esa-sceva/satcom-chunk-collection huggingface dataset:
# LSTNet: A Reference-Based Learning Spectral Transformer Network for Spectral Super-Resolution
## 3 Proposed Method
### Spectral Reconstruction Module  
As shown in Figure 1, the SRM module is composed of three backbone sub-networks. The architecture of each backbone sub-network is illustrated in Figure 3. The reason we call them backbone sub-networks is because the role of the STM module is to borrow the spectral information from ref-HSI, while the role of the SRM is to store the mapping priors in the form of the learned parameters in the neural network.  
To mitigate the gradient disappearance problem, which can occur in deep networks, a global residual connection was adopted. The overall network was formed by stacking a set of residual dual-channel attention blocks (RDCA) in order to form a deep network for spectral reconstruction. Each RDCA consists of a local residual connection, a set of convolutional layers having 3 \(\times\) 3 kernel size, and two-channel attention sub-modules. Based on the global and local residual connections, the network allows for the abundant low-frequency features of the input data to be bypassed and the high-frequency features to be sufficiently extracted. In the RDCA, average channel attention and channel max attention were used to exploit the adaptive learning of channel weights. Similar to CBAM [43], we exploited the inter-channel relationships of features through the use of two pooling layers.  
Figure 3: Illustration of the backbone network.  
Figure 2: Illustration of the feature extraction sub-network.  
The average pooling layer calculates the average spatial statistics, and the max-pooling layer calculates distinctive object features. In contrast to CBAM, we further calculate the two-channel reallocated features separately. The two features are then combined through a concatenation function. Therefore, in the backbone network, feature redistribution between channels benefits learning of the prior information of spectral features.  
In the SRM, three backbone sub-networks were used to learn the spectral reconstruction process: the backbone head only takes the input MSI as an input to learn the preliminary features, while the backbone body and tail take the transformed hard and soft attention spectra as inputs, along with the features from the previous level, as shown in Figure 1. In summary, the transformed spectral features and spectral priors are combined in the SRM to boost the accuracy of spectral reconstruction.
# Urban Road Anomaly Monitoring Using Vision-Language Models for Enhanced Safety Management
## 2 Methods
### Model Selection  
Our approach commenced with an initial screening of object detection models, Vision-Language Models, and image classification models. This preliminary analysis highlighted the inherent features of these models, their effectiveness for real-time assessments, and their adaptability for monitoring road anomalies and describing road waterlogging.  
Based on this evaluation, we selected the InternVL model due to its high applicability. Additionally, we chose ResNet34 as a reference model to facilitate result testing and comparison. This decision enhances the analytical depth of our research scenario and establishes a solid foundation for subsequent optimization and experimentation (Table 5).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Level** & **Types** & \multicolumn{2}{c}{**Number of Data Points**} \\  & & & **(Testing Dataset)** \\ \hline
1 & GREEN & Normal & 675 & \\  & & Flooding (depth \(\leq\) 0) & 39 & 714 \\ \hline
2 & LIGHT & Garbage & 364 & \\  & GREEN & Traffic jam & 437 & 844 \\  & & Flooding (10 \(<\) depth \(\leq\) 150) & 43 & \\ \hline
3 & & Bad road & 214 & \\  & & Fallen tree & 296 & \\  & YELLOW & Fire & 305 & 1476 \\  & & Traffic accident & 433 & \\  & & (depth \(>\) 150 mm) & 228 & \\ \hline  & Total & & 3034 & 3034 \\ \hline \end{tabular}
\end{table}
Table 4: The testing dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & & & **Suitability for** & **Suitability for** & **Suitability for** \\  & & **Main Features** & **Real-Time** & **Urban Road** & **Urban Road** \\  & & & **Monitoring** & **Anomaly** & **Waterlogging** \\  & & & **Classifications** & **Monitoring** \\ \hline
1 & ResNet34 & A fast, simple & Medium & & Medium-Low \\  & (Image & convolutional neural & It focuses on image & High \\  & classification & network that & classification rather & It focuses on image \\  & & performs & than real-time & classification. & depth. It should be used in conjunction \\  & & classification. & processing. & & used in conjunction with other models. \\ \hline
2 & YOLO & Real-time processing & High & & Medium-Low \\  & (You Only Look & with rapid inference & It is fast in inference & It is suitable for \\  & Once) & speeds, well-suited & speed and efficient & It is suitable for \\  & (Object detection & for detecting & in object detection \\  & model) & multiple objects for & capabilities. & classifying & other models for \\  & & specific classes [49]. & & & optimal results. \\ \hline
3 & & High detection accuracy with & Low & & Medium-Low \\  & Faster R-CNN & Region Proposal & Less suitable due to & Highly suitable for \\  & (Object detection & Networks (RPN), & slower inference & Accurate anomaly \\  & model) & ideal for detailed & speeds compared to & detection and \\  & & segmentation & single-stage & classification. \\  & & tasks [50]. & & & Medium-Low \\  & & & & \\ \end{tabular}
\end{table}
Table 5: The comparison of popular vision models.
