esa-sceva/satcom-chunk-collection huggingface dataset:
# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 3 Fundamentals and Basic Definitions  
### Histological Image Segmentation  
Histology is the science dedicated to the study of biological tissues at a microscopic level. Histological images are digital representations of tissue slides, usually obtained by optical microscopy after application of specific dyes, such as H&E. These images capture a complex cellular and tissue architecture, and are fundamental for understanding tissue morphology and diagnosing diseases, especially in the field of pathology. In biomedical practice, histological images allow the identification of cellular patterns, anatomical structures and alterations that may indicate pathological processes, such as inflammation, dysplasia or cancer [27,28].  
The role of histological images in medical diagnosis is irreplaceable. In diagnostic pathology, they are a basis for the diagnosis of several diseases, guiding therapeutic and prognostic decisions. Furthermore, they are essential in biomedical research, for example, to understand molecular mechanisms of diseases or evaluate responses to experimental treatments [29].  
However, manual analysis of these images, traditionally performed by experienced pathologists, faces significant challenges. This process is time-consuming, subject to human fatigue, and often presents inter- and intra-observer variability, i.e., different specialists may interpret the same image differently [30]. Furthermore, the complexity of histological images, with their color variations, morphological patterns, and heterogeneous textures, makes the task even more challenging. The increase in the volume of exams due to the growing demand for health services also overloads professionals, highlighting the need for automated methods to assist in the analysis [28].  
In this context, the segmentation of histological images emerges as a fundamental step in automated analysis. Segmentation aims to identify and delimit specific structures within images, such as cell nuclei, glands, collagen fibers, or tumor regions. This step is essential to extract quantitative information that complements the qualitative analysis performed by experts [27]. Practical applications of segmentation include tumor detection in histological sections [31,32], quantification of cell nuclei for tumor health studies [33], assessment of degrees of inflammation or fibrosis [30], and prognostic analysis based on morphological characteristics [34].  
Despite its potential, segmentation of histological images presents significant technical difficulties. Color and intensity variations introduced by different staining protocols,texture patterns, and overlapping cellular structures make the problem challenging for traditional algorithms. In addition, artists generated during slide preparation, such as folds or bubbles, can interfere with the accuracy of segmentations [29]. These difficulties have motivated the development of more robust approaches, such as the use of deep neural networks and, more recently, models based on generative adversarial networks (GANs).
## 5 Experiments and Results
### Dataset  
Data quality is a crucial factor that directly affects the performance of supervised learning algorithms. The utilization of a representative and high-quality dataset is critical for achieving optimal accuracy and performance [37]. In this study, we obtained the dataset from the DFUC2021 challenge organized by the Medical Image Computing and Computer-Assisted Intervention (MICCAI) society [16]. The proper licensing was also secured for this research, ensuring that all ethical and legal requirements were met. Upon initial preprocessing, we observed that the dataset's class distribution was imbalanced, with 621, 2555, 227, and 2552 instances belonging to the classes both, infection, ischemia, and none, respectively.
# Fast and Effective Tag Searching for Multi-Group RFID Systems
## 7 Performance Evaluation
### Validity Verification of the Proposed Protocols  
As we know, existing tag searching protocols cannot effectively solve the multi-group tag searching problem because they cannot ensure the searching results in each group satisfy  
Figure 4: Illustration of the A-Search tag searching protocol.  
the predefined reliability requirements. To demonstrate the multi-group tag searching validity of the B-Search, G-Search and A-Search, we propose a baseline protocol for comparison.  
The reader can confirm whether a wanted tag is the target tag by polling it. To reduce the time spent on polling, the reader can first broadcast the group ID. Local tags immediately check whether their group ID matches the broadcast one. If so, it remains active. The reader then broadcasts the member ID of wanted tags in the group, and the active local tag checks if its own member ID is the same as the broadcast one. For each member ID, we can reserve a one-bit slot for identifying. When the local tag's member ID are polling, the tag replies with \"1\". Otherwise, the tag replies with \"0\". \(T_{bl}\) indicates the time taken by the baseline protocol to complete the multi-group tag searching. Simply,  
\[T_{bl}=(16\cdot T_{c}+T_{i})\cdot G+(80\cdot T_{c}+T_{i}+T_{s})\cdot\sum|W_{g}|,\qquad g\in\{1,2,\ldots,G\}. \tag{18}\]  
Based on Equation (18), the tag searching efficiency of the baseline protocol is only associated with the number of wanted tags in each group and the number of groups. In Table 1, we set the target tags ratio \(\lambda=0.3\), the reliability requirement \(P_{req}=0.9999\), the group interval \(r=10\), and the number of local tags in each group \(|L_{g}|=1000\). The table shows the searching time of baseline protocol, B-Search, G-Search, and A-Search. It is obvious that our proposed three multi-group tag searching protocol is far superior to the baseline protocol.
