esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature Extraction and Recognition of Chinese Mitten Crab Carapace Based on Improved MobileNetV2
## 2 Materials and Methods
### Methods
#### 2.3.3 Loss Function  
The loss function plays a crucial role in model training, serving as a key factor in ensuring effective learning and accurate predictions by the model. Traditional classification loss functions utilize the Softmax loss function [28], which primarily maps output features to the probability range (0, 1) and classifies these features based on the probabilities. Although it ensures the separability of classes in recognition tasks, it lacks constraints on intra-class and inter-class distances. This limitation makes it less suitable for tasks requiring fine-grained individual identification, such as those distinguishing individual Chinese mitten crabs.  
To address this issue, this paper employs the ArcFace loss function [29]. This has proven effective in enhancing feature separability in facial recognition tasks, which is analogous to the challenge we face in identifying individual Chinese mitten crabs based on subtle differences in carapace features. The purpose of ArcFace is to encourage the learned feature vectors so that they cluster more closely together in angular space while larger angular distances are maintained between inter-class feature vectors, thereby improving the discriminability of the features. The specific expression is shown as follows:  
\[L_{\text{ArcFace}}=-\frac{1}{N}\sum\limits_{i=1}^{N}\ln\frac{\text{e}^{s(\cos (\theta_{y_{i}}+m))}}{\text{e}^{s(\cos(\theta_{y_{i}}+m))}+\sum\limits_{j=1,j \
eq y_{i}}^{C}\text{e}^{s\cos\theta_{j}}} \tag{6}\]  
In the equation, \(N\) is the number of sample batches in the training batch, \(y_{i}\) is the true label of the sample, \(\theta_{y_{i}}\) is the angle between the feature vector of the sample \(i\) and the weight vector of its corresponding category \(y_{i}\), \(s\) is the scaling factor used to control the radius of the feature space, and \(m\) is the margin added to increase the angular separation between feature vectors, thus enhancing the model's discriminative ability.
# Apollonian Packing of Circles within Ellipses
## Appendix A Deduction of Hyperbola Parametrization Formulas
### Parametrization in the General Case  
Parametrization formulas may be deduced by expressing point coordinates as complex coordinates and by applying rototranslation to the hyperbola in the canonical reference frame. A point \(P\) of hyperbola \(\mathscr{I}\) in the canonical reference frame is identified by the following complex number:  
\[z_{P}=x_{P}+iy_{P}=a\cosh t+ib\sinh t, \tag{12}\]  
In Figure 10(b), the first step of the transformation to the general form is shown. Rotation by an angle \(\varphi\) is applied, and a transformed hyperbola \(\mathscr{I}^{\prime}\) is obtained. In the final arrangement, focal line \(F_{1}F_{2}\) is tilted by angle \(\varphi\) with respect of the \(x\)-axis; thus, since foci \(F_{1}\) and \(F_{2}\) are identified by complex numbers \(z_{1}=x_{1}+iy_{1}\) and \(z_{2}=x_{2}+iy_{2}\), respectively,  
\[\varphi=\text{Arg}(z_{1,2}),\quad z_{1,2}=z_{2}-z_{1}. \tag{13}\]  
From Equation (13) and usual relations between hyperbola focal distance and axis length, Equation (11) is obtained. Point \(P\) of the hyperbola is transformed into point \(P^{\prime}(x_{P^{\prime}},y_{P^{\prime}})\), corresponding to the following complex number:  
\[\begin{split} z_{P^{\prime}}&=x_{P^{\prime}}+iy_{P^ {\prime}}=e^{i\varphi}z_{P}=(\cos\varphi+i\sin\varphi)(a\cosh t+ib\sinh t)=\\ &=(\pm a\cos\varphi\cosh t-b\sin\varphi\sinh t)+i(\pm a\sin\varphi \cosh t-b\cos\varphi\sinh t)\end{split} \tag{14}\]  
Figure 10: (**a**) Hyperbola in the canonical reference frame and (**b**) after rotation by an angle \(\varphi\). (**c**) Hyperbola after rototranslation.  
