esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Cervical Proprioceptive Reposition Sense Measurement  
The evaluation of cervical JREs using the CROM device followed the established protocols [23]. Cervical JREs refer to the discrepancies between an individual's perceived or intended head position and the actual head position in space, and these errors are quantified in degrees (\({}^{\circ}\)). The participant assumed an upright seated position, maintaining a straight back, with their head oriented straight ahead and their feet planted firmly on the ground. This neutral head position was adopted as the baseline for the assessment. All participants were provided with comprehensive instructions about the study procedure, and standardized guidelines were administered during the testing phase. To eliminate visual and extraneous physical cues, participants were blindfolded using a travel eye mask, and a Velcro strap was utilized to minimize unnecessary movements of the trunk and shoulders throughout the cervical JRE testing (Figure 1).  
To maintain uniform measurement conditions, the examiner firmly affixed the CROM unit to the participant's head with the help of a Velcro strap. Simultaneously, the magnetic yoke of the CROM device was positioned on the participant's shoulder, aligning the arrow symbol with the north direction. This setup allowed for the calibration of the CROM device to establish a neutral position. The CROM device, well known for its accuracy in quantifying cervical range of motion, demonstrated consistent intratester reliability, with values ranging from 0.62 to 0.91, and inter-tester reliability, with values ranging from 0.74 to 0.87 [24].  
To evaluate the cervical JREs, the examiner took charge of directing the participant's head to a specific target position, precisely set at 50% of the maximum cervical range of motion range previously determined by the examiner. The participant received instructions to maintain their head in this designated position consistently for a duration of 3 s. After this period, participants were asked to mentally note this target position. Following that, the examiner gradually guided the participant's head back to the initial neutral position. Subsequently, the participant was instructed to actively replicate the target position by adjusting their head. The degree of disparity between the participant's repositioned head and the actual target position was measured as the repositioning accuracy error, quantified in degrees.  
Cervical JRE testing encompassed two planes of motion: sagittal (flexion and extension) and transverse (right and left rotation). The order of testing across these directions  
Figure 1: Cervical joint reposition error assessment using CROM unit.  
was randomized using a straightforward chit method. To ensure robustness, each direction of cervical JRE testing was conducted in triplicate, and the mean error derived from these trials was employed for subsequent analysis. To maintain consistency, the testing process was overseen by a single investigator, and participants received no feedback during the testing procedure.
# A Study on Aeroengine Conceptual Design Considering Multi-Mission Performance Reliability
## 4 Reliability-Based Multi-Design Point Methodology
### Problem Statement
#### 4.3.3 Constraints  
Except for the constraint of the design variables, thrust-to-weight ratio (\(R_{TW}\)) is also employed as the constraint, which is a comprehensive index to measure the aeroengine performance ability. The formula of \(R_{TW}\) can be presented as  
\[R_{TW}=\frac{Fn_{SLS,\ A/B-On}}{g\cdot W_{Engine}} \tag{29}\]  
where, \(g\) is gravitational acceleration; \(W_{Engine}\) is the quality of engine, which is estimated as [43]:  
\[\begin{array}{ll}W_{Engine}=&0.0146\mathrm{T}_{\mathrm{ET}}^{2}+3.949\pi+151 6.8R_{\mathrm{BP}}^{2}-0.045W_{std}^{2}-0.426\pi\mathrm{T}_{ET}+9.684R_{BP}T_{ ET}\\ &+0.0392\mathrm{T}_{ET}W_{std}-74.37\pi R_{BP}-1.646\pi W_{std}-12.87R_{BP}W_{ std}-44.21\mathrm{T}_{ET}\\ &+690.9\pi-13429R_{BP}-0.3169W_{std}+30759.267\end{array} \tag{30}\]  
where, \(\pi\) is the total pressure ratio.  