# A Credit-Based Congestion-Aware Incentive Scheme for DTNs
## 5 Performance Evaluation
### Performance Comparison
#### 5.2.4 Influence of TTL  
The impact of TTL on delivery ratio is shown in Figure 13. It can be seen that the delivery ratio increases as the TTL increases for all schemes. This is because the messages with larger TTL can stay longer in the buffer and the delivery chances are better. The delivery ratio of the direct scheme is the lowest. This is because each node only stores its own messages and cannot forward messages to other nodes with higher delivery probability. As a result, some messages are discarded due to the overflow of buffer or expiration of TTL. The delivery ratio of the CBCAIS scheme is much higher than SMART, Multicenter, or GTA when TTL is larger. This is because nodes are more congested when TTL is larger, and CBCAIS can effectively avoid node congestion.  
Figure 14 shows the impact of TTL on delay. It can be seen that delivery delay increases as TTL increases for all schemes. This is because, as the TTL increases, nodes can store and deliver more messages with longer delay. The delivery delay is largest in the direct scheme when TTL is no larger than 12 h, because a node cannot forward messages to others with smaller delivery delay. The delivery delays of other schemes are larger when TTL is larger than 12 h, because more messages with longer delay are delivered. The delivery delay of CBCAIS is slightly larger than SMART, Multicent, and GTA. This is because more messages with longer delivery delay are delivered, and many messages are forwarded to the forwarding nodes with more free buffer but a slightly larger delivery delay to avoid being discarded.  
Figure 15 shows the impact of TTL on overhead ratio. In the direct scheme, the overhead ratio is always 1, because the messages are not forwarded by other nodes. For other routing schemes, more messages are successfully delivered as TTL increases, so the overhead ratio will decrease. The overhead ratio of CBCAIS is lower than SMART, Multicent, and GTA, as it can reduce message discarding and avoid many invalid forwards.  
The impact of TTL on energy is shown in Figure 16. It can be seen that energy increases as TTL increases for all schemes. This is because messages have more chances to be forwarded with longer TTL, leading to more energy consumption. In the direct scheme, the energy consumption is lowest because the messages are not forwarded by other nodes. The energy consumption of CBCAIS is slightly higher than SMART, Multicent, and GTA, as it can deliver messages more effectively.  
Figure 13: Delivery ratio for varying TTL.  
Figure 16: Energy for varying TTL.  
Figure 14: Delivery delay for varying TTL.  
Figure 15: Overhead ratio for varying TTL.
## 2 Description of Type, Shape, and Properties of Fibers  
Fibers used as an additive in mixtures to improve the properties of ultra-high performance concrete (UHPC) can be classified according to the material from which they are produced, e.g., steel (ST), synthetic (polpropylene--PP, polyvinyl alcohol--PVA, polyethylene--PE, polyester--PES, polyoxymethylene--POM or polyolefin--POL), inorganic (basal--B, glass--G) or carbon (C). They are also divided into two categories according to their modulus of elasticity, which can be higher or lower compared to the UHPC matrix [23]. Furthermore, fibers can be classified by their length, that is, ultra-short with L\({}_{\text{f}}\leq\) 8 mm, short with 8 mm \(\leq\) L\({}_{\text{f}}\leq\) 13 mm, long with 13 mm \(\leq\) L\({}_{\text{f}}\leq\) 30 mm, and ultra-long with L\({}_{\text{f}}\)? 30 mm. They can also be classified according to their shape, such as straight, twisted, hooked, wavy, with round ends, paddle-shaped ends, etched, indented, or thickened ends [114]. Other important parameters include the cross-sectional diameter and the surface area of the fiber that adheres to the matrix. When selecting fibers for use, three main requirements should be considered: firstly, the compatibility of the fiber material properties with concrete; secondly, the optimal shape factor, which allows the composite to behave appropriately after matrix cracking; and the thirdly, the matrix-fiber interaction, enabling stress transfer [115; 116; 117]. Table 1 presents the physical and mechanical properties, as well as the geometry of selected fibers that have been used as components in UHPC mixtures. Figure 1 shows the fibers used by the authors of this publication in various combinations of fibers during tests of their impact on different UHPC properties.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Fiber Type**} & **Density (g/cm\({}^{3}\))** & **Length (mm)** & **Diameter (mm)** & **Tensile Strength (MPa)** & **Elastic Modulus (GPa)** & **Maximum Elongation (\%)** \\ \hline steel & 7.5–7.9 & 6–62 & 0.1–1.0 & 345–2900 & 200–250 & 0.5–4.0 \\ polypropylene & 0.90–0.95 & 12–50 & 0.015–0.6 & 270–760 & 1.5–11.0 & 10–80 \\ polyvinyl-alcohol & 1.2–1.3 & 6–12 & 0.01–0.04 & 770–2500 & 16–42 & 5.5–8.0 \\ polyethylene & 0.92–0.97 & 12–19 & 0.023–1.0 & 80–3800 & 5–116 & 3–100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical, mechanical properties, and geometry of fibers used in HPC and UHPC.
