esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Shake Table Study
### 3.1. Similitude Requirements  
The theory of modeling presents an elaborate framework that identifies critical parameters essential for establishing relationships among different geometric features, intrinsic material properties, initial conditions that act as the basis for analysis, boundary limitations  
Figure 4: Drawings of a representative TnT BRB system: (**a**) Overall elevation showing connector plates at both ends, (**b**) detail of gusset-plate connection, (**c**) plan view of tube, and (**d**) plan view of fully assembled brace.  
that define the framework of the models, and the numerous environmental factors that can affect both models and prototypes throughout their duration. Understanding the complex interplay among these assorted elements is crucial for achieving a deep comprehension of how one entity interacts and operates in relation to another within the modeled system. A key principle that supports the creation of correlation functions, which illustrate and clarify the relationship between the model and its respective prototype, is well-established in the discipline as the principle of similitude [28].  
In this theoretical framework, developing and analyzing a structural model requires a methodical approach that follows specific criteria to link the model to its original prototype. These criteria are based on modeling theory principles and explained through dimensional analysis, which examines the physical phenomena affecting structures under various conditions. In dynamic model testing, dimensional analysis helps researchers and engineers understand the principles governing scaling laws for key variables such as force and time, thereby improving comprehension of the complex interactions during testing [29].  
This research endeavor utilized a meticulously crafted artificial mass simulation model, which was specifically engineered to fulfill a variety of precise similitude requirements pertaining to both geometric configurations and load-bearing parameters. While the model largely complied with the established material specifications mandated by the relevant scientific standards, it is important to note that there was a significant deviation in terms of the mass density parameter. In order to effectively rectify this discrepancy and maintain the integrity of the simulation, steel blocks were strategically incorporated into the slab, thereby ensuring the preservation of the overall stiffness of the model. Each individual block utilized in this simulation measured an impressive 1750 mm in length, 570 mm in width, and 60 mm in height, with a substantial weight of 430 kg attributed to each unit. A total of fourteen such blocks were meticulously welded together to form a cohesive structure, resulting in a cumulative weight that reached an impressive total of 6 tons. The physical variables that were meticulously measured and obtained from this experimental setup are comprehensively outlined and presented in Table 1 for further analysis and reference.
# Laser Phase Noise Compensation Method Based on Dual Reference Channels in Inverse Synthetic Aldar
## 2 Models and Methods
### Proposed Method Using Dual Reference Channel
In summary, we represent the DPN estimation process using dual reference channels as follows:  
\[\hat{\phi}(t) =h_{D_{1}}(t)^{*}\phi_{\tau_{1}}(t)+h_{D_{2}}(t)^{*}\phi_{\tau_{2 }}(t) \tag{14}\] \[=\sum_{n=0}^{N-1}\delta(t-n\tau_{1})^{*}\phi_{\tau_{1}}(t)+\sum_{ m=N\beta}^{M-1}\delta(t-n\tau_{2})^{*}\phi_{\tau_{2}}(t).\]  
This method strategically combines the advantages of both long and short-delay channels, as illustrated in the flowchart of \[=\phi_{L}(t-N\beta\tau_{2})-\phi_{L}(t-(N\beta+1)\tau_{2})+\phi_{ L}(t-(N\beta+1)\tau_{2})-\phi_{L}(t-(N\beta+2)\tau_{2})+... \tag{11}\]0 The longer delay DP compensates for the primary component of the target DPN, effectively reducing excessive cascade orders and minimizing error accumulation. Meanwhile, the short delay DP focuses on decreasing the RPN within the mismatch region, enhancing compensation speed while maintaining accuracy.
# High-Durability Concrete Using Eco-Friendly Slag-Pozzolanic Cements and Recycled Aggregate
## 2 Slag-Pozzolanic CEM V Characteristics
### Basic Cement Qualities
#### 2.2.2 Cement Properties and Composition  
Following the manufacture of the four different above-mentioned CEM V cement types (1), (2), (3), and (4) (see Section 2.1), cement testing was conducted to determine the key characteristics [49]. Fundamental characteristics are cement true density and specific surface expressed as a Blaine-value, illustrated in Table 3 for the manufactured CEM V cement types, as well as for the CEM I basic product.  