The \(R_{TW}\) is a nonlinear implicit function of \(\mathbf{x}\), and ought not be less than the original value. Then the constraint function of \(R_{TW}\) is presented as  
\[R_{TW}(\mathbf{x})\geq R_{TW,Origin} \tag{31}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cycle Parameter (\(\mathbf{x}\))** & \(\pi_{F}\) (\(\mathbf{x_{1}}\)) & \(\pi_{C}\) (\(\mathbf{x_{2}}\)) & \(T_{ET}\)/K (\(\mathbf{x_{3}}\)) & \(R_{BP}\) (\(\mathbf{x_{4}}\)) & \(W_{std}\)/(kg\(\cdot\)s\({}^{-1}\)) (\(\mathbf{x_{5}}\)) \\ \hline Lower & 3.2 & 9.5 & 1650 & 0.6 & 110 \\ Upper & 3.6 & 10.5 & 1750 & 1.0 & 130 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Cycle parameter design range.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Operating** & **Atmospheric** & \multirow{2}{*}{**Alt (km)**} & \multirow{2}{*}{**Ma**} & \multirow{2}{*}{**Control Schedule**} & \multicolumn{3}{c}{**Overall Performance Requirements**} \\ \cline{5-8}
**Condition** & **Environment** & & & & & & \multicolumn{1}{c}{**Fn [KN]**} & \multicolumn{1}{c}{**\(SFC\) [kg/(h\({}^{+}\)KN)]**} & \(R_{TW}\) \\ \hline SLS & ISA & 0 & 0.00 & Fixed NL, AH-on & / & / & \(\geq\)6.75 \\ TKO & Hot day, +15K & 0 & 0.25 & Fixed NLcor, AFB-off & \(\geq\)68.21 & / & / \\ SBC & ISA & 11 & 0.90 & Fixed NL, AH-off & \(\geq\)18.48 & \(\leq\)92.02 & / \\ SpP & ISA & 19 & 2.00 & Fixed NH, AH-on & \(\geq\)29.57 & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overall performance requirements in flight profile.
# A Military Object Detection Model of UAV Reconnaissance Image and Feature Visualization
## 1 Introduction
YOLOv2 [7] proposed various improvements to YOLOv1, including batch normalization technology, a high-resolution classifier, a new backbone network, and an anchor frame mechanism. YOLOv3 [8] explored multi-scale feature maps for detection in which the feature map size was designed according to the size of the prior anchor, introduced a more powerful backbone Darknet-53, and replaced softmax by logistic regression in prediction. YOLOv4 [9] further improved the performance of YOLOv3 by introducing a series of practical skills, which took CSPDarkNet53 as the backbone network, replaced the ReLU activation function with Mish activation function, and added data augmentation skills such as CutMix, Mosaic, and label smoothing. It is common to say that the computation complexity of two-stage algorithms is relatively higher than that of one-stage algorithms since it includes a candidate box selecting process. It is hard to meet the needs of real-time detection for UAV reconnaissance. Therefore, a one-stage algorithm is more suitable, and YOLOv5 is one of the most popular and effective one-stage object detection algorithms. However, it is not the best choice to apply YOLOv5 to object detection of UAV images directly. First of all, fog blur and motion blur are the two most common distortions for UAV reconnaissance images, which have not been considered in the original YOLOv5. The detection accuracy of the original YOLOv5 on blurred images is very low, i.e., the generation ability in practice is poor. Then, the view angle of UAV images is different from that of the common images, and the objects in the UAV image are usually small, which will also affect the performance of YOLOv5. Moreover, the explanation of the model is still an open problem. The main contributions of our work can be summarized as:  
1. We constructed the UAV reconnaissance image tank database UAVT-3 for detection, which includes 1263 images, 7 scenes, 3 types of tanks, and 2241 marked objects. 2. In order to improve the ability to detect small objects and blurred objects, we improved YOLOv5 by using data augmentation of blurred samples, adding a larger feature map in the neck, and optimizing the loss function and anchor priors. In order to improve the explanation, we introduced the feature visualization technique of Class Action Mapping (CAM) to explore the mechanism of the proposed model.
