esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
As mentioned above, the interest for this study was generated by the findings of our previous investigation, which resulted in a high percentage of positive samples of feeds and milk to the targeted pathogens, including AMR and MDR isolates. Therefore, this investigation aimed to conduct a more thorough analysis of the certain types of samples, which in connection to feeds, referred additionally to the analysis of their ingredients produced by the respective farmers. With regards to milk, the analysis was targeted to the raw milk tank, which had proven a reliable sampling point for in-farm monitoring [2], and the milking machines that may provide favourable conditions for the formation of biofilms exhibiting strong resistance to antibiotics and antiseptics [6,7]. The analysis of the samples collected from the milk tank and the milking shells was positive to _E. coli_ in only 3 of the samples that were tested (5.7%, 3 of 53). These three samples were swabs from milking machines: two from Farm A and one from Farm D (Table 1). Considering that the detection of the certain pathogen implies faecal contamination, the small percentage of _E. coli_-positive samples of this category (samples of milk tank and swabs of milking shells) can be considered an indication of good hygiene for Farms C and E. To the contrary of _E. coli_, _Staphylococci_ were detected in all the samples collected from the milk tank (three farms positive to _S. aureus_ and two to CoNS), a finding that can be considered consistent with staphylococcal mastitis being a common problem in the study farms, and the certain sampling point (milk tank) being suitable for its routine monitoring [2]. Another finding that further supports the proposed correlation between mastitis and positivity of milk to _Staphylococci_ was that positive results were recorded for the same group of pathogens in the milking shell swabs, in all farms that use milking machines (Farms A, C, D, and E). Comparing these results to those from the milk tank, with regards both to the pathogens detected and the AMR profile, concludes that they are not consistent, which indicates that milking machines are infected with pathogens that do not always appear in the milk tank (Tables 2 and 3). This finding could be associated with the milking machines providing a substrate which favours formation of microbial biofilms, harbouring pathogens that are less likely to be detected in other sites within the farm [8]. Notably, only 1 of the 14 (7.1%) AMR isolates of all the study pathogens that were detected in milk (_Staphylococci_ and _E. coli_) appeared in the milk tank; the rest (92.9%, 13 of 14) were detected in the milking shells (Tables 1 and 2). Based on the above, AMR testing in samples collected from the milking shells is rendered advisable for improving in-farm AMR monitoring and complimentary to that conducted in samples of milk. In
## 4 Results and Discussion
### Major Cracks and Stress Variations
#### 4.2.2 Locations and Depths of the Major Cracks
Two major cracks were obtained through the difference analysis of horizontal stresses (or transfer process analysis). The first major crack (trailing edge crack) was near the measuring point 11 and on the left side of the measuring point 21. The second major crack (front edge crack) passed through the following areas from top to bottom: between points 12 and 13, between points 22 and 23, and between points 32 and 33. The determined positions and depths of the two cracks were basically the same as those observed in the experiment.
## 4 Implementation
### Proposed Scheme
#### 4.3.1 Oversampling  
When the frame rate of a rolling shutter camera is at least two times larger than the packet rate of light sources, the data packet is sampled multiple times causing the oversampling effect. It occurs because of the frame rate variation effect, then we need to use the repetition code to transmit data. When the packet is sampled more than once, errors of packet merger are created at the receiver side. The SN is added to the DS to deal with this problem because it improves the receiver's ability to decrease the frame rate variation effect. Redundant data were removed from the receiver when the same SN value is recognized in the DS of different packets. The receiver side can exclude consecutive packets with the same SN value and select packets with consecutive SN values (n - 1, n, n + 1) for the merger, as illustrated in Figure 13.  
In our proposed algorithms, the data sub-packets are added to the data packet; this is repeated N times to reduce the loss rate of the packets while the camera cannot capture the time between the  
Figure 12: MIMO–COOK data packet structure.  
Figure 13: Merging procedure of MIMO COOK scheme in the time domain.  
sub-packets. To enhance the reliability of the OCC system, we need to prevent the missing of packets when there is a change in the frame rate of the camera. For this, the calculation of the N value must conform to the following equation:  
\[N\geq\frac{(T_{cam})_{\text{max}}}{DS\_length} \tag{6}\]  
where \(T_{cam}\) is the inter-frame interval between two consecutive images, and DS_length is the data packet interval.
