55–60] Hz** \\ \hline On (\(\sigma\)) & 0.0077 V & 0.0061 V & 0.0081 V & 0.0089 V \\ Off (\(\sigma\)) & 0.1000 V & 0.0860 V & 0.0690 V & 0.1014 V \\
20 lg(On/ Off) & –22.27 dB & –22.98 dB & –18.61 dB & –21.13 dB \\ \hline \end{tabular}
\end{table} 
Table 1: Steady-state standard deviations (\(\sigma\)) of residual vibration for different experimental cases. Figure 14: Frequency-domain residual vibration with frequencies [35-70-105] Hz in open loop (black) and closed loop (red) of the adaptive active vibration control system. Figure 13: Time-domain residual vibration with frequencies [35-70-105] Hz in open loop (black) and closed loop (red) of the adaptive active vibration control system.
## 3 Analysis of Study Methods
### Mental States  
In this section, we tabulate and analyze the common standard questionnaires used in studies (Figure 4). The acronyms and full names of common standard questionnaires are tabulated in Table 2. As the mental state measurement is a common goal regardless of whether the VR mode is 360-degree video or 3D virtual environment, we combine analysis across both types of VR mode.  
From a tabulation and analysis of the questionnaires employed by these studies, we find that PANAS is the most common standard questionnaire used with 7 out of 12 studies using PANAS. Following this, PRS and ROS are used in three studies, both of which measure restoration. Restoration is a common prediction due to the attention restoration theory, a main theory behind the positive beneficial effect of nature. Finally, SVS is used in 2 studies. Besides psychological surveys, physiological measurements like ECG and skin conductance are also used in 2 studies.  
The infrequent use of physiological measurements such as ECG and EEG suggests an area of future research. Physiological measurements may serve as more objective  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Acronym** & **Expanded** \\ \hline PANAS [37] & Positive And Negative Affect Scale \\ ROS [38] & Restoration Outcome Scale \\ PRS [39] & Perceived Restroviruses Scale \\ SVS [40] & Subjective Utility Scale \\ SSQ [41] & Simulator Sickness Questionnaire \\ \hline \hline \end{tabular}
\end{table}
Table 2: Common standard questionnaires used in studies. Acronyms and the expanded name of the standard questionnaires are shown.  
Figure 4: Tabulation of common standard questionnaires and physiological measurements used in 12 studies. ECG includes measurements derived from ECG such as heart rate and heart rate variability.  
measurements unlike psychological questionnaires, which can be biased by location and intervention methods and expectations.
## 2 Materials and Methods
### Chromatin Immunoprecipitation Assays  
Chromatin immunoprecipitation (ChIP) assays were performed using GC-1 cells and the SimpleChIP(r) Plus Sonication Chromatin IP Kit (Cell Signaling Technology, Danvers, MA, USA) according to the manufacturer's instructions with minor modifications. Forty-eight hours after plating cells in two 75 cm\({}^{2}\) cell culture flasks, proteins were cross-linked to DNA by adding 1% formaldehyde directly to the media and incubating for 30 min at 37 \({}^{\circ}\)C, followed by quenching with 0.4 M glycine for 5 min. Cells were washed twice with ice-cold PBS, pelleted, followed by nuclei preparation and chromatin fragmentation. Sonication was performed on ice using a Q125 Sonicator (Qsonica, Newtown, CT, USA) with 1 s ON/1 s OFF pulses at 45% amplitude for 4 min of sonication time to obtain DNA fragments between 200 and 700 bp in size. Protein-DNA complexes were incubated using 1 uL of normal rabbit antibody (Cat.: 2729, Cell Signaling), 10 uL of Jun rabbit monoclonal antibody (Cat.: 9165, Cell Signaling Technology, Danvers, MA, USA) or 3 uL of Jund rabbit polyclonal antibody (Cat.: GTX79259, GeneTex, Irvine, CA, USA) overnight at 4 \({}^{\circ}\)C with rotation, followed by immunoprecipitation using ChIP-Grade Protein G Magnetic Beads. Beads were washed using low salt and high salt buffers. Cross-links were reversed by addition of 200 mM of NaCl and proteins digested using 50 \(\mu\)g/mL of proteinase K and incubation at 65 \({}^{\circ}\)C for 2 h. DNA fragments were purified using spin columns. Quantitative PCRs were performed using previously described primers targeting the proximal region of the mouse _Gjal_ promoter [13]. Amplification of a region of the second intron of _Rpl30_ (Cat.: 7015, Cell Signaling Technology, Danvers, MA, USA) served as a negative control for Jun and Jund DNA recruitments. Quantitative PCRs were performed using the PerfeCTa(r) SYBR(r) Green FastMix(r) Reaction Mix (Quanta Biosciences) on a CFX Connect(tm) Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA). ChIP results were confirmed via three separate experiments.
# Primary Seal Deformation in Multipane Glazing Units
## 1 Introduction
### State of the Art  
It has been recognised that IGU durability is mainly dependent on the primary seal [18], hence this aspect has been extensively investigated [19]. A thorough state-of-the-art review of a large number of primary seal systems and comparison of the performance thereof is presented by [PERSON] et al. [2]. [PERSON] et al. [20, 21] addressed the influence of spacer material on the inside surface condensation. In [22] [PERSON] describes the impact of the spacer rigidity and thermal expansion coefficient (CTE) on the PIB seal deformation under various loadings. It was found that a stainless-steel spacer had less effect on the change in the PIB cross-section due to the lower CTE, while an aluminium spacer had the largest effect due to the high CTE. In order to homogenise the strain in the primary sealant, a new warm edge system, i.e., Kodispace, was developed [14]. In such a double glass unit (DGU) there is a primary seal only between two glass panes, while in multipane glazing there are more interacting systems, which significantly complicates IGU design.  
