esa-sceva/satcom-chunk-collection huggingface dataset:
# Edge Server Deployment Strategy Based on Queueing Search Meta-Heuristic Algorithm
## 5 Experimental Assessment
### Analysis of Simulation Results
#### 5.2.3 Performance Trend Analysis with the Number of Base Stations  
The performance of various algorithms is evaluated based on the average number of hops for user requests as the number of base stations increases from 100 to 180. The results are presented in Figures 8 and 9 using bar charts with error bars. Each bar represents the average hop count of an algorithm for a specific number of base stations, and the error bars indicate the standard deviation of 10 independent runs, reflecting the variability in algorithm performance due to different initial conditions.  
As shown in Figure 8, the average number of hops for each algorithm decreases as the number of base stations increases. This is primarily because, with the increase in the number of base stations, the likelihood of a user being covered by a closer base station rises, reducing the number of hops needed to forward the user's request to the edge server (ES). The bar chart clearly demonstrates that QSA consistently achieves the lowest average hop count across all base station densities, with smaller error bars compared to other algorithms. This indicates that QSA not only performs better in minimizing user request delays but also exhibits greater stability under varying initial conditions.  
To further assess how the number of base stations (BSs) affects algorithm performance, we evaluate the average number of hops as the BS count increases from 100 to 180. The results, presented in Figures 8 and 9, show that QSA maintains its superior performance regardless of the BS density. The smaller error bars for QSA suggest that its performance is less sensitive to variations in initial populations, further confirming its robustness and reliability in edge server deployment scenarios.  
Figure 8: Average hop count variation with increasing base stations.  
Figure 9 shows that computational time increases linearly with the number of base stations. This is because a larger number of base stations expands the solution space, requiring more time for the algorithms to explore and identify optimal deployment solutions.
# Reducing PAPR with Low Complexity Filtered NOMA Using Novel Algorithm
## 3 Simulation Results
### Complexity  
In this segment, we have estimated the complexity of the system by considering \(n\) = 64, S = 4, and \(p\) = 4 given in Table 3 and Figure 7. The number of additions and multiplications required by SLM (S = 4 \(p\) = 4) for 16-QAM is 12,288, 20,480, 64-QAM is 16,384, 24,576, and 256-QAM is 33,088, 32,768, respectively. Hence, it is concluded that the complexity of the framework will increase with high order transmission schemes such as 256-QAM.  
Figure 5: BER performance.  
Figure 6: PSD of F-NOMA and NOMA with and without SLM.
# A Corpus-Based Study of Linguistic Deception in Spanish
## 2 Automated Deception Detection
### The Role of Linguistic Variables in the Computational Analysis of Deception  
As has been seen, deception detection can play a role in the investigation of different security issues, civil cases, and even some types of crimes, and, according to the Institute for Linguistic Evidence (ILE) ([[https://linguisticevidence.org/](https://linguisticevidence.org/)]([https://linguisticevidence.org/](https://linguisticevidence.org/)), accessed on 4 July 2021) paradigm, standards for forensic computational linguistic methodology include that forensic linguistics provides an empirical analysis grounded in linguistic theory [11]. Furthermore, the adoption of totally automated deception detection methods and mixed machine-human methods entail some basic stages: choosing an appropriate linguistic level, properly codifying the variables of analysis, engaging in statistical analysis, and conducting validation testing.  
These kinds of analyses can make use of variables from different linguistic levels, namely, the phonemic, morphometric, lexical, syntactic, semantic, and pragmatic. As stated in [11], forensic methods dealing with written data have focused on analytical units at the character, word, sentence, and text levels. Specifically, some studies, such as [14], present automated methods for deception detection operating at the character level, whose analytical units include, among others, single characters, punctuation marks, or character-level n-grams (units of adjacent characters). At the word level, analytical units can be word-level n-grams [15], lexical semantics [16], and vocabulary richness [17]. Sentence-level analytical units can include part-of-speech (POS) tags [18], sentence type [19], average sentence length [20], and average number of clauses per utterance [21]. At the textual level, analytical units can include text length [22] and discourse strategies [23], to name but a few. The easiest patterns to detect by machine are character and word level features. On the contrary, at other linguistic levels, automatic pattern detection is harder, especially with forensic data, as they are often messy. For instance, sentence level features can be extracted automatically, but most parsers require human revision of the output to ensure the accuracy of the analysis.  
