esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Fundamentals of Terahertz Communication
### The Terahertz Spectrum
#### 2.1.2 Shorter Wavelengths  
Terahertz frequencies with a range from 30 \(\upmu\)m to 3 mm can be designed and fabricated in a compact size and with a lighter weight due to the short wavelengths of the THz frequency range [31]. This is far different for microwave wavelengths, which range from 1 mm to 1 m (corresponding to 300 MHz-300 GHz), whereas millimeter wavelengths span 1 mm to 10 mm (30 GHz-300 GHz) and THz wavelengths are typically below 1 mm (300 GHz-10 THz), requiring larger, less compact parts [32]. For instance, antennas based on THz technology are capable of a physical dimension size of less than 1 mm and, thus, can be incorporated directly on the chips of portable communication devices [17; 33]. The recent development of integrated photonics as a large-scale system solutions allows us to directly emit THz signals with devices of less than 10 mm\({}^{2}\) in size with up to 30% less power consumption than comparable systems [21; 34]. Such elements are especially valuable in applications where size and mass are factors, such as in satellite communication. The relationship between the wavelength (\(\lambda\)) and frequency (f) in electromagnetic waves is given by the following equation:  
\[\lambda=\frac{c}{f} \tag{3}\]  
Here, c = 3 \(\times\) 10\({}^{8}\) m/s is the speed of light. For THz frequencies ranging from 0.1 to 10 THz, \(\lambda\) falls in the range of 30 \(\upmu\)m-3 mm, enabling compact and lightweight component designs for communication systems.  
The diffraction-limited beamwidth for a system with an aperture diameter D is expressed as follows:  
\[\theta=\frac{1.22\lambda}{D} \tag{4}\]visualization of atmospheric absorption and its impact on THz signal propagation, emphasizing the severe attenuation that occurs as frequencies increase beyond 500 GHz. The figure plots attenuation (in dB/km) against frequency, illustrating key absorption peaks caused by molecular interactions with water vapor, oxygen, and other atmospheric gases. It highlights that THz waves experience dramatic signal loss beyond 500 GHz, where absorption can exceed 100 dB/km, severely restricting long-range transmission. However, the figure also identifies a lower-loss transmission window around 680 GHz, which presents a promising opportunity for optimized high-speed THz communication. The high-absorption regions correspond to the major spectral absorption lines of water vapor (~557 GHz, 752 GHz) and oxygen (~60 GHz, 118 GHz), making these frequencies unsuitable for long-distance communication. This visualization underscores the importance of applying frequency selection, adaptive multi-band switching, and AI-driven signal processing to mitigate the atmospheric losses. Additionally, hybrid THz-FSO systems and beamforming techniques could improve the feasibility of using THz links in real-world conditions.
## 4 Research Methodology
### Window Analysis  
Cooperation among concurrent and inter-time-based analysis is the supposed window analysis where DEA is used consecutively on the overlying periods of continuous width (called a window). Once the window width has been quantified, all explanations within it are observed and surveyed in an inter-time-based method denoted as nearby inter-time-based analysis. The technique was primarily suggested by [PERSON], et al. [44] to calculate efficiency in cross-sectional and time fluctuating data. Additionally, when window-DEA is used, the number of explanations taken into interpretation is increased fundamentally by a factor equivalent to the window's width, which is valuable when dealing with insignificant model sizes as it raises the judgment ability of the method. Consequently, two following features should be prepared to accept when selecting window width:  
* The window should be wide enough to integrate the least number of DMUs for the required judgment
* But it should also be narrow enough to guarantee that technological change within is insignificant. Therefore, it will not permit confusing or partial assessments among DMUs fitting to distant apart periods.  
For modeling, consider n DMUs number (n = 1, 2, 3...N) in T period (\(t\) = 1, 2, 3, 4... T). It has been observed that all r inputs are used to generate s outputs. Therefore, the sample has N \(\times\) T observations.  
