esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup
At a next step, the phase sensitivity of the iterative phase retrieval method and off-axis digital holography have been compared (see Figure 4). For this purpose, a particularly designed and in-house manufactured fully transparent test target, with the elements etched into to the glass to a depth of 79.1 nm (\(L=\lambda/8\) for 633 nm wavelength), was created. With an approximate refractive index n of the glass of 1.5, the resulting phase step \(\Delta\phi\) was:  
\[l_{vir}=l_{geo}/n \tag{1}\]1  
\[l_{vir}=l_{geo}/n \tag{1}\]2 Recorded (**a**) diffraction pattern and (**b**) digital hologram with a conventional positive USAF test target serving as the object under investigation. The enlarged area in the bottom-right corner highlights the difference in the recording pattern caused by the carrier frequency introduced by the separate reference wave in off-axis digital holography. The corresponding amplitude reconstructions are shown in (**c**,**d**). The resolution and the signal to noise ratio were used as objective quantitative measures in support of this comparison. It can be appreciated that the iterative phase retrieval approach performed significantly better in terms of resolution and signal to noise ratio. The abscissa in the graphs at the bottom of Figure 4a,b correspond to the pixel position and the ordinate to phase in radians. Moreover, an averaged cross-section across the square can be seen at the left side of Figure 4a, with the same parameter representation of the axis. The phase difference obtained for the square in comparison to its surrounding was 0.346 rad, which matched well with the expected phase step of 0.393 rad. For the iterative phase retrieval approach, the third element in group five could still be resolved (40.3 lp/mm, resulting in a smallest resolvable structure of 12.04 um in width), whereas for the digital off-axis hologram group 3 element 4 could only be resolved (11.3 lp/mm, resulting in a smallest resolvable structure of 44.19 um in width). The significantly improved resolution and signal to noise ratio for the phase retrieval approach in comparison to off-axis digital holography were due to the application of various speckle decorrelated diffraction patterns resulting in reducing speckle noise and different incident angles on the specimen, as in incoherent imaging [12]. According to Reference [13], the signal to noise ratio was calculated as the ratio between:  
\[l_{vir}=l_{geo}/n \tag{1}\]3  
with \(\upmu\) representing the mean value and \(\sigma\) the standard deviation. The SNR obtained for the digital holographic phase reconstruction was 6.08 and 22.78 for the quantitative phase image obtained via the iterative approach. Moreover, any environmental changes in the two interfering arms, caused for instance by heat, were likewise recorded and may hence negatively affect the result.
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 4 Proposed Image Compression and Encryption Algorithm
### Compression with Confusion-Diffusion through Kd-Tree, Wavelet Tree and Huffman Coding  
In the second stage, we performed joint compression and encryption via Kd-tree, wavelet tree and Huffman coding. A detailed explanation is given below.
# Profile of Accelerations and Decelerations in Young Basketball Players
## 2 Materials and Methods
### Procedure  
According to the [PERSON] et al. [41] guidelines, the use of the ultra-wide band (UWB) was explained, considering 21 points out of 23. For the description of the methodology for the use of MEMS, 17 points were considered out of 20. The rest of the items cannot be explained, as the authors did have not this information.  
For data collection, a UWB system served as the reference system, complemented by tracking devices carried by players, namely IMUs. The UWB system comprised six antennae that functioned as both transmitters and receivers of radiofrequency signals. These antennae served distinct purposes, with five designated as tags and one serving as the master antenna responsible for managing the others (synchronization) and overseeing time management. The antennae, particularly the master antenna, automated the localization of devices within the play area using Time Difference of Arrival (TDOA).  
Upon antenna activation, the system, through an algorithm, determined the position of each antenna. Each device recorded 18 position data points per second (X, Y coordinates), along with corresponding timestamps. Based on positional data and time, the devicecalculated velocity (V = e/t). Velocity calculations employed differential Doppler, and acceleration was subsequently derived from velocity.  
After each game session, the data were downloaded to a computer (PC). The analysis software used was Spro 1.9.1 (RealTrack Systems, Almeria, Spain), compatible with the Windows operating system. This software facilitated the analysis of raw data, and various algorithms were developed for acceleration and deceleration analysis. Using the speed data, the software calculated acceleration and deceleration by applying mathematical derivatives to the velocity variable.