## 1 Introduction
### Novelty of This Research  
The selection methodology for composite material for the design and manufacture of landing gear struts is different than that for conventional material and was not identified in the literature. In this context, a comprehensive selection methodology for composite material has been developed and proposed which is considered to be the novel contribution of this research. Initially four different composite materials, two each from carbon- and glass-fiber families were considered and analyzed using classical lamination theory and beam theory for thin and thick laminates, respectively. The categorization of these materials was performed based on a high strength-to-weight ratio and qualification using the Tsai-Wu failure criterion. Once the composite pre-preg materials, one each from carbon- and glass-fiber families, were selected, a complete design and analysis of main landing gear struts was undertaken in relation to given landing loads and other design constraints. Based on a comprehensive analysis of landing gear struts according to the airworthiness standards FAA FAR-23 [18], a selection methodology for composite material for the manufacture of main landing gear struts is proposed and is shown in Figure 2. The material contributing towards qualification of the main landing gear strut with respect to the aforementioned criteria,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**S.No** & **Author** & **Aircraft Weight** & **Landing Gear Type** & **Fiber** & **Matrix** & **Remark** \\ \hline \multirow{3}{*}{1} & [PERSON] et al. & \multirow{3}{*}{1550 kg} & \multirow{3}{*}{Tri-Cycle Type} & \multirow{3}{*}{Glass Fiber} & \multirow{3}{*}{Epoxy} &
\begin{tabular}{c} Designed analytically \\ only without \\ computational and \\ experimental approach. \\ \end{tabular} \\ \cline{1-1}  & & & & & & & \\ \hline \multirow{3}{*}{2} & [PERSON] et al. & \multirow{3}{*}{-} & Hybrid strut & \multirow{3}{*}{-} & Drawings and basic \\  & & & comprising metal and & & & (parent). Materials not \\ \cline{1-1}  & & & composite portion & & & revealed. \\ \hline \multirow{3}{*}{3} & [PERSON] [15] & \multirow{3}{*}{9207 kg} & Applied only to the & \multirow{3}{*}{Carbon Fiber} & \multirow{3}{*}{Epoxy} & Landing gear \\  & & drag brace (F-16) & & & & \\ \cline{1-1}  & & & & & & by 15\% using RTM after \\ \cline{1-1}  & & & & & & mass production \\ \cline{1-1}  & & & & & (6000 parts). \\ \hline \multirow{3}{*}{4} & [PERSON] [16] & \multirow{3}{*}{9207 kg} & Applied only to the & \multirow{3}{*}{Carbon Fiber} & \multirow{3}{*}{Epoxy} & TiMMC provides almost \\ \cline{1-1}  & & drag brace (F-16) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of fiber-reinforced composite landing gears and associated components found in the literature.  
with a maximum strength-to-weight ratio, was finally recommended for the manufacture of the main landing gear struts for a given aircraft.
# Atom Probe Tomography for Catalysis Applications: A Review
## 3 Case Studies
### Catalysts as Unsupported Nanoparticles
#### 3.3.2 Imaging Core-Shell Au@Ag Nanoparticles--Embedding Method  
Au@Ag nanoparticles are used for preferential oxidation reactions (PROX) [125], for example the preferential oxidation of CO in presence of H\({}_{2}\), important for fuel cell applications, and it is crucial to understand the structure/composition relationships to improve their synthesis and catalytic activity. Instead of using the electrophoresis method, an embedding method was used [123; 124]. The first step consists of dispersing the nanoparticles on a substrate. Here, Au@Ag nanoparticles were dispersed in water, and subsequently deposited on a Si substrate (Figure 8a). The nature of the substrate and its structure are important parameters in the success of the sample preparation. The sample is then incorporated into a solid matrix, to obtain a continuous evaporation field during APT and to improve the data reconstruction. The solid matrix should be such that the field of evaporation is similar to that of the materials of interest. Different coatings were tested: Pt(C) from electron beam assisted deposition within the FIB instrument, and Cr by sputter coating (Figure 8b,c). The remaining of the sample preparation consists of the regular FIB lift-out method (Figure 8d). The authors claim that sandwiching the nanoparticles between two solid layers leads to the least artefacts during data reconstruction.  
