esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Approach
### Integrated Gradients (IGs)  
Integrated gradients (IGs) are a frequently used attribution-based XAI method to identify the words that strongly influence the target prediction. Attribution-based methods use DNN predictions to estimate the significance of the input characteristics, which reveal reasons for the model predictions [38]. IGs are a local explainability method that can be applied to any differentiable DNN model with several advantages over other attribution methods [38]. The IG XAI method can be easily implemented in DNNs compared to other approaches as well [38]. A reference baseline must be defined to get the feature attributions using IGs. IGs generate feature attributions in comparison to this baseline that do not contain informative content [39]. For image-based DNN models, the baseline input can be a black image, whereas for text classification models, it may be a zero embedding vector [15]. This reference baseline can be identified as a neutral baseline, which is defined to indicate the absence of input features.  
\[IG_{i}(x,x^{\prime})::=(x_{i}-x^{\prime}_{i})\times\int_{\alpha=0}^{1}\frac{ \partial f(x^{\prime}+\alpha\times(x-x^{\prime}))}{\partial x_{i}}d\alpha \tag{1}\]  
Let's define \(f:\mathbb{R}^{n}\rightarrow[0,1]\) to represent the deep neural network and let \(x\in\mathbb{R}^{n}\) be the input while \(x^{\prime}\in\mathbb{R}^{n}\) is the baseline input [15]. IGs are computed by accumulating the gradients at all points, which are evaluated along a straight-line path from the baseline \(x^{\prime}\) to the input \(x\)[15]. IGs are defined as the path integral of the gradients computed along a straight-line path from the baseline \(x^{\prime}\) to the input \(x\). Equation (1) defines the IG along the \(i\)-th dimension for an input \(x\) and baseline \(x^{\prime}\)[15]. The \(\frac{\partial f(x)}{\partial x_{i}}\) in Equation (1) refers to the gradient of the model \(f\) with respect to the \(i\)-th dimension [38]. This reference baseline intends to get a high entropy prediction with greater uncertainty. Subsequently, the input features are added to the reference baseline, which interpolates towards the complete input, which reduces the uncertainty present in the absence of the input features [38]. Then, integrated gradients are derived by accumulating these gradients.  
When applying IGs in the context of NLP DNNs, \(x\) is the concatenated embedding of the input sequence. The attribution for each token is computed by aggregating the attributions of its corresponding embedding [38]. We used padding tokens to define the reference baseline during our experiments. This is because the padding token is a neutral token that does not impact model predictions. In the IG method, we should specify the number of steps that should be taken to reach the baseline with reference to the actual input of the model. The authors of the IG paper recommend having step sizes between 20 and 300 [15]. The model provides an attribution value for each input feature during the calculation steps, indicating how much it influenced the target prediction.
## 2 Theory and Methods
### Model of the Optomechanical System  
2.2.1. Optical System Design  
In order to satisfy the performance parameter and the structure types of the optical system, the optical system was designed and optimized based on ZEMAX optical design software in our previous studies [8], as shown in Figure 2.  
Figure 1: Schematic diagram of energy transmission between two facets.  
Figure 2: Optical system based on an aspheric surface camera.  
For imaging systems, the modulation transfer function (_MTF_) is the most comprehensive standard among all optical system performance standards. Figure 3 shows the image intensity map, starting from a periodic target, its intensity changes in the form of a sine curve, and finally through the lens imaging. Due to factors such as aberration, diffraction, assembly, and calibration errors, the image quality declines. Definition terms are shown in Equation (4):  
\[\left\{\begin{array}{l}\text{Modulation}=\frac{I_{\text{max}}-I_{\text{min} }}{I_{\text{max}}+I_{\text{min}}}\\ \\ MTF=\frac{\text{Modulation of the image}}{\text{Modulation of the object}}\end{array}\right. \tag{4}\]  
where _MTF_ represents a function of spatial frequency, and the unit is lp/mm. Therefore, _MTF_ represents the relationship between the modulation transfer from the object through the lens to the image and the spatial frequency.  
In addition to evaluating whether the image quality and other indicators of the system meet the required standards, tolerance should also be considered during the evaluation process. For general equipment, structures with larger tolerances are easier to install. For instruments that require higher precision, the required tolerances are smaller, often exceeding the capabilities of current processes, and it is difficult to successfully process and assemble.  