# Development of a Maintenance and Spare Parts Distribution Model for Increasing Aircraft Efficiency
## 4 Simulation Model Results
### System Dynamics Model Definition
#### 4.1.4 Forecasting Sub-Model for Maintenance Requirements  
This sub-model forecasts the traffic volume of flights and the expected operating hours for the aircraft fleet. The forecast is built based on the analysis of historical data and by applying moving average or linear regression forecasting methods. Based on this forecast and on the spare parts characteristics of the airplane, the number of components of the spare part per airplane, and the spare part failure probability, the maintenance requirements are determined for each maintenance location of the system dynamics model.
## 5 Findings and Discussion
### Similarity of Economic Sustainability  
Regarding the economic sustainability of steel structures, designers and constructors share similar opinions on its indicators. As shown in Table 9, construction cost, maintenance cost, operating cost, repair cost, dismantling cost, and waste disposal cost are included in the economic sustainability dimension. The cost of developing steel structure projects is higher than that of developing concrete structure projects, which draws both designers and constructors to carefully predict building investment. This study shows that the investment amount is crucial in determining whether a steel structure project is accepted by the developer [55]. It implies that the investment amount of the steel structures project will help the contractor successfully win the bid and help the developer abandon those projects that are not \"value for money\" in time.  
The similarity of this dimension of indicators is also attributed to the transparency of a steel structure project's cost in procuring the services of design, contracting, and maintenance. Most parts of steel structures are produced in factories and transported to construction sites for assembly and installation. This means that metal materials and maintenance occupy a more significant proportion of steel structures' construction cost. Thus, the maintenance cost and operating cost are important indicators for managing investment amount better. This research finding provides contractors with more clear guidance about the development of the economic sustainability of steel structures. According to some historical studies [56], the cost issue is one of the most significant barriers to implementing off-site construction projects (e.g., steel structures). Therefore, while the findings concur with previous studies on the importance of construction cost of steel structures, the study modifies the existing economic sustainability indicators from a life cycle costing perspective.
## Materials and Methods  
### Patients and Devices  
From January 2010 to December 2020, all patients (n\({}^{\circ}\)154) with a histological diagnosis of orophyneal squamous cell carcinoma undergoing RT (with or without chemotherapy) at the Careggi University Hospital (Florence, Italy) were selected. Of the originally enrolled sample, 27 patients who had undergone surgery and/or chemotherapy without RT and 5 patients who had received palliation were excluded. A further 58 patients were also excluded due to the lack of PAN prior to the start of RT, since PAN had been performed in other institutions or more than 40 days before treatment. Then, 5 patients were excluded because they had performed pre-RT MST elsewhere. Finally, 2 and 7 patients were excluded due to the presence of motion and metal artefacts that significantly degraded the quality of pre-RT MST images (Figure 1).  
Exclusion criteria:  
* Underage patient (<18 years);
* No histological diagnosis of OPC;
* No RT;
* No pre-RT PAN examination;
* PAN has not been performed during the 40 days prior to MST examination;
* No pre-RT MST examination;
* Poor MST image quality due to motion and/or metal artefacts.  
Inclusion criteria:  
* Adult patient (\(\geq\)18 years);
* Histological diagnosis of OPC;
* RT for OPC;
* Pre-RT MST examination;
* PAN carried out during the earlier 40 days to MST examination;
* Absence of motion and/or metal artefacts.  
The final sample consisted of 50 patients with histologically confirmed squamous cell OPC (range 43-81 years; 37 males, mean age 62.3; 13 females, mean age 64.7) who carried out pre-RT MSCT and PAN during the 40 days prior to MSCT imaging.  
All MSCT examinations were carried out with a 128-detector row helical CT scanner (SOMATOM Definition Flash, Siemens Healthcare, Erlangen, Germany) with a field of view extended from the skull base to the thoracic inlet. The following parameters were used: tube voltage 120 kV, current \(\times\) exposure time 186 mAs, rotation time 0.5 s, pixel size 0.465 mm, both thickness and reconstruction intervals 1 mm, beam collimation 128 \(\times\) 0.6 mm, and pitch 0.80. Post-processing, 1-mm-thick sections were obtained on axial, sagittal, and coronal planes oriented on the longitudinal axis of the tooth in question to simulate cross-section obtained by Dentascan software. The bone window was used for image evaluation.  