Subsequently, translation is applied, transforming midpoint \(O(0;0)\) into point \(C\), the midpoint of segment \(F_{1}F_{2}\), corresponding to complex number \(z_{C}=(z_{1}+z_{2})/2\), giving rise to Equation (12). In Figure 11c, the last step of the transformation to the general form is shown. Point \(P^{\prime}\) of hyperbola \(\mathcal{I}^{\prime}\) is transformed into point \(P^{\prime\prime}(x_{P^{\prime\prime}};y_{P^{\prime\prime}})\) of translated hyperbola \(\mathcal{I}^{\prime\prime}\), corresponding to the following complex number:  
\[\begin{split} z_{P^{\prime\prime}}&=x_{P^{\prime \prime}}+iy_{P^{\prime\prime}}=z_{C}+Z_{P^{\prime\prime}}=(x_{C}+iy_{C})+(x_{P^ {\prime}}+iy_{P^{\prime}})=\\ &=(x_{C}\pm a\cos\varphi\cosh t-b\sin\varphi\sinh t)+i(y_{C}\pm a \sin\varphi\cosh t-b\cos\varphi\sinh t).\end{split} \tag{12}\]  
By separating real and imaginary parts, Equations (10) is obtained.  
Coordinates \(z_{R}\) of a point \(R\in F_{1}F_{2}\) may be expressed by means of the following parametrization:  
\[z_{R}=(1-k)z_{1}+kz_{2} \tag{13}\]  
which conveys \(R\equiv F_{1}\) for \(k=0\) and \(R\equiv F_{1}\) for \(k=1\); for any \(R\), \(k=\overline{F_{1}R}/\overline{F_{1}F_{2}}\).  
Point \(A_{1}\in F_{1}F_{2}\) is defined by number \(r_{1}=\overline{A_{1}F_{1}}\) and corresponds to value \(k=\overline{A_{1}F_{1}}/\overline{F_{1}F_{2}}=r_{1}/\overline{F_{1}F_{2}}\). By means of the last remark and by rearranging Equation (13), Equation (13) is obtained.
## 3 Method
### Heterogeneous Encoder  
MaxViT employs a multi-axis attention mechanism to assign weights to feature maps, as illustrated in Figure 2A,B. Multi-axis attention can be divided into two distinct components: block attention and grid attention. Block attention involves partitioning the feature map into a series of non-overlapping windows, with self-attention calculations conducted within each distinct window. In contrast, grid attention does not employ a fixed window size for the division of the feature map. For a feature map of dimensions \(H\times W\times C\), the grid attention mechanism uses an attention window of size \(G\times G\), thereby converting the feature map dimension to \(\left(G\times\frac{H}{G},G\times\frac{W}{G},C\right)\). The combination of block attention and grid attention allows MaxViT to balance local and global information processing. Although MaxViT effectively captures local information through block attention, it is important to note that organs in the human body are not isolated; there is connective tissue between organs. MaxViT's operation of segmenting the feature map into multiple patches causes it to overlook the information of the connective tissue. Therefore, the Heterogeneous encoder incorporates CSWin alongside MaxViT. Cross Shape Window attention mechanism of CSWin is illustrated in Figure 2C,D: CSWin's self-attention organizes the attention into horizontal and vertical stripes, enabling CSWin to effectively supplement the missing connective tissue information in MaxViT.  
Figure 3 depicts the structure of the Heterogeneous encoder. Heterogeneous encoder is divided into two parts: the MaxViT encoder and the CSWin encoder. The feature map is first processed by MaxViT, after which the weighted feature map is processed by the decoder and weighted into the feature map about to be input to CSWin. Finally, CSWin weights the feature map, and the feature map output by CSWin also needs to enter the decoder. The workflow of the Heterogeneous encoder is shown in Equation (2).  
