esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Policy Learning Methods from Biological Behavior: A Systematic Review
## 5 Learning Based on Interpretability and Safety: Causal Policy Learning  
As mentioned in Section 3. \(A\), supervised learning-based BC methods suffer from causal confusion [97] as they ignore the causal consequences of expert-environment interactions. Although GAIL improves the interaction between the agent and environment, according to the architecture and complexity of its generative models, more complex and powerful models create black-box models that lack interpretability. Deep learning-based neural networks can output strategies or reward functions from data inputs; however, they cannot provide an intrinsic causal relationship between the behavior and the reward behind them [112]. This means that while uninterpretable strategies may be valid, there is no guarantee that such uninterpretable strategies are free from errors [112; 113]. This lack of transparency and interpretability limits the further application of models in the industry because users cannot fully trust them. If targeted interventions can be made to determine the correct causal relationship in the model design, this will help provide interpretability to the algorithm.  
Causal inference is the process of determining causality based on the causes that lead to the occurrence of an outcome [114]. However, it is sometimes difficult to draw direct causal relationships from observations. To explain with a simple example, even if the collected data exhibit a simultaneous increase in the number of people eating ice cream and the number of people drowning, it does not show that there is a causal relationship between the two, ice cream eating and drowning. This is because the summer temperature increase might be the cause. That is, \"correlation\" is not \"causation\". Determining causality is an important and challenging task in policy learning because the relationship between two different actions is often difficult to determine.  
In this section, we reviewed the policy-learning approach using a causal inference framework. The basic concepts of the causal inference framework and policy learning methods using causal relationships between variables were introduced. The listed methods for constructing the structural causal model (SCM) include the basic method based on the directed acyclic graph (DAG) and the extended method based on Bayesian networks and neural network difference equations. After determining the causal effects between the variables, the policy can be optimized using the GAIL or IRL algorithms. The advantages of CPL are its robustness and interpretability. Therefore, it has a good scope for industrial applications.
###### Abstract  
The packing characteristics of aggregates are very important for aggregate blend design, which is closely related to the performance of mixtures. In this study, an indoor packing experiment was designed to investigate the behaviors of single-size particle packing and two-size particle packing. The effects of particle composition, particle size and size ratio, particle morphology on packing characteristics were also evaluated. Two kinds of aggregates (crushed stone and gravel) with significant morphological differences were selected for the test. The angularity of the aggregates was quantitatively analyzed using the variance of mean curvature (\(S_{Cm}^{2}\)) of particle surface in accordance with the 3-D scanning measurements. Based on the test results, the packing characteristics of aggregates were analyzed using the air void content (\(V_{a}\)) and the packing function index (\(I_{pf}\)) proposed in this paper. It is shown that the analysis results of packed ideal spheres cannot be directly used to describe the packing characteristics of aggregates. Particle morphology has a significant impact on packing characteristics, especially on the \(V_{a}\). The \(V_{a}\) of packed aggregates with poor angularity is significantly smaller than that with good angularity. \(I_{pf}\) can be used to quantitatively distinguish the packing function of particles. The test results show that the packing function of particles cannot be simply divided into the skeleton building and air voids filling. Generally, the particles in packed blend have both of these functions. The packing function of particles depends not only on the particle size, but also on the composition of particles with different size. When the size ratio and volume ratio are the same, the packing characteristics of the two-size particle blends will still change with the change of the particle size. The exploration of packing behaviors of single- size and two- size particle aggregates is helpful for analyzing the packing behaviors of blends with multi-size particles.  