PAN was performed via the Orthoceph OC200 D (Instrumentarium Dental, Tuusula, Finland). It was a digital panoramic radiograph with a rotation time of 17.6 s, 66 kV, and 4.2-7.7 mA.  
MSCT and PAN images were displayed on a 20-inch medical monitor with a 3-megapixel Barco display (Barco, Kortrijk, Belgium) and 2048 \(\times\) 1536 resolution. The software programs originally provided with the systems were used for image evaluation.
## 5 Conclusions  
A novel spectral displacement formulation (SDF) was proposed for analyzing functionally graded material (FGM) plates. This formulation extends the displacements into the Chebyshev series in the thickness direction, resulting in a quasi-3D shear deformation theory with adjustable truncation numbers. By incorporating isogeometric analysis (IGA) for the in-plane discretization, the proposed method can handle FGM plates with complex geometries and various boundary conditions.  
Numerical results of the square, rectangular, circular, and L-shaped FGM plates demonstrate the effectiveness of the SDF-based IGA, especially its ability to approach exact 3D elasticity solutions, similar to the well-known Carrera's unified formula (CUF). Compared with the CUF, an important improvement of the SDF is that it is extended from the classical plate theory, which naturally avoids the shear locking problem. This feature makes the SDF-based IGA versatile for analyzing FGM plates of arbitrary thicknesses.  
Numerical tests indicate that, for displacement analyses, using SDF truncation numbers \(M=3\) and \(N=3\) is generally enough. However, larger truncation numbers such as \(M=5\) and \(N=6\) are recommended for stress analyses. Of course, the current discussions are limited, and appropriate settings may be changed in different scenarios. Anyhow, the high precision and adjustability of the present method have manifested its great potential in various applications of FGM plates, including strain-based or stress-based reliability analysis, optimization design, thermoelasticity analysis, viscoelasticity analysis, fatigue analysis, and fracture analysis.  
Conceptualization, S.Y., Z.C. and X.S.; methodology, S.Y.; software, S.Y. and X.S.; validation, Y.Z. and Z.C.; formal analysis, S.Y.; writing--original draft preparation, S.Y.; writing--review and editing, S.Y. and X.S.; supervision, Z.C., Y.Z. and H.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number U2241263.  
Not applicable.  
Data available on request due to restrictions e.g., privacy or ethical.  
Thanks to the magazine editors for their careful work, and to the reviewers for their valuable comments.  
The authors declare no conflict of interest.
# Research on Multi-Physical Field Coupling Simulation of Local Electrochemical Machining
## 3 Simulation Result Analysis  
### The Influence of Two-Phase Flow Field and Temperature on the Conductivity Distribution  
#### 3.1.1 Two-Phase Flow Field and Temperature Field Distribution  
According to Formula (16), the conductivity of the electrolyte is the result of the combined influence of the bubble rate \(\phi\) and temperature \(T\) of the electrolyte. With the increase in the bubble rate \(\phi\), the conductivity will decrease, and when the bubble rate is 1, the conductivity will be 0. With the increase in the temperature \(T\), the conductivity will increase, so it is necessary to analyze the two-phase flow field and temperature field distribution in the machining process.  
Figure 6 shows the two-phase flow field distribution and the evolution process of the local electrochemical machining of the pre-shaped convex contour at different processing times of 1 s, 250 s, 500 s, and 744 s. The legend represents the volume fraction of the electrolyte, also equal to \((1-\phi)\), the dark red represents the electrolyte, and the dark blue represents the air. It can be seen that there is an obvious gas-liquid two-phase interface in the simulation process, the electrolyte always flows close to the workpiece wall, and the rest is air. Since the conductivity is not zero only in the region where the electrolyte is present, therefore, the two-phase flow field plays a major role in the conductivity distribution.  
Figure 5: Simulation models and modules.  
Figure 7 shows the temperature field distribution and the evolution process of local electrochemical machining of the pre-shaped convex contour at different processing times of 1 s, 250 s, 500 s, and 744 s. The legend represents the temperature, the yellow area represents the high temperature, and the dark red represents the initial temperature. As can be seen from the figure, the distribution of temperature field is basically consistent with that of two-phase flow field. The main reason is that the heat generated in the processing zone is mainly taken away by the electrolyte. When the electrolyte flows through the machining gap, the electrolyte temperature rises from the initial 293.15 K to about 296 K, and slowly rises with the feed depth. When the feed reaches the final position, the maximum temperature of the machining gap reaches about 299 K. The temperature rise of surrounding air is very small because the heat conduction coefficient of air is far less than that of the electrolyte. The temperature field plays a minor role in the conductivity distribution and only determines the maximum conductivity of the machining area.