Figure 2: (**A**) Block attention mechanism of MaxViT. (**B**) Grid attention mechanism of MaxViT. (**C**) Vertical attention mechanism of CSWin. (**D**) Horizontal attention mechanism of CSWin.  
\[\begin{cases}x_{out-M}=MaxViT(x),MaxViT\ part\\ x_{out-C}=CSWin(x\odot OC-S(x_{out-M})),CSWin\ part\end{cases} \tag{2}\]  
\(x\) is feature map with a size of \(H\times W\times C\), \(x_{out-M}\) represents the feature map output by \(MaxViT()\), \(\odot\) the feature map with a size of represents the Huffman dot product, \(x_{out-C}\) is feature map output by \(CSWin()\).
## 4 Software Architecture  
### Overall Architecture Design  
Based on the analysis of system requirements, this paper presents a classic layered software architecture, as shown in Figure 3. In the layered architecture [25], the various levels are isolated and decoupled, making the software easy to test and maintain, with better scalability and reusability.  
The overall architecture is divided into four layers: the user layer, the access layer, the logic layer, and the software and hardware resource layer. The access layer is mainly used for data interaction between the modules of the user layer and the logic layer. The other layers are detailed below.  
This layer has two parts: the client GUI and the scenario simulator. Every user operation is passed to the logic layer to process, and then the client GUI is updated to display related data. The scenario simulator primarily injects scenario data.
* Logic Layer This layer is the core of the entire system, receiving operations from the user layer on the database or the physical resources while receiving and processing events from the physical resources. It is mainly responsible for scenario and node management, communication testing, application testing, and other services, and conducts data management and hardware operations based on business logic.
* Resource Layer This layer has physical resources in addition to the database used in classical information systems. The database contains user information, scenario information, node information, test execution information, and test result data. The physical resources mainly refer to the testbed nodes and the DUTs.
## 4 Results and Discussion
### Pile-Soil Separation Characteristics  
In the test, the failure process of the drill-expanded concrete pile and the soil was recorded throughout the entire loading process. Figure 9a shows the failure process of the undisturbed soil around the pile under different loads. At the beginning of the load application, the load was small, and no cracks appeared between the upper part of the expanded body and the soil. As the applied load increased, the model pile moved down  
Figure 8: Load and settlement of pile top curve.  
wards, and the soil above the expanded body appeared to separate from the pile body. With the continuous increase in the load, the separation width and scope gradually increased as well. The separation became increasingly pronounced, and the cracks penetrated into the soil on either side along the upper edge of the expanded body. The crack depth gradually increased as the applied load increased. However, since the test utilized clayey soil and the entire loading period was short, the soil above the expanded body did not fall significantly. Figure 9b shows the failure process of the sand around the pile. For sand, as the load increases, there is no separation between the upper part of the expanded body and the sand. The sand moves downward with the pile, and at the same time, there is a phenomenon of downward collapse on the surface of the sand around the pile top.  
Figure 10 is a schematic diagram of undisturbed soil cracking under force at the tip of the expanded body. Due to the unique features of drill-expanded concrete piles, cracks occur in the soil at the tip of the expanded body. When the pile is subjected to a downward load, the expanded body will exert downward pressure on the soil below it, and the soil at the tip of the expanded body will generate a strong crack propagation force (G) due to the stress concentration. The size of the crack propagation force is relative to the load applied to the pile. The soil at the tip of the expanded body possesses a crack propagations resistance (R). The crack propagation resistance is related to the soil properties, such as the soil's cohesive force and the angle of its internal friction. Crack propagation resistance also relates to the soil's stress state. When the crack propagation force exceeds the crack propagation resistance of the soil at the tip of the expanded body, the crack will propagate further and expand (\(\Delta\)a is the newly expanded crack length).
