esa-sceva/satcom-chunk-collection huggingface dataset:
# Motion Target Localization Method for Step Vibration Signals Based on Deep Learning
## 3 Description of Our Method
### Feature Extraction
#### 3.2.1 Global Channel Attention Module  
During the propagation of vibration waves, they are often affected by attenuation and multipath effects, leading to signal amplitude reduction, energy loss, and temporal domain distortion. Consequently, in deep learning tasks for vibration signal feature representation, learning features from individual channels alone often fails to capture the multidimensional characteristics of footstep vibration signals comprehensively. Since the signals from different channels originate from the same footstep-induced structural vibrations, they exhibit spatial correlation. Therefore, this paper introduces the CAM mechanism to construct a global channel attention module, which enhances attention to channels that are closer and less affected by interference by extracting inter-channel correlation information and adjusting weights, thereby achieving effective complementary information among channels.  
Given the spatial correlation between signal channels, this paper introduces the CAM mechanism to enhance the model's ability to identify key channels by dynamically adjusting weights, thereby effectively complementing information between channels and obtaining global signal features. Generally, vibration sensors closer to the moving target collect more accurate and reliable signals because they contain significant peaks and valleys with less energy attenuation. To this end, the CAM module first performs global max pooling and average pooling operations on the segmented signal matrix \(\mathbf{U}_{j}^{r}\) to extract maximum value statistical features \(\mathbf{f}_{{}_{M\times}}^{r}\in\mathbb{R}^{M\times 1}\) and average value statistical features \(\mathbf{f}_{{}_{M\times}}^{r}\in\mathbb{R}^{M\times 1}\) for each sensor data channel. The max pooling focuses on the vibration peaks associated with events such as foot contact with the ground and lift-off, capturing the extrema characteristics of gait. Meanwhile, the average pooling emphasizes the overall trend and steady state of the signal, providing information on the stability and rhythm of the gait. These statistical features are then input into a shared network composed of multiple perceptrons, or multilayer perceptrons (MLPs), where the outputs of the shared network are element-wise summed to merge the advantages of the two pooling strategies. The weight vector \(\mathbf{w}^{r}\in\mathbb{R}^{M\times 1}\) is generated by processing through the sigmoid function to represent the importance of different channels. Equation (4) shows this process:  
\[\mathbf{w}^{r}\!\!=\!\!\sigma(MLP(\mathbf{f}_{{}_{M\times}}^{r})+MLP(\mathbf{f }_{{}_{M\times}}^{r})) \tag{4}\]  
where \(\sigma\) represents the sigmoid function. Finally, the weighted feature matrix \(\overline{\mathbf{U}}^{r}\in\mathbb{R}^{M\times N_{\text{s}}}\) is obtained by multiplying the weight vector \(\mathbf{w}^{r}\) with the signal matrix \(\mathbf{U}_{j}^{r}\):  
\[\overline{\mathbf{U}}^{r}\!\!=\!\!\mathbf{w}\times\mathbf{U}_{j}^{r} \tag{5}\]
## 4 Challenges and Prospects  
### Improved Computing Performance  
Although the use of coarsening technology can significantly improve the calculation efficiency, there is still a gap compared to the calculation requirements on an industrial scale, especially for long-time simulation, because the simulation usually requires a small time step and high spatial resolution. At present, there are some solutions, such as the hybrid Euler-Lagrange method, combining the energy minimization multiscale model (EMMS) and DDPM [131; 132], or the recurrence CFD method to realize long-time simulation by capturing system characteristics and then extrapolating data at longer intervals [133; 134]. But it still depends on improvements in computing performance. In order to reduce the computing time and improve computing efficiency, the development of a parallel computing strategy is worthwhile. Some hybrid computing modes combining a graphics processing unit (GPU) and central processing unit (CPU) [135], and virtual computing experimental methods [136], have emerged. More performance improvement calculation methods are needed, which will help to improve the adaptability of the computational fluid dynamics-discrete phase method to various physical and chemical process simulations.
# Machine Learning-Based Lie Detector Applied to a Novel Annotated Game Dataset
## 2 Related Work  
Different approaches and techniques have been applied for the lie detection task, with physiological cues being widely and commonly used. The most popular one is the polygraph, commonly known as a lie detection machine. Other approaches have used brain activity in order to detect deception by utilizing different neuro-imaging methods, such as fMRI [10; 20; 21; 22]. For example, [PERSON] [22] compared brain scans from volunteers in a lie-detection experiment in which some participants were asked to lie and others had to tell the truth. It was found that when people were telling the truth, the brain region associated with sureness was activated, while in the case of lies, the area associated with mental imagination was activated. Similarly, the brain's hemoglobin signals (fNIRS) or electrical activity (EEG) can be measured to define physiological features for lie detection [23; 24; 25; 26].  