# Strategies for Dielectric Contrast Enhancement in 1D Planar Polymeric Photonic Crystals
## 4 Role of the Dielectric Contrast in DBRs
This effect is clearly visible in Figure 3c, where the spectral position of the PBG is plotted versus the effective refractive index of the investigated structures. In the figure, we can observe that the lowest \(\lambda_{\text{max}}\) value corresponds to the Hy:PVK DBR, while the largest value corresponds to the CA:TiO\({}_{2}\)-PVA DBR. These results confirm that DBRs fabricated with polymer media with an engineered refractive index show rather large and intense PBGs. To date, these structures have not been investigated for applications requiring accurate light control. On the other hand, as discussed previously, the dielectric contrast that characterizes them is competitive with inorganic DBRs and microcavities. In the next paragraph, we will try to assess the possibility of obtaining strong light confinement with polymer structures through the comparison with inorganic microcavities in which the effect was demonstrated.
# Research on Fracture Mechanism and Stability of Slope with Tensile Cracks
## 4 Results Analysis
### The Effects of Parameters on the Stability of Slope
#### 4.2.5 The Effect of Optimal Crack Depth on the Safety Factor  
In this study, the optimal crack depth is computed by the principle of minimum safety factor of slope. Therefore, to find the optimal crack depth for any slope, a dimensionless coefficient \(\delta^{\prime}/H\) was introduced to illustrate this. It can be seen in Figure 12(a) that \(\delta^{\prime}/H\) increases with the increase of cohesion, and the maximum value is \(\delta^{\prime}/H=0.26\), which means that the optimal crack depth is about one-fourth of the slope height. However, in Figure 12(b), \(\delta^{\prime}/H\) decreases with the increase of the internal friction angle, and the maximum value is \(\delta^{\prime}/H=0.28\). According to the comparison of \(\delta^{\prime}/H\) obtained by the different internal friction angle and cohesion change, the results show that the maximum of \(\delta^{\prime}/H\) from the two methods are approximately equal. For the crack depth, bigger value is not necessarily better, and the optimal crack depth explains this reason. The results of the above two cases show that no matter how the cohesion and internal friction angle change, the optimal crack depth will eventually become consistent. In fact, when choosing the crack depth, this study introduces the dimensionless coefficient \(\lambda\) to determine the optimal crack depth. \(\lambda\) is not a constant, the value range is \(\lambda\in\) [0-2], and its value changes with the change of cohesion and internal friction angle. As pointed out by [PERSON] [9] and [PERSON] [10], the crack depth should not exceed half of the slope height, and it represents the coefficient \(\delta^{\prime}/H<0.5\). By contrast, the optimal crack depth in the two cases is far less than half of the slope height, and it can be used as a reference for stability analysis of the slope with tensile cracks.  
Figure 13: Relationship between safety factor and \(\delta^{\prime}/H\).