In their meta-analysis of computational deception detection, [24] explored 44 studies and a set of 79 cues, which seemed reasonably consistent across previous literature. Despite some inconsistencies, the authors reported some common conclusions from the poll of studies reviewed: in broad terms, liars experienced greater cognitive load than truth-tellers; using fewer words related to cognitive processes, they used more negative emotion words, detached themselves from the events narrated, and used fewer sensory-perceptual words. Nonetheless, words expressing uncertainty were found indicative neither of deception nor of truth. All in all, the results varied across the studies according to event type, involvement, intensity of interaction, and motivation, among other variables.
## 3 Controller Design  
### Controller Design  
For simplifying the controller development, by using the backstepping method, several variables are designed as below  
\[\left\{\begin{array}{l}\xi_{1}\ =\ x_{1}-x_{1d}\\ \xi_{2}\ =\ k_{1}\xi_{1}+\dot{\xi}_{1}\ =\ x_{2}-x_{2d}\\ x_{2d}\ =\ \dot{x}_{1d}-k_{1}\xi_{1}\\ \xi_{3}\ =\ x_{3}-x_{3d}\end{array}\right. \tag{15}\]  
where \(k_{1}\) is non-negative; \(\xi_{1}\) stands for the tracking error; \(x_{2d}\) denotes the virtual control law of the state \(x_{2}\); \(\xi_{2}\) stands for the discrepancy between the actual state \(x_{2}\) and the virtual control \(x_{2d}\); \(x_{3d}\) denotes the virtual control law of the state \(x_{3}\); \(\xi_{3}\) stands for the discrepancy between the actual state \(x_{3}\) and the virtual control \(x_{3d}\).  
_Step 1:_ On basis of the Formulas (12) and (15) and differentiating \(\xi_{2}\), there is  
\[\begin{array}{l}\dot{J}\dot{\xi}_{2}\ =\ \dot{J}\dot{x}_{2}-\dot{J}\dot{x}_{2d}\\ \ =\ \frac{\partial x_{s}}{\partial x_{1}}x_{3}-mgd_{4}\cos(\beta_{0}+x_{1})-A_{ f}S_{f}(x_{2})-Bx_{2}+\Delta(t)-\dot{J}\dot{x}_{2d}\\ \ =\ \frac{\partial x_{s}}{\partial x_{1}}(\xi_{3}+x_{3d})-mgd_{4}\cos(\beta_{0}+ x_{1})-A_{f}S_{f}(x_{2})-Bx_{2}+\Delta(t)-\dot{J}\dot{x}_{2d}\end{array} \tag{16}\]  
From (16), the virtual control \(x_{3d}\) is constructed as  
\[\left\{\begin{array}{l}x_{3d}\ =\ \left(\frac{\partial x_{s}}{\partial x_{1}} \right)^{-1}(x_{3d}+x_{3 ds}),x_{3 ds}\ =\ x_{3 ds1}+x_{3 ds2}\\ x_{3 ds}\ =\ mgd_{4}\cos(\beta_{0}+x_{1})+A_{f}S_{f}(x_{2})+Bx_{2d}+\dot{J}\dot{x}_{2d }-\xi_{1}\\ x_{3 ds1}\ =\ -k_{2}\xi_{2},x_{3 ds2}\ =\ \frac{-k_{2}\xi_{2}c_{1}^{2}}{k_{2 }c_{2}\xi_{2}\tanh(\xi_{2}/b_{1}(t))+b_{1}(t)}\end{array}\right. \tag{17}\]where \(k_{2}\) and \(k_{2s}\) are positive constants; \(b_{1}(t)>0\) and \(\int_{0}^{t}b_{1}(v)dv\leq\tau_{1}<+\infty\), with \(\tau_{1}\) being positive.  