The main drawback of the above techniques, however, is their invasive and expensive nature, due to the need for special instruments to allow data collections. This has led to the emergence of less obtrusive approaches involving verbal and non-verbal cues. Several studies focused on utilizing thermal imaging to perform the deception detection task since skin temperature has been shown to significantly rise when subjects are lying [7; 27]. Furthermore, speech was also explored [28; 29], e.g., by extracting features based on transcripts, part of speech (PoS) tags, or acoustic analysis (Mel-frequency cepstral coefficients).  
The use of several modalities for lie detection was also investigated to see its impact in improving detection algorithms. In [30; 31; 32], both verbal and non-verbal features were utilized. The verbal features were extracted from linguistic features in transcriptions, while non-verbal ones consisted of binary features containing information about facial and hands gestures. In addition, [PERSON] et al. [32] introduced dialogue features, consisting of interaction cues. Other multi-modal approaches combined the previously mention verbal and non-verbal features together with micro-expressions [3; 4; 5], thermal imaging [33], or spatio-temporal features extracted from 3D CNNs [34; 35].  
In the last decade, there has been a growing interest in the use of facial images to perform lie detection, often based on micro-expressions [3; 4; 5; 13; 15] or facial action units [14], achieving the current state-of-the-art accuracy. Table 1 below shows an overview of the major related works outlined in this section.
## 1 Introduction
### Water Scarcity
The pilot MD system for seawater desalination - which exhibited an electrical conductivity (ED) of 37-40 mS/cm - exhibited a thermal SEC of 208 kWh/m\({}^{3}\) and an electrical SEC of 5-20 kWh/m\({}^{3}\)[10]. However, the water cost was hardly comparable due to the large data variations and different applications from the literature [11]. Unfortunately, no reliable energy and cost data on a solar-powered FO desalination system can be found. The electrical SEC of pervaporation technology indicated a value < 0.3 kWh/m\({}^{3}\), but a considerable thermal energy was required for heating and maintaining the feed stream [12]. Various models suggested have shown that CDI could operate with a SEC of less than 1 kWh/m\({}^{3}\) for low-salinity brackish water but remains less energy-efficient than RO [13; 14; 15]. In a pilot 1-kW photovoltaic (PV)-powered membrane CDI system for treating 6700-uS/cm brackish water, the system exhibited a low SEC (the sum of battery, pump and power supply associated with an electrode) in the range of 0.7-1.1 kWh/m\({}^{3}\) for producing 5-m\({}^{3}\)/d potable water [16]. For other electrochemical systems, an EDI system performed with an SEC in the range of 0.3-0.7 kWh/m\({}^{3}\) when treating water with a TDS of 5 g/L [17]. However, these SEC values were not consistent, as many values stem from bench-scale systems operating with a low-feed salinity and salt removal. Comparatively, in a field demonstration of a solar-powered ED reversal system in rural India for treating ground water (salinity of 2100-2500 uS/cm), the solar system produced 6 m\({}^{3}\)/d, with a SEC of 1.7 kWh/m\({}^{3}\) and an estimated leveled cost of water of US$ 1.9/m\({}^{3}\)[18]. A RO system typically exhibited a SEC in the range of 0.6-4 kWh/m\({}^{3}\) for desalinating brackish groundwater (depending on the salinity of the water and the size of the system) at a low water cost of US$ 0.2-0.4/m\({}^{3}\)[19]. Hence, PV-powered membrane filtration (PV-membrane) systems appear attractive for small-scale (~1 m\({}^{3}\)/d), distributed and robust systems where no electricity is present [20], and it is envisaged that these can potentially help break the paradigm of the water-energy nexus [3].
## 4 Discussion
### Data Privacy, Security, and Cybersecurity Challenges in HMGT Systems  
The integration of IoT and AI technologies into aviation health monitoring systems presents new opportunities for enhancing safety and operational efficiency. However, the adoption of these technologies also introduces significant challenges related to data privacy, security, and cybersecurity.  