# Space Node Topology Optimization Design Considering Anisotropy of Additive Manufacturing
## 3 Results
### Anisotropic Optimization Analysis of Space Nodes
#### 3.4.2 Anisotropic Optimization of Space Nodes  
In addition to optimization with isotropic materials, this section performed topology optimization on nodes that incorporated anisotropic materials. Before optimization, the anisotropy parameters were taken as material properties and substituted into the space node model in Section 3.1 for optimization to compare the similarities and differences in optimization with and without taking into account anisotropy.  
The results are shown in Figure 15.  
As shown in Figure 15, the topology optimization of anisotropic materials resulted in quite differently shaped nodes compared to the results of isotropic optimization. In the X printing direction, since the force in the X direction was the largest but the material was the weakest when scanning in the Z direction, the shear modulus became weaker, and the shear force was affected, generating more hole areas. Compared with the results when scanning in the Y direction, larger holes also appeared in the center of the node. When printing in the Y direction and the Z direction, the difference between different scanning  
Figure 15: Shape and displacement nephogram under anisotropic optimization: (**a**) X-direction print, Y-direction scan; (**b**) X-direction print, Z-direction scan; (**c**) Y-direction print, X-direction scan; (**d**) Y-direction print, Z-direction scan; (**e**) Z-direction print, X-direction scan; (**f**) Z-direction print, Y-direction scan.  
directions was small, but it can be seen that the shape was different. During Y-direction printing, due to the weak material properties in the Y-direction, more material accumulated on the boundary of the fixed end to provide more structural stiffness. When printing in the Z direction, the difference in the scanning direction resulted in the two sides being significantly affected by the shear force, while X-direction scanning weakened the shear stiffness on both sides of the node. Therefore, there was a little more material buildup at the boundary relative to the Y direction.  
As shown in Figure 16, in the cases of isotropic optimization and anisotropic optimization, there was an optimal manufacturing solution for this node, i.e., printing in the Y direction and scanning in the Z direction. In this paper, the optimal manufacturing direction was shown at the blue flower, where the maximum deformation after optimization of the two was closest. However, in other directions, the optimization results considering the anisotropic parameters performed better than the isotropic optimization. In summary, it is necessary to take the manufacturing direction into account when optimizing space nodes that require greater stiffness.
## 3 Results
### Experiments
#### 3.2.2 Experiment B  
The second experiment was carried out on a two-layer structure, as shown in Figure 9. The upper layer was made of polymethyl methacrylate (PMMA), with a sound velocity of 2337 m/s and a thickness of 20 mm. The lower layer was the same specimen used in experiment A. The two-layer structure was immersed in water, and the distance between the array center and the upper PMMA surface was 10 mm. The tilt angle of the linear array was set to 8 degrees, and the rest of the experimental parameters remained the same as in experiment A.  
Figure 10a shows the experimental result reconstructed by the proposed method. It can be seen that the proposed method can compensate for the tilt angle very well and can achieve a satisfactory result in the detection of two-layer structures. Figure 10b shows the normalized amplitudes of these four SDHs in the experimental result reconstructed by  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**SDH 1**} & \multicolumn{2}{c}{**SDH 2**} & \multicolumn{2}{c}{**SDH 3**} & \multicolumn{2}{c}{**SDH 4**} & \multicolumn{2}{c}{**Average**} & \multirow{2}{*}{**Time**} \\ \cline{2-2} \cline{5-10}  & **API** & **FWHM** & **API** & **FWHM** & **API** & **FWHM** & **API** & **FWHM** & **API** & **FWHM** & **Cost** \\ \hline Time- & & & & & & & & & & & \\ domain & 0.7640 & 1.0125 & 0.7374 & 1.0125 & 0.7204 & 1.0323 & 0.6993 & 1.0687 & 0.7302 & 1.0315 & 255.89 s \\ TFM & & & & & & & & & & & \\ Proposed & 0.6473 & 0.9652 & 0.6503 & 0.9375 & 0.6834 & 0.9750 & 0.6956 & 1.0125 & 0.6691 & 0.9726 & 4.25 s \\ Difference & \(-15.3\%\) & \(-4.8\%\) & \(-11.2\%\) & \(-7.4\%\) & \(-5.1\%\) & \(-5.6\%\) & \(-0.5\%\) & \(-5.3\%\) & \(-8.4\%\) & \(-5.7\%\) & \(-98.3\%\) \\ \hline \hline \end{tabular} Note: the unit of FWHM is millimeters (mm).  
\end{table}
Table 4: APIs, FWHMs and time costs with different imaging methods in experiment A.  