packing characteristics; aggregate; particle size; morphology; mixture design Article
# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
## 5 Conclusions
)achieved reductions in the mean absolute error (MAE) of 7.3%, 14.8%, and 10.6%, respectively, reduced root mean square error (RMSE) by 6.05%, 15.9%, and 7.5%, and lowered the maximum error (Max Error) by 15.9%, 28.6%, and 13.6%, respectively. Looking ahead, future work will focus on optimizing the model's structure, exploring new attention mechanisms and fusion strategies, and extending the model to other energy forecasting domains such as wind and solar energy. Additionally, efforts will be made to improve the model's efficiency in practical applications through techniques like model compression and hardware acceleration, supporting smarter grid scheduling and better operational efficiency. Conceptualization, W.G. and L.W. ; methodology, W.G. and S.L. ; software, W.G. ; validation S.L. ; formal analysis, X.L. ; investigation, W.G. and X.L. ; resources, L.W. ; data curation, L.W. ; writing--original draft preparation, W.G. and L.W. ; writing--review and editing, X.L. ; visualization, S.L. ; supervision, L.W. ; project administration, L.W. ; and funding acquisition, L.W. All authors have read and agreed to the published version of the manuscript. Not applicable. The data and the code of this study are available from [[https://github.com/RenHongjin6/CNN-MHSA-LSTM-GAM-CAM-Network](https://github.com/RenHongjin6/CNN-MHSA-LSTM-GAM-CAM-Network)]([https://github.com/RenHongjin6/CNN-MHSA-LSTM-GAM-CAM-Network](https://github.com/RenHongjin6/CNN-MHSA-LSTM-GAM-CAM-Network)) (accessed on 6 December 2024). Authors [PERSON] and [PERSON] were employed by the company State Grid Electric Power Research Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# A Compact Quad-Element UWB-MIMO Antenna System with Parasitic Decoupling Mechanism
## 5 Comparison with the Existing Literature  
The proposed MIMO system is designed for UWB applications. The existing literature reports several UWB-MIMO antennas. However, the proposed design is highly competitive to the reported designs in terms of isolation, size/profile and MIMO parameters. This MIMO antenna not only has a compact size but also exhibits efficient performance. A comprehensive comparison with the recent literature is shown in Table 1. The proposed work is compared with planar two-port and four-port reported designs. It has the smallest MIMO antenna dimensions of \(40\times 43\times 1\) mm\({}^{3}\) and highest isolation of 20 dB in comparison with reported designs in Table 1. Moreover, proposed design has desirable values of ECC, CCL and TARC and the highest values of gain and efficiency in comparison, whereas most of the reported designs in the table are not evaluated in terms of CCL and TARC.  
Figure 10: \(E\) and \(H\) plane radiation patterns: (**a**) 4 GHz; (**b**) 6 GHz; (**c**) 10 GHz.
## 2 Material and Data Collection
### Focal-Loss Function and Label-Smoothing
#### 2.4.1 Focal-Loss Function Principle  
Cross-entropy loss is a type of loss function that is often used in network training. In the face of unbalanced data sets, it has a relatively low recognition degree with a few samples. \"Focal loss is a cross-entropy loss of dynamic scaling. By inserting a dynamic scaling factor, the weight of both challenging and easy samples can be adjusted in the training process, thus biasing the center of gravity towards the more challenging samples.\" [27]. The method is as follows:  
Cross-entropy loss: Cross-entropy loss is based on a binary classification, which takes the following form:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Set** & **Intact Soybean Sample** & **Broken Soybean Sample** \\ \hline Original data set & 439 & 92 \\ \hline Data-augmentation data set & 787 & 829 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of samples in a dataset.  
Figure 3: Image preprocessing of soybean. (**a**) Original image of soybeans, (**b**) gray image of soybeans, (**c**) outline image of soybeans, (**d**) circumscribed rectangles of soybeans, and (**e**) single soybean image.  
\[\text{CE}(\text{p},\text{y})=\left\{\begin{array}{cc}-\log(\text{p})&\text{if y}=1 \\ -\log(1-\text{p})&\text{otherwise}.\end{array}\right. \tag{1}\]  
where y takes values of 1 and \(-1\) to represent the foreground and background, respectively. The value of p ranges from 0 to 1, which is the probability that the model predicts belonging to the foreground.  
Defining a function about p:  
\[\text{P}_{\text{t}}=\left\{\begin{array}{cc}\text{P}&\text{if y}=1\\ 1-\text{p}&\text{otherwise}.\end{array}\right. \tag{2}\]  
Combined with the above formula, there is the following formula.  
\[\text{CE}(\text{p},\text{y})=\text{CE}(\text{P}_{\text{t}})=-\log(\text{P}_{ \text{t}}) \tag{3}\]  
Balanced cross entropy: Common solutions to class imbalance. A weight factor \(\alpha\in[0,1]\) is introduced, which is \(\alpha\) when it is a positive sample and \(1-\alpha\) when it is a negative sample. Therefore, the loss function formula is as follows:  
\[\text{CE}(\text{P}_{\text{t}})=-\alpha_{\text{t}}\log(\text{P}_{\text{t}}) \tag{4}\]  
Focal loss: Although the balanced cross entropy solves the problem of positive and negative samples, the difficulty in distinguishing samples will still have an impact on network training, so a modulation factor is introduced to focus on the difficult samples, and the formula is as follows:  
\[FL(p_{t})=-(1-p_{t})^{7}log(p_{t}) \tag{5}\]  
As a parameter, \(\gamma\) is in the range of [0, 5]. When \(\gamma\) becomes 0, it becomes the initial cross-entropy function.