The precision of the lens aperture radius of curvature is 1 mm, the element thickness and the air interval are 0.01 mm, the element eccentricity is 0.02 mm, and the element assembly tilt is 0.02\({}^{\circ}\). Modify the default tolerance function, select the sensitivity analysis method, and finally perform the tolerance analysis. The average _MTF_ value is used as the evaluation standard, and the tolerance analysis of the aspheric camera optical system is performed. The analysis results are shown in Figure 3.  
After setting the tolerance of the optical system, the optical system has a 50% probability of reaching 0.43 at the cut-off frequency of 63 lp/mm and a 90% probability of reaching 0.36, which can meet the imaging requirements of aerial cameras. The aerial camera lens consists of a lens, a press ring, a spacer ring, a lens barrel, and a flange. Figure 4 shows the two-dimensional structure of the camera.
# Localization of GNSS Spoofing Interference Source Based on a Moving Array Antenna
## 3 Proposed Methods
### The CRB of the Method Proposed in This Article
^{-1} \tag{27}\]  
where \(\mathbf{R_{1}}\), \(\mathbf{R_{2}}\), and \(\mathbf{R_{3}}\) are defined as follows. \[\mathbf{R_{1}}=E\bigg{[}\bigg{(}\frac{\partial^{2}Inf(\hat{\mathbf{u}},\hat{\mathbf{v}}, \mathbf{\Phi})}{\partial\mathbf{p_{t}}\partial\mathbf{p_{t}}^{T}}\bigg{)}\bigg{]}=\bigg{(} \frac{\partial\mathbf{u}}{\partial{p_{t}}^{T}}\bigg{)}^{T}\mathbf{Q_{u}}^{-1}\frac{ \partial\mathbf{u}}{\partial{p_{t}}^{T}} \tag{28}\]  
\[\mathbf{R_{2}}=E\bigg{[}\bigg{(}\frac{\partial^{2}Inf(\hat{\mathbf{u}},\hat{\mathbf{v}}, \mathbf{\Phi})}{\partial\mathbf{p_{t}}\partial^{T}}\bigg{)}\bigg{]}=\bigg{(}\frac{ \partial\mathbf{u}}{\partial\mathbf{p_{t}}^{T}}\bigg{)}^{T}\mathbf{Q_{u}}^{-1}\frac{ \partial\mathbf{u}}{\partial\mathbf{v}^{T}} \tag{29}\]  
\[\mathbf{R_{3}}=E\bigg{[}\bigg{(}\frac{\partial^{2}Inf(\hat{\mathbf{u}},\hat{\mathbf{v}}, \mathbf{\Phi})}{\partial\mathbf{v}\partial^{T}}\bigg{)}\bigg{]}=\bigg{(}\frac{ \partial\mathbf{u}}{\partial\mathbf{v}^{T}}\bigg{)}^{T}\mathbf{Q_{u}}^{-1}\frac{\partial \mathbf{u}}{\partial\mathbf{v}^{T}}+\mathbf{Q_{v}}^{-1} \tag{30}\]  
Based on the inverse formula of the block matrix in [37], the following CRB calculation equation can be given. \[CRB(\mathbf{p_{t}})=\Big{(}\mathbf{R_{1}}-\mathbf{R_{2}}\mathbf{R_{3}}^{-1}\mathbf{R_{2}}^{T} \Big{)}^{-1}=\mathbf{R_{1}}^{-1}+\mathbf{R_{1}}^{-1}\mathbf{R_{2}}\Big{(}\mathbf{R_{3}}-\mathbf{R _{2}}^{T}\mathbf{R_{1}}^{-1}\mathbf{R_{2}}\Big{)}^{-1}\mathbf{R_{2}}^{T}\mathbf{R_{1}}^{-1} \tag{31}\]
# Joint AP Association and Bandwidth Allocation Optimization Algorithm in High-Dense WLANs
## 3 Network Model and Design Requirements
### Network Model
#### 3.2.2 Transmission Time  
Transmission time demanded by client \(i\) is the length of time to transmit the data that the client requires from AP \(j\). It is formulated as follows.  
\[T_{ij}=\frac{S_{i}}{r_{i}}=\frac{B_{i}}{r_{i}}T_{j} \tag{2}\]  
where, \(T_{j}\) is the total transmission time of AP \(j\), which satisfies \(T_{j}=\sum_{i=1}^{n}T_{ij}\), \(T_{ij}\) is the transmission time demanded by client \(i\) after the association is made to AP \(j\); \(s_{i}\) is the size of data that client \(i\) requires; \(r_{i}\) is the transmission rate of client \(i\) after the association; \(B_{i}\) is the size of data required by client \(i\) during per unit of time.