Figure 8: Normalized amplitudes of the results reconstructed by TFM and the proposed method.  
Figure 9: The geometry of the two-layer structure in experiment B.  
the proposed method. We also calculated the FWHM for every SDH and the SNR of the result, as listed in Table 5. As shown in Table 5, the proposed method achieved an average FWHM of 1.012 mm in the detection of the two-layer structure, and the SNR of the image was 26.18. This result is consistent with the results in experiment A. In fact, due to the presence of the water, this experimental object should be considered a three-layer structure. Therefore, the experimental results demonstrate the effectiveness of the proposed method for oblique incidence detection of multi-layer structures. Furthermore, Table 5 shows that the proposed method only costs 3.02 s to reconstruct an image with a size of 200 \(\times\) 300 on an ordinary PC.
# Optimizing Session-Aware Recommenders: A Deep Dive into GRU-Based Latent Interaction Integration
## 3 Proposed Method
### Problem Statements
#### 3.2.2 Context Generation Module  
The output from the embedding module constitutes the input for the context generation module. The primary objective of this module is to identify and capture latent-interactive information, herein referred to as 'Latent-context'. The process for generating this content information commences with the input of the embedding-transformed representation into a GRU, designated as the GRU (Inner). That is, \(R_{\begin{subarray}{c}\mu i,i\\ 1\end{subarray}}\), \(R_{\begin{subarray}{c}\mu i,i\\ 2\end{subarray}}\), \(\ldots\), \(R_{\begin{subarray}{c}\mu i,i\\ \end{subarray}}\), goes through the GRU (Inner) and produces the latent context \(C_{\begin{subarray}{c}\mu i^{\text{11}}_{1},C_{\begin{subarray}{c}\mu i^{ \text{21}}_{1},C_{\begin{subarray}{c}\mu i^{\text{11}}_{1},\ldots,\end{subarray} }\end{subarray}}\end{subarray}}\), \(\ldots\), \(C_{\begin{subarray}{c}\mu i^{\text{11}}_{1}\end{subarray}}\). Within this neural network framework, user information is subject to a learning process.  
Upon each iteration through the GRU, two distinct outcomes are yielded: the hidden state and the current prediction representation. The prediction representation, as derived from the GRU, is conceptualized as Latent-context and is subsequently stored within a dedicated Latent-context list. This list is further employed by another GRU, named GRU (Outer). The rationale for selecting this specific information as Latent-context, is based on the methodology of generating the prediction representation at any given temporal point. This involves using both the hidden state and the current interaction record as inputs. The hidden state represents a cumulative preference profile, developed through the analysis of historical interaction records up to the current moment. Consequently, the prediction representation, grounded in these derived preferences, facilitates the generation of user-specific items of potential interest.
# Solving the Time-Varying Inverse Kinematics Problem for the Da Vinci Surgical Robot
## 2 Kinematics Analysis  
### Forward Kinematics Analysis  
During surgery using the Da Vinci medical robot, dual articulated arms (slave manipulators) work as the right and left hand of the surgeon, respectively. The coordinate frame of the master/slave manipulator is set the same as the coordinate frame of human sight, i.e., the world coordinate frame. Therefore, in Figure 1, the origin of the base coordinate frame of the slave manipulator is located at the bottom of the robot frame. The \(Z_{0}\)-axis is upward, along the robot frame.  
Before surgery, the angles of passive joints \(M\), \(N\), and \(P\) and the position of the \(M\) joint on the frame are adjusted manually and then remain fixed during surgery. The joints that mainly assist the surgeon are the active joints of the slave manipulator: \(Q\), \(E\), \(G\), \(K\), \(H\), \(I\), and \(J\) in Figure 1b.  
The DOFs of the slave manipulator includes four DOFs: \(I_{0}\), \(\theta_{1}\), \(\theta_{2}\) and \(\theta_{3}\). The parts 6, 7, and 8 in the active arm belong to the parallelogram mechanism (i.e., these three parts cannot move freely but under the motion constraint of the parallelogram mechanism in Figure 1c. Then only parts 4, 5, 8 and 9 can move freely in the slave manipulator, i.e., part 5 rotates around part 4, there is rotation between part 6 and part 5, and there is rotation between part 9 and part 8. Therefore, the entire slave manipulator will have 6 DOFs when the additional 3 DOFs of the \(H\) joint are considered.