## 4 Analysis, Comparison and Discussion of the Results
In the following, the periodic factors \(W_{N}^{kn}\) define the equidistant points on the unit circle in the complex plane [64]:  
\[W_{N}=e^{-j\Omega_{0}}=e^{-j2\pi/N} \tag{7}\]  
\[X[k]=\frac{1}{N}\cdot\sum_{k=0}^{N-1}W_{N}^{kn} \tag{8}\]  
FFT procedures, as with the ordinary Fourier transform, are based on the decomposition of the considered interval of length \(N\) into a number of subintervals, in principle \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Measuring Device** & **Frequency Range (Hz)** & **Dominant Frequency (Hz)** & **Amplitude** & **Vertical Displacement (mm)** \\ \hline RTS Leica TS50 & 26 Hz & 2.5555 & 0.5200 & 5.18 \\ Instantel Miniate Plus & 250 Hz & 3.307 & 0.307 & - \\ Polytec PDV 100 & 2.4 kHz & 1.484 & 1.559 & 5.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Analysis: laboratory monitoring results. equidistant. Importantly, when using FFT algorithms, the length of the interval \(N\) is usually an integer power \(M\) of 2, which can be written mathematically as [64]:  
\[N=2^{M} \tag{9}\]  
The usefulness of the procedure is mainly reflected in the increase in the computational efficiency of the analysis, as some mathematical operations are easier to perform in frequency space than in time space. The main advantage is practically reflected in the special fast algorithms for the calculation of the DFT known collectively as the FFT. Mathematically, the most widely used FFT algorithm (the Cooley-Tukey algorithm) can be written as [64]:  
\[X_{\text{K}}=\sum_{m=0}^{N-1}x_{n}e^{-\frac{(2\cdot\pi+n}{N}}\,n=0,\,\,\ldots,N-1 \tag{10}\]  
The Instantel Blastware software package allows the frequency content of waveforms to be analysed using the dominant frequency for each waveform. The dominant frequency is defined as the frequency with the largest amplitude in the spectrum. As can be seen from Table 9, the frequency of signal acquisition has a major impact on the results of dynamic response measurements. The RTS Leica TS50 geodetection equipment has a sampling frequency of 26 Hz, while the Polytec PDV 100 has a sampling frequency of 2.4 kHz. The pre-calculated values of the dominant frequencies differed. With the RTS Leica TS50 measuring equipment, this value was 2.55 Hz, and with the Polytec PDV 100 measuring equipment, it was 1.48 Hz. The explanation for this deviation is the wide frequency distribution of the measured signals, which was made possible by the Polytec PDV 100, which samples more quickly and accurately. Despite this difference, the results for the vertical displacement measurement with the two measuring devices were very similar: 5.18 and 5.24 mm, respectively. From the above, it can be concluded that the RTS Leica TS50 surveying equipment is sufficiently accurate for measuring displacements. Comparing the results obtained with the Instantel Minimate Plus, it can be seen that they deviate. The converted dominant frequency was 3.30 Hz, which is the maximum. Unfortunately, this equipment cannot measure real vertical displacement.
# Progress in THz Rectifier Technology: Research and Perspectives
### THz Diode Technology: State of the Art of Metal Insulator Metal (MIM) Diode
For the fabrication of MIM diode, the aluminum (work function of 4.28 eV) is used for the preparation of the bottom electrode, and aluminum dioxide Al\({}_{2}\)O\({}_{3}\) (having electron affinity of 1.25 eV) is used as a barrier layer after plasma oxidation of aluminum. For the top electrode, chromium is used, having sufficiently higher work function (4.5 eV). The substrate on which the MIM diode is fabricated is a microscopic glass slide, which is optically flat and smooth on both sides. The whole structure is printed on 1.5 \(\upmu\)m of SiO\({}_{2}\) deposited on Si. The diode is realized by considering a thin-film insulator layer Al\({}_{2}\)O\({}_{3}\) with thickness of 3 nm sandwiched between two different metals Al/Cr with thickness of 100 nm. The results obtained indicate a maximum current density (J-V) of 2 \(\times\) 10\({}^{-4}\) A/cm\({}^{2}\) at 0.8 V and \(N\)L of 3.1 at 0.8 V. The latest studies on MIMs discussed in this subsection are summarized in Table 3.
## 4 Alternative Solutions for UAV Stabilizing Mechanism
### Comparison of 3-SRR and 3-SRR/SRU PMs  
All three mechanisms have demonstrated robust performance. However, in the case of the 3-RRS PM, the stabilization of the plate necessitated movement in the \(x\) and \(y\) directions in a free manner. Consequently, a comparison was conducted solely between the translational motion of the moving plate for the 3-SRR/SRU PM and the 3-SRR PM, both utilizing the proposed strategy. This comparative analysis provides valuable insights into the effectiveness and characteristics of translational motion for these two mechanisms under the given strategy.  