In (17), \(x_{3 da}\) stands for the model-based feedforward compensation term that is utilized to achieve accurate model compensation and then improve the tracking performance. \(x_{3 ds}\) consisting of the linear feedback term \(x_{3 ds1}\) and the nonlinear feedback term \(x_{3 ds2}\) denotes the robust control law that is employed to remove the impact of external disturbances on the tracking performance.  
Putting (17) into (16), one has  
\[\begin{array}{ll}\dot{\xi}_{3}&=u_{a}+u_{s}-\varphi_{2}+q(t)-\dot{x}_{3d}\\ &=\varphi_{2}+\dot{x}_{3d}-\frac{\dot{x}_{3s}}{\dot{x}_{1}}\xi_{2}-k_{3}\xi_ {3}-\frac{k_{3s}\xi_{3}\xi_{2}^{2}}{k_{3s}\xi_{3}z\tanh(\xi_{3}/b_{2}(t))+b_{2 }(t)}-\varphi_{2}+q(t)-\dot{x}_{3d}\\ &=-k_{3}\xi_{3}-\frac{\dot{x}_{3s}}{\dot{x}_{1}}\xi_{2}-\frac{k_{3s}\xi_{3} \xi_{2}^{2}}{k_{3s}\xi_{3}z\tanh(\xi_{3}/b_{2}(t))+b_{2}(t)}+q(t)\end{array} \tag{18}\]  
The control diagram of the lifting mechanism is provided in Figure 2 below.
# Human Activity Recognition for Assisted Living Based on Scene Understanding
## 6 Design and Implementation
### Forwarding Detected Activities to the SmartCare Gateway  
As described in Section 3, the SmartCare Gateway receives information from several devices, one of which is the HAR system. Communication with the Gateway is carried out using the MQTT protocol by publishing messages. We use serialized JSON objects with information about the current activity of the supervised person.  
On initialization, the Gateway subscribes to the following topics:  
* gateway/register;
* video-monit/1/activity/response-get;
* video-monit/1/activity/response-async-get.  
At start-up, the video monitoring system subscribes to:  
Figure 11: Communication diagram.  
Figure 10: Communication using priority queues.  
* gateway/discover;
* video-monit/1/activity/get;
* video-monit/1/activity/async-get.  
It must also make itself known to the Gateway by publishing a serialized JSON object containing system data on the gateway/register topic. The video-monit subtopic is the name of the HAR bridge used for the proposed module. The module communication and initialization process can be followed in Figures 11 and 12, both the HAR system and the SmartCare Gateway subscribe to the MQTT Agent for this connection.  
Topic description:  
* gateway/register and gateway/discover are used for a handshake protocol: bridges from each device in the network (including the HAR system) subscribe to gateway/discover to receive a \"get acquainted\" message from the server, after which they publish device details to gateway/register to get recognized by the server.
* video-monit/1/activity/get and video-monit/1/activity/response-get: under the video-monit/bridge name we have 1/device enrolled with a resource of type activity/. The Gateway publishes an inquiring operation on the _get_/subtopic and the video monitoring system responds on the response-get/subtopic with the patient activity details. This message exchange is represented in Figure 13.
* video-monit/1/activity/async-get and video-monit/1/activity/response-async-get: similar with the above get/response-get topics, an asynchronous behavior is triggered by an 'on'/'off' message. An example of asynchronous communication is illustrated in Figure 14.  
Figure 12: Initialization sequence for the connection between the HAR module and the SmartCare Gateway.
## 3 Results  
### Objective 1: Data Procurement  
Objective 1 was to _establish effective data procurement methods for both before and after planned disturbances_ and was achieved by testing various data collection and processing parameters with UAS imagery at multiple prescribed burn and selective timber harvest sites in the Central Hardwood Forest (CHF) region. Key UAS data collection variables were tested for two types of land cover (prairie and forest) surrounding their respective natural disturbance-based management (NDBM) treatments (Tables 12 and 13).