\(DMU^{n}_{t}\) shows a \(DMU^{n}\) in t period with an input vector consists of r dimension as follows:  
\[X^{n}_{t}=(X^{n}_{1t}.X^{n}_{2t}.X^{n}_{3t}.X^{n}_{4t}\dots X^{n}_{rt})\, \tag{56}\]  
Meanwhile, the input vector consists of s dimensions as follows:  
\[Y^{n}_{t}=(Y^{n}_{1t}.Y^{n}_{2t}.Y^{n}_{3t}.Y^{n}_{4t}\dots Y^{n}_{st})\, \tag{57}\]  
window (\(Y_{kw}\)) has been started in time K (1 \(\leq\) k \(\leq\) T) with width W (1 \(\leq\) w \(\leq\) t-k).  
Therefore, inputs matrix for window analysis are as follows:  
\[X_{kw}=(X^{1}_{k},\dots,X^{N}_{k},\ X^{1}_{k+1},\dots X^{N}_{k+1},\dots X^{1} _{k+w},\dots X^{N}_{k+w}) \tag{58}\]  
And outputs matrix for window analysis are as follows:  
\[Y_{kw}=(Y^{1}_{k},\dots,Y^{N}_{k},\ Y^{1}_{k+1},\dots Y^{N}_{k+1},\dots Y^{1} _{k+w},\dots Y^{N}_{k+w}) \tag{59}\]  
Since **CCRIO**, **BCCIO** are applied the input-oriented window analysis based on CRS are as follows [45]:  
\[Min\theta \tag{60}\]  
\[St. \tag{61}\]  
\[\theta X^{\prime}_{n}-\lambda X_{kw}\geq 0 \tag{62}\]  
\[\lambda Y_{kw}-Y^{\prime}_{t}\geq 0 \tag{63}\]  
In Equation (60), \(\theta\) is a scalar that determines the rate of decrease in inputs, and \(\theta=1\) is an efficient unit. Meanwhile, \(X_{kw}\) is an inputs matrix in the k period with w width, and \(Y_{kw}\) is outputs matrix in the k time with w width. Finally, \(\lambda\) is a vector with N \(\times\) 1 dimension includes constant numbers or reference set weights.  
For the window analysis, the following formula for N DMUs is applicable:  
\[\text{K}=\text{T}-\text{W}+1 \tag{64}\]  
N: The number of Decision-Making Units (DMUs) or cement companies in this paper; T: Period; W: The number of windows.
## 2 Materials and Methods
### Surface Modification of Poly(S/DVB)HIPEs  
The layer-by-layer polyelectrolyte multilayers (LbL) technique was used to modify the polyHIPE surface. Poly(S/DVB)HIPE was cut into 2 cm thick sample. Then, a vacuum pump was applied to run the coating solutions through poly(S/DVB)HIPE. The LbL technique is composed of two coatings: the primary coating and the secondary coating. For the primary coating, a positively charged solution of PDADMAC was run through poly(S/DVB)HIPEs for 2 min, then the poly(S/DVB)HIPE was rinsed with DI water 2 times. Next, a negatively charged solution of PSS was run through poly(S/DVB)HIPE for 2 min, then the poly(S/DVB)HIPE was rinsed with DI water. The deposition of the primary layer contained six layers. For the secondary coating, tetraethylenepentamine (TEPA) or polyethyleneimine (PEI) polymer solution were used as the termination coating, as shown in Figure 1. Finally, the sample was dried at room temperature for 24 h.  
The use of polyHIPE modified with primary (TEPA) and secondary (PEI) amines is the economic option. The adsorption of CO\({}_{2}\) on amine-functionalized modified polyHIPE proceeds predominantly by the carbamate mechanism, as shown in Scheme 2.  
