esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Compared with other types of sensors, FBG sensors have many outstanding advantages, such as dust prevention, anti-electromagnetic interference, moisture resistance, and high-temperature resistance, so they have been widely promoted in the field of coal mines. By analyzing the kinematics model of the hydraulic support, [PERSON] et al. [21] proposed an FBG inclination sensor for monitoring the attitude of the hydraulic support. [PERSON] et al. [22] proposed an FBG displacement sensor for analyzing roof separation and studying displacement changes during blasting. [PERSON] et al. [23] designed a full rod FBG force-measuring bolt and system, which realized the continuous monitoring of the roof pressure of the coal mine roadway. [PERSON] et al. [24] developed a new type of coal mine roof safety control monitoring system with FBG material as a sensing element and transmission medium, further preventing catastrophic roof collapse accidents. In view of this, scholars have gradually begun to study the FBG curvature sensor of scraper conveyors. The first measurement of curvature made using Bragg gratings written in separate cores of a multi-core fiber was reported by [PERSON] et al. [25]. Local three-dimensional curvature can be measured by embedding three or more non-aligned strain sensors in the core of an optical multi-core fiber cross-section [26]. [PERSON] et al. [27] proposed a precise compensation model based on the rotation error angle. When the rotation error angle is within the range of 0-90\({}^{\circ}\), according to the strain of FBG obtained by numerical simulation, the radius of the curvature is inversely calculated by the compensation model. Based on optical fiber sensing technology and the structure of the scraper conveyor, a three-dimensional curvature sensor of FBG is designed and developed. Combined with the curvature information of the sensor and the idea of differential geometry, the three-dimensional bending shape fitting perception and reconstruction of the scraper conveyor is realized, and the bending test experiment platform of the scraper conveyor is built to prove the feasibility of the method.
## 3 Research Methodology
### The Super-SBM DEA Model with Undesirable Output  
Modern production methods have not only increased labor productivity but also produced abundant and inexpensive manufactured goods to improve people's standard of living. Industrial production also inevitably generates a large amount of pollutants such as wastewater, exhaust gas, and waste residue (undesired outputs). In this study, the eco-efficiency of each city is calculated using the Super-SBM DEA model with undesirable output via the data envelopment analysis (DEA) method. The DEA model does not need to set a specific function model and can evaluate the efficiency of decision-making units with multiple inputs and multiple outputs. The traditional DEA model is mainly based on radial and angular considerations, which requires inputs and outputs to be improved in the same proportion and does not measure the feasibility of slack variables, overestimating the decision-making efficiency units. The super-efficient SBM model based on non-expected output corrects the problem that the radial model does not include slack variables for the inefficiency measurements, and reaches the frontier with the shortest path so that the decision-making unit can reach the most efficient frontier surface at the least cost ([PERSON], 2015). Several scholars have utilized this method to measure eco-efficiency, thus justifying the methodology of this study ([PERSON], 2015).  
\[\rho=\min\frac{1+\frac{1}{m}\sum_{i=1}^{m}\frac{s_{i}^{x}}{x_{i0}}}{1-\frac{1 }{s_{1}+s_{2}}(\sum_{k=1}^{s_{1}}\frac{s_{k}^{y}}{y_{k0}}+\sum_{l=1}^{s_{2}} \frac{s_{l}^{z}}{z_{l0}})}\]  
\[x_{i0}\geq\sum_{j=1,\
eq 0}^{n}\lambda_{j}x_{j}-s_{i}^{x},\forall i \tag{6}\]  
\[y_{k0}\leq\sum_{j=1,\
eq 0}^{n}\lambda_{j}y_{j}+s_{k}^{y},\forall k\]  
\[z_{i0}\geq\sum_{j=1,\
eq 0}^{n}\lambda_{j}z_{j}+s_{l}^{z},\forall l\]  
\[1-\frac{1}{s_{1}+s_{2}}(\sum_{k=1}^{s_{1}}\frac{s_{k}^{y}}{y_{k0}}+\sum_{i=1} ^{s_{2}}\frac{s_{l}^{z}}{z_{l0}})>0\]  
\[s_{i}^{x}\geq 0,s_{k}^{y}\geq 0,s_{i}^{z}\geq 0,\lambda_{j}\geq 0,\forall i,j,k,l\]  
Suppose there are n decision units, each of which contains three elements: inputs, desired outputs, and undesired outputs, represented by three vectors (X, Y, and Z), where x\({}_{i0}\), y\({}_{i0}\), and z\({}_{i0}\) are the values of inputs, desired outputs, and undesired outputs of the ith decision unit, respectively, and s\({}^{x}\), s\({}^{y}\), and sb are the slack values of inputs, desired outputs, and undesired outputs and are the weight vectors. \(\rho\) is the objective function, and \(\rho=1\), i.e., s\({}^{x}=0\), s\({}^{y}=0\), and s\({}^{z}=0\), means the decision unit is valid; if \(\rho<1\), it means the decision unit is nonvalid and there is room for improvement.
