esa-sceva/satcom-chunk-collection huggingface dataset:
# Vibration and Reliability Analysis of Non-Uniform Composite Beam under Random Load
## 3 Numerical Studies, Results and Discussion
### Vibration Responses under Random Base Motion Excitation  
In order to study the influences of the random base motion excitation on the vibration characteristics of non-uniform composite beam, the vibration responses of non-uniform composite beam with \(b_{0}=0.005\) m, \(h_{0}=0.001\) m, \(n=2\), \(m=2.5\) and \(h=1.5\) under base motion with random frequency and amplitude of acceleration and harmonic base motion were simulated and compared based on the proposed iterative method. The frequency and amplitude of acceleration of random load accord with \(\varphi\sim N\left(200,\left(200/15\right)^{2}\right)\) rad/s and \(A_{a}\sim N\left(50,10^{2}\right)\) m/s\({}^{2}\), respectively, while the constant frequency and amplitude of acceleration of the base motion are 200 rad/s and 50 m/s\({}^{2}\), which are the mid-values of the random base motion excitation case. The vibration responses in time domain at the free end of the non-uniform composite beam are shown in Figure 2a,b. The power spectral density (PSD) plots of the time domain signal are shown in Figure 2c,d. The PSD function of MATLAB software is used to convert the time-domain vibration response into power/frequency (dB/Hz) with decibel notation definition of the corresponding power concept, which makes the PSD analysis results easier to read.  
Figure 2: _Cont._  
From Figure 2a,b, while the frequency and amplitude of acceleration of random base motion excitation change with Gaussian distribution, with the increment of time, the deflection at the free end of the non-uniform composite beam will not approach a steady state with variable amplitude. This fully shows the randomness characteristics of the random load. However, for a non-uniform composite beam excited by base motion with constant frequency and amplitude of acceleration which are the average of the ones of the random load, the vibration responses (deflection) at the free end experience a process from transient to steady state with the time increment. The amplitude at the free end of beam under a random load is larger than the one under base motion with constant frequency and amplitude of acceleration, and the variation trend of deflection is unpredictable. From Figure 2c,d, PSD of time domain signal under random frequency and amplitude has obvious 'volatility'. This kind of uncontrollability explains the significance of studying the vibration reliability under a random load.
## 2 Materials and Methods
### End-to-End Evaluation  
After individually evaluating and selecting the best models for lesion detection and classification, the models were combined sequentially to develop the complete pipeline. In this setup, all the predicted bounding boxes generated by the detection model were passed directly to the classifier without applying additional filters or thresholds. This evaluation was conducted on the entire test dataset to assess the pipeline's overall performance using end-to-end metrics, capturing its ability to detect and classify adrenal lesions in a fully automated workflow.  
To ensure a comprehensive evaluation of the complete ML pipeline, we created a multiclass confusion matrix (described in Table 3). The first column represents all samples analyzed by both stages of the pipeline that resulted in adenoma predictions: (1) correct classifications, (2) incorrect classifications as non-adenoma lesions, or (3) incorrect predictions due to the absence of lesions. The second column follows the same logic for non-adenoma predictions. The third column includes samples that only passed through the detection stage, because no lesions were detected by the DL model. To populate this matrix, a predefined IoU threshold was used to determine whether lesions were correctly localized by the detection model. A higher IoU threshold enforces stricter detection criteria.  
The computed end-to-end metrics are derived from this multiclass confusion matrix. We calculated macro, weighted, and class-specific metrics, for a holistic evaluation of our pipeline. Macro metrics provided an unweighted average across all classes, treating each class equally, regardless of its frequency. Weighted metrics, on the other hand, accounted for the imbalance in class distribution, offering insights into the pipeline's performance in proportion to the occurrence of each class. Finally, class-specific metrics allowed us to evaluate the model's performance in individual classes.  
All the experiments were made in a Docker environment using a container based on the pytorch/pytorch:2.5.0-cuda12.4-cudan9-runtime image. The system was configured with Python 3, essential development tools, and the required Python libraries, including Detectron2, PyRadiomics, and scikit-learn. The host computer had a 12 th Gen Intel(R) Core(TM) i7-12700 CPU, 32 GB DDR4 RAM, and an NVIDIA GeForce RTX 3060 GPU with CUDA 12.4 and cuDNN 9 for efficient training. This environment ensured reproducibility and consistency across all experiments.