The amount of CO\({}_{2}\) gas adsorbed by poly(S/DVB)HIPEs was determined using a gas chromatography instrument. The samples were loaded into a sample tube 2 \(\times\) 25 cm. Before carrying out the adsorption studies, the poly(S/DVB)HIPEs sample in the reactor was pretreated with N\({}_{2}\) (80 mL/min). Then, the gas was switched to 15 vol% of CO\({}_{2}\) (15 mL/min) at room temperature and desorption was applied with N\({}_{2}\) (80 mL/min). Finally, adsorption capacity of poly(S/DVB)HIPEs was calculated by Equation (1) below.  
\[\mathrm{Q_{ads}}=\frac{\mathrm{FC_{in}t_{st}}}{\mathrm{M}}, \tag{1}\]  
where Q\({}_{\mathrm{ads}}\) dynamic adsorption capacity, mmol CO\({}_{2}\)/g, F = total flow rate, mol/min, C\({}_{\mathrm{in}}\) = the concentration of CO\({}_{2}\) entering the reactor, vol%, M = the weight of the adsorbent, g, and t\({}_{\mathrm{st}}\) = the stoichiometric time corresponding to CO\({}_{2}\) stoichiometric adsorption capacity, min.
## 3 Discussion  
We verify the proposed phase-compensation-based calculation method and amplitude tapering through simulations. We used the \(8\times 8\) random grid array in Figure 7 with the cavity-backed patch antenna operating at 10 GHz. For the array factor, Equation (10) was used, with \(\phi=0^{\circ}\), \(\theta=30^{\circ}\) beam scanning. Amplitude tapering was obtained by deriving the values of \(N_{0}\), \(N_{1}\), and \(c\), and SLL was \(-25\) dB for the \(1\times 8\) conformal array, based on the expression in Section 2.3, and multiplying them by assigning amplitudes in the x- and y-directions.  
Figure 13 shows the obtained amplitude curve applied to the actual x-direction. \(A=0.5\), \(N_{0}=N_{1}=3\) is set, and \(c\) is set as \(c_{0}=c_{1}=0.3563\), which is \(-25\) dB for the first SLL based on Figure 11.  
Figure 14a shows the \(\phi=0^{\circ}\)-plane (XZ plane) array factor pattern of a \(8\times 8\) conformal array obtained by the phase compensation method and the expression based on the optimization data. Figure 14b shows the \(\phi=0^{\circ}\)-plane 2D radiation pattern of the \(8\times 8\) random grid array to which the calculated amplitude and phase and uniform feeding with amplitude tapering are applied. The pattern shown in Figure 14a is similar to that of an \(8\times 8\) planar array with dx = 0.5\(\lambda\) (15 mm) and dy = 0.5\(\lambda\) (15 mm) on the left side of \(\theta=30^{\circ}\), except for \(\theta=-90^{\circ}\). The SLL is higher than that on the right side of \(\theta=30^{\circ}\). This is because SLL is relatively high due to beam scanning and the reduction in the gain of the main beam  
Figure 12: Application of the calculated amplitude based on the optimization trend: (**a**) conformal array shape with amplitude applied and (**b**) \(1\times 16\) conformal array radiation pattern vs. linear array radiation pattern.  
Figure 13: Amplitude tapering curves applied to actually validation cases.  
due to the structure and nonperiodicity. For the simulated radiation pattern (Figure 14b), SLL near the right side of \(\theta=30^{\circ}\) and at \(\theta=-90^{\circ}\), which were high due to the effect of the element pattern in Figure 6c, decreased. Compared with the uniform feeding pattern, the first SLL decreased from \(-11.4\) to \(-21.3\) dB. The SLL applied in the actual array factor was \(-25\) dB, which changed to \(-21.3\) dB depending on the error in the developed relationship, difference in structure, and application of element patterns. We confirmed that the phase was calculated based on Equation (8) using the element locations, and the \(AF_{3D}\) amplitude in Equation (10) could be predicted from the optimization results. Thus, we can predict and control an adjacent pattern having an SLL similar to that of the desired pattern for an arbitrary 3D array without repeated optimization.