Recently, multipane IGUs have appeared on the market; [PERSON] et al. analysed and compared their performance in [23]. The authors exposed pressing issues that are specific to multipane IGUs. Namely, in multipane glazing the intermediate chambers are highly insulated by the other chambers, therefore high cavity temperatures due to solar energy absorption can pose a problem for the robustness of such units. There are several layers of PIB and their mechanical behaviour can differ significantly from one layer to another and also along the circumference. The design requirements for such a complex structure were partially disclosed in [10].  
A numerical approach is one of the possibilities to gain insight into the mechanical behaviour of an IGU. [PERSON] [24] performed compressive tests of edge seal spacers and built a numerical model for the sake of comparison. [PERSON] et al. [25] used the finite element method (FEM) to analyse the thermal displacements of the spacer frame and glass panes. However, to obtain the PIB response, the entire IGU should be exactly modelled, which requires very dense mesh and expensive analyses.
# Study on Stress-Strain Relationship of Coir Fiber-Reinforced Red Clay Based on Duncan-Chang Model
## 5 Discussion  
[PERSON] et al. [34] added coir fiber to expansive soil and found that under the same confining pressure, the sample had maximum deviatoric stress when the fiber content was 0.6%, which was similar to our research results. The reason for the difference in the optimal fiber content between the two studies may be due to the different types of reinforced soil tested. In addition, our study of coir fiber-reinforced red clay showed that its deviatoric stress was higher than that of the coir fiber-reinforced expansive soil studied by [PERSON] et al. [34]. The reason may be due to the different test methods. Our study adopted the consolidated undrained test method, and the strength of the consolidated soil was higher than that of the unconsolidated soil. [PERSON] et al. [34] found that the cohesion of the soil was highest when the fiber content was 0.6%. When it exceeded 0.6%, the cohesion of the soil would decrease. This trend is the same as the change rule in our study, which found that the cohesion of soil increased first and then decreased with increasing fiber content, and reached a maximum at 0.3%.  
All of the formulas of the Duncan-Chang model are calculated under the condition that \(\sigma_{3}\) is constant, that is to say, it is obtained under the condition that the lateral stress is constant and the axial stress is increased. The problems of stress path and dilatancy are not considered, and the influence of the intermediate principal stress cannot be reflected, which is its limitation. In this paper, samples with a diameter of 39.1 mm and a height of 80 mm were used for research. If conditions permit in the future, samples with larger sizes can be used for research, such as 69.1 mm \(\times\) *125 mm, which can better reflect the stress and deformation of coir fiber-reinforced red clay in practical engineering. The deformation mechanism of coir fiber in soil can also be further investigated by microscopic experiments.
## 2 Materials and Methods
### Calculation of Traffic Density and Distance Cruise Ships Pass from the Island Shores
Each table is related to one high-risk navigational and environmental region. Information is given for each month (August-October 2014 and June-July 2015) and cumulatively. Data available from each table are the number of cruise ships that pass <3 M from shore, 3-6 M from shore and >6 M from shore, the retention period <3 M from shore expressed in minutes and the average retention period expressed in minutes. Cumulative data are expressed as totals and give a summary of cruise ships' impact on the designated area. The number of cruise ship transits through Svetac--Bisevo, Susac--Lastovo and Lastovo-Mljet passages were calculated separately, as well as number of transits through the Central Adriatic Separation Scheme (Tables A5-A8). Information is given for each month of the research and cumulatively. The comparison between routes that used the Central Adriatic Separation Scheme and routes that kept north of the Central Adriatic Separation Scheme offers an important conclusion regarding cruise ships' navigational practice in the central and south Adriatic east coast. The number of cruise ships that used the Central Adriatic Separation Scheme were counted from cruise traffic monitoring. In order to make a comparison, the number of routes that passed north of the Central Adriatic Separation Scheme were calculated. Susac island was taken as a reference; it is the island located north of the Central Adriatic Separation Scheme, it is positioned on the southern point of the longitudinal corridor that cruise ships frequently use, and it is the closest land from the Central Adriatic Separation Scheme. Palagruza island, being a remote island at the immediate border to the Central Adriatic Scheme, does not offer reliable data and was not taken as the reference. The calculation was carried out as follows: From Table A2, cumulative information related to the number of cruise ships that passed 3-6 M (265 cruise ships) and <6 M (30 cruise ships) from Susac island were added (data < 3 M were not taken into consideration, as cruise ships that passed <3 M from the shore passed 3 M-6 M from the shore too). From the result, the number of vertical routes that transited through the Susac-Lastovo passage (58) were subtracted (Table A6), because for the comparison, only horizontal routes had to be taken in consideration. The final result was: 237 cruise ships kept north of the Central Adriatic Separation Scheme passing in the vicinity of Susac island, while 187 cruise ships used the Central Adriatic Separation Scheme.
## 4 Performance Metric  
This paper aims to analyze the performance of MLLMs from multiple perspectives by considering various evaluation metrics that effectively reflect user satisfaction with model responses. To achieve this, we employ a diverse set of widely used and well-established evaluation metrics, each with its own unique characteristics. Specifically, we utilize BLEU, ROUGE, METEOR, S-BERT, MoverScore, and CIDEr as our primary evaluation criteria.