# Design of a Laparoscopic Robot System Based on Spherical Magnetic Field
## 1 Introduction  
### Background  
The field of surgery has significantly advanced from traditional open surgery to minimally invasive surgery [1], and now there are new trends in this field aiming to meet the demands of surgeons and patients. One of these trends is the procedure known as single-incision laparoscopic surgery (SILS), which is attracting a great deal of attention worldwide because of the benefits that this method can offer. In SILS, medical instruments and the laparoscope are operated through a single incision in the derma of the patient's abdominal cavity. Compared with traditional laparoscopic surgeries, SILS has several possible benefits including reduced operative complications, reduced postoperative pain, and better cosmetic results [2]. In SILS, however, because the surgical instruments share a common incision, the interferences between the laparoscope and other medical instruments constrain the dexterity of surgical instruments and affect the field of views of the laparoscope.  
To solve these problems and take account clinical needs and technical requirements [3], fully inserted laparoscopes are designed. The interferences and visual affection can be alleviated by keeping the laparoscope away from the surgical incision. For a fully inserted laparoscope, the main challenge lies in the position and orientation control. Position and orientation here refer to the position and orientation of the robot relative to abdominal wall. In order to get clear and broader visual feedback in SILS, the position and orientation of the laparoscope should be controllable.
## 2 Materials and Methods
### Histological Evaluation Procedure  
We collected the immunohistochemical data carried out by our Pathological Anatomy department, such as tumor size (T, measured in mm), histological subtype (ductal, lobular, other special types), estrogen receptor expression (ER, Pos/Neg), progesterone receptor expression (PgR, Pos/Neg), histological grade (G, Elston-Ellis scale: 1, 2, 3), cellularmarker for proliferation (Ki67, Pos/Neg with cut-off 20%), human epidermal growth factor receptor-2 (HER2/neu: 0, 1+, 2+, 3+), tumor multiplicity (Pos/Neg), presence of carcinoma in situ associated with invasive component (Pos/Neg), and also the sentinel lymph nodes status (N, Pos/Neg) required in the associated supervised approach.  
The tumor grade G was defined by the Elston-Ellis modification of the Scarff-Bloom-Richardson grading system on a three-grade scale, i.e., grade G1 (low grade), G2 (intermediate grade) or G3 (high grade), where a lower grade indicates a better prognosis [24]. The histological exam was performed through multiple biopsy sampling with 14-16 G core under ultrasound guidance. Immunohistochemistryichemical expression is categorized according to the following molecular subtypes on the basis of the St. Gallen convention [25], using a threshold equal to 20% for Ki67 [26]: luminal A (ER+ and/or PR+, HER2- and low Ki67), luminal B (ER+ and/or PR+, and HER2+ or HER2- with high value of Ki67), HER2 positive (ER/PR- and HER2+) and triple negative (ER-, PR- and HER2-).
## Materials and Methods
### Spectral Similarity Metrics  
To test the spectral similarity between Scots pine canopy sunlit fraction pixels observed from different view angles, we used the spectral information divergence measure (SID) [30]. SID is the discrepancy of the probability distribution between two spectral vectors. [PERSON] [31] has tested several common spectral similarity algorithms using synthetic and measured Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. They concluded that SID is more effective in mapping and detecting targets, and is less sensitive to spectral noise compared with other tested spectral metrics.
