esa-sceva/satcom-chunk-collection huggingface dataset:
# Aerothermoelastic Analysis of Conical Shell in Supersonic Flow
## 1 Introduction
With [PERSON]'s principle, the effects of the mass fraction, agglomeration parameters and distribution pattern of the agglomerated carbon nanotubes on aeroelastic stability were investigated by [PERSON] et al. [31]. [PERSON] and [PERSON] [32] studied the aeroelasticity of a conical shell under yaw supersonic flow. However, there is little literature on aerothermoelastic analysis of conical shells. [PERSON] et al. [33] investigated the aerothermoelastic stability of a composite truncated conical shell under simply supported boundary conditions in supersonic airflow. According to the displacements given by double trigonometric series, [PERSON] et al. [34] analyzed the nonlinear vibration of a conical shell which was subjected to aerodynamic load and thermal load under simply supported boundaries. During the modeling process, the displacement components were approximate, and the governing equations of motion ignored the effect of in-plane inertia. For conical shells with simply supported boundary conditions, the double trigonometric function can be used as the trial function, and the results obtained are acceptable. However, obtaining simple trial functions for more general constraints is challenging, which limits further study of the vibration characteristics of conical shells. It can be noted from the above review that at present, the aerothermoelastic analysis of conical shells is mainly focused on conical shells with simply supported boundaries, and there is a lack of aerothermoelastic research on conical shells with more general constraints. Therefore, in the present paper, the aerothermoelastic characteristics of a functionally graded conical shell with classical boundary conditions in supersonic flow are investigated. Based on [PERSON]'s shell theory, the kinetic energy and potential energy of the conical shell are obtained. According to the linearized first-order potential theory and the temperature assumption along the thickness direction, the aerodynamic loads and thermal loads are calculated. By taking the characteristic orthogonal polynomial series which are constructed using a Gram-Schmidt procedure as the admissible functions, the mode function of the conical shell is obtained using the Rayleigh-Ritz method. The dynamical equation of the functionally graded conical shell is established by a Lagrange equation. Taking conical shells under free-clamped boundaries as the research object, the effects of thermal and aerodynamic loads on natural vibration characteristics are analyzed. Then, on this basis, the effects of length-to-radius ratio, thickness-to-radius ratio, semi-vertex angle, aerodynamic load and thermal load on the aerothermoelastic stability of conical shells are investigated.
## 3 Experimental Setup
### Data-to-Image Conversion
#### 3.4.2 Three-Phase DWT Image Construction  
To generate DWT-based input images, the three-phase current data were first converted from 1D sequences into 2D arrays using sequential reshaping. Each phase signal was then decomposed using a Daubechies 4 (db4) wavelet up to Level 3.  
Since the time-series data were aligned horizontally due to the reshape method, the horizontal detail coefficients (cH) were selected from the wavelet-decomposed components. As each cH component output was in grayscale, the grayscale images of phases A, B, and C were mapped to the R, G, and B channels, respectively, to form a three-channel DWT-based RGB image.
## 2 Background and Related Works
### Aspect-Based Sentiment Analysis
#### 2.2.1 Aspect Extraction  
Aspect extraction aims to extract the set of aspects mentioned in a given text. It is an essential step in opinion mining. The first work on aspect extraction was introduced by [PERSON] and [PERSON], 2004 [16]. Although they considered explicit and implicit aspects, they only dealt with the former ones. The main idea of their approach was to design a set of rules based on statistical observations. [PERSON] and [PERSON], 2007 [17] improved this method by assuming that the product class is known a priori. Based on that assumption, they proposed an algorithm that detected whether a noun refers to an aspect by calculating the pointwise mutual information between the product and the noun. [PERSON] et al. [18] improved [PERSON] and [PERSON]'s method by assuming that the aspects of a product are more frequent in reviews than in general text. [PERSON] et al., 2015 [19] proposed a novel method to detect aspects by using rules that consider the dependency relations between aspects and the opinion words.  
Aspect extraction can be conceptualised as a sequential labelling problem. In this case, the extracted aspects are terms rather than categories. The most popular methods in this context are Hidden Markov Models (HMM) and Conditional Random Fields (CRF). The authors in [20] used a lexicalised HMM to extract opinions along with their explicit aspects. [PERSON] and [PERSON], 2010 [21], [PERSON] et al., 2010 [22] and [PERSON] and [PERSON], 2010 [23], employed CRF to extract explicit aspects.  