# Depth-Extrapolation-Based True-Amplitude Full-Wave-Equation Migration from Topography
## 3 Examples
### Canadian Foothills Model  
The Canadian Foothills model (shown in Figure 10) is a very classic SEG (Society of Exploration Geophysicists) model, which is often used to test the imaging performance of the migration algorithm in irregular topography scenario. This model shows the geological features from the Canadian Foothills in northeastern British Columbia [17]. The number of shots is 277, and the shot interval is 90 m. The first shot is placed at x = 0 m. There are 480 traces per shot. The offsets range is \(-\)3600 m to 3600 m for all shots except roll-in and roll-out. Figure 11 presents the topographic profile. The imaging result is shown in Figure 12. In this experiment, we select three logs (the location of log L3, log L4, and log L5 is shown in Figure 10). The comparison of the imaging amplitude and the theoretical amplitude of these three logs is shown in Figure 13.  
From Figures 10 and 11, we can see that the surface conditions of this model are very complicated. The height difference between the highest point and the lowest point on the surface is 1537 m and there is a severe lateral velocity variation on the surface. In Figure 12, we can see that the imaging result can describe well the structures. All the faults are clearly visible. In Figure 13 we can find that the imaging amplitude curves are in good agreement with their theoretical curves. In this experiment, the deepest reflector is almost 9 km deep but our method can still accurately maintain the amplitude. These results prove that our method has good amplitude-preserving performance.  
Figure 10: The velocity field of the Canadian Foothills model. The yellow lines show the locations of log L3, log L4, and log L5.
# Cracking and Toughening Mechanisms in Nanoscale Metallic Multilayer Films: A Brief Review
## 3 Basic Mechanical Properties
### Experiment Methods
#### 3.1.1 Nanoindentation  
Nanoindentation is the most popular protocol in assessment of mechanical properties for NMMFs. A scanning electron micrograph of the nanoindentation residual impression of an Ag/Co NMMFs is shown in Figure 7a [37]. The hardness and elastic modulus are measured with nanoindentation [38; 39]. In addition, this technique is also available for measuring strain rate sensitivity, the stress exponent for creep, activation energy and fracture behavior [40; 41; 15; 42; 43]. A typical set of load-displacement data are presented in Figure 7b, which also serves to define some experimental quantities. The key quantities are the peak load \(P_{\text{max}}\), the maximum displacement \(h_{\text{max}}\), and the projected contact area of the impression \(A\). For nanoindentation, the contact stiffness with the contact area in conjunction provides a valid measurement of the hardness \(H\) and elastic modulus \(E_{\textit{eff}}\) as:  
\[H=P_{\text{max}}/A \tag{5}\]  
and:  
\[E_{\textit{eff}}=\frac{1}{\beta}\frac{\sqrt{\pi}}{2}\frac{S}{\sqrt{A}} \tag{6}\]  
where \(\beta\) is a constant which depends on the geometry of the indenter. The effective modulus, which is responsible for the fact that elastic deformation occurs in both the specimen and the indenter, is given by:  
Figure 6: **(a)** XRD profiles of Ag/Al multilayer films with varying \(h\), showing epitaxial character for all multilayer films. Only a single peak was present when \(h\) reduced to 25 nm. As \(h\) decreased to 10 nm, satellite peaks of multiple orders began to appear; **(b)** XRD results of Mg/Ti NMMFs with \(h\) ranges from 2.5 nm to 200 nm. The primary diffraction peaks correspond to Mg (0002) and Ti (0002) (the basal planes). For large individual layer thickness, Ti (10-10) reflections also become visible. It is observed that the peak breadth becomes larger with decreasing \(h\). Satellite peaks start to emerge around the primary peaks at small \(h\). **(c)** The interface structure evolution with layer thickness \(h\) in (111) Cu/Co multilayers. Note that the interface is coherent at \(h<25\) nm, while the interface is semicoherent at \(h>25\) nm [4; 20; 32].  
\[\frac{1}{E_{eff}}=\frac{1-v^{2}}{E}+\frac{1-v_{i}^{2}}{E_{i}} \tag{7}\]  
where \(E\) and \(v\) are the [PERSON]'s modulus and [PERSON]'s ratio for the specimen, and \(E_{i}\) and \(v_{i}\) are the same quantities for the indenter [38].  