# STGAT: Spatial-Temporal Graph Attention Neural Network for Stock Prediction
## 4 Experiments  
### Datasets and Experimental Setup  
In this study, we used the stocks of the CSI 500 Index and the S&P 500 Index sourced from the Choice Financial Terminal as selected stock samples. The CSI 500 Index comprises 500 small and medium companies with capitalization from China's A-share market, covering a wide array of industries. These companies are characterized by substantial growth potential, though they tend to exhibit higher volatility. In contrast, the S&P 500 Index consists of 500 large-cap, highly liquid companies listed on the New York Stock Exchange and NASDAQ, representing key sectors of the U.S. economy. The constituent companies are typically blue-chip stocks, renowned for their stability and strong market presence, with broad representation across industries such as technology, finance, and consumer goods. Daily market data for the stocks in the CSI 500 and S&P 500 indices covered the period from January 2015 to April 2024. This timeframe was selected to capture multiple market regimes, including pre-COVID stability (2015-2019), pandemic-induced volatility (2020-2021), and post-pandemic recovery with monetary policy shifts (2022-2024). Additionally, it aligns with the availability of high-frequency granular data in both A-share and US stock markets after 2015, enabling robust decomposition of trend-seasonal-residual components. The datasets include 2170 and 2330 trading days, respectively.  
Neural networks are highly sensitive to data relationships and outliers, and they require complete input data with no missing values. Therefore, it is critical to perform outlier detection and processing on time series data to prevent individual stocks from distorting the overall dataset. Missing values in stock data can stem from issues such as delayed listings, IPO delays, or errors in data reporting. To address this, we exclude ST stocks and those with considerable missing data. After filtering, the dataset for A-shares includes 304 stocks, while the dataset for U.S. stocks contains 462 stocks, as shown in Table 1.  
The experiments in this work were conducted using Python 3.11.8, running on a system with an Intel Core(TM) i5-12400F processor, an NVIDIA GeForce RTX 4060 Ti, and 12 GB of memory. The initial learning rate was set to \(1\times 10^{-3}\), and the batch size was configured to 16.
# Development of a Novel Open Control System Implementation Method under Industrial IoT
## 4 Case Study
### Discussion  
From the proposed open CNC demonstration, it can be seen that, compared to current open control prototypes, the proposed open control system implementation method has two significant characteristics:  
* It does not aim to construct an open control system according to a fixed control task granularity standard. Instead, it is based on the control domain's formal description, which is written in a configuration file. In the sense of the openness criteria, the proposed implementation method improves interoperability and extendibility by elevating the abstract level.
* It concentrates on adopting virtualization technologies to decouple control tasks from heterogeneous infrastructures as much as possible. The addresses of the virtualized control tasks are written in a configuration file. Therefore, compared to component  
Figure 11: Activity diagram of the scheduler task.  
and service-based open control prototypes, the proposed implementation method supports higher portability and scalability.  
The proposed open control system implementation method, moreover, enables the break of the traditional control system's stand-alone operation pattern. For instance, the Interpretation container on the cloud in the above open CNC demonstration can be accessed by multiple Look-ahead Velocity-Planning containers from different factory-level edges. Further, the Look-ahead Velocity-Planning container can serve multiple Interpolation wasn modules from different actuator-level edges.