Some studies have employed the multi-label classification approach to extract aspect categories from a text. First, they define a set of aspect categories. Then, they develop a system to map sentences to aspect categories from the defined set. For example, the au  
Figure 1: Components of the review of a restaurant.  
[PERSON] in [24] developed five binary SVM classifiers. Each one specifies whether a sentence belongs to a specific category or not. The NLANGP team [25; 26] also modelled aspect category detection as a multi-class classification problem using logistic regression for each category. [PERSON] [27] proposed a separate maximum entropy classifier with syntactic features (e.g., words, lemmas). Then, they applied heuristics to the outputs of the classifiers to determine the categories of each sentence. Following this approach, i.e., multi-label classification, we have developed a system to extract aspects from reviews, as explained in Section 3.3.
## 5 Engineering Validation
### Identification Results and Discussion  
The dynamic response was used to identify the tension based on the proposed algorithm. Firstly, the time-varying frequency of the acceleration signal was extracted using TFR techniques. The window function in STFT was also chosen as 256 based on the discussion in the workshop experiment. Moreover, the best identification range is between 5.0 to 7.0 Hz. Thus, the 15 th, 16 th, and 17 th frequencies of cables were identified clearly from the reassignment spectrogram, as shown in Figure 16a. It is clear to see from Figure 16a that the structural frequency changes more frequently than the results of the workshop environment. Moreover, the identified time-varying frequency was used to calculate the tension by the 15 th order frequency based on Equation (12), as shown in Figure 16b.  
Figure 14: Acceleration of the experimental cable.  
Figure 15: (**a**) The stress of the girder and (**b**) GPS displacement of the tower.  
The cable tension increased from 4325 \(\pm\) 30 kN to 4425 \(\pm\) 25 kN as the trucks moved over the bridge, as shown in Figure 16b. The tension variation was smaller than the truck's static weight of 117.6 \(\pm\) 3.92 kN because the loads were spread over two cables upstream and downstream. Moreover, the increasing moment of cable tension during Stage 1 in two cases coincided with the stress results, as shown in Figure 17a. The loading trucks passed through the mid-span area and moved to the South Tower during this period. Meanwhile, there was a sudden jump at about 200-225 s (Stage 2 in Case 1) when the trucks crossed the bottom region of the cable. The stress simultaneously decreased due to the compression of the steel box girder by the trucks. Therefore, the variation of identified tension is simultaneously changed with the stress of the girder. It is proved that the time-varying characteristics of cable tension can be extracted based on the proposed algorithm.  
For the GPS displacement shown in Figure 17b, there is still a high correlation between the GPS displacement and the trend of cable tension. However, the variation of GPS displacement was ahead of the identified tension because the displacement of the tower is a global parameter controlled by other cables. It can be found from the figure that the time to change in advance will be reduced as the truck gets closer to the location of the measured cable.
## 2 Preliminary
### Process Mining  
Process mining has been used in the field of process management to support the analysis of business process based on the event logs. During process mining, specific data mining algorithms are applied to event log data in order to identify trends, patterns, and details contained in event logs recorded by an information system. Process mining aims to improve process efficiency and understanding of processes.  
There are three classes of process mining techniques. This classification is based on whether there is a prior model and, if so, how the prior model is used during process mining [2].  
* Discovery The discovery technique is used in process mining, usually when a prior model does not exist. Based on the event log, a new model is constructed based on low-level events.
* Conformance Checking Conformance checking is used when there is a prior model. The existing model is compared with the process event log. Discrepancies between the log and model are analyzed.
* Performance Mining The process model based on a prior model or generated from a discovery technique is extended with performance information such as processing times, cycle times, waiting times, etc.