# Continuous Recognition of Teachers' Hand Signals for Students with Attention Deficits
## 3 Experimental Results
Therefore, we select \(Th_{t}\) as 1.5 s for practical applications. Our continuous recognition approach provides three-class recognition and considers the time lag to avoid unintended and unnecessary rapid changes. The performances and advantages of our method are competent and feasible for applications in the classroom. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Video** &
\begin{tabular}{c} **Recognized** \\ **Signal** \\ \end{tabular} & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** & **F\({}_{1}\) Score** \\ \hline \multirow{6}{*}{\(1\)} & _Pointing toleft_ & & 100.00\% & 98.10\% & 89.47\% & 94.44\% \\ \cline{2-6}  & _Pointing toright_ & 90.16\% & 97.67\% & 88.61\% & 82.35\% & 89.36\% \\ \cline{2-6}  & _Non-pointing_ & & 82.26\% & 98.33\% & 98.08\% & 89.48\% \\ \cline{2-6}  & Macro Average & - & 93.31\% & 95.01\% & 89.97\% & 91.09\% \\ \hline \multirow{6}{*}{\(2\)} & _Pointing toleft_ & & 100.00\% & 97.64\% & 78.57\% & 88.00\% \\ \cline{2-6}  & _Pointing toright_ & 91.30\% & 100.00\% & 89.29\% & 85.71\% & 92.31\% \\ \cline{2-6}  & _Non-pointing_ & & 83.56\% & 100.00\% & 100.00\% & 91.04\% \\ \cline{2-6}  & Macro Average & - & 94.52\% & 95.64\% & 88.09\% & 90.45\% \\ \hline \multirow{6}{*}{\(3\)} & _Pointing toleft_ & & 100.00\% & 93.79\% & 69.44\% & 81.96\% \\ \cline{2-6}  & _Pointing toright_ & 83.66\% & 89.19\% & 89.06\% & 82.50\% & 85.71\% \\ \cline{2-6}  & _Non-pointing_ & & 75.73\% & 91.92\% & 90.70\% & 82.54\% \\ \cline{2-6}  & Macro Average & - & 88.31\% & 91.59\% & 80.88\% & 83.40\% \\ \hline \multirow{6}{*}{\(4\)} & _Pointing toleft_ & & 92.93\% & 96.00\% & 94.85\% & 93.88\% \\ \cline{2-6}  & _Pointing toright_ & 90.18\% & 95.83\% & 95.50\% & 71.88\% & 82.14\% \\ \cline{2-6}  & _Non-pointing_ & & 86.14\% & 93.50\% & 91.58\% & 88.78\% \\ \cline{2-6}  & Macro-Average & - & 91.63\% & 95.00\% & 86.10\% & 88.27\% \\ \hline \multirow{6}{*}{\(5\)} & _Pointing toleft_ & & 85.11\% & 93.55\% & 90.91\% & 87.91\% \\ \cline{2-6}  & _Pointing toright_ & 86.24\% & 92.68\% & 97.18\% & 88.37\% & 90.47\% \\ \cline{2-6}  & _Non-pointing_ & & 84.34\% & 87.41\% & 80.46\% & 82.35\% \\ \cline{2-6}  & Macro Average & - & 87.38\% & 92.71\% & 86.58\% & 86.91\% \\ \cline{2-6}  & _Pointing toleft_ & & 95.61\% & 95.82\% & 84.65\% & 89.24\% \\ \cline{2-6}  & _Pointing toright_ & 88.31\% & 95.07\% & 91.93\% & 82.16\% & 88.00\% \\ \cline{2-6}  & _Non-pointing_ & & 82.41\% & 94.23\% & 92.16\% & 86.84\% \\ \cline{2-6}  & Macro-Average & - & 91.03\% & 93.99\% & 86.32\% & 88.03\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of the proposed recognition method.