Figure 8: Investigation of Au@Ag nanoparticles prepared by the embedding method. (**a**) Dispersion of nanoparticles on Si substrate; (**b**) same sample after coating with Cr and Pt(C) strip; (**c**) schematic representation of the embedding method; (**d**) tip-sample after lift-out and before FIB milling: Au@Ag nanoparticles are sandwiched between the two solid layers; (**e**) atom map of a single nanoparticle highlighting the heterogeneous Ag surface coverage; and (**f**) 2 nm slice through the reconstruction showing the presence of an internal cluster. Panels (**a**,**b**,**d**): reprinted from [123], Copyright (2015), with permission from Elsevier. Panels (**c**,**e**–**f**): reproduced from [124]. Copyright (2014), John Wiley & Sons, Ltd.  
The APT analysis reveals the presence of Au-Ag nanoparticles with a size of \(\sim\)15 nm made of Au core and Ag shell. However, the Ag surface coverage is not homogeneous, as depicted in Figure 8e, and the Ag surface concentration depends on the curvature of the specimen and on the size of the facets offering a way to fine-tune the synthesis of nanoparticles. More interestingly, Na and N species were detected in specific regions and are believed to originate from the nanoparticle synthesis [126]. Figure 8f represents a 2 nm cross-sectional slice where only Ag is depicted, highlighting the inhomogeneity of Ag at the surface, as well as the presence of Ag clusters within the nanoparticle. This study paves the way to better understand the residual concentration of spectator species and their influence on the synthesis and catalytic activity.
## 7 Composite Carbonaceous-Titanium Dioxide and the Photocatalytic Activities
The rise of efficiency was attributed to the electron-scavenging properties of MWCNTs [141]. In different studies, efficiencies of oxidized MWCNTs in MWCNT\({}_{\text{ox}}\)-TiO\({}_{2}\) systems were compared to plain TiO\({}_{2}\) powders: P25, bare anatase, and bare rutile in degradation of diclofenac (DIC), a popular NSAID, under light irradiation at 254 nm and 366 nm [143]. Complete removal of DIC was achieved after 30 min of irradiation with the formation of eight photoproducts. However, the MWCNT\({}_{\text{ox}}\)-TiO\({}_{2}\) system was not as efficient as P25 and pure anatase. Several hypotheses were given as possible explanations to this difference in another paper of the authors, all closely related to e\({}^{-}\)-_h\({}^{+}\)_ interactions in the composite system [144]. In other studies, various CNT-TiO\({}_{2}\) systems with the addition of silica, nitrogen-, or urea-doped were used for the photodegradation of caffeine, diclofenac, ibuprofen, and 4-chlorophenol [145; 146; 147; 148]. The reason for the addition of SiO\({}_{2}\) to the TiO\({}_{2}\) system was to prevent the transformation of active anatase to inactive rutile phase. Acid-treated CNT-TiO\({}_{2}\)-SiO\({}_{2}\) revealed better photocatalytic activity than the non-treated system and pure anatase. Furthermore, oxygenation and addition of H\({}_{2}\)O\({}_{2}\) to the solution improves the photocatalytic activity of CNT-TiO\({}_{2}\) since H\({}_{2}\)O\({}_{2}\) acts as an electron scavenger [143].