# Rethinking the Operation Pattern for Anomaly Detection in Industrial Cyber-Physical Systems
## 2 Related Work
### Automatic Features  
To improve the features' ability, some studies prefer to use machine learning methods to choose or combine the raw features, e.g., [PERSON] et al. [9] used principal component analysis (PCA) to process the raw features. [PERSON] et al. [21] further proposed combining echo state networks (ESNs) and auto-encoders (ESN-AE) to extract features and detect anomalies in industrial systems. [PERSON] et al. [30] proposed using combinations of convolution layers and pooling layers to extract features and identify anomalies using a siamese convolutional neural network (SCNN). [PERSON] et al. [31] also proposed using CNN to conduct feature extraction and classification tasks. The automatic features achieve a good performance and could save effort in feature extraction; however, these features make the classification results difficult to explain.
## 5 Experimental Results and Analysis
### Performance under a Single Anomaly Scenario

Table 4: Detection performance of the proposed model under the bias scenario. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Signal** & **Acc** & **Sens** & **Prec** & **F1** \\ \hline
1 & 95.91 & 94.83 & 96.93 & 95.87 \\
2 & 96.05 & 95.10 & 96.94 & 96.01 \\
3 & 95.86 & 94.73 & 96.93 & 95.81 \\
4 & 95.01 & 93.76 & 96.17 & 94.95 \\
5 & 94.85 & 93.76 & 95.84 & 94.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detection performance of the proposed model under the injection scenario. Figure 5 illustrates the graphical depiction results of the CWT-CNN model under different anomaly scenarios of five different signals, respectively. It can be seen that the detection performance generally varies among the different sensor signals. Specifically, for signal 4 and signal 5 (i.e., acceleration and torque of wheel), across all five anomaly scenarios, the evaluation metrics of the two signals are lower than those of signal 1, 2, and 3. This is partly because there is the possibility of a large variation between consecutive acceleration signal observations compared with the other three speed-related sensor signals, which have a relatively steady variation over time. The torque of wheel signal has a similar phenomenon of detection performance to acceleration signals due to the two being correlated in terms of the physical invariant [53].
# AMBIQUAL: Towards a Quality Metric for Headphone Rendered Compressed Ambisonic Spatial Audio
## 3 Subjective Listening Tests Using the MUSHRA Methodology
### Experiment 1-Single Point Audio Sources  
Both listening quality and localization accuracy were evaluated using single point audio sources. Twenty-one assessors (20 male and 1 female) took part in the listening tests. The average age was 32 and the age range was between 20 and 53. Among assessors, there were 9 experienced listeners (professional audio engineers and academics with prior experience of similar tests) 12 semi-experienced listeners (post-graduate students in TUD and TCD). Following the ITU-T recommendation, subjects who gave the hidden reference a score of less than 90 for more than 15% of the test items were excluded. Results of three outliers were excluded. The AmbiX decoder [25] with the SADIE II database [26] was used to render the Ambisonic audio for binaural subjective presentation.  
Figure 2: WebMUSHRA Graphical User Interface.
# Research Progress and Typical Case of Open-Pit to Underground Mining in China
## 3 Typical Case: Integrated Demonstration of Xinqiao Iron Mine
### Determining the Crown Pillar Thickness at Xinqiao Mine  
The stability of the crown pillar plays a crucial role in ensuring the safe transition from open-pit to underground mining at Xinqiao Mine. The ore body characteristics below the \(-156\) m level combined with complex hydrogeological conditions pose significant challenges to crown pillar stability. To address this, a combination of theoretical calculations and numerical simulations was employed to assess the stability of both the original crown pillar and the composite pillar under varying mining span conditions. This approach provides both the theoretical foundation and engineering support necessary for determining the optimal crown pillar thickness.  
Theoretical calculations employ methods such as the thickness-to-span ratio, load transfer line intersection, and structural mechanics to analyze the safety thickness of the original crown pillar. The results show that as the pillar thickness increases, the stability of the original crown pillar improves progressively. However, when the mining span exceeds 40 m, the pillar's stability reaches a critical threshold. Therefore, the theoretical analysis recommends that during the transition from open-pit to underground mining, the mining span should be kept under 40 m to ensure the stability and safety of the crown pillar.  