On the one hand, these technologies themselves have protective mechanisms for solving these problems. IoT devices and AI systems use sophisticated encryption techniques to protect data transmitted across networks. This encryption ensures that data, from sensor outputs to maintenance logs, remain confidential and tamper-proof during transmission. AI algorithms are adept at monitoring network traffic for unusual patterns that may indicate a security breach or attempted intrusion. By continuously learning from network data, these AI systems can quickly detect and alert human operators to potential threats, often before any harm is done.  
On the other hand, there are potential risks and vulnerabilities in IoT and AI applications. Despite advances in encryption, data transmitted from IoT devices can be intercepted by unauthorized entities if not properly secured. This risk is particularly acute when devices use unsecured communication channels or when encryption protocols are outdated. AI systems themselves can be targets for cyberattacks. Adversarial attacks, where malicious inputs are designed to deceive AI algorithms, can lead to incorrect system outputs or even system failure, posing serious risks to aircraft safety. IoT devices are inherently connected, making them vulnerable to cascading failures if one device is compromised.  
The interconnected nature of these devices means that a single point of failure can have widespread consequences.  
As the number of connected IoT devices within aviation systems grows, scaling security measures appropriately becomes a significant challenge. Ensuring consistent security across all devices requires substantial resources and continuous oversight. The absence of standardized security protocols across the IoT industry complicates the implementation of effective security measures. Without industry-wide standards, each device might require unique handling, increasing the complexity of securing aviation systems.  
Integrating new security technologies with existing aviation infrastructures is both technically challenging and costly. Older systems may not support the latest security measures, requiring either costly upgrades or custom solutions that may not offer the same level of protection. This involves not only implementing advanced technological solutions but also continuously evaluating and adapting these solutions to address emerging threats. Collaboration across the aviation industry to develop standardized security protocols will be crucial in mitigating risks effectively. As these technologies continue to evolve, so too must the strategies to protect the data they handle, ensuring that the benefits of the IoT and AI are realized without compromising the safety and privacy of aviation operations.
## 2 Theoretical Background
### Linear Discriminant Analysis (LDA)  
When working with large, high-dimensional sets of data, it is necessary to reduce the dimension of the dataset to interpret the information and manipulate it for the classification or clustering tasks at hand. In this context, different techniques have been developed for supervised, when there is previous knowledge about the classes within the data, and unsupervised methods, with no prior knowledge of the classes, with LDA being one of the main techniques for supervised dimension reduction. LDA focuses on linear combinations of the variables of features to maximize the between-class scatter matrix (\(S_{b}\)) to separate the classes as much as possible while minimizing the within-class scatter matrix (\(S_{w}\)) to make the clusters more compact [37]. Equations (32) and (33) present the procedure to calculate \(S_{b}\) and \(S_{w}\), where \(k\) is the dimension of the subspace defined as \(k=C-1\), \(C\) is the number of classes, \(m_{k}\) is the mean value of the class \(C_{k}\), \(m\) is the global mean value, \(n_{k}\) is the number of samples, and \(x_{i}\) is the \(i\)-th sample of class \(C_{k}\).  
\[S_{b}=\sum_{k}n_{k}(m_{k}-m)(m_{k}-m)^{T} \tag{32}\]  
\[S_{w}=\sum_{k}\sum_{i\in C_{k}}(x_{i}-m_{k})(x_{i}-m_{k})^{T} \tag{33}\]  
The optimal subspace \(G=(g_{1}\),..., \(g_{k})\) is obtained by optimizing Equation (34), with the solution being given by the k eigenvectors of \(S_{w}{}^{-1}S_{b}\) associated with the highest eigenvalues.  
\[\begin{array}{c}maxTr\ G^{T}S_{b}G\\ U\Once the subspace \(G\) is obtained, the original data, \(X\), are projected onto \(G\) to transform it into the new space, \(Y\), as shown in Equation (35).  