# Deep Compressed Sensing Generation Model for End-to-End Extreme Observation and Reconstruction
## 2 Related Background
### Compressed Sensing Using Generative Models  
In 2017 [PERSON] et al. proposed a CSGM that combines compressive sensing with generative models to get rid of the forced sparsity constraint on signals from traditional compressive sensing, such as VAE and GAN, which were based on generative models of neural networks that showed unexpected results in generation data. The generative model maps the low-dimensional latent representation space to the high-dimensional sample space:  
\[\mathbf{x}=\mathbf{G}(\mathbf{z})\;,\;\mathbf{G}(\mathbf{z})\in\mathbb{R}^{\text{N}} \tag{5}\]  
\(\mathbf{z}\in\mathbb{R}^{\text{K}}\) is the low-dimensional signal of the latent space. \(\mathbf{G}\) is the generative model. \(\mathbf{x}\) is the generated signal. During training, the generative model learns mappings from low to high dimensions such that the generated vectors are similar to the training dataset. Therefore, any pre-trained generative model is used to roughly learn the probability distribution of the training dataset samples and attempt to assign the training set with high probability to the more likely latent vectors.  
The literature [19] proposed the set-restricted eigenvalue condition (S-REC):  
\[\parallel\mathbf{\Phi}(\mathbf{x}_{1}-\mathbf{x}_{2})\parallel\geq\gamma\parallel\mathbf{x}_{ 1}-\mathbf{x}_{2}\parallel-\delta \tag{6}\]  
Let \(S\in\mathbb{R}^{\text{N}},\mathbf{x}_{1},\mathbf{x}_{2}\in S\) are any natural vectors that exist. \(\gamma>0\) is a constant. \(\delta>0\) is an additive slack term. The observation matrix \(\mathbf{\Phi}\) satisfies the S-REC.  
Compared with the RIP condition of traditional compressed sensing, the S-REC relaxes the sparsity requirement of the observed vector, making its reconstruction process similar to the minimization reconstruction process of Equation (4):  
\[\hat{\mathbf{z}}=\underset{z}{\text{arg}min}\parallel\mathbf{y}-\mathbf{\Phi}\mathbf{G}_\mathbf{{\theta}}(\mathbf{z})\parallel_{2}^{2} \tag{7}\]  
\(\hat{\mathbf{z}}\) is the optimized latent vector. Different from Equation (4), the reconstruction idea of CSGM is finding the optimal latent input \(\mathbf{z}\) among the random input to minimize the error expectation. The back-propagation gradient descent method is used to find the optimal latent input \(\mathbf{z}\) within the generative model of the neural network. CSGM successfully combines compressed sensing with neural networks. However, the optimization of its reconstruction method requires thousands of gradient descent, leading to slow reconstruction. The observation matrix \(\mathbf{\Phi}\) is an untrainable random matrix, leading to the reconstruction limitation.
## 1 Introduction
As a result, the proposed method makes a solution for an MKP by combining sequential actions made by an agent in each state. Our main contributions are as follows:  
* We propose a DRL-based method to solve MKP, in which the DNN model is extensively trained with various combinations of random items and knapsacks. The trained single DNN model has the capability of solving diversified MKPs with untrained instance sets of items and knapsacks. * We simplify the action space for MKP to make it possible to train the model in a scalable manner. The proposed method combines greedy sorting algorithms with the DRL training process, and the size of the action space is fixed to the number of items regardless of the number of knapsacks. It simplifies state transition pattern so that the agent can identify a change in the environment quickly during the training process. * We adopt the Asynchronous Advantage Actor-Critic (A3C) [20] to expedite the learning process for DNN model. The training is performed asynchronously with multiple learners. A global model is shared with each learner, which contributes to the global model updating at the end of each episode. * The experiments show that the DNN model can be successfully trained in a variety of configurations in terms of the number and capacity of knapsacks, and the number, weight, and profit of items. It is demonstrated that even when the weight and profits of items have a nonlinear relationship, the proposed method achieves comparable performance to an optimal policy. The remainder of this article is organized as follows. In Section 2, we will summarize existing work on knapsack problems. In Section 3, we introduce a DRL method with a sorting algorithm for solving MKP. Section 4 compares our method with other baselinemethods by evaluating computational results on random, linear, and quadratic instances. Finally, we conclude this paper in Section 5.