Equation (7) is suitable to a thin film on a substrate as long as the multilayer and substrate have the same elastic properties. If the multilayer and substrate are elastically inhomogeneous, a simple model developed by [PERSON] and [PERSON] [45] is used in order to avoid the substrate effects on the intrinsic modulus of the NMMFs as:  
\[\frac{1}{E}=\frac{1}{E_{i}}+(\frac{1}{E_{s}}-\frac{1}{E_{m}})\exp(-\alpha\frac {hf}{h_{0}}) \tag{8}\]  
where \(\alpha\) is a constant, \(h_{f}\) is the multilayer thickness and \(h_{0}\) is the penetration depth.
# A Study on Multiple Factors Affecting the Accuracy of Multiclass Skin Disease Classification
## 3 Skin Disease Classification
### Choice of Network Architectures  
Using off-the-shelf deep networks, which have been proven effective in massive and challenging datasets, is common practice in image classification. In this paper, two network architectures, AlexNet and ResNet50, were used to construct our models for skin disease classification.  
AlexNet [20] is a well-known CNN that won the ImageNet contest in 2012. It achieves 37.5% top-1 error rates and 17.0% top-5 error rates on test data. It consists of five convolutional layers and three fully connected layers, as shown in Figure 2. It has over 60 million parameters and 65,000 neurons. The network uses ReLU nonlinearity to accelerate the training and employs various techniques to prevent overfitting, such as using dropout and data augmentation.  
ResNet [21] is a very deep residual learning framework developed to reduce the effort of network training. It is easy to optimize and can gain accuracy from increased network depth. The original ResNet with 152 layers won first place in the ILSVRC 2015 classification task and achieves 3.57% error on an ImageNet test set. ResNet introduces a shortcut connection technique to overcome the degradation problem, i.e., the accuracy gets saturated and degrades rapidly when the network depth increases.  
Figure 1: Sample images from the skin disease dataset.  
Figure 2: Architecture of AlexNet.  
A smaller version of ResNet50 consists of four stages, as shown in Figure 3. It was initially trained to classify 1000 classes on the ImageNet dataset and has a fully connected layer with a size of 1000.
# Multimodal Unsupervised Speech Translation for Recognizing and Evaluating Second Language Speech
## 4 Experimental Results
### Proficiency Evaluation Test  
#### 4.2.1 Speech Database  
We also performed the proficiency evaluation test using the rhythm and stress scores with other fluency features. A speech dataset was selected from the English read speech dataset read by non-native and native speakers for the spoken proficiency assessment. The dataset is a corpus of English speech sounds spoken by Koreans and 7 American English native speakers (references) for experimental phonetics, phonology, and English education, and is designed to see Korean speakers' intonation and rhythmic patterns in English connected speech and the errors which Korean speakers are apt to make in pronunciation of segments. Each utterance was scored by human expert raters on a scale of 1 to 5. In this study, the gender and spoken language proficiency levels were evenly distributed among the speakers. Table 3 shows scripts samples. The speech dataset comprised 100 non-native speakers, and for each speaker, 80 sentences were used for training and another 20 sentences, not included in the training dataset, were used for testing.  
For speech conversion and augmentation, an additional 7 American English native speakers (3 males and 4 females), and for each speaker, 100 sentences, were used, and frame alignment of the dataset was not performed. We used the WORLD package [49] to perform speech analysis. The sampling rate of all speech signals reported in this paper was 16 kHz. The frame shift length was 5 ms and the number of fast Fourier transform (FFT) points was 1024. For each extracted spectral sequence, 80 Mel-cepstral coefficients (MCEPs) were derived.