## 6 Resulting Graph Visualizations
### Visualizing Attacks by Count  
Figure 7 depicts the full picture of the attack data binned with respect to the number of occurrences (count). The star motif in the red box is the Reconnaissance port scan sample shown in Figure 2. The top right of Figure 7 has more IPv6 addresses compared to Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ID** & **From** & **To** & **Total\_Dur** & **Avg\_Dur** & **Total\_Bytes** & **Avg\_Bytes** & **Count** & **CountBin** \\ \hline edge\_21 & 143.88.11.14 & 143.88.11.1 & 1,267,576.92 & 2.6 & 40,376,997 & 82.73 & 488,029 & 5 \\ edge\_35 & 143.88.255.10 & 10.0.10.1 & 114.42 & 0 & 605,569,716 & 90.06 & 6,724,017 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Non-Attack points of interest (count).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ID** & **From** & **To** & **Total\_Dur** & **Avg\_Dur** & **Total\_Bytes** & **Avg\_Bytes** & **Count** & **CountBin** \\ \hline edge\_0 & 143.88.2.10 & 143.88.7.15 & 353,248.5 & 0.212616 & 2,654,582,328,320 & 1,597,760 & 1,661,440 & 3 \\ edge\_3 & 143.88.2.10 & 143.88.7.12 & 778,386.3 & 0.691353 & 925,758,636,800 & 822,247.5 & 1,125,888 & 4 \\ edge\_257 & 143.88.5.12 & 143.88.5.1 & 943,576.7 & 1.87766 & 36,458,752 & 72.55069 & 502,528 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Reconnaissance points of interest (average bytes).  
Example data points for all attack tactics by count are presented in Table 7. The maximum count was 6,724,017, the minimum count was 1, and the average count was 3,864,567.
# The Effect of Preprocessing on Arabic Document Categorization
## 5 Experiment and Results  
### Arabic Data Collection  
Unfortunately, there is no free benchmarking dataset for Arabic document categorization. For most Arabic document categorization research, authors collect their own datasets, mostly from online news sites. To test the effect of preprocessing on Arabic DC and to evaluate the effectiveness of the proposed preprocessing algorithm, we have used an in-house corpus collected from the dataset used in several published papers for Arabic DC and gathered from scanning well-known and reputable Arabic news websites.  
The collected corpus contains 32,620 documents divided into 10 categories of News, Economy, Health, History, Sport, Religion, Social, Nutrient, Law, and Technology that vary in length and number of documents. In this Arabic corpus, each document must be assigned to one of the corresponding category directories. The statistics of the corpus are shown in Table 4.  
Figure 1 shows the distribution of documents in each category in our corpus. The largest category contains around 6860 documents, whereas the smallest category contains nearly 530 documents.
## 4 Function Design of the Back-End Service Layer
### Router Management  
Router management refers to handling of REST-style uniform interfaces and URL resource parsing based on requests from the front-end application layer. It is implemented using the Express application framework in the node.js development platform. All REST-style uniform interfaces are defined in the \"routes\" file of the project. The router finds the corresponding business logic processing based on the URL in the request from the front-end application layer.  
The initial steps for building the router are as follows:  
Step 1. Import the Express router module, then instantiate the router through the express.Router() method.  
Step 2. Build the router table using the Express router mechanism. The routes are defined by the app.method(path, handler) method. Here, app is the cloud server object of Express, method is the HTTP request method, path is the URL resource accessed by the browser, and handler is the callback function when the router is triggered, which is responsible for executing the corresponding resource's business logic.  
After the router is initially built, whenever a user makes a request from the front-end, the URL resource in the request is parsed through the body-parser middleware. Then, the server IP address and target router information for processing the request are obtained and matched to the corresponding router. After receiving the resource, the router triggers the callback function to perform database operations, returns the results from the database, encapsulates the data in JSON format, and returns them to the front-end application layer.
## 3 Results and Analysis
This demonstrates that the optimizer has learned to adapt the bitrate under the conditions to improve the general QoE. From a very poor QoE range (1-2 MOS) in the figure of almost 25% of the total, the optimizer has reduced the percentage to 10%. Finally, 90% of the values obtained represent good quality (higher than 3.5) from the 65% obtained without optimizer. With the actual experimental setup, the background traffic generator configuration creates uncontrolled randomness on the impairments. This leads to a lack of sufficient bandwidth for the proper provision of the content service during minimal time slots. As a result of this, image artifacts are generated, and the QoE (MOS) results in very low scores, despite adjusting the streaming bitrate according to the profiles. This justifies why there are still low MOS values (10%) with the optimizer enabled. Figure 4: Training reward chart.