To further assess the stability of the crown pillar, numerical simulations were conducted, with the results for the composite crown pillar thickness shown in Figure 8. The simulation results show that, with a fixed mining span, the maximum tensile stress in the original crown pillar decreases as the pillar thickness increases, while the tensile stress safety factor increases with the thickness. For the composite pillar, the application of a 1 m thick reinforced concrete layer results in a more even distribution of stress, effectively reducing tensile stress and enhancing the pillar's safety and stability. This indicates that the composite pillar provides clear stability advantages in high-stress environments.  
Figure 8: Numerical simulation model of the thickness of the top column of the composite realm. Generated by ANSYS (ANSYS, Inc., Canonsburg, PA, USA), version 2022 R1. Available online: [[https://www.ansys.com/](https://www.ansys.com/)]([https://www.ansys.com/](https://www.ansys.com/)) (accessed on 28 July 2025).  
Based on the results of both theoretical calculations and numerical simulations, the research team recommends a 24 m thick original rock crown pillar for the ore body above the \(-\)180 m level, ensuring that the tensile stress safety factor remains at or above 1.2. This design not only satisfies safety requirements but also meets the technical demands for ground pressure control and resource recovery during the transition from open-pit to underground mining. Furthermore, this design considers both the economic viability and practical feasibility of operations, providing a valuable reference for pillar design in similar mining projects.
# Research on Rapid Recognition of Moving Small Targets by Robotic Arms Based on Attention Mechanisms
## 4 Experimental Results and Discussion
### Object Recognition Images  
The prediction function of the enhanced YOLOv8 network model was utilized to evaluate the test set, with the results depicted in Figure 15. Within the previously described operational environment, the processing time for each image through the improved model was allocated as follows: 0.6 milliseconds for preprocessing, 95.6 milliseconds for inference, and 0.5 milliseconds for post-processing. Analyzing the displayed image alongside the prediction time reveals that the advanced YOLOv8 network is capable of identifying small target fruits both accurately and swiftly. As it has achieved good accuracy in multiple fruit category recognition tasks, the model has good generalization performance.  
Figure 14: (**a–c**) represent the heatmaps of the original model identifying apples, oranges, and bananas, respectively. (**d–f**) represent the heatmap effects of our improved model identifying the same fruits.  
Figure 15: Recognition effect diagram of the improved YOLOv8 algorithm.
# Design of Outer Membrane Vesicles as Cancer Vaccines: A New Toolkit for Cancer Therapy
## 2 Vaccines in Cancer Therapy  
### Historical Overview  
A cancer vaccine either prevents infections with cancer causing viruses, treats existing cancer, or prevents the development of cancer in certain high-risk individuals [3; 6; 21]. Vaccination against an infectious neoplastic agent can be categorized based on three clinical applications: (1) a prophylactic vaccine to prevent infection or acute disease; (2) a therapeutic vaccine to treat an established infection before a malignancy has been induced; or (3) a therapeutic vaccine to treat the infection after the malignant tumor has developed. Prophylactic vaccines are mainly used in viral infections associated with a high risk of cancer, for example, the human papillomavirus (HPV) vaccine [22]. Prophylactic vaccines therefore seem to be an ideal approach to limit the risk of cancer; however, it is difficult to conduct prophylactic trials as the response to the vaccine cannot be evaluated within a short period of time, when compared with suppression of infection or disease in therapeutic vaccine trials [23]. Advances in targeted therapies have reinvigorated interest in cancer immunotherapy because (1) therapeutic vaccines employed as a monotherapy could deliver a targeted immune-mediated effect in tumors, and (2) the advantage of cancer vaccines in comparison with passive immunotherapy is that T-cell driven responses and associated memory may assist during disease recurrence [24]. Since our immune system is built to target and destroy \"non-self,\" theoretically, cancer vaccination is the safest, most natural, and effective therapeutic approach against cancer. Owing to their ability of immune regulation and presenting multiple heterologous antigens to host cells, OMVs can be a new option for design as an effective therapeutic vaccine to deliver tumor antigens or small molecule drugs to APCs or even targeted cancer cells through genetic engineering.  
To date, a group of cancer vaccine models have been developed, including whole-cell and lysed-cell vaccines, gene-modified tumor vaccines, heat shock proteins, peptide-based vaccines, naked DNA, viral vectors, and ex vivo dendritic cell (DC) vaccines [25; 26; 27; 21]. These vaccine models present a good example for the theory that \"prevention is better than cure\". Their advancement and application will ensure a decrease in cancer incidence globally. At present, the promotion of cancer vaccines in clinical practice is positive, and in general, acceptance of these vaccines is positive. Issues still to be addressed are design of effective cancer vaccines, lack of familiarity with cancer vaccine models, varied beliefs regarding the risk of acquiring infections, and concerns regarding age specifications for the vaccines [3; 28; 29]. Our focus is on improving the protective effect of the vaccine stimulating to the host and the delivery efficiency of the vaccine vector. Moreover, we believe that OMVs as a tumor antigen carrier can provide us with a novel strategy to design cancer vaccines.