# Multi-Object Deep-Field Digital Holographic Imaging Based on Inverse Cross-Correlation
## 5 Discussion
### Axial Resolution  
The axial resolution in 3D imaging refers to the ability to distinguish objects along the optical axis. In digital holography, it can be quantified as the minimum axial distance required for two planes to be distinguished clearly. This resolution can be expressed using Equation (6) [41], where \(\lambda\) represents the wavelength; \(z\) is the diffraction distance between planes; \(M\) and \(N\) are the number of samples in the transverse and longitudinal directions of the photodetector, respectively; and \(\delta\) represents the pixel size.  
\[R=\frac{8\lambda z^{2}}{MN\cdot\delta^{2}} \tag{6}\]  
In this section, we investigate whether the current algorithm impacts axial resolution. Experimental results are presented in Figure 11. Figure 11a,c show the inverse cross-correlation matrix and its schematic diagram of the rectangular box when two closely spaced planes are considered (i.e., when the planes are separated by approximately the thickness of the polyester mask plate). Notably, even when the two planes are in close proximity, the algorithm can effectively distinguish between them, clearly indicating that they belong to separate planes. However, it should be noted that the diffraction distance at this point is 15.7 cm, which--according to Equation (6)--generates an axial resolution of 5.99 mm; this is greater than the mask plate thickness and therefore cannot be resolved.  
Figure 11: Inverse cross-correlation matrices and their schematic diagram of the rectangular box. (**a**) Schematic diagram of the rectangular box at close proximity. (**b**) Schematic diagram of the rectangular box at 0.61 cm apart. (**c**) Inverse cross-correlation matrix at close proximity. (**d**) Inverse cross-correlation matrix at 0.61 cm apart.  
When the two planes are positioned with an approximate separation of 6 mm, and the far plane's diffraction distance on the CCD remains at 15.7 cm, the precise separation of the two planes can be achieved. The respective diffraction distances of the two planes are 15.77 cm and 15.16 cm, with a spacing of 0.61 cm, making them resolvable. Figure 11b,d illustrate their respective inverse cross-correlation matrix and schematic diagram of the rectangular boxes. The phase distribution is depicted in Figure 12. The color bar values indicate the phase magnitude.  
The experimental results revealed that the proposed algorithm accurately localizes the object's position when the two planes are in close proximity. Consequently, its axial resolution is solely determined by the characteristics of the digital holographic optical path itself, and the algorithm does not impose any limitations on its size.
## 4 Proof of Concept Implementation  
In this section, a proof of concept implementation is developed to demonstrate the technological feasibility of the model synchronization approach. An overview of the basic architecture of our proof of concept implementation is illustrated in Figure 3.  
The different mechanisms of the architecture will be discussed in the following except for the knowledge base mechanism. This mechanism is included in the proof of concept implementation for completeness, but its automation is outside the scope of this paper (i.e., in our study, we address this mechanism manually).
## 4 Results and Discussion
### Kriging with an External Drift (KED)  
Improvements to accuracy for monthly rainfall interpolations in KED approach were realized by the use of elevation, extracted from a digital elevation model, and geographic coordinates as the secondary information. In the area without data, elevation, mainly extracted from a DEM, represents easily available data, which can be used to be incorporated into multivariate geostatistics of rainfall. A minimal improvement in the interpolation accuracy for monthly rainfall occurred since elevation and geographic coordinates were used as secondary variables, considering that a significantly smaller error of estimates was not achieved. Figure 5 shows the rainfall maps for 12 months obtained by using the KED technique. The results of the correlation coefficients of measured versus predicted monthly rainfall, which ranging from 0.67 to about 0.85 (Figure 2 and Table 2), attest that the KED interpolator, in few months, operated better than the COK interpolator, which was, however, the best methods for most months. In fact, differently from the IDW and the OK, the MAE and RMSE results indicated that the KED method was the best approach in some autumn and winter months (Table 3).