An important parameter with regard to chemical composition and environmental impact is the CaO-content of the various cement products. The lower the content of CaO, the lower the amount of ECO\({}_{2}\) (see Section 1.1) emitted. On the other hand, the CaO-containing components in hydrated cement are most sensitive to any aggressive attack; therefore, reducing the CaO content in the blended cement is a key condition for the expected improvement in the durability of the cement composites, see Section 2.4. Figure 2 illustrates the CaO content derived by X-ray fluorescence.  
Figure 1: XRD diagrams displaying angle 2\(\Theta\)-intensities of cementitious constituents fly ash (V) and furnace slag (S), whereby: Alite—3 CaO \(\times\) SiO\({}_{2}\); Belite—2 CaO \(\times\) SiO\({}_{2}\); Celite—2 CaO \(\times\) Al\({}_{2}\)O\({}_{3}\); Ferrite—4 CaO \(\times\) Al\({}_{2}\)O\({}_{3}\)\(\times\) Fe\({}_{2}\)O\({}_{3}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cement Type** & **True Density [kg/m\({}^{3}\)]** & **Blaine-Value [cm\({}^{2}\)/g]** \\ \hline CEM I 32.5 R & 3088 & 3560 \\ (1) CEM V/A (S-V) 32.5 R & 2876 & 5343 \\ (2) CEM V/B (S-V) 32.5 N & 2857 & 4392 \\ (3) CEM V/A (S-V) 32.5 R & 2741 & 4484 \\ (4) CEM V/B (S-P) 32.5 N & 2790 & 4770 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cement true density and Blaine value.  
Figure 2: CaO content of the various CEM V cement types.  
Figure 1 clearly shows the reduced amount of CaO in CEM V cements (replaced by SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) from cementitious constituents, see Table 1) compared to Portland clinker CEM I. Particularly, the CEM V/B with a much higher proportion of additives shows a comparable low CaO content.  
Cement analysis via X-ray diffraction (Figure 3a,b) shows an amorphous content in each case, expressed by a slightly broader and diffuse appearance of the peaks, whereby the glassy, noncrystalline phases originate from blast furnace slag and fly ash, cf. Figure 1. Referring to this, cement type (1) and (2) present the highest amorphous content. Furthermore, CEM V/B (S-P) shows the lowest level of amorphous components due to the well-crystallized Clinoptilite (see also [47]).
# A Novel Integrated Gliding and Flapping Propulsion Biomimetic Mana-Ray Robot
## 2 The Design of the Manta Ray Robot
### The Gliding System Design of the Manta Ray Robot
#### 2.3.1 The Gliding Motion Analysis of the Manta Ray Robot  
To describe the motions of the manta ray robot in six degrees of freedom (DOF), we used six independent coordinates (Table 2) to represent the position and attitude. The six DOF movements of the prototype were surge, roll, and heave, which refer to the longitudinal, sideways, and vertical displacements, and roll, pitch, and yaw, which describe the rotations about the longitudinal, transverse, and vertical axes. The general motions of the manta ray robot could be described by the flowing vectors: \(\boldsymbol{\eta}=[x,y,z,\theta,\psi]^{T}\), \(\boldsymbol{v}=[u,v,w,p,q,r]^{T}\), and \(\boldsymbol{\tau}=[X,Y,Z,K,M,N]^{T}\), where \(\boldsymbol{\eta}\) denotes the earth-fixed position and attitude vector, \(\boldsymbol{v}\) denotes the body-fixed linear and angular velocity vector, and \(\boldsymbol{\tau}\) is used to describe the forces and moments acting on the robot in the body-fixed frame. We picture the body-fixed and inertial reference frames in Figure 3.  