## 5 Computational Analysis
### Response Analysis with SFD Nonlinear Oil Film Force and Support Nonlinear Restoring Force
Near the second and third critical speeds, the curve with only the SFD oil film force added shows an increase of approximately 1.5 Hz in the second-order frequency and a slight decrease of 0.1% in amplitude, with the third-order frequency remaining unchanged and the amplitude increasing by 0.5% compared to the curve without nonlinear forces. After the introduction of the support nonlinear force, there is a significant increase in both amplitude and frequency near the second and third critical speeds. Consequently, the oil film gap \(C\) was adjusted from 9.6 \(\times\) 10\({}^{-6}\) m to 1.1 \(\times\) 10\({}^{-5}\) m. With both nonlinear forces in effect, the second-order peak value decreased by 2%, and the third-order peak value increased by 8%. Additionally, due to the increase in the second-order frequency, the second critical speed approached the third critical speed, with both peaks being constrained to the same value by the SFD. Next, nonlinear oil film forces and nonlinear restoring forces were applied to the three bearings, respectively. The data for the forces applied are as follows: (1) Support 1(\(C\) = 5.5 \(\times\) 10\({}^{-5}\), \(k_{\text{bnl}}\) = 1 \(\times\) 10\({}^{8}\)); (2) Support 2(\(C\) = 1.4 \(\times\) 10\({}^{-5}\), \(k_{\text{bnl}}\) = 8 \(\times\) 10\({}^{8}\)); (3) Support 3(\(C\) = 1.1 \(\times\) 10\({}^{-5}\), \(k_{\text{bnl}}\) = 2 \(\times\) 10\({}^{9}\)). The axial trajectories of the three bearings are shown in Figure 21. It can be observed that after applying both nonlinear forces (oil film and restoring), bearing 1 exhibits the disappearance of bi-stability near the third critical speed, with its axial trajectory returning to a perfect circle; bearing 2 maintains a perfect circular axial trajectory even after applying both nonlinear forces; and for bearing 3, the amplitudes and axial trajectories near the second and third critical speeds tend to converge, but the third-order trajectory shows slight irregularity. Figure 20: Amplitude–frequency characteristics for bearing 3 with both nonlinear forces applied.
# Measurement and Analysis of Vibration Levels for Truck Transport Environment in Korea
## 1 Introduction
The first is the average method of all recorded events under various conditions (the average composite spectra) [15; 23], and the other connects the highest PSD level breakpoints by considering all various recorded PD spectra [11; 23; 32]. In particular, the latter method produces conservative results that include all results measured under different conditions. Research on the vehicle transport environment in Korea is at a rudimentary stage. In particular, no research on the vibration level for representative trucks and road types applied to Korea's domestic freight transport is available. These studies are needed to compare domestic transport vibration levels with those presented by international standards (ASTM: West Conshohocken, USA, ISTA: Chicago, USA) and to simulate damage to packaged products by domestic transport environments. Therefore, we attempted to measure the dynamic vibration level of the distribution environment in Korea, construct the PSD profile for conducting the transport simulation in the lab and compare the results with the dynamic level of the distribution environment in other countries. The specific research objectives include:  
1. Analysis of the transport vibration levels for trucks and routes representative of Korea's freight transport and development of a simulation protocol for this truck transport environment. Qualitative analysis of the characteristics of Korea's truck transport environment by comparing the truck transport vibration levels measured in the pre-planned test route with exiting standardized test protocols. * Proposal of optimum cushioning packaging design level of packaging products to prevent damage caused by vibration and shock during transport.
# A Review on the Importance of Microbial Biopolymers Such as Xanthan Gum to Improve Soil Properties
## 4 Xanthan Gum Biopolymer  
### Xanthomonas Campstris Species  
Xanthan gum (C\({}_{35}\)H\({}_{49}\)O\({}_{29}\)) is a polysaccharide formed by aerobic fermentation of sugar of the bacterial species _Xanthomonas campstris_ and is commonly used as a food additive due to its characteristics as a hydrocolloid rheology modifier [20; 21]. It is an anionic polysaccharide composed of D-uronic acid, D-mannose, pyruvylated mannose, 6-0-acetyl D-mannose, and 1,4-linked glucan [19]. The chemical structure is led by the linear chain 1,4-linked \(\beta\)-D glucose backbone with two tri-saccharide elements pairing the side chain on every glucose element. The other side chain is composed by a D-glucuronic acid element-linked between two D-mannose elements [15; 19]. The xanthan gum-produced by the type strain of the bacterial species is considered by the US Food and Drugs Administration (FDA) as an additive for industrial food; it is of great interest because it has rheological characteristics not found in other polymers [16]. _Xanthomonas campestris_ is an aerobic and microaerophilic bacterium, so it is easily cultivated in the laboratory with an optimum growth temperature of about 25-30 \({}^{\circ}\)C. This species grows rapidly and produces turbidity in the medium within approximately 2 or 3 days of cultivation with an optimal pH between 6 and 8 [17].