\[Y=\ G^{T}X \tag{35}\]
# A Novel Spatiotemporal Analysis Framework for Air Pollution Episode Association in Puli, Taiwan
## 3 Proposed Methods
### Pollution Episode Segmentation
To evaluate the correctness of the segmented shots, each image frame in the time series was labeled as cut or non-cut by an expert who has worked in this area for three years. In the machine learning literature, the commonly used performance measures, namely, precision, recall, and F1-Score, for a two-class classification problem (cut or non-cut in our case) are as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]  
\[Recall=\frac{TP}{TP+FN} \tag{4}\]  
\[\text{F1}-\text{Score}=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{5}\]  
where \(TP\), \(FP\), and \(FN\) are the numbers of true positive, false positive, and false negative samples, respectively. Each of the measures gauges the classification's performance from a different perspective. Precision focuses on the classification correctness of the samples that the algorithm recognizes as positive, while recall emphasizes how many true positive samples are correctly recognized by the algorithm. Precision and recall are contrasting measures. A perfect recall can be obtained at the cost of reduced precision by letting the algorithm recognize more samples as positive with a lower classification threshold value. The F1-Score is a balance measure that takes into account both precision and recall simultaneously. After performing the SBD methods on the PM\({}_{2.5}\) alert category time series, a number of episode shots are obtained. Each shot is a sequence of alert category maps within a time period and potentially elucidates a particular pollution episode. To efficiently represent the  
Figure 3: Four Sobel edge filters for detecting vertical, horizontal, and two diagonal edges. pollution episode shot, the gait energy image (GEI) scheme is adopted to reserve the main pollution variations in the spatiotemporal domain in a single image. GEI representation was proposed in [19], where the human walking gaits are extracted for individual recognition. The GEI is the image obtained by calculating the mean gray image over the sequence of human walking videos in the time sequence. In the context of our research, the GEI is the mean gray image over the sequence of alert category maps in the segmented shot.
## 2 Material and Methods
### UPLC-MS/MS and Chromatographic Conditions  
The sample analysis was accomplished by ACQUITY UPLC system coupled to a triple quadrupole detector (TQD) mass spectrometer (Waters Corporation, Milford, MA USA). Chromatographic separation of 5-AIQ and IS were performed on an Acquity CSH\({}_{18}\) (2.1 \(\times\) 100 mm; 1.7 \(\mu\)m) column. A mixture of ACN and 10 mM ammonium acetate in ratio of 65:35 was used for isocratic elution of analyte and IS, at flow rate of 0.3 mL/min. The column was heated to 40 \(\pm\) 5 \({}^{\circ}\)C to maintained constant temperature throughout the analysis and the auto-sampler temperature was fixed to 15 \(\pm\) 5 \({}^{\circ}\)C.  
The TQD equipped with turbo ion source (Z spray) was used for electrospray ionization (ESI) in positive mode and the quantification of the analyte and IS were performed by multiple reaction monitoring (MRM) mode. The transition from precursor to product ion of 145.0 \(>\) 117.4 and 145.0 \(>\) 91.0 were used as qualifier and quantifier for 5-AIQ while 384.0 \(>\) 138.1 was used for IS, respectively. The TQD parameters: capillary voltage (2.41 kV); ion source temperature (150 \({}^{\circ}\)C); desolvation temperature (350 \({}^{\circ}\)C); desolvation gas (nitrogen) flow (600 L/hr); collision gas (argon) flow (0.15 mL/min) and dwell time (0.160 s) were carefully optimized to produce maximum responses. The molecule related parameters: cone voltage and collision energy were 50 V & 20 V and 22 eV & 30 eV, respectively, for 5-AIQ and IS, respectively (Table 1). The MassLynks and TargetLynks (version 4.1) were used for sample acquisition and processing, respectively.
## 2 Underwater Wireless Sensor Network
### Communication Architecture
#### 2.3.1 Static UWSNs  
This type of architecture employs 2D and 3D architecture [29; 32; 33]. In 2D architecture, the sinks are deployed in water surface while underwater gateways and ordinary sensor nodes are deployed on the ocean floor. These sensor nodes forward the packets using acoustic links to underwater gateways. Then, these gateways forward the packets to the sinks using acoustic links [2; 14; 19; 33]. On the other hand, in 3D architecture, sensor nodes are deployed at different depths and equipped with an acoustic modem, while sink nodes are equipped with both radio and acoustic modems. These sensors detect the events from underwater environments and forward the information to the sink nodes using acoustic links. Sink nodes then forward the packets to the onshore base station using radio links.