The translational distance in the 2D plane for each mechanism is calculated using the Pythagorean theorem:  
For the 3-SRR/SRU:  
\[\mathrm{T}=\sqrt{\mathrm{X}^{2}+\mathrm{Y}^{2}}=\sqrt{24^{2}+4^{2}}=24.31\ \mathrm{mm} \tag{7}\]  
For the 3-SRR:  
\[\mathrm{T}=\sqrt{\mathrm{X}^{2}+\mathrm{Y}^{2}}=\sqrt{7.5^{2}+15^{2}}=16.77 \ \mathrm{mm} \tag{8}\]  
The analysis of the graphs presented in Figure 15, as well as the calculations based on Equations (7) and (8), reveal that the 3-SRR PM undergoes a translational motion of approximately 16.77 mm, while the 3-SRR/SRU PM experiences a translational motion of around 24.31 mm. It is noteworthy that both mechanisms effectively stabilize the plate in the z direction.  
Figure 14: 3D simulation of the 3-SRR PM when \(\alpha=\beta=20^{\circ}\) and \(\Delta\mathrm{Z}=0.02\mathrm{m}\) using the proposed strategy.  
These results demonstrate that both mechanisms successfully achieve the desired goal of plate stabilization, with only a negligible drawback. The 3-SRR PM, with its lesser translational motion, offers the advantage of easier assembly by avoiding the use of universal joint assembly. Consequently, for the remainder of the paper, the focus will be on the utilization and analysis of the 3-SRR mechanism, which proves to be a suitable choice for the intended purpose.
#### 1.1.2 Covering Materials  
Based on the covering materials, greenhouses can be divided into four different cases, which are described and explained below:  
* _Glass greenhouses_: Glass greenhouses have walls and roofs made of glass, providing excellent light transmission and aesthetic appeal. They are often used for commercial flower production and high-value crops. Because of their exceptional light transmission, resilience, and aesthetic appeal, glass greenhouses are well suited to temperate settings with steady sunlight and mild temperatures. They work incredibly well for high-value crops that need exact temperature and light control. Because of their classic, aesthetically pleasant design, these greenhouses are frequently chosen in urban and suburban settings. Because of their durability, they also make a suitable investment for permanent buildings.
* _Plastic film greenhouses_: Plastic film greenhouses are the most common type covered with polyethylene or other greenhouse-grade plastic. They are cost-effective and versatile, making them popular for various crops and growing conditions. Plastic film greenhouses are an affordable and adaptable option for multiple regions, particularly those with temperature swings. They are appropriate for large-scale agricultural operations and temporary or seasonal use since they are simple to install and replace. Because plastic sheets provide good light diffusion, they improve crop growth conditions. This form of greenhouse is popular for commercial farming, where maintenance costs and convenience are important factors.
* _Polycarbonate greenhouses_: These use polycarbonate panels as the covering material, providing good insulation and durability. Polycarbonate greenhouses are desired for impact resistance, superior insulation qualities, and lightweight portability. Because of these characteristics, they work effectively in arid climates with severe weather, such as hail, torrential rain, and high winds. Materials made of polycarbonate efficiently insulate and keep internal temperatures steady, which makes them perfect for areas with wide temperature swings. Because of their strength and portability, they are also a popular option for hobby and home greenhouses.
* _Shade house greenhouses_: Shade houses are designed to provide reduced light intensity for shade-loving plants. They have a shade cloth covering that filters sunlight to the desired level. Shade house greenhouses are ideal for hot, sunny settings, such as tropical and subtropical countries, where reducing heat and light intensity is essential. They offer shading rather than complete enclosure. These structures improve airflow and ventilation, which makes it easier for young seedlings or plants that like shade to flourish. To provide ideal crop-growing conditions during the hottest times of the year, shade rooms are frequently utilized in nurseries and propagation houses. They are also frequently used on a seasonal basis.  
Figure 2: Different types of greenhouses based on design and shapes: (**a**) gable roof greenhouses, (**b**) Quonset (hoop) greenhouses, (**c**) gothic arch greenhouses, and (**d**) sawtooth greenhouses.