# Spoken Language Identification System Using Convolutional Recurrent Neural Network
## 4 Proposed Spoken Language Identification System
### Selected Features  
To reduce signal redundancy and improve accuracy in the spoken LID system, we propose combining MFCC and GFCC features [40].
# Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment
## 4 Observer Design
### Strong Tracking UKF  
Equations (16) and (21) show the UKF computing procedure for computing Cholesky decomposition, which requires the variance matrix to be positive, but in some cases, the matrix, \(p_{k}^{+}\), cannot be kept as a positive-definite matrix, then, the algorithm would be terminated. To improve the astringency of the algorithm, in this study, the strong tracking algorithm is introduced.  
The strong tracking UKF (STUKF) uses the residual of state estimation value to correct the a posteriori variance matrix. Define innovation as:  
\[Y_{err}=y_{k}-\hat{y}_{k} \tag{27}\]  
And innovation \(Y_{err}\) satisfies:  
\[Y_{\text{err}}^{\text{T}}Y_{\text{err}}\leq S\cdot trE\Big{(}Y_{\text{err}}Y_ {\text{err}}^{\text{T}}\Big{)} \tag{28}\]  
where \(S\geq 1\) represents the threshold coefficient.  
The STUKF computing procedure is as follows:  
**Step 1** Calculate residual and residual variance matrix.  
\[\begin{gathered} Y_{\text{err}}=y_{k}-\hat{y}_{k}\\ C_{0,k}=\begin{cases}\ Y_{\text{err}}Y_{\text{err}}^{\text{T}}&k=1\\ \frac{\beta C_{0,k}+Y_{\text{err}}Y_{\text{err}}^{\text{T}}}{1+\beta},&k>1 \end{cases}\end{gathered} \tag{29}\]  
where \(\beta\) is the forgotten factor (\(\beta<1\)) is to decrease the influence of early information to enhance the tracking ability.  
**Step 2** Calculate the suboptimal fading factor at the current time.  
\[\begin{gathered}\rho_{k}=\begin{cases}\rho_{0}&\rho_{0}\geq 1\\ 1&\rho_{0}<1\end{cases}\\ \rho_{0}=\frac{N_{k}}{M_{k}}\\ N_{k}=tr\big{(}C_{0,k}-R\big{)}^{T}\\ M_{k}=tr\bigg{(}\sum\limits_{2n}^{i=0}W^{(i)}\Big{(}\hat{y}_{k}^{(i)}-\hat{y} _{k}\Big{)}\Big{(}\hat{y}_{k}^{(i)}-\hat{y}_{k}\Big{)}^{T}\bigg{)}\end{gathered} \tag{30}\]  
where \(tr()\) denotes the trace of the matrix.  
**Step 3** Update the variance matrix.  
\[P_{k}^{-}=\rho_{k}\sum_{2n}^{i=0}W^{(i)}\left(\hat{x}_{k}^{(i)}-\hat{x}_{k}^{-} \right)\left(\hat{x}_{k}^{(i)}-\hat{x}_{k}^{-}\right)^{T}+Q_{k-1} \tag{31}\]  
The next step is the same as the traditional UKF.