# Multimodal Few-Shot Learning for Gait Recognition
## 3 Experiment
### Datasets and Evaluation Metric  
We gathered gait information data from 30 adults aged 20 to 30 years. The insole was used to collect the data while the subjects walked for approximately 3 minutes. The data that were gathered during this time included approximately 151 unit steps on average per subject, and the entire dataset consisted of 4544 unit steps. In the experiment, we set the standard length to be \(d=87\).  
As shown in Figure 5, we split the data into three sets--_training_, _unknown known_, and _unknown unknown_. First, we randomly selected 16 out of the 30 subjects, and allocated 100% of the unit steps to the _training_ dataset, which was used to train the CNN, RNN, and ensemble models independently. Second, we selected 7 out of the remaining 14 subjects arbitrarily. For each subject among the selected people, \(n=10\) unit steps were utilized to train the OSVM algorithm and the decision boundary of  
Figure 4: Illustration of gait recognition using the trained model. In the example, unit step \(\mathbf{s}_{*,\mu}\) is recognized as that of the “green” subject, whereas unit step \(\mathbf{s}_{*,w}\) is not recognized.  
the subject was determined. Except for these \(n\) unit steps, all unit steps of the selected 7 subjects are allocated to the _unknown known_ test dataset. Finally, all unit steps of the remaining 7 subjects are allocated to the _unknown unknown_ test dataset [17]. The number of unit steps in the _training_ dataset is approximately 2423, and the number of unit steps of the _unknown known_ test and the _unknown unknown_ test datasets were approximately 990 and 1060, respectively. We repeated generating the datasets 20 times. For each dataset, we trained and tested the network independently and reported the averaged evaluation metrics. For a unit step in the _unknown known_ test dataset, we define a _true positive_ (TP) if a unit step is recognized correctly, and a _false negative_ (FN) otherwise. In contrast, for a unit step in the _unknown unknown_ test dataset, we define a _true negative_ (TN) if a unit step is not recognized as any subject in the _unknown known_ test dataset, and a _false positive_ (FP) otherwise. We report the true positive rate \(TPR=\frac{TP}{TP+FN}\), the true negative rate \(TNR=\frac{TN}{TN+FP}\), and the accuracy \(ACC=\frac{TP+TN}{TP+FN+TP}\).
## 4 Conclusions
\begin{tabular}{l l} DER & Distributed Energy Resources \\ EPDS & Electric Power Distribution Systems \\ EV & Electric Vehicles \\ PCC & Point of Common Coupling \\ HV/MV/LV & High/Medium/Low Voltage \\ SPC & Switching Power Converter \\ LVR & Line Voltage Regulator \\ OLTC & On-Load Tap Changer \\ RPC & Reactive Power Compensator \\ ZSCC & Zero Sequence Current Compensator \\ D-STATCOM & Distribution Static Synchronous Compensator \\ DVR & Dynamic Voltage Restorer \\ UPQC & Unified Power Quality Conditioner \\ BESS & Battery Energy Storage System \\ PV & Photo Voltaic \\ BRC & Balancing Reactive Compensator \\ ABC & Adaptive Balancing Compensator \\ SVC & Static var Compensator \\ CPC & Current’s Physical Components \\ BCC & Balancing Capacitive Compensator \\ ABCC & Adaptive Balancing Capacitive Compensator \\ \end{tabular}   
**Notations**  
\begin{tabular}{l l} \(U,a\) & phase to ground rms voltage, Stokvis' rotation operator \\ \(\underline{Y_{ab}^{load}},\underline{Y_{bc}^{load}},\underline{Y_{ca}^{load}}\) & equivalent admittances of the load in the delta equivalent scheme \\ \(\underline{G_{ab}^{load}},\underline{G_{bc}^{load}},\underline{G_{ca}^{load}}\) & equivalent conductances of the load in the delta equivalent scheme \\ \(\underline{B_{ab}^{load}},\underline{B_{bc}^{load}},\underline{B_{ca}^{load}}\) & equivalent susceptances of the load in the delta equivalent scheme \\ \(\underline{I^{load+}},\underline{I^{load-}},\underline{I^{load0}}\) & phasors of positive, negative and zero sequence components of the phase \\ currents at the three-phase unbalanced load \\ \(\underline{I^{comp+}},\underline{I^{comp-}},\underline{I^{comp0}}\) & phasors of positive, negative and zero sequence components of the phase \\ \(\underline{I^{s+}},\underline{I^{s-}},\underline{I^{s0}}\) & currents at three-phase source \\ \(\cos\theta^{s+}\) & power factor on the positive sequence \\ \(B_{ab}^{comp},\underline{B_{bc}^{comp}},\underline{B_{ca}^{comp}}\) & the equivalent susceptances on the sides of the delta scheme of \\  & the compensator \\ \(\underline{I^{load}_{a}},\underline{I^{load}_{b}},\underline{I^{load}_{c}}\) & the reactive components of the currents on the load phases; \\ \(\underline{Q_{a}^{load}},\underline{Q_{b}^{load}},\underline{Q_{ca}^{load}}\) & reactive powers on the load phases \\ \(\underline{P_{a}^{load}},\underline{P_{b}^{load}},\underline{P_{c}^{load}}\)