## 4 Model of Primary Resources  
In this article, to model the components of the primary resource, a recursive occupancy distribution is used [60]. In line with the notation adopted in this article, for Erlang, Engset, and Pascal traffic this distribution can be written in the following way:  
\[\left[P_{n}\right]_{V_{s}}=\frac{1}{n}\left\{\sum_{i\in M_{s}^{\rm Er}}A_{i,s} ^{\rm Er}t_{i}[P_{n-t_{i}}]_{V_{s}}+\sum_{j\in M_{s}^{\rm En}}A_{j,s}^{\rm En }(n-t_{j})t_{j}\Big{[}P_{n-t_{j}}\Big{]}_{V_{s}}+\sum_{l\in M_{s}^{\rm Pa}}A_{ l,s}^{\rm Pa}(n-t_{l})t_{l}\big{[}P_{n-t_{l}}\big{]}_{V_{s}}\right\}, \tag{1}\]  
where the distribution \(\left[P_{n}\right]_{V_{s}}\) determines the occupancy probability \(n\) AU in the primary resource \(s\) with the capacity of the resource \(V_{s}\). In Figure 1, \(A_{i,s}^{\rm Er}\) denotes the value of Erlang traffic, whereas the values of Engset (\(A_{j,s}^{\rm En}(n)\)), and Pascal traffic (\(A_{l,s}^{\rm Pa}(n)\)), offered to individual primary resources \(s\), are determined on the basis of the following dependencies [61]:  
\[\forall_{1\leq s\leq r}\forall_{j\in M_{s}^{\rm En}}\,A_{j,s}^{\rm En}(n)= \alpha_{j,s}^{\rm En}\Big{[}S_{j,s}^{\rm En}-n_{j,s}^{\rm En}(n)\Big{]}, \tag{2}\]  
\[\forall_{1\leq s\leq r}\forall_{l\in M_{s}^{\rm Pa}}\,A_{l,s}^{\rm Pa}(n)= \alpha_{l,s}^{\rm Pa}\Big{[}S_{l,s}^{\rm Pa}+n_{l,s}^{\rm Pa}(n)\Big{]}, \tag{3}\]  
where:  
\(i,j,l\) indexes for call classes of Erlang, Engset, and Pascal traffic, respectively,  
\(\alpha_{j,s}^{\rm En}\) average traffic intensity of class \(j\) for Engset traffic generated by one free source in the resource \(s\),  
\(\alpha_{l,s}^{\rm Pa}\) average traffic intensity of class \(l\) for Pascal traffic generated by one free source,  
\(S_{j,s}^{\rm En}\) the number of traffic sources of class \(j\) of the Engset type, related to the primary resource \(s\),  
\(S_{l,s}^{\rm Pa}\) the number of traffic sources of class \(l\) of the Pascal type, related to the primary resource \(s\),  
\(n_{j,s}^{\rm En}\) the number of traffic sources of class \(j\) of the Engset type, serviced in the occupancy state \(n\) AU of the primary resource \(s\),  
\(n_{l,s}^{\rm Pa}\) the number of traffic sources of class \(l\) of the Pascal type, serviced in the occupancy state \(n\) AU of the primary resources \(s\).  
The blocking probability in such a system results from the finite capacity of the resource and can be determined for calls of class \(c\) by the following formula:  
\[\left[E_{c}^{X}\right]_{V_{s}}=\sum_{n=V_{s}-(t_{c}-1)}^{V_{s}}\left[P_{n}\right] _{V_{s}}\quad\text{dla }c\in M_{s}^{X}. \tag{4}\]  
Formula (4) determines the sum of the blocking states for calls of class \(c\), that is, those states in which the number of free AUs in the resource is lower than the number of AUs demanded to set up a connection of class \(c\).
# Performance Analysis of Two-Hop mmWave Relay Nodes over the 5G NR Uplink Signal
## 2 System Model and Basic Assumptions
### Uplink mmWave MIMO A&F Implementation  
In this subsection, we propose the scheme to simulate the uplink mmWave MIMO A&F relay node with a standardized 5G signal by 3 GPP. Here, we present an L1 A&F conventional, as shown in Figure 5. In the first phase, the mmWave IQ signal is captured with the mmWave receiver chains (\(\mathbf{M}_{\text{RR}}\)) through the SimulinkTM software, which will be explained in detail in the next section.  
After acquiring the received information, the signal is saved in the buffer (processing signal block). Thus, the \(\mathbf{G}_{\text{AF}}\) of the relay node is applied to the stored signal. Finally, the amplified signal is transmitted to the destination by means of the Transmitter \(\mathbf{M}_{\text{RT}}\) block, which is simulated as the RF transmit chains of an SDR platform using the SimulinkTM program. It should be noted that this is an easy implementation and cheap to build. However, the main drawback is that with a low Signal-to-Noise Ratio (SNR), the noise and interference are amplified; therefore, its performance deteriorates.