# Analysis of Sealing Characteristics of Lip Seal Rings for Deep-Sea Separable Pressure Vessels
###### Abstract  
Deep-sea pressure vessels are specialized pressure vessels designed for automatic deployment from underwater to the surface. These vessels find extensive applications in underwater life-saving and transportation. Their interiors are furnished with a pair of sealing rings, one of which is lip-shaped, and the other is a convex shape, to ensure a dependable seal. With increasing water depth, the sealing rings experience augmented pressure, resulting in a gradual pressing of the rings into the sealing groove. Using ANSYS workbench finite element software, a two-dimensional axisymmetric lip seal finite element model using forces for overall constraint was established, the complete process of progressive pressing into the sealing groove was simulated, and the deformation, contact stress, maximum shear stress, and von Mises stress distribution was also simulated. We also conducted a comparative analysis of lip seals under low and high-water pressure sealing conditions. The findings of the study indicate that when subjected to a combined effect of the installation pre-tightening force and the working water pressure, the lip seal experiences complete compression into the sealing groove at a specific water depth. When subjected to the simultaneous influence of water pressure on the sealing ring material and friction force on the contact surface, two extremes of contact stress manifest in the primary sealing zone of the lip seal. These extremes have the capacity to elevate the contact stress and the effective sealing width, ultimately leading to an improvement in the sealing performance. Concurrently, as the water pressure gradually increases, the inner concave circle of the sealing ring experiences stretching, leading to a reduction in stress concentration, equivalent stress, and shear stress to a considerable extent. This mechanism ensures that the lip-shaped sealing ring retains sufficient strength. This study offers a viable solution for conducting sealing studies on deep-sea separable pressure vessels.  
lip seal; seal performance; contact stress extremum 2023  
3
## 3 Results and Discussion
### Optical Performances
Another comparison from previous work is presented in Table 3. This work provides a higher optical performance among other applications of PC on PSC. The comparison is given in terms of the specific wavelength that is 446 nm, where the solar spectrum \(I_{AM1.5}\) has a high intensity [39]. \begin{table}
\begin{tabular}{c c c} \hline \hline \(r\) **(nm)** & \(a\) **(nm)** & \(J_{sc}\) **(mA/cm\({}^{2}\))** \\ \hline (Flat PSC) & (Flat PSC) & 24.470 \\ \hline
50 & & 24.659 \\
100 & & 25.428 \\
150 & 500 & 26.153 \\
200 & & 26.467 \\
225 & & 26.473 \\ \hline  & 450 & 26.251 \\
225 & 562.5 & 26.187 \\
750 & 25.220 \\  & 1125 & 24.618 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The photocurrent density (\(J_{sc}\)) for certain variations of the radius (\(r\)) and lattice constant (\(a\)) of PC and flat PSC in this work. Figure 4: Absorption spectra for PSC without (flat) and with 225 nm radius of nanodisk photonic crystals. The photonic crystals lead to the absorption enhancement of PSC.
# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 5 Discussion
### Analysis in Relation to Learning Rate  
In this paper, the Adam optimization algorithm is adopted to carry out gradient descent, which required the setting of learning rate. Learning rate determines how far the weights will move in the gradient direction in a batch. If the learning rate is very low, the optimization time will become very long. On the contrary, if the learning rate is high, the training may not converge. In order to test the influence of different learning rates on the classification accuracy of RFPNet, experiments were carried  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attribute** & **Method** & **Accuracy (\%)** \\ \hline \multirow{8}{*}{Traditional methods} & SPM [14] & 77.69\(\pm\)1.01 \\  & LDA [17] & 60.32\(\pm\)1.20 \\  & PLSA [61] & 89.60\(\pm\)0.89 \\  & S-UFL [62] & 74.84 \\  & SIFT+BoVW [63] & 75.63 \\  & RF [64] & 89.29 \\  & Features integration [65] & 88.64 \\ \hline \multirow{8}{*}{Deep learning methods} & LPCNN [59] & 89.88 \\  & WMS-RFPNet & 83.2 \\ \cline{1-1}  & MFPNet & 86.52 \\ \cline{1-1}  & MRNet & 88.67 \\ \cline{1-1}  & WD-RFPNet & 88 \\ \cline{1-1}  & WPNP-RFPNet & 90.63 \\ \cline{1-1}  & WMAM-RFPNet & 90.43 \\ \cline{1-1}  & **RFPNet** & **91.5** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of classification methods based on the SIRI-WHU dataset.  
out on the UC Merced dataset and the SIRI-WHU dataset respectively. With other parameters of RFPNet network unchanged, the learning rate is adjusted to 0.01, 0.001, 0.0001, and 0.00001, as shown in Figure 16. The trend of the UC Merced dataset's accuracy is roughly the same as that of the SIRI-WHU dataset. When the learning rate is set to 0.01, the classification results of the two datasets diverge. When the learning rate is 0.0001, the training accuracy reaches the best result, and when the learning rate is 0.00001, it is in a declining trend.