## 3 Methods
### Segmenter
#### 3.1.5 Decoder Path  
The up-sampling pathway adopts a symmetrical architecture utilizing transposed convolutions to facilitate hierarchical feature fusion. Each up-sampling module initially performs bilinear interpolation to double the spatial resolution, followed by a \(3\times 3\) convolutional layer equipped with batch normalization for effective feature transformation. Subsequently, a ReLU activation function is applied to introduce non-linearity. During the decoding process, the up-sampled feature maps are concatenated along the channel dimension with their corresponding encoder features through skip connections, enabling the seamless integration of high-level semantic information with low-level spatial details. The concatenated features are further refined via dense convolutional blocks, which promote feature reuse and enhance multi-scale representation capability. The decoder progressively reconstructs the spatial resolution through four successive up-sampling stages, each doubling the resolution, culminating in a \(1\times 1\) convolutional layer that projects the refined features onto the target classes for end-to-end pixel-wise prediction. This architectural design ensures precise segmentation performance while maintaining computational efficiency through systematic feature recombination.
## 4 Case Study of a Suspension Bridge
### Reliability Analysis Based on the DBN Approach  
Since structural failure is determined by the internal force of the bridge components, an appropriate FE model is essential to conduct the structural mechanical analysis. An FE of the bridge was developed in the commercial software ANSYS (Figure 11).  
Girders and pylons were modeled with Beam188 elements, and the main cables and girders were modeled with Lin180 elements. The main cable and the pylon were connected with coupled degrees of freedom, while the bottom parts were fully constrained. The initial tension forces in the main cable and suspenders were introduced as pre-stressed tension.  
Using the FE model, the structural response can be approximated through the DBN. Initially, 20 uniformly-distributed training samples were selected as the input data. Corresponding structural response including cable forces and bending moments, were computed using the FE model and were used as the output data. The DBN was treated following the framework presented in Figure 3. As an  
Figure 10: Stochastic load model for dense traffic flow.  
Figure 9: Probability distribution of V6 trucks: (**a**) PDF of GVWs; (**b**) relation between the GVW and the axle weight.  
example, the approximated cable force is presented in Figure 12. It can be observed that the response surface has captured the nonlinearity of structural mechanics.  
In general, there are three types of failure modes related to bridge components: suspended fracture, main cable rupture, and girder bending moment failure. The reliability index of each bridge component is shown in Figure 13.  
Figure 11: Finite element model in ANSYS of the suspension bridge.  
Figure 12: Simulated response surface and training samples of the cable force based on DBNs.  
Figure 13: Reliability index of the bridge components.  
It can be observed that the suspender had a relatively lower reliability index, while that of the main cable was the highest. Therefore, the first component more likely to fail should be the suspender. The most important step for the system reliability evaluation is to develop the fault tree of the structural system. The fault tree is composed of failure sequences, which can be evaluated based on the \(\beta\)-bound method.
# Compression Challenges in Large Scale Partial Differential Equation Solvers
## 2 Compression Aspects of PDE Solvers  
PDE solvers have a requirement profile for compression that differs in several aspects from other widespread compression demands like text, image, video, and audio compression. The data to be compressed consists mainly of raw floating point coefficient vectors in finite difference, finite element, and finite volume methods.
## 2 Related Work  
### Background of Speech Emotion Recognition  
Emotions are psychological states triggered by neurophysiological changes, and they are related differently to thoughts, feelings, behavioral responses, and varying degrees of happiness or unhappiness. Currently, there is no scientific consensus on the definition of emotions, which often intertwine with feelings, temperament, personality, character, and creativity. Emotions consist primarily of subjective experiences, physiological responses, and behavioral reactions, playing a crucial role in building interpersonal relationships. They can be recognized through speech, facial expressions, and body language. Basic emotions include surprise, joy, disgust, sadness, anger, and fear, while complex emotions, such as contempt, amusement, and embarrassment, are blends of multiple feelings and are more challenging to identify.  
Speech emotion recognition (SER) technology aims to identify human emotions through voice. Typically, people are not very accurate in recognizing others' emotions, making emotion recognition a burgeoning field of study where appropriate technology can enhance accuracy. The core of this process lies in identifying emotions from speech without considering cognitive content. SER mainly involves two processes, feature extraction and emotion classification, and it holds significant potential for applications in security, healthcare, entertainment, and education. The task of emotion recognition is complex due to the highly subjective nature of emotions, and there is yet to be a unified standard for classifying or measuring them.  
As illustrated in Figure 1, an SER system consists of modules for speech signal input, pre-processing, feature extraction and selection, classification, and emotion recognition. The ability to extract robust emotional features from speech is crucial to the success of an SER system. Studies have shown that extracting features across multiple modal [17; 18; 19; 20; 21; 22; 23] and multiple scales can significantly improve the accuracy of emotion recognition.