## 1 Introduction
However, this complete substitution raises notable concerns, as the prediction model and cost function become \"black box\" systems, obscuring relationships among internal constraints and significantly limiting their capability for multivariable optimization. As such, it remains questionable whether this ANN-based control approach strictly qualifies as an MPC method. Consequently, further research efforts are still required to explore neural network-based strategies within predictive control architectures, particularly to address parameter sensitivity issues in PMSM drive applications. In this paper, a specific type of RNN known as the echo state network (ESN) is employed to enhance the parameter robustness of the MPC strategy for PMSM control. Compared with conventional neural networks, the ESN exhibits distinctive advantages in its training mechanism [21]. Structurally, the ESN adopts a three-layer framework consisting of an input/output layer and a dynamic reservoir. The reservoir comprises sparsely interconnected neurons with fixed, randomly initialized weights. The training process only involves linear regression to adjust the output weights for approximating the input-output relationship. This architecture significantly reduces computational complexity compared to back-propagation base neural networks, making it particularly effective for real-time parameter variation compensation through online learning. Rather than replacing the entire MPC architecture, the proposed ESN-MPC approach substitutes only the predictive model component. This adjustment achieves enhanced performance without sacrificing the inherent advantages of conventional MPC, preserving multivariable constraint handling capability. There are three main contributions in this paper, summarized as follows:  
1. A hybrid ESN-MPC framework is proposed. The trained ESN replaces only the prediction model in the MPC controller framework and maintains the MPC-based cost function by keeping the core MPC structure. 2. The proposed strategy enhances the robustness of the parameters. The ESN-MPC strategy is initially trained offline using data collected from a real PMSM-MPC control environment. With an online learning algorithm, the proposed controller can make rapid adjustments to mitigate parameter mismatches under real-time deployment. 3. Compared with traditional MPC, the proposed ESN-MPC approach demonstrates equivalent dynamic performance while achieving superior steady-state performance under parameter mismatch conditions. The remainder of this article is organized as follows. Section 2 details the implementation of a conventional FCS-MPC strategy in PMSM control. Section 3 details the proposed ESN-MPC strategy. The results of several simulation studies are presented in Section 4, validating the proposed strategy through comparisons with different methods. Finally, conclusions are drawn and suggestions for future work are presented in Section 5.
# Lightweight Physical Layer Aided Key Agreement and Authentication for the Internet of Things +
## 3 Physical Layer Aided Authentication and Key Agreement (PL-AKA)
### Proposed PL-AKA Protocol  
In this subsection, we propose a PL-AKA, which prevents severe network congestion in core networks and minimizes the computational complexity for authentication of low-cost IoT devices in MIoT systems. To this end, the notion of local security is investigated, and the PHY-CRAM [8] is applied to a conventional AKA protocol with a novel integration strategy to resist impersonation attacks from malicious intruders. A BS plays a crucial role in authenticating an IoT device through the PHY-CRAM scheme, which is employed to alleviate traffic loads in core networks in the proposed protocol. Here, the PHY-CRAM can effectively protect the attacks by preemptively detecting a forged signal at a BS. Although the extra burden of a BS arises from the preemptive authentication, it is relatively small because the PHY-CRAM using channel state information (CSI) does not require high computational complexity. On the other hand, the PHY-CRAM itself may not provide acceptable authentication performance under bad communication environments (e.g., low SNR), whereas it enables fast authentication with low complexity. Therefore, it is crucial to design a novel integration strategy, which exploits advantages of both the PHY-CRAM and cryptography-based authentication.