## 1 Introduction and Background
### The Merged Open Annotation Model (OA)  
The W3C Open Annotation Community Group was established in 2011 to specify an extensible, interoperable framework for representing annotations, by aligning the OAC and AO data models. Figure 1 illustrates the draft Open Annotation (OA) model [8], which has emerged from the alignment of the two models. A comparison of the key differences between the OAC and OA models is presented in Table 1.  
In Figure 1, an OA _Annotation_ instance, _Anno1_ represents the reification of an _annotates_ relationship between a _Body_ (the comment or data being attached by the annotator) and one or more _Target_ resources [where the Body is \"about\" the Target(s)]. Body and Target resources can be of any type e.g., image, audio, video or text-based formats including HTML. Annotation provenance is recorded via properties, (see top section of Figure 1), that describe the context in which the annotation was created, including the time of creation and the human or software agent responsible for creating the annotation. Annotation bodies can be identified by URI or included as inline content (_i.e._, where the content is embedded in the _Annotation_) using properties from the W3C's \"Representing Content in RDF\" [9] ontology, as illustrated in the bottom left of Figure 1.  
Figure 1: The Open Annotation (OA) model. Many Annotations apply to segments of resources, such as a region of an image, or a paragraph of text within a larger document. In these cases, instead of targeting the URI that identifies the entire resource, an Annotation will target a _SpecificResource_ representing the segment of the resource. The extent of the segment is described by a _Selector_ and the complete resource is specified by _hasSource_. The OA community group is working on defining a set of common subclasses of _Selector_, including _FragmentSelector_, which can be used to specify URI fragments using existing fragment schemes
###### Abstract  
The aim of this research was to confirm the possible synergic effects of sasam-Kyeongokgo (SKOG) on the anti-tumor activity of gefitinib using athymic nude mice bearing the human non-small-cell lung squamous cell carcinoma (NSCLC) NCI-H520 cell line after continuous oral combination treatment provided daily for 35 days within a timeframe of 5 min, as a developing process of novel preventive and therapeutic regimes for various types of lung cancer. After 35 days, BW, tumor volume and weight, and lymphatic and periovarian fat pad weight measurements, as well as serum IFN-\(\gamma\) and IL-6 level, NK cell activity, and splenic cytokine content measurements, together with histopathological observations, and immunohistochemistry measurements of the treated and control mice, were performed. The results suggest that the co-administration of SKOG 400, 200, or 100 mg/kg with gefitinib markedly increased the anti-tumor activity of gefitinib through immunostimulatory effects and also dramatically inhibited cancer cachexia through the immunostimulatory effect, potentiating the anti-tumor activity of gefitinib, with favorable anti-cachexia effects. Therefore, the co-administration of over 100 mg/kg of SKOG and gefitinib can provide an effective novel treatment regimen for various lung cancer patients.  
sasam-Kyeongokgo; gefitinib; synergic effects; lung cancer; anti-tumor activity; carcinoma; nude mice +
Footnote †: journal:  
to achieve health and longevity in middle-aged and elderly people in Korea since ancient times [10; 11].  
Thus far, Kyeongokgo has been experimentally proven to show various pharmacological effects, including anti-hyperlipidemic [10], antioxidant [11], anti-inflammatory [12], immunomododulatory [11], osteoporosis-treating [13], hair-growth-promoting [14], anti-fatigue and athletic-performance-improving [15], growth-promoting [16], anti-aging [17], and antibacterial effects [18]. In addition, we have already evaluated the respiratory function improvement effects of Adenophorae Radix [9], Kyeongokgo [19], and Sasam-Kyeongokgo [20] through in vivo animal experiments. Therefore, an appropriate combination of sasam-Kyeongokgo and gefitinib is expected to synergistically increase the anticancer effect of gefitinib on lung cancer.  
In this study, as part of the effort to develop novel anticancer therapies for lung cancer patients, the effects of sasam-Kyeongokgo on the anticancer activity of gefitinib were evaluated in vitro using human NSCLC, NCI-H520 cells. Subsequently, after the oral administration of sasam-Kyeongokgo to athymic nude mice transplanted with the NCI-H520 lung cancer cell line, indices related to anticancer and immune activation effects, such as the tumor and lymph organ weight and histopathological changes, as well as the levels of cytokines in the blood and immune organs, were monitored.