Assuming that the robot moves in an ideal fluid with a constant speed of V\({}_{0}\), ignoring the water's viscous and inertial forces, the motion equations of the robot in the vertical plane can be simplified as follows:  
\[m\ddot{u} = (G-B)\sin\theta-F_{X} \tag{1}\] \[m\ddot{w} = (G-B)\cos\theta-F_{Z}\] (2) \[B = B_{0}+\Delta B\] (3) \[G = G_{0}+\Delta G\] (4) \[F_{X} = \frac{1}{2}\rho u^{2}S_{x}C_{x}\] (5) \[F_{Z} = \frac{1}{2}\rho u^{2}S_{y}C_{y} \tag{6}\]  
Figure 2: The structural model and prototype of the manta robot. (**a**) The three-dimensional overall structural model of the manta robot; (**b**) the manta ray robot prototype on display; (**c**) the buoyancy-adjustment system three-dimensional model; (**d**) the flexible pectoral fin model; (**e**) the mass-adjustment system’s three-dimensional model.  
where \(G\), \(G_{0}\), and \(\Delta G\) denote the gravity of the robot, the gravity of the robot without the mass block, and the gravity of the mass block; \(B\), \(B_{0}\), and \(\Delta B\) denote the buoyancy, the buoyancy equal to the robot gravity, the adjustable amount of the buoyancy system; \(F_{X}\) and \(F_{Z}\) are used to describe the fluid resistances in the \(x\)-axis and the \(z\)-axis; \(m\) and \(\rho\) denote the mass of the robot and the density of the water; \(S_{x}\) and \(S_{y}\) are used to describe the maximum transverse cross-sectional area and the maximum longitudinal cross-sectional area; and \(C_{x}\), \(C_{y}\) denote the hydrodynamic parameters. The force analysis diagram of the robot gliding up and down is shown in Figure 4.
## 3 A Taxonomy of Measuring Methods, Techniques, and Sensors in Physiological-Based DMSs
### Skin Conductance Measurement (SC)  
Skin conductance measurement is a method of recording electrodermal activity by applying an external current to the skin. From an electrical measurement perspective, this is considered a two-wire resistance measurement. To achieve better contact between the skin and the electrodes, it is recommended to use disposable electrodes made of Ag/AgCl. In 2018, [PERSON] et al. [95] presented the development of an embedded device for SC measurement on the wrist to detect driver drowsiness in real time. In 2020, [PERSON] [96] proposed a dual-channel EDA sensor for acquiring data from the hands. The author pointed out the need for two sensor channels for SC measurement while driving, as the movement of the hands on the steering wheel led to significant motion artefacts. With a single-channel SC sensor, it is very difficult to separate the motion artefact from sympathetic activity. In 2024, [PERSON] et al. [26] presented a study on collecting physiological signals in real driving scenarios, on different road types and different sensor electrode locations, to determine the appropriate placement for obtaining a high-quality signal. The EDA measurement electrodes were placed in two locations--on the right palm (Figure 8a) and on the middle and index fingers of the right hand (Figure 8b). The presented results show that the EDA signals from the right palm were less affected by the movement of the hand during driving for all road types. In 2020, [PERSON] et al. [97] used a wristband sensor (Empatica E4) for continuous measurement of SC in real-world driving scenarios with different road intersections (Figure 8c). For assessment of the driver's stress level, they introduced an Electrodermal Impact Index, calculated as the product of the number of SCR peaks and their average amplitude. In 2018, [PERSON] et al. [98] presented a study on identifying dangerous driver states using a combination of driver characteristics, vehicle kinematics, and physiological indicators. The EDA signal was recorded via two electrodes attached to the palm of the non-dominant hand using a fingerless glove (Figure 8d). Simulated driving tests were conducted with twenty-one participants. The ECG, RR, and SC signals were used to classify sleep deprivation (sleepiness versus alertness), and the authors reported a high achieved accuracy of 98.8%. In 2022, [PERSON] et al. [99] presented their development of a sensing platform with a flexible EDA sensor mounted on an off-the-shelf steering wheel  
Figure 7: Ultrasonic sensor for acquisition of the driver’s BP and HR signals.  
sleeve (Figure 8e). They investigated the influence of different forces applied by the driver's hand gripping the sensor when holding the steering wheel while driving.  