## 2 Related Work
Previous research endeavors have been dedicated to identifying effective approaches to overcome these challenges. The integration of FL and IoT has transitioned from a conceptual possibility to a practical reality, facilitated by the emergence of mini-computers equipped with GPUs, commonly known as \"edge GPUs\". These devices enable the processing of multimedia data at the edge, giving rise to the concept of \"edge AI\" [22; 23; 24; 25; 26]. This evolution has spurred increased research initiatives exploring the application of AI at the edge. For instance, in a study conducted by [22], researchers utilized the NVIDIA Jetson Nano to develop a model for detecting vehicles and pedestrians on rural roads. Through extensive testing of well-established models, including MobileNetv1, MobileNetv2, Inceptionv2, Pednet, and Multiped, they demonstrated the effective functionality of these models on the NVIDIA Jetson Nano. Another exploration by [PERSON] et al. [24] delved into the feasibility of on-device FL. In this investigation, Android smartphones and the Nvidia Jetson TX2 played a pivotal role. These devices were leveraged to train a model following the federated learning methodology through the utilization of the Flower framework. The training process involved the CIFAR-10 dataset for the Nvidia Jetson TX2 and the Office-31 dataset for Android clients. The findings from this study provide compelling evidence for the viability of training AI models on edge devices, especially with lightweight models such as ResNet18. Moreover, [PERSON] et al. [26] harnessed the power of FL to train a lightweight anomaly detection model tailored for industrial control systems. By embracing FL, the learning process was significantly expedited, with completion times reduced to a matter of minutes. The research involved the use of four NVIDIA Jetson Nanos as federated clients, demonstrating the potential for efficient AI training on edge devices. In order to address the aforementioned security and privacy issues associated with sustainable smart city applications, adversarial training strategies have been implemented within the framework of federated learning. These methodologies have been specifically developed to overcome the challenges that arise from clients having limited datasets, with the ultimate goal of developing a robust global model. This approach exhibits potential for improving the security and privacy aspects of smart city applications and is viable when federated clients are outfitted with GPU support.
# Image Quality and Information Parameters of Electronic Portal Imaging Devices
## 5 Conclusions  
In this study, we investigated the influence of source-phantom distance and monitor unit (MU) settings as LINAC irradiation parameters on the image quality of electronic portal imaging systems (EPIDs) using quantitative quality metrics. Image quality was evaluated using the QC-3V EPID phantom, showing its successful applicability in quality control procedures. In addition, metrics analyzed in the spatial domain, such as the SNR, and spatial frequency-dependent image quality metrics associated with the signal and noise transfer characteristics of detectors can be determined. In this study, we examined parameters such as the aMTF, CTFnorm, SNFR and IC. The SNFR proves to be valuable for assessing the overall system output performance and can be conveniently evaluated in a clinical setting. Additionally, IC values, either integrated or as a function of frequency (IC (f)), are useful metrics for estimating both image quality and the performance of the EPID. The aforementioned metrics can be regularly employed for evaluating the performance of LINAC and EPID systems, complementing existing image quality procedures. They can aid in optimizing various settings during portal imaging procedures and checking deviations in EPID performance. In addition, our results with respect to distance may be of importance in the realization of three-dimensional organ images when a two-dimensional EPID image is acquired. Further work may incorporate our findings in EPID dosimetry applications.  
Conceptualization, M.K.T., N.K. and I.S.K.; methodology, M.K.T. and I.S.K.; software, M.K.T.; validation, C.M., N.K. and I.V.; formal analysis, I.S.K.; investigation, I.S.K.; resources, G.F., V.P., A.A., G.K. and A.E.; data curation, M.K.T.; writing--original DRaft preparation, M.K.T., I.S.K. and N.K.; writing--review and editing, M.K.T., I.V., C.M., V.P., A.A., N.K. and I.S.K.; visualization, M.K.T.; supervision, N.K. and I.S.K.; project administration, N.K. and I.S.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
[PERSON] is an employee of Elekta.