## 4 The Evaluation Results and Discussions
### Experimental Results and Discussions for the Proposed Method
#### 4.3.1 Multi-Condition Training Mode

Table 6: The state error rate and WER (%) of the baseline, MSPP, FMSE, MMSE, and IRM with multi-condition training for the test set in noise environment “**jackhammer**”. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Signal-To-Noise Ratio (SNR)**} \\ \hline \hline  & \(-\)**6 dB** & \(-\)**3 dB** & **0 dB** & **3 dB** & **6 dB** & **12 dB** \\ \hline \multirow{6}{*}{state error rate} & baseline & 0.951 & 0.928 & 0.895 & 0.849 & 0.807 & 0.717 \\ \cline{2-7}  & MSPP & 0.949 & 0.925 & 0.895 & 0.852 * & 0.809 * & 0.723 * \\ \cline{2-7}  & FMSE & 0.954 * & 0.930 * & 0.899 * & 0.856 * & 0.811 * & 0.729 * \\ \cline{2-7}  & MMSE & 0.968 * & 0.952 * & 0.929 * & 0.899 * & 0.867 * & 0.794 * \\ \cline{2-7}  & IRM & 0.950 & 0.925 & 0.891 & 0.850 * & 0.803 & 0.718 * \\ \hline \multirow{6}{*}{WER (\%)} & baseline & 66.1 & 62.1 & 57.0 & 49.8 & 44.8 & 34.4 \\ \cline{2-7}  & MSPP & 65.5 & 61.0 & 54.9 & 48.9 & 43.2 & 34.7 * \\ \cline{2-7}  & FMSE & 69.2 * & 63.3 * & 57.2 * & 50.4 * & 44.8 & 35.3 * \\ \cline{2-7}  & MMSE & 70.3 * & 66.8 * & 60.4 * & 55.0 * & 49.7 * & 40.1 * \\ \cline{2-7}  & IRM & 65.8 & 61.9 & 56.5 & 50.4 * & 44.4 & 34.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The state error rate and WER (%) of the baseline, MSPP, FMSE, MMSE, and IRM with multi-condition training for the test set in noise environment “**white**”. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Signal-To-Noise Ratio (SNR)**} \\ \hline \hline  & \(-\)**6 dB** & \(-\)**3 dB** & **0 dB** & **3 dB** & **6 dB** & **12 dB** \\ \hline \multirow{6}{*}{state error rate} & baseline & 0.958 & 0.936 & 0.898 & 0.850 & 0.796 & 0.689 \\ \cline{2-7}  & MSPP & 0.957 & 0.933 & 0.899 * & 0.851 * & 0.799 * & 0.694 * \\ \cline{2-7}  & FMSE & 0.956 & 0.932 & 0.892 & 0.840 & 0.782 & 0.676 \\ \cline{2-7}  & MMSE & 0.964 * & 0.946 * & 0.917 * & 0.878 * & 0.832 * & 0.730 * \\ \cline{2-7}  & IRM & 0.958 & 0.936 & 0.898 & 0.851 * & 0.795 & 0.690 * \\ \hline \multirow{6}{*}{WER (\%)} & baseline & 65.3 & 61.7 & 55.1 & 48.2 & 41.5 & 31.1 \\ \cline{2-7}  & MSPP & 65.5 * & 60.2 & 54.6 & 47.9 & 41.7 * & 32.3 * \\ \cline{2-7}  & FMSE & 70.4 * & 64.5 * & 56.2 * & 48.2 & 41.0 & 31.2 * \\ \cline{2-7}  & MMSE & 68.3 * & 63.6 * & 57.3 * & 51.4 * & 44.9 * & 34.2 * \\ \cline{2-7}  & IRM & 65.9 * & 61.4 & 54.8 & 48.2 & 41.2 & 31.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The state error rate and WER (%) of the baseline, MSPP, FMSE, MMSE, and IRM with multi-condition training for the test set in noise environment “**engine**”. that MSPP promises to promote the recognition accuracy of noisy speech features even when MSPP does not pre-process the training set for the acoustic model. It reconfirms our previous statement: MSPP has a generalization capability to conquer unseen noise.