In summary, for each measurement method, detection technique, and sensor type, Table 1 presents the advantages and disadvantages, as well as the physiological parameters that can be monitored.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Detection** & **Sensor Type
# A Study on the Super Resolution Combining Spatial Attention and Channel Attention
## 3 Proposed Method  
This paper proposes a super resolution method using CSBlock and various feature extractions for high-frequency enhancement in images. In general, image quality has a great influence on high-frequency features, and for this purpose, the high-frequency features are emphasized by applying a CSBlock structure that combines channel attention and spatial attention. To enable effective training of deep networks, an attention block combining CSBlock and skip connection was applied. The attention block was used to emphasize features using the structure of Shallow Feature Extraction and Deep Feature Extraction with attention block, and feature utilization was performed through connections. In this way, both shallow features including shape information and deep features with emphasized high-frequency components were extracted from the insufficient information of low resolution. Then, super resolution was performed by expanding the feature maps through the upsampling process of various feature maps with high-frequency features emphasized. Figure 7 is the structure of the proposed super resolution.
# Dynamic Compensation of a Fluxgate Magnetometer Based on a Hybrid Optimizing Algorithm
## 3 Simulation and Signal Test  
In order to verify the effect of the dynamic compensator proposed in the previous section based on the GA-SA algorithm for the hybrid optimization solution, a simulation and an experiment were carried out, respectively.
# Forecasting the Number of Road Accidents in Polish Provinces Using Trend Models
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data/Model** & **Annual Data** & **Monthly Data** \\ \hline Exponential & y = \(1605.6e^{-0.005\text{x}}\) & y = \(1308.3e^{-4E-06\text{x}}\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.9468\) & R\({}^{2}\) = \(0.0019\) \\ \hline Linear & y = \(-49.516\text{x}\) + \(1456.8\) & y = \(-0.0061\text{x}\) + \(1364.2\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.9544\) & R\({}^{2}\) = \(0.004\) \\ \hline Logarithmic & y = \(-359.1\text{ln}(x)\) + \(1688.1\) & y = \(-260.2\text{ln}(x)\) + \(3878.5\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.8715\) & R\({}^{2}\) = \(0.0042\) \\ \hline Polynomial of 2 nd degree & y = \(0.8747\text{x}^{2}\) \(-68.759\text{x}\) + \(1530.5\) & y = \(6E\text{-}06\text{x}^{2}\) \(-0.5033\text{x}\) + \(11742\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.963\) & R\({}^{2}\) = \(0.0116\) \\ \hline Polynomial of 3 rd degree & y = \(-0.0076\text{x}^{3}\) + \(1.1243\text{x}^{2}\) \(-71.007\text{x}\) + \(1535.1\) & y = \(3E\text{-}09\text{x}^{3}\) \(-0.0003\text{x}^{2}\) + \(12.994\text{x}\) \(-176022\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.9631\) & R\({}^{2}\) = \(0.0144\) \\ \hline Polynomial of 4 th degree & y = \(-0.0326\text{x}^{4}\) + \(1.4274\text{x}^{3}\) \(-19.501\text{x}^{2}\) + \(35.49\text{x}\) + \(1393.7\) & y = \(-1E\text{-}11\text{x}^{4}\) + \(2E\text{-}06\text{x}^{3}\) \(-0.1254\text{x}^{2}\) + \(3495.8\text{x}\) \(-4E\text{+}07\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.9721\) & R\({}^{2}\) = \(0.1273\) \\ \hline Polynomial of 5 th degree & y = \(-0.0041\text{x}^{5}\) + \(0.1948\text{x}^{4}\) \(-3.0764\text{x}^{3}\) + \(19.076\text{x}^{2}\) \(-\) & y = \(3E\text{-}15\text{x}^{5}\) \(-6E\text{-}10\text{x}^{4}\) + \(5E\text{-}05\text{x}^{3}\) \(-2.1244\text{x}^{2}\) + \(45221\text{x}\) \\ \cline{2-3}  & 97.781\text{x}\) + \(1523.2\) & \(-4E+08\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.9759\) & R\({}^{2}\) = \(0.1385\) \\ \hline Polynomial of 6 th degree & y = \(0.0003\text{x}^{6}\) \(-0.0258\text{x}^{5}\) + \(0.7421\text{x}^{4}\) \(-9.6906\text{x}^{3}\) + \(y\) = \(6E\text{-}18\text{x}^{6}\) \(-2E\text{-}12\text{x}^{5}\) + \(2E\text{-}07\text{x}^{4}\) \(-0.0091\text{x}^{3}\) + \(282.88\text{x}^{2}\) \\ \cline{2-3}  & 57.881\text{x}^{2}\) \(-196.26\text{x}\) + \(1598.8\) & \(-5E+06\text{x}\) + \(3E\text{-}10\) \\ \hline \multirow{2}{*}{Potentiometric} & y = \(2004.2\text{x}^{-0.302}\) & y = \(7319.3\text{x}^{-0.178}\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.7814\) & R\({}^{2}\) = \(0.002\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Trend models for Podlaskie province.
## 3 Results and Discussion
### NDVI for Plant Health  
As presented in Table 3, this study determined NDVI values of plants within the GSIs using the multispectral images obtained from the UAV platform and categorized them into three plant-condition classes: excellent, good, and poor. This classification criterion was adopted, and color scale patterns were modified based on published resources [29; 41]. The matching vegetation index color patterns at six levels depicted the three classes along the color scale ranging from \(-1.0\) to \(1.0\). Additionally, minimal and maximal NDVI values were extracted and classified into classes, as detailed in Table 3. As shown in Figure 5, RGB and NIR images were processed and used to generate NDVI maps. NDVI maps of the bioretention behind the CBEIS building of MSU campus were analyzed for two different time points (May and June in 2021). In these NDVI maps, green represents healthy vegetation, yellow indicates senescent or declining vegetation, orange and brown represent soil and dead vegetation, and red denotes the areas with no vegetation such as water bodies or equipment. For further exploration of the condition of orange and red areas in GSIs where NDVI values were less than 0, it is essential to investigate the presence of long-standing water, as this is a crucial issue affecting GSI performance. The central area of red color in the NDVI map increased from May to June. This was attributed to increased rainfall events in summer leading to stagnant water in the pond in the center area of this bioretention. Stagnant water in a pond not only results in decreased levels of dissolved oxygen, but also fosters the growth of harmful organisms, deteriorates water quality, and adversely affects plant (or grass) health. This observation suggests the need to consider appropriate plants and to replace typical plant and grass in the central area of the GSIs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classes** & **Color** & **NDVI Scale** & **Color** & **NDVI Scale** \\ \hline Excellent & & 0.67\(\sim\)1.00 & 0.34\(\sim\)0.67 \\ \hline Good & & 0.00\(\sim\)0.34 & 0.00\(\sim\)0.34 \\ \hline Poor & & \(-\)0.34\(\sim\)\(-\)0.67 & \(-\)0.67\(\sim\)\(-\)1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ranges of NDVI values for plant conditions [25; 29]. Two sets of colors and associated NDVI scales are used to precisely distinguish the plant health conditions of each class (excellent, good, and poor).