## 1 Introduction
A unique feature of ice in the 0.1-2.0 THz radiation window is that it has an absorption coefficient (\(\alpha\)) that is much less than that of liquid water [14; 15; 16; 17; 18]. For example, \(\alpha\) of ice is in the order of 5.0 cm\({}^{-1}\) compared to 150-200 cm\({}^{-1}\) for liquid water at -1.0 THz, and 50 cm\({}^{-1}\) compared to 300 cm\({}^{-1}\) at 2.0 THz. The non-bulk-water elements do not show the same change in \(\alpha\) on freezing as bulk water does. Thus, when biological tissues are frozen, the bulk water content changes from being the principal absorber, to becoming a minor contributor to the absorption of THz radiation. It is imperative that accurate, temperature dependent, THz dielectric properties of frozen and non-frozen biological tissues be better understood over the 0.1 to 2.0 THz band. The variation in these dielectric properties may provide insights into the nature of the tissues that is not available with other investigation modalities. Such new techniques would complement the established tissue characterisation methods. Current tissue characterisation techniques tend to depend on the interpretation of the tissue appearance of X-ray, visible light or ultrasound images. The potential advantage in studying the frequency dependent, temperature dependent, changes in THz dielectric parameters of biological tissues is that a system of characterisation of the tissues can be developed which is dependent only on the capacity of the apparatus at hand, thus reducing the need for subjective operator interpretation. When an attenuated total reflection (ATR)apparatus is coupled with a temperature variation apparatus, the tissue characterisation does not depend on interpretation, but simply a comparison to an established dataset. The ATR/temperature variation apparatus thus offers the ability for rapid characterisation of the tissues with minimal preparation and minimal operator training. The Synchrotron's high brightness source facilitates the systematic application of this method by offering rapid evaluation, high spectral quality and excellent spatial resolution.
## 3 Results
### Performance of Raw Data Models and Extracted HC Feature Models  
The testing accuracy of the models trained on raw sensor signals was compared with that of the results above for the random forest and logistic regression classifiers.  
From Figure 6, in both validation techniques, the models performed better when trained on HC features than raw data, resulting in an absolute percentage difference of 30% and 25% for k-fold and LOSO, respectively, on the random forest. This is attributed to the interpretability of HC features, as they are designed with insight information regarding each activity. Table 1 presents the detailed characteristics of the extracted features, presenting a meaningful representation of the activities and leading to improved performance. Inputting raw data into the models creates a challenge in this area, as the models might not interpret the complex patterns that they are learning.  
In both experiment modules, the random forest achieved the highest accuracy. This attributed to:  
1. [label=()]
2. Its ensemble learning property, which combines the predictions of multiple decision trees, allowing it to adapt well to different activity characteristics, even where they may exhibit complex and diverse patterns.
3. Its measure of feature importance, selecting the most relevant features that contribute more to the model's predictive performance. Where features are crucial to distinguishing between activities, it assigns higher importance to these features.
4. Its robustness in handling imbalanced classes, which is the case in our dataset, as seen in Tables 4 and 5.
## 2 Analysis of the Reverse Torque and Vibration in the Mud Pump  
Due to the mid-hanging configuration of the deepwater RMR pump, it generates reverse torque and vibration in the mud return pipeline connected at both the upper end and the lower end during startup and operation. The return pipeline for the mud may be damaged, which will affect the stability and safety of both the mud pump and the return pipeline. In this section, the reverse torque and the vibration of the mud pump were analyzed using the ANSYS (Version 2020 R1) software. Since the mud pump used in the RMR technique is a ten-stage mud pump, a single-stage pump was first used for the analysis, and the error between the theoretical calculations and the simulation analysis was compared to ensure the error was within a reasonable range, thus proving the correctness of the analysis. Then, the mud pump (ten-stage pump) was analyzed based on the setup adopted for analyzing the single-stage pump.  
Figure 1 shows the flowchart of the main analysis process. Firstly, the reverse torque of the single-stage mud pump was theoretically calculated and compared with the numerical simulation results as well as the error analysis. Secondly, the fluid model was established; its mesh division, materials, and boundary conditions were set; and the mud pump's reverse torque and rotational speed were derived from the changes in the relationships. Finally, the rotor model of mud pump was established; its mesh division, materials, and boundary conditions were set; and the relationships between the mud pump's vibration and the motor's input parameters were obtained.