## 3 Flight Controller Design
### Position Integral Backstepping Controller Design
\Big{\|}-\varepsilon_{xz}\Big{)}-k_{z1}z_{1}^{2}-k_{z2}z_{2}^ {2}-w|z_{2}|+|z_{2}||\frac{d_{\theta}}{J}|\] \[\leq -\lambda_{min}(\mathbf{Q}_{xz})\Big{\|} \begin{bmatrix}\mathbf{e} _{x}\\ z_{1}\end{bmatrix} \Big{\|}\Big{(}\Big{\|} \begin{bmatrix}\mathbf{e}_{x}\\ z_{1}\end{bmatrix} \Big{\|}-\varepsilon_{xz}\Big{)}-k_{z1}z_{1}^{2}-k_{z2}z_{2}^ {2}-|z_{2}|\Bigg{(}w-\frac{d_{\theta}^{+}}{J}\Bigg{)} \tag{90}\]  
By choosing \(w\) which is shown in (60), (90) meets  
\[\dot{V}\leq-\lambda_{min}(\mathbf{Q}_{xz})\Big{\|} \begin{bmatrix}\mathbf{e} _{x}\\ z_{1}\end{bmatrix} \Big{\|}\Big{(}\Big{\|} \begin{bmatrix}\mathbf{e}_{x}\\ z_{1}\end{bmatrix} \Big{\|}-\varepsilon_{xz}\Big{)}-k_{z1}z_{1}^{2}-k_{z2}z_{2}^ {2}-\eta|z_{2}| \tag{91}\]  
and consequently \(\dot{V}<0\) if  
\[ \begin{bmatrix}\mathbf{e}_{x}\\ z_{1}\end{bmatrix} \
otin\mathcal{E}_{1} \tag{92}\]  
where  
\[\mathcal{E}_{1}=\left\{ \begin{bmatrix}\mathbf{e}_{x}\\ z_{1}\end{bmatrix} \in\mathbb{R}^{4\times 1}:\left\| \begin{bmatrix}\mathbf{e}_{x}\\ z_{1}\end{bmatrix} \right\|\leq\varepsilon_{xz}\right\} \tag{93}\]  
The control force (37) can be realized because it depends on the system states and parameters only. However, the control torque proposed in (54) needs the information of \(\phi_{1}\) and \(\phi_{2}\). Refs. [9; 12; 13] perform the numerical differentiation to estimate \(\phi_{1}\) and \(\phi_{2}\) from \(\phi_{1}\) and \(\phi_{2}\), and then apply the low-pass filter to inhibit the effects of the measurement noise. However, a phase delay will be induced inevitably due to the use of a low-pass filter. Instead of taking the numerical differentiation, in this study, an analytical form of the position integral backstepping controller is proposed. The associated detailed derivations are presented in the next section.
## 4 Conclusions  
The present work considers the ambient temperature as one of the key parameters affecting the drying performance of the industrial corn drying. The artificial neuron network was applied to predict the drying performance of the drying system. The drying kinetics, the exergy exchange performance, as well as the heat recovery performance of the drying system were investigated. The detailed conclusions depending on the results are listed as follow:  
1. the exergy efficiency of the heat exchanger is significantly affected by the ambient temperature, where the exergy efficiency is proportion to the ambient temperature. Moreover, the exergy efficiency of the heat exchanger varies from 0.249 to 0.648 for the whole drying process;
2. the far-infrared wavelength of the radiators are found to be ranged from 7.61 to 8.15 um, and the recovered radiant exergy for 17 October, 30 October, and 19 November are, respectively, ascertained to be 571.12, 621.17, and 650.43 MJ;
3. based on the analysis of the ambient conditions on the drying performance of the drying system, two prediction models with (IANN) and without (OANN) considering the ambient conditions were established and compared with each other. The results showed that IANN model has a better prediction performance, which indicates that the ambient conditions should be taken into consideration when establishing the prediction model;
4. the best performance for prediction of moisture content, specific recovered exergy, and exergy efficiency for corn industrial drying are found to be at the training epochs of 30, where the regression coefficient of determination for \(MC\), \(Er\), and \(\eta_{ex,h}\), respectively, are 0.998, 0.992, and 0.980; mean squared error of 0.067, 0.00296, and 0.0057; mean absolute error of 0.16, 0.04, and 0.0063.  
Figure 10: The proposed control model structure of industrial corn drying system.  
This indicates that the established IANN model has excellent prediction performance and can be used in engineering practices.  
Data curation, B.L., C.L. ([PERSON]) and J.H.; Formal analysis, B.L.; Funding acquisition, C.L. ([PERSON]); Investigation, B.L., C.L. ([PERSON]) and J.H.; Methodology, B.L.; Project administration, C.L. ([PERSON]); Software, B.L. and J.H.; Supervision, C.L. ([PERSON]); Writing--original draft, B.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China No. 31,671,783; No. 31,371,871) and Science and Technology Planning Project of Guangdong Province, China (No. 2014B020207001).  
The authors would like to thank the editors and reviewers for their valuable and constructive comments.  
The authors declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.