# Service Life Prediction and Life Cycle Costs of Light Weight Partitions
## 2 Background
Several studies have delved into the life cycle management of infrastructure in extreme conditions, including [PERSON] et al., who emphasize the importance of a risk-based seismic life-cycle cost-benefit analysis for bridge retrofit assessment, which integrates probabilistic seismic hazard models and fragility assessments to optimize retrofit strategies [35]. [PERSON] et al. and [PERSON] et al. researched the life-cycle performance, cost, and reliability of reinforced concrete highway bridges in high seismic areas [36; 37]. [PERSON] et al. investigate the life-cycle seismic and environmental performances of buildings by using alternative concretes, linking seismic resilience with sustainability in building materials [38]. [PERSON] et al. propose a framework for assessing the life cycle resilience of RC structures in multi-hazards, and consider the effects of corrosion on seismic risks and resilience [39]. These studies underscore the critical need to integrate maintenance, material selection, and hazard assessment into infrastructure life cycle management, with the aim of ensuring resilience and sustainability in the face of extreme events. This section has provided a thorough overview of various research studies that focus on the service life expectancy and life cycle costs of building components, and has emphasized their importance in helping to ensure sustainability, longevity, and cost-effectiveness in construction. It has covered various topics, ranging from the deterioration and maintenance of exterior claddings to the life cycle assessment of different building materials. The discussion of the integration of advanced technologies like AI and Building Information Modeling (BIM) into these studies has highlighted the significant progress that has been made in predicting service life and managing life cycle costs more efficiently. In addition, the impact of climate change on building materials and methods also emerged as a recurring theme, illustrating the need for the construction need to adopt adaptable and sustainable practices. All of these studies underscore the importance of developing comprehensive approaches to building design and maintenance when seeking to meet the challenges of a changing environment.
## 4 Photon Diagnostics Systems
### Invasive Photon Diagnostics Systems
#### 4.2.3 Imagers  
There are almost 30 imaging units distributed over the photon tunnels which serve different purposes and therefore have different resolutions, fields of view, and geometries [60]. All of them contain one or more scintillators, mostly Ce:YAG, sometimes additionally polycrystalline diamond, and all but one type have stationary optics with sCMOS GigE cameras and fixed focus lenses.  
* Transmissive imagers (1 per FEL) are closest to the source and have the thinnest scintillators to allow transmitting the beam for recording another image of the same photon pulse at a downstream imager. By this method beam pointing and beam offset data can be obtained simultaneously.
* The SR imagers (1 per FEL) are optimized for highest photon sensitivity to detect spontaneous radiation from single undulator segments when applied in conjunction with the K-mono in undulator commissioning. Their optical resolution is 25 \(\upmu\)m (FWHM) and field of view (FOV) 26.6 \(\times\) 15 mm\({}^{2}\) using YAG:Ce and ceramic Gd2O2S:Pr scintillators.
* The FEL imagers (1 per FEL) are optimized for detailed spatial characterization of the FEL beam to measure the transverse intensity profile with beam position, size and shape. Their optical resolution is 28 \(\upmu\)m (FWHM) and FOV is 16 \(\times\) 22 mm\({}^{2}\). These imagers have redundancy scintillators of several different materials.
* Pop-in monitors (15 in total) are the basic imagers for beam finding and alignment. These monitors are placed downstream of major optical elements like mirrors and monochromators. Their horizontal FOV is large as to cover the variable beam offset without scintillator or optics movements. Various geometries are employed. Most devices put the scintillator at 45\({}^{\circ}\) to the XFEL beam, but some have the scintillator at normal incidence and an additional optical mirror. Optical resolutions range from 35 to 83 \(\upmu\)m (FWHM) and FOVs from 22.7 \(\times\) 40 up to 150 \(\times\) 30 mm\({}^{2}\).
* Exit slit imagers are installed on the two exit slits of the SASE3 monochromator for beam alignment, but more importantly to deliver single-pulse soft X-ray spectra with a resolution of \(\Delta\)E/E \(\geq\) 10\({}^{-5}\)