# Multi-Label Weighted Contrastive Cross-Modal Hashing
## 4 Experiment
### Hyperparameter Sensitivity Analysis
#### 4.6.1 Trade-Off Parameter \(\lambda\)  
As shown in Equation (12), the hyperparameter \(\lambda\) is a trade-off factor of linear and non-linear weights. We observe the performance change of MLWCH by varying \(\lambda\). From Figure 7, when we set \(\lambda\) to 0.3, 0.6, and 0.2 on MIRFLICKR-25K, NUS-WIDE, and MS COCO, respectively, our method obtains the best performance. Under this circumstance, the cross-modal hashing model focuses almost equally on linear and non-linear semantic similarity between instances. Undoubtedly, this manner is essential for comprehensively expressing complex cross-modal semantic relationships.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**MS COCO**} & \multicolumn{3}{c}{} \\ \cline{2-7}  & **I2T** & \multicolumn{3}{c}{**T2I**} & \\ \cline{2-7}  & **16 Bits** & **32 Bits** & **64 Bits** & **16 Bits** & **32 Bits** & **64 Bits** \\ \hline MLSCH & 0.7990 & 0.8212 & 0.8509 & 0.8298 & 0.8567 & 0.8861 \\ MLWCH & **0.8072** & **0.8271** & **0.8579** & **0.8350** & **0.8614** & **0.8924** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance of MLWCH compared with MLSCH in terms of MAP@50 on MS COCO. The best results are bold-font.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**MIRFLICKR-25K**} & \multicolumn{3}{c}{} \\ \cline{2-7}  & **I2T** & \multicolumn{3}{c}{**T2I**} & \\ \cline{2-7}  & **16 Bits** & **32 Bits** & **64 Bits** & **16 Bits** & **32 Bits** & **64 Bits** \\ \hline MLSCH & 0.9079 & 0.9234 & 0.9377 & 0.8756 & 0.8916 & 0.8992 \\ MLWCH & **0.9216** & **0.9310** & **0.9450** & **0.8912** & **0.8987** & **0.9102** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance of MLWCH compared with MLSCH in terms of MAP@50 on MIRFLICKR-25K. The best results are bold-font.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**NUS-WIDE**} & \multicolumn{3}{c}{} \\ \cline{2-7}  & **I2T** & \multicolumn{3}{c}{**T2I**} & \\ \cline{2-7}  & **16 Bits** & **32 Bits** & **64 Bits** & **16 Bits** & **32 Bits** & **64 Bits** \\ \hline MLSCH & 0.8365 & 0.8457 & 0.8634 & 0.7803 & 0.7894 & 0.7934 \\ MLWCH & **0.8459** & **0.8546** & **0.8765** & **0.7886** & **0.7965** & **0.7990** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance of MLWCH compared with MLSCH in terms of MAP@50 on NUS-WIDE. The best results are bold-font.
# Coherence Enhancement Based on Recursive Anisotropic Scale-Space with Adaptive Kernels
###### Abstract  
The reduction of unnecessary details is important in a variety of imaging tasks. Image denoising can be generally formulated as a diffusion process that iteratively suppresses undesirable image features with high variance. We propose a recursive diffusion process that simultaneously computes the local geometrical property of the image features and determines the size and shape of the diffusion kernel, leading to an anisotropic scale-space. In the construction of the proposed anisotropic scale-space, image features due to undesirable noise are suppressed while significant geometrical image features such as edges and corners are preserved across the scale-space. The diffusion kernels are iteratively determined based on the local geometrical properties of the image features. We demonstrate the effectiveness and robustness of the proposed algorithm in the detection of curvilinear features using simple yet effective synthetic and real images. The algorithm is quantitatively evaluated based on the identification of fissures in lung CT imagery. The experimental results indicate that the proposed algorithm can be used for the detection of linear or curvilinear structures in a variety of images ranging from satellite to medical images.  
Keywords: curvilinear structure enhancement; coherence enhancement; anisotropic diffusion; anisotropic scale-space; structure tensor; local orientation +
Footnote †: journal: Applied Sciences