###### Abstract  
Ground subsidence monitoring by Synthetic Aperture Radar interferometry (InSAR) over Arctic permafrost areas is largely limited by long revisit intervals, which can lead to signal decorrelation. Recent satellite missions such as COSMO-Skymed (X-band) and Sentinel-1 (C-band) have comparably short time intervals of a few days. We analyze dense records of COSMO-Skymed from 2013 and 2016 and of Sentinel-1 from 2016, 2017, and 2018 for the unfrozen period over central Yamal (Russia). These years were distinct in environmental conditions and 2016 in particular was unusually warm. We evaluate the InSAR-derived displacement with in situ subsidence records, active-layer thickness measurements, borehole temperature records, meteorological data, C-band scatterometer records, and a land-cover classification based on Sentinel-1 and -2 data. Our results indicate that a comparison of seasonal thaw evolution between years is feasible after accounting for the early thaw data gap in InSAR time series (as a result of snow cover) through an assessment with respect to degree-days of thawing. Average rates of subsidence agree between in situ and Sentinel-1 (corrected for viewing geometry), with 3.9 mm and 4.3 mm per 100 degree-days of thaw at the test site. X-band and C-band records agree well with each other, including seasonal evolution of subsidence. The average displacement is more than twice in magnitude at the active-layer monitoring test site in 2016 compared to the other years. We further demonstrate that InSAR displacement can not only provide information on the magnitude of ground thaw but also on soil properties through analyses of seasonal evolution in extreme years.  
Keywords:permafrost; subsidence; InSAR +
Footnote †: journal: Remote Sensing, 11, 1865; doi:10.3390/rs11161865
# Potential of Room Acoustic Solver with Plane-Wave Enriched Finite Element Method
## 3 Numerical Experiments
### Sound Propagation in a Single Room  
#### 3.2.1 Problem Description and Numerical Setup  
Figure 2 portrays a single room with area \(S_{\text{a}}\) of 39.92 m\({}^{2}\), including a small rectangular area S assuming a loudspeaker where \(\Gamma_{\text{V}}\) is treated as the vibration boundary. The room's boundaries comprise a weakly absorbing impedance boundary \(\Gamma_{\text{z,1}}\) and an impedance boundary \(\Gamma_{\text{z,m}}\) of honeycomb-backed  
microperforated panel (MPP) absorber with Helmholtz resonator type sound-absorption characteristics. The weakly absorbing surface \(\Gamma_{x,1}\) has real valued impedance corresponding to normal incidence sound absorption coefficient \(\alpha_{0}=0.05\). For the MPP sound absorber, the sound absorption characteristics are frequency dependent in both \(\alpha_{0}\) and \(z_{\mathrm{n}}\), as shown in Figure 2. A theoretical impedance model [24; 50] considering a lim MPP was used to calculate the \(z_{\mathrm{n}}\). The geometrical parameters of MPP are 0.5 mm in hole diameter, 1 mm in plate thickness, 0.75% in perforation ratio, and 1.13 kg/m\({}^{2}\) in surface density. The backing honeycomb core thickness is 0.015 m. With those specifications, the MPP absorber shows peak sound absorption at around 1 kHz. We applied \(v=1\) m/s for the vibrating surface, assuming a speaker cone.  
Figure 3a,b show two PUFEM meshes, Mesh 1 and Mesh 2, with different spatial resolutions. Both meshes consist of elements larger than the wavelength of upper-limit frequency. Mesh 1 is a uniform mesh discretized with square elements of 0.2 m having 1.47 times larger size than the wavelength at 2.5 kHz. Mesh 2 is a non-uniform mesh discretized by rectangular elements of 0.2-0.4 m having 2.97 times larger size at maximum. The total numbers of elements \(N_{\mathrm{ele}}\) and nodes \(N_{\mathrm{node}}\) are 922 and 994, respectively, for Mesh 1. They are 267 and 307, respectively, for Mesh 2. The constant \(C\) in Equation (10) was set as 5-14 with one interval. For the reference solution, two fine meshes with different spatial resolution were used for different frequency ranges. We used a mesh discretized with 0.01 m square elements with 400,720 DOF for analyses up to 1.5 kHz. Its spatial resolution is 22 elements per wavelength at 1.5 kHz. At higher frequencies, we used a mesh with 1,599,840 DOF discretized by 0.005 m square elements. Its spatial resolution is 27 elements per wavelength. Standard FEM analysis used the same meshes as those used for a reference solution.  
Figure 2: Single-room model including a vibration boundary, and impedance boundaries of weakly absorbing surfaces and honeycomb-backed microperforated panel absorbers: S and R respectively represent sound source and receivers.