# Time-Frequency Analysis of Daily Activities for Fall Detection
## 3 Results
### Characteristics of Rigorous Activities and Falls  
As previously shown in Figure 3, rigorous activities such as running, jumping and falling exhibit peak acceleration ranges that overlap with one another in the time domain. Therefore, traditional thresholding-only methods for fall detection in the time-domain experience low specificity unless when thresholds are set too low. However, when these critical thresholds are raised to eliminate false positive detection, sensitivity (i.e., detection of true positives) decrease as some falls may be missed.  
To resolve this, we perform additional analysis of the acceleration data in the frequency domain. Sample time, time-frequency, and PSD plots of the acceleration signal are shown in Figure 4 for rigorous activities. For each activity, time-domain waveforms for Acc and (Acc)\({}_{\text{yz}}\) are provided in the first and second rows, respectively. Time-frequency and PSD information are given in the third and fourth rows for each activity. These results are also summarized in Table 1.  
For running, peak acceleration ranged between 2.2-2.8 g, with peak PSD ranging between 3.5-5.5 Hz. Energy spectra below 3.5 Hz were appreciably lower. For jumps occurring in the forward and vertical direction, peak acceleration was observed between 2-4 g with distinctly different peak energy ranges, 1-2 Hz for forward jump; 2.5-3.5 Hz for vertical jump). Finally, for fall events, we observed peak acceleration between 2.5-5.4 g while peak energy occurred between 2-3.5 Hz.
## 3 Performed Full-Scale/Model Test Comparisons
### Typical Full-Scale/Model Test Discrepancies  
From the above literature reviews, it is clear that most full-scale/model test comparisons arrived at the conclusion that the discrepancies between the full-scale measurement results and the wind tunnel data were noticeable. The typical discrepancies can be summarized as below:  
1. Mean and/or peak negative pressures at the flow separation regions on low-rise building roofs were usually underestimated in the wind tunnel [7; 9; 13; 14; 15; 24];
2. The local fluctuating pressures attributable to vortex shedding on high-rise building models sometimes differed from those on the prototypes [27];
3. The aero-elastic model tests for high-rise buildings were found to underestimate the real dynamic structural responses in the intermediate-frequency range [4];
4. Differences in the rms values of the acceleration samples between the full-scale measurements and the force balance model tests were sometimes in the range of 4-25% for high-rise buildings [32; 34; 35; 36; 37];
5. The vertical VIV amplitudes of long-span bridges measured using section models and aero-elastic models were much lower than those observed with the prototypes [42; 44];
6. The pressure fluctuations measured on the prototypes followed a non-Gaussian distribution, whereas the model test samples followed a Gaussian distribution [37; 47; 48; 50];
7. Coherences between wind pressure samples at separated locations were stronger for model tests than for the full-scale measurements [21; 50].
## 2 LEO Satellite Networks Model
### Setup and Update of Link State
The threshold \(T_{1}\) and \(T_{2}\) meet \[(T_{2}-T_{1})\cdot L_{\max}=\left[I_{avg}-O_{avg}\right]\cdot t_{c}\] (7) \[(1-T_{2})\cdot L_{\max}=\left[I_{avg}-O_{avg}\right]\cdot t_{c}\] (8) Considering the extreme situation, we get \[T_{1}=\min(\max(1-\frac{2\left[I_{avg}-O_{avg}\right]\cdot t_{c}}{L_{\max}},0 ),1)\] (9) \[T_{2}=\min(\max(1-\frac{\left[I_{avg}-O_{avg}\right]\cdot t_{c}}{L_{\max}},0 ),1)\] (10)  
The link state is marked as Free State (FS) when \(q\) is below \(T_{1}\) and is considered to be Busy State (BS) if \(q\) is between \(T_{1}\) and \(T_{2}\). It is defined as Congested State (CS) when \(q\) exceeds \(T_{2}\). To monitor and control the load effectively, if the link state is BS or CS, the satellite should send a notification including the traffic reduction ratio \(X\) to its neighbor and request it to decrease the input packet rate to \(I_{avg}\cdot X\). When the satellite enters BS, assuming the desired time for a satellite to reside in FS is set to be \(t_{s}\), the traffic reduction ratio is calculated by Equations (11) and (12). \[T_{2}=\min(\max(1-\frac{\Big{[}l_{avg}-O_{avg}\Big{]}\cdot t_{c}}{L_{\max}},0),1) \tag{11}\]  
\[X=\min(\max(\frac{I_{s}}{I_{avg}},0),1) \tag{12}\]  
When the satellite enters CS, it should require its neighbor to stop transmitting the packet immediately. Therefore, \(X\) is set to be 0.