# Probabilistic Forecasting of Electricity Demand Incorporating Mobility Data
## 2 Materials and Methods
### Dataset and Data Pre-Processing  
Several real-world data sources are used to evaluate and demonstrate the effectiveness of the proposed method. This study focuses on data from three years: 2020, 2021, and 2022, onset and during the COVID-19 pandemic as three different scenarios to capture and illustrate the forecasting uncertainty. The data are from New South Wales (NSW), Australia's most populous state; a similar range of data is used, dating from 12 February-13 March.  
To achieve optimal data quality, the entire dataset is examined, and missing data points are populated by neighborhood values. The data sources of this study are as follows:  
* Electricity demand data: AEMOs' open dataset is used, which contains accumulated daily electricity demand sampling rate (30 min) [3].
* Energy and weather data: OpenNEM, an open platform for National Electricity Market (NEM) data, is used, which contains time series hourly weather (temperature), renewable and non-renewable energy generation, and consumption data [61].
* Mobility data: The mobility data is obtained through the Google COVID-19 Community Mobility Reports and have six location-specific metrics (parks, workplaces, residential, retail and recreation, and grocery and pharmacy) [62].
## 3 Experiments and Results  
### Experimental Scenario  
In order to verify the effectiveness of the method proposed in this paper, spoofing detection and direction-finding experiments were carried out on the roof of the aerospace information research institute in Beijing. The experimental scenario is shown in Figure 8. The experimental equipment mainly includes a data processing unit, an external clock, a spoofer device, four antennas, and an omnidirectional transmitting antenna. Among them, the data processing unit is composed of four GNSS boards and a computing core. The GNSS board receives observation data and ephemeris data, DOAs are calculated in the computing core, and the real-time calculation results are output by the upper computer. The GNSS receiver board model is Unicorecomm UB480 (UB480 is a GNSS receiver board produced by Unicorecomm company. And the Unicorecomm is a company from Beijing, China), and the price is about USD 360. The antenna model is HIGH GAIN HG-ZMYH3181 (HIGH GAIN is a company from Shanghai, China), and the price of a single antenna is about USD 290. The computing core baseboard is XC7Z035 from Xilinx Company (San Jose, CA, USA). The spoofer is a generative spoofer device. In order to improve the electromagnetic environment and weaken the influence of multipath, some absorbing materials are added around the four antennas.
# Evaluation of Viscoelastic and Rotational Friction Dampers for Coupled Shear Wall System
## 4 Numerical Analysis Using STERA 3D
### Numerical Model of VE Damper  
Figure 21 illustrates the numerical model of the VE damper, represented by a four-element system consisting of a spring \(K_{1}\), a parallel damper \(C_{1}\), a second damper \(C_{2}\), and an additional spring \(K_{2}\)[22]. The components are labeled as follows: (1) elastic-plastic spring, (2) nonlinear Maxwell body, and (3) nonlinear viscous element. The parameters \(K_{1},C_{1}\), and \(C_{2}\) are derived from empirical formulas dependent on the shear strain \(\gamma\) as indicated by Equations (6)-(8). These parameters exhibit power-law scaling with respect to \(\gamma\), while an exponential decay term introduces a time-dependent component. The bilinear stiffness behavior of the damper is captured by the spring \(K_{2}\) in Equation (9). Where \(A_{s}\)\(d\), and \(\theta\) indicate the area, the thickness of the VE material, and the room temperature, respectively. Table 6 shows the parameters for adjusting the original coefficients in Ref. [22] determined from the comparison of the hysteresis of the VE damper between the element test and the numerical analysis, as shown in Figure 22. A parametric study was conducted to determine the optimal parameters for Equations (6)-(9), calibrated using element test data. For the VE damper, the 1.0 Hz hysteresis response was selected as the reference case, as it closely matches the fundamental frequency of the six-story frame structure.  
\[K_{1} =2.83(x_{1})\times 10^{-1}\gamma^{-0.715(x_{2})}(A_{s}/d)\text{exp }[-0.017(\theta-20)]\] (6) \[C_{1} =4.76(x_{3})\times 10^{-1}\gamma^{-