## 2 Materials and Methods
### Evaluation of the Images
The original image and the corresponding binary image with the calculated ferret diameter and the edge detection image with the length and width of the melt pool, respectively, for the green and blue channels. \[M(u,v,c)=\sqrt{G_{x}^{2}+G_{y}^{2}} \tag{6}\]  
The final edge selection \(M_{T}\)(\(u\),\(v\),\(c\)) was then performed using a hysteresis thresholding method. In the hysteresis thresholding method, two thresholds, T1 and T2, were used to first search for pixels in the image that are greater than T2. Afterward, the edges adjacent to these pixels that are larger than T1 were marked as edges. Here T1 = 5% and T2 = 9% of the maximum intensity in the image. These threshold value pairs have a large margin at which the edges of the melt pool are detected. Only for very large and small threshold values the detection along the edge can be incorrect, which leads to interruption of the detection along the edge line. The melt pool width \(w_{melt}\) was calculated from the vertical axis of the ellipse and the length \(l_{melt}\) from the horizontal axis. \[w_{melt}=\max[M_{T}(u)]-\min[M_{T}(u)] \tag{7}\]  
\[l_{melt}=\max[M_{T}(v)]-\min[M_{T}(v)] \tag{8}\]  
Eventually, the values of the melt pool size, the ferret diameter, the axis of the ellipse, and the position of the melt in the kerf for the green and blue color channels were stored for each individual frame. Finally, a moving average was calculated since individual peaks interfere with the evaluation, with a window width of 300 frames (15 ms) for stainless steel and zinc-coated steel, respectively, with a window width of 15 frames for aluminum, due to the shorter sampling rates for aluminum. A flowchart describing the image evaluation for the determination of spectral and geometric information from the melt pool is illustrated in Figure 3.  
In the following evaluation of complete and incomplete cuts, the thermal emissions from the process zone in the green and blue spectral range from the melt are evaluated. These geometric and spectral components of the melt area are denoted as the green and blue  
Figure 3: Flowchart of the image evaluation to determine spectral and geometric information from the melt pool.
## 2 Related Works
### Other Approaches to Mitigate ADI  
Beyond signal processing and learning-based techniques, simulation-driven approaches have also been employed to analyze and evaluate the impact of ADI, particularly in over-the-horizon (OTH) radio communication systems. One such study, conducted by [PERSON] and [PERSON] [18], utilized software simulation to model the propagation characteristics of radio waves within complex atmospheric conditions over non-uniform terrestrial surfaces.  
Their analysis was grounded in a detailed digital elevation model (DEM) representing terrain data from Wuxi province to four distinct provinces across China. Key simulation parameters included the transmission frequency, antenna height, elevation angle, polarization mode, propagation angle, and propagation distance. These parameters were meticulously varied to assess their influence on radio wave propagation loss under atmospheric duct conditions.  
The simulation results indicated significant signal attenuation across the tested routes, with calculated propagation losses of approximately 150 dB for 100 km links from Wuxi to Hangzhou, Shanghai, and Zhoushan, and a notably higher loss of 237.5 dB on the Wuxi-Nanjing path. These findings underscore the severity of ADI effects in long-distance, low-angle radio transmission scenarios and highlight the utility of simulation tools in pre-deployment analysis and planning for robust network coverage in OTH communication environments.
## 4 Training and Performance Evaluation of the CCBA Model
### Performance Evaluation and Results
#### 4.3.1 Classification Accuracy
Although classifiers from previous works, which are the CNN-LSTM using MFCC [21] and the multi-stream CNN using multiple features [38], achieved the highest classification accuracies on clean audio (94.9% and 94.4%, respectively), which is approximately 4% higher than the CCBA, their accuracies significantly decrease when WGN is introduced, particularly for the CNN-LSTM using MFCC. In terms of average accuracy across all SNRs, despite a 1.7% decrease compared to the teacher classification system, the CCBA demonstrates a significant advantage over previous works, leading the existing systems by 21.5% and 3.7%, respectively.