## 3 Results and Discussion
### Results of MSCR Test on Original and RTFO-Aged Binders  
The statistical analysis results from Table 7, alongside the graphical data from Figure 5, give a thorough assessment of the impact of incorporating TLA and SBS into PG64-22 asphalt binders. These findings focus on % recovery and non-recoverable compliance Jnr, offering insight into the potential for high-performance asphalt binders in pavement engineering.  
The control binder, PG64-22, shows a poor performance with low % recovery and high Jnr values. Statistical analysis confirms these results as significant (S) when compared to binders with TLA and SBS additions, particularly under RTFO conditions. Adding 10% or 20% TLA alone improves performance but remains statistically non-significant (N) in most comparisons under original conditions. However, under RTFO conditions, the performance differences become statistically significant, showing the contribution of TLA to binder stability after aging.  
Among the tested combinations, 10% TLA with 15% SBS and 20% TLA with 15% SBS show an almost identical performance in % recovery and Jnr. Both combinations achieve a % recovery exceeding 80% in the original condition and 88% after RTFO aging, along with negligible Jnr values \(<\) 0.01 kPa\({}^{-1}\). This similarity indicates that 15% SBS is the optimal threshold for achieving high-performance properties, regardless of whether 10% or 20% TLA is used. Statistical analysis supports this observation, showing no significant differences (N) between these two combinations, reinforcing their equivalence in performance. These findings emphasize the critical role of SBS in achieving high-performance asphalt binders suitable for heavy traffic and extreme climatic conditions.  
In conclusion, the integration of TLA and SBS into PG64-22 produces high-performance asphalt binders capable of meeting the demands of modern pavement engineering. The combination of 20% TLA and 15% SBS is recommended as the optimal formulation, balancing elasticity, rutting resistance, and long-term durability, paving the way for resilient and sustainable road infrastructure.
## 3 Verification of the Constitutive Model
### LSFRC Beam Performance Test  
The verification object selected for this study is the laminated fabric steel fiber concrete beam subjected to static testing, as documented in the literature [15]. To establish a 3D solid model, the structural dimensions of the beam are set to 150 mm \(\times\) 150 mm \(\times\) 550 mm in FEM ABAQUS software. A four-point bending load method is employed, wherein the PC and FRC units are represented by C3D8R units. Both units are embedded within the reinforcement bars using the embedded region contact method in the FRC solid model. Displacement coupling is utilized for the loading method. The material parameters used in the analysis are presented in Table 1.  
As illustrated in Figure 5, the CDP model adopted in this paper was employed to predict the load-displacement curve of the layered steel fiber-reinforced concrete tension-bent beam, as presented in the literature [15], based on the test values and finite element analysis results. The simulation results presented in this paper are in close agreement with the experimental data observed in the rising section of the curve, when compared with the values reported in the literature. Secondly, following the peak load, the CDP model is able to accurately predict the reduction in stiffness of the structure, resulting in a distinct decline in the curve. As the load is increased further, the proportion of load borne by steel fibers in the tensile crack zone of concrete increases. This has the effect of slowing down the expansion of the crack and improving its toughness. Consequently, the test curve rises once more. Finally, the simulation results of the CDP model demonstrate a gradual tendency towards stability, which is in good agreement with the experimental values. This indicates that the model is reliable and accurate.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Compressive Strength/MPa** & **Tensile Strength/MPa** & **Elastic Modulus/GPa** & **Poisson’s Ratio** \\ \hline RC & 35.6 & 2.82 & 39 & 0.2 \\ SFRC & 37.3 & 3.54 & 41 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters [15].  
Figure 4: Validation of SFRC cell stress–strain curves: (**a**) axial compressive curve; (**b**) axial tensile curve.