## 4 Artificial Intelligence (AI)-Enabled 6G Networks
### Intelligent Routing  
To manage the network traffic efficiently and to fulfill the QoS requirements of 6G applications, several routing strategies have been developed. However, the traditional routing protocols developed using meta-heuristic approaches become computationally expensive with increasing traffic variability. Consequently, ML and DL-based approaches  
Figure 8: Deep Reinforcement Learning (DRI)-based data caching in the Internet of Things (IoT) environment.  
have been proposed to address the shortcomings of the traditional methods. Figure 9 shows an example of DL-based intelligent routing in an IoT environment. [PERSON] et al. [56] proposed a real-time deep CNN-based routing algorithm in a wireless mesh network backbone. A CNN model with two convolutional layers and two fully connected layers is trained periodically using the continuous stream of network data. [PERSON] et al. [57] proposed a DRL-based routing in software-defined data center networks by recombining network resources (such as cache and bandwidth) based on their effectiveness in reducing delay and then using DRL for routing with the recombined state. The employed DNN model within DRL consists of two fully connected hidden layers with 30 neurons each. The CNN model in the actor and critic networks of DRL consists of two max pooling layers, three convolutional layers with eight filters, and one fully connected layer with 30 neurons. A Relu activation function is employed in all the layers.
## 2 Literature Review  
A detailed survey of existing studies are presented in this section. [PERSON] et al. [14], have proposed wide and deep Convolutional Neural Network (CNN) to capture the periodicity from State Grid Corporation of China (SGCC) dataset. However, the time complexity of wide and deep CNN model is very high, being a hybrid model. The number of non-fraudulent customers is greater than the number of fraudulent customers, which causes the class imbalance problem. The authors have addressed the issue of class imbalance using large scale data in [19]. For this purpose, the authors proposed Random undersampling Boosting (RusBoost) and Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) for classification using real smart meter data from commercial and industrial zones [19]. However, the limitation of random undersampling is the underfitting problem, biased selection of samples, and removal of useful information from the majority class. A metaheuristic technique, namely, the Binary Black Hole Algorithm (BBHA), is used to select the most representative features using real time-series data collected from a Brazilian agency in [20]. However, challenges in BBHA include being stuck in local minima and class imbalance problem [21]. The authors have proposed a Clustering technique by Fast Search and Find of Density Peaks (CFSFDP) combined with Maximum Information Coefficient (MIC) based on the method in [22]. They use an Irish dataset of real smart meter project [23]. The dataset consists of residential, small and medium size enterprises with 5000 customers within 500 days. However, observer meters are installed for smart meter security. The installation and maintenance costs of hardware resources are very high. Authors have combined K-means clustering and Deep Neural Network (DNN) to secure the smart meter [17]. This combined approach is used to detect the anomalies in normal electricity usage of Irish data. [PERSON] et al. [18] have proposed finite mixture clustering model and genetic programming to discover new characteristics for theft detection. It is applied on the customer behavior trails from 2009-2010 in Ireland. The main concern of the study is feature engineering, rather than accurate classification. However, it has high FPR, which leads to high on-site inspection cost. A detailed literature review is presented in Table 2.  
Figure 1: Two months electricity consumption data of a normal customer.
## 3 Materials and Methods
### Identification and Quantification of Volatile Compounds
#### 3.7.3 GC-MS  
GC-MS analysis was conducted using a GC-MS QP2010 Ultra system (Shimadzu, Japan) and HP-INNOWax capillary column (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m). High-purity helium (>99.99%) at a flow rate of 1.0 mL/min was used as the mobile phase. The temperature of the injector and transfer line was set at 250 \({}^{\circ}\)C. The GC program consisted of an initial temperature of 45 \({}^{\circ}\)C for 5 min, raised to 230 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C/min, and held for 5 min. The MS run program parameters consisted of the electron impact ionization mode, an ion source temperature of 200 \({}^{\circ}\)C, an m/z range of 30-500, and scanning at 0.3 s intervals. The retention indices (RI) were calculated after analyzing C7-C30 n-alkanes. The mass spectra were compared to those available in the National Institute of Standards and Technology (NIST) 20/20s database based on the RI of authenticated standards. The volatile compounds were quantified by comparing their peak areas with the peak area of the internal standard, and multiplying by the concentration of the internal standard.