# Enhancing Multimodal Emotion Recognition through Attention Mechanisms in BERT and CNN Architectures
###### Abstract  
Emotion detection holds significant importance in facilitating human-computer interaction, enhancing the depth of engagement. By integrating this capability, we pave the way for forthcoming AI technologies to possess a blend of cognitive and emotional understanding, bridging the divide between machine functionality and human emotional complexity. This progress has the potential to reshape how machines perceive and respond to human emotions, ushering in an era of empathetic and intuitive artificial systems. The primary research challenge involves developing models that can accurately interpret and analyze emotions from both auditory and textual data, whereby auditory data require optimizing CNNs to detect subtle and intense emotional fluctuations in speech, and textual data necessitate access to large, diverse datasets to effectively capture nuanced emotional cues in written language. This paper introduces a novel approach to multimodal emotion recognition, seamlessly integrating speech and text modalities to accurately infer emotional states. Employing CNNs, we meticulously analyze speech using Mel spectrograms, while a BERT-based model processes the textual component, leveraging its bidirectional layers to enable profound semantic comprehension. The outputs from both modalities are combined using an attention-based fusion mechanism that optimally weighs their contributions. The proposed method here undergoes meticulous testing on two distinct datasets: Carnegie Mellon University's Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) dataset and the Multimodal Emotion Lines Dataset (MELD). The results demonstrate superior efficacy compared to existing frameworks, achieving an accuracy of 88.4% and an F1-score of 87.9% on the CMU-MOSEI dataset, and a notable weighted accuracy (WA) of 67.81% and a weighted F1 (WF1) score of 66.32% on the MELD dataset. This comprehensive system offers precise emotion detection and introduces several significant advancements in the field.  
deep learning; CNN; BERT; emotion recognition; attention mechanism; cross-modal emotion recognition +
Footnote †: journal: Applied Sciences
# A Multi-Scale Attention Fusion Network for Retinal Vessel Segmentation
## 3 Methodology
### MsAF Block  
The channel attention mechanism focuses on adjusting the weights of different channels in the network's feature maps to enhance useful features and suppress those that are less relevant to the current task. On the other hand, the spatial attention mechanism is concerned with how the network prioritizes different spatial positions in the image, allowing for a selective emphasis on crucial areas. Inspired by [16; 29], we simultaneously introduced both channel attention and spatial attention mechanisms, designing a novel MsAF block. As illustrated in Figure 3, we applied channel attention to low-level features and spatial attention to high-level features. The final step involved merging the features extracted from each attention mechanism, enabling the network to comprehensively understand the content of the image. We describe the detailed operation below.  
First, we defined the low-level features as \(X_{L}\) and the high-level features after the upsample operation as \(X_{H}\). For the low-level features \(X_{L}\), global average pooling was applied to compress the features. Subsequently, two fully connected (FC) layers and an activation function were utilized to obtain channel-wise dependencies \(Y_{L}^{\prime}\). This can be expressed as  
Figure 3: Illustration of the MsAF block. The MsAF block fuses channel attention from low-level features and spatial attention from high-level features.  
Figure 2: Illustration of the MsFE block. The MsFE block extracts features at different scales by using convolutional kernels of different sizes.  
\[Y_{L}^{\prime}=\theta(f_{2}(\delta(f_{1}(AvgPool(X_{L}))))), \tag{1}\]  
where \(f_{1}\) and \(f_{2}\) denote the fully connected layers, \(\theta\) denotes a sigmoid function, and \(\delta\) denotes a ReLU function. Then, the channel attention map can be represented as  
\[Y_{L}=X_{L}\cdot Y_{L}^{\prime}, \tag{2}\]  
where \(Y_{L}\in R^{H\times W\times C}\).  
For the high-level features \(X_{H}\), we initially performed two pooling operations, concatenated the resulting two feature maps, and then utilized a \(7\times 7\) convolution to generate the spatial attention map \(Y_{H}^{\prime}\). This can be expressed as  
\[Y_{H}^{\prime}=\theta(f^{7\times 7}([AvgPool(X_{H});MaxPool(X_{H})])), \tag{3}\]  
where \(\theta\) denotes a sigmoid function. Then, the final spatial attention map \(Y_{H}\) can be represented as  
\[Y_{H}=X_{H}\cdot Y_{H}^{\prime}, \tag{4}\]  
where \(Y_{H}\in R^{H\times W\times C}\).  
We concatenated the obtained channel attention map \(X_{L}\) and spatial attention map \(X_{H}\), performed a \(3\times 3\) convolution operation, and generated a multi-scale attention fusion feature map Y. This can be represented as follows:  
\[Y=f^{3\times 3}([X_{L};X_{H}]), \tag{5}\]  
where \(Y\in R^{H\times W\times C}\), and \(f^{3\times 3}\) represents the convolution operation.