## 3 Results and Discussion
### Photocatalytic Activity Tests
#### 3.1.1 Influence of Cu Content on the Hydrogen Production under Visible Light
Therefore, these values made the 1.08 Cu_ZnO catalyst able to produce hydrogen in the presence of visible light both from thermodynamic and kinetic points of view thanks to the presence of the metal dopant that inhibited the recombination rate of the electron-hole pairs. Figure 1: Influence of Cu content on hydrogen production under visible light after 4 h of visible irradiation time. Glycerol initial concentration: 5 wt %; catalyst dosage: 1.5 g/L; solution pH: 6; visible light intensity: 32 mW/cm\({}^{2}\).
# Current Challenges in _Yersinia_ Diagnosis and Treatment
###### Abstract  
_Yersinia_ bacteria (_Yersinia enterocolitica_, _Yersinia pseudotuberculosisis_) are commonly found in nature in all climatic zones and are isolated from food (mainly raw pork, unpasteurized milk, or contaminated water), soil, and surface water, rarely from contaminated blood. _Yersinia_ infection occurs through sick or asymptomatic carriers and contact with the feces of infected animals. The invasion of specific bacterial serotypes into the host cell is based on the type 3 secretion system (T3 SS), which directly introduces many effector proteins (_Yersinia_ outer proteins--Yops) into the host cell. The course of yersiniosis can be acute or chronic, with the predominant symptoms of acute enteritis (rarely pseudo-appendicitis or septicemia develops). Clinical and laboratory diagnosis of yersiniosis is difficult. The infection requires confirmation by isolating _Yersinia_ bacteria from feces or other biological materials, including lymph nodes, synovial fluid, urine, bile, or blood. The detection of antibodies in blood serum or synovial fluid is useful in the diagnostic process. The treatment of yersiniosis is mainly symptomatic. Uncomplicated infections (diarrhea and abdominal pain) usually do not require antibiotic therapy, which is indicated in severe cases. Surgical intervention is undertaken in the situations of intestinal necrosis. Given the diagnostic and therapeutic difficulties, this review discusses the prevalence of _Y. enterocolitica_ and _Y. pseudotuberculosisis_, their mechanisms of disease induction (virulence factors and host response), clinical manifestations, diagnostic and preventive methods, and treatment strategies in the context of current knowledge and available recommendations.  
Yersinia enterocolitica; Yersinia pseudotuberculosisis; clinical symptoms; diagnosis; treatment
# Multi-Means Comprehensive Forecasting Technology for Water Inrush in Long Tunnels
## 2 Engineering and Geological Overview  
The tunnel project is situated in Fuzhou, Fujian Province, featuring a total length of 9270 m with a single bore accommodating two lines. The site's elevation ranges from 21 m to approximately 605 m, with a maximum tunnel depth of about 540 m.  
The tunnel site is predominantly characterized by the Neocathaysian structural system, oriented NNE. The fault scale and intensity, along with the dynamic metamorphic rock's width and depth within the structural belt, increase progressively from the northwest to the southeast. The area contains several groups of nearly parallel fractures trending NEE and NNW. The primary group trends NE at 65-75\({}^{\circ}\), while the secondary group trends NW at 330-340\({}^{\circ}\). The NE-trending fractures are primarily compression-torsional and often filled, whereas the NW-trending fractures are tension-torsional. The regional structural configuration is depicted in Figure 1.  
Fault fracture zones intersecting the tunnel alignment and lithologic contact zones are predominantly distributed along gullies and depressions on both sides of the geomorphic features. These areas exhibit developed cracks, fragmented rock masses, and enhanced water conductivity and abundance. Tectonic fissure water primarily occurs along structural fracture zones, forming localized water collection channels with significant water volume and well-developed fissure water systems. The tunnel site encompasses 14 faults, 21 intrusion contact zones, and six joint-intensive zones, most of which follow gullies or intersect surface water near the alignment. The rock mass adjacent to these structural zones is highly fractured and may establish hydraulic connections with surface water at angular intersections, creating potential water conduits that could lead to water seepage and inrush during tunnel excavation.  
The stratigraphic lithology of the tunnel site, from top to bottom, consists of silty clay in the Quaternary residual slope deposit (Qel + dl) at the surface layer, underlain by crystalline tuff lava from the third member of the Upper Jurassic Nanyuan Formation (J\({}_{3}\)nc), which includes rhyolitic tuff, siltstone, dacite, and other related rock types.
## 2 Related Works
### Motivation  
The performance analysis of most of the conventional relay-aided communication systems is performed based on the assumption that the fading channel between the source-relay and relay-destination links follows the same family as that of the fading PDFs, i.e., the channel PDF remains the same for the complete lifetime of the communication system. However, in 5G scenarios, this assumption is not always met as the fading conditions continue varying rapidly. Therefore, the performance of the system needs to be calculated repeatedly for all possible fixed and static fading scenarios. Therefore, a universal framework is required which can accommodate every fading scenario, and the computational resources can be saved by eliminating the need to derive the expressions repeatedly.