## 2 Background
### The LSTM Model  
Recurrent neural networks (RNNs) are fundamentally different from the feed-forward neural network. In RNN, the temporal correlations between previous information and current circumstance is modeled explicitly, which is aiming at solving sequence-based problems. However, it is difficult to learn long-range dependencies with traditional RNNs due to the gradient vanishing or exploding problem [26; 27]. In order to overcome these issues, in [16], [PERSON] et al. propose the long short-term memory (LSTM) architecture that is further improved by [PERSON] et al. [28]. The structure of the LSTM unit and the sequence classification model are depicted in Figures 4 and 5. In brief, a gating mechanism is introduced by their works, which provides avenues for increasing network insensitivity to gap length. The formulations of all function nodes in an LSTM unit are given by:  
\[f_{t}=\sigma\left(W_{fx}x_{t}+W_{fh}h_{t-1}+b_{f}\right), \tag{1}\]  
\[i_{t}=\sigma(W_{ix}x_{t}+W_{ih}h_{t-1}+b_{i}), \tag{2}\]  
\[\widetilde{C}_{t}=\phi\big{(}W_{\widetilde{C}x}x_{t}+W_{\widetilde{C}h}h_{t-1 }+b_{g}\big{)}, \tag{3}\]  
\[o_{t}=\sigma(W_{ox}x_{t}+W_{oh}h_{t-1}+b_{o}), \tag{4}\]  
\[C_{t}=\widetilde{C}_{t}\odot i_{t}+C_{t-1}\odot f_{t}, \tag{5}\]  
\[h_{t}=\phi(C_{t})\odot o_{t}. \tag{6}\]  
where \(W_{fx}\), \(W_{fh}\), \(W_{ix}\), \(W_{ih}\), \(W_{\widetilde{C}x}\), \(W_{\widetilde{C}h}\), \(W_{ox}\), and \(W_{oh}\) are weight matrices corresponding to the input of activation functions; \(\sigma\) and \(\phi\) represent the sigmoid activation function and tanh function respectively; and \(\odot\) means an element-wise multiplication. In the deep learning literature, there are three gates in an LSTM block, the forget gate \(f\), the input gate \(i\), and the output gate \(o\), and all of them take advantage of the sigmoid output range from 0 to 1. It is intuitive that the decisions for the three gates are dependent on the previous output \(h_{t-1}\) and the current input \(x_{t}\). Specifically, the input gate, forget gate, and output gate are responsible for deciding what to preserve in the internal state, what to forget from the previous state, and which input signals should pass from \(\widetilde{C}t\) to the output \(h_{t}\), respectively. In application, the dimension of input signal \(x_{t}\) should be specified at first as a hyperparameter, and it depends on the dimension of all internal vectors. The relationship between input \(x_{t}\) and output \(h_{t}\) is associated with the memory cell state  
Figure 3: Sketch map of the feature maps obtained by LeNet-5 [25]. The descriptor of input image is generated in the second stage of neural network.  
\(C_{t-1}\) and intermediate output \(h_{t-1}\) at the \(t-1\)th time step. It means that utilizing the LSTM block, we can construct transformation from the previous input and current input to the current output, and we can realize equalization to mitigate ISI with consideration of information brought by all kinds of symbol arrangement. Such equalization contributes to both BER reduction and data rate increasing, taking advantage of long-short-term memory.
## 2 Overview of the Power Ramp-Rate Control Methods
### Exponential Moving Average (EMA) Method  
The EMA method is an extension of the SMA. However, this method takes priority on recent samples by using an exponential weighting function as follows [16; 17]:  
\[\mathrm{P_{ess}[k]=\sum_{n=0}^{W-1}\alpha\big{(}(1-\alpha)^{n}P_{pv}[k-n]\big{)} -P_{pv}[k]} \tag{5}\]  
where the exponential smoothing factor \(\alpha\) is between 0 and 1. As \(\alpha\) becomes closer to 1, more priority is given to older samples. As this method gives more priority to recent samples, it is more suitable for smoothing power peaks at the output while maintaining the mean value.
# Effect of Nitrogen Atoms in the CNT Structure on the Gas Sensing Properties of PANI/CNT Composite
## 2 Materials and Methods
### Gas Sensing  
When synthesizing PANI/CNT composites, not only core-shell structures but also free PANI is formed. The study of gas sensitivity on bulk composites does not allow separating the contribution of free polymer from the contribution of PANI/CNT core-shell structures. Therefore, the measurements were carried out on PANI/CNT nanostructures containing 1-5 nanotubes. The content of free PANI in such structures is low. Nanostructures were isolated from the polymer volume by ultrasound in isopropyl alcohol. The processing time varied from 10 to 30 min for different samples, which is due to different degrees of polymer interaction in composites, and was chosen empirically. The suspension was spin-coated onto the multi-electrode array containing 16 independent gold contacts (2 \(\upmu\)m gap).  
Gas sensitivity was studied in a 2 mL chamber with the flow-through gas supply system. The resistance of nanostructures was measured on an E4980A LCR-meter (Agilent Technologies, Santa Clara, CA, USA) at room temperature. Ammonia gas of a certain concentration (2, 4, 6, 8 and 10 ppm) was passed through the measuring chamber. Degassing was carried out by pumping air through the cell. Three measurements were carried out per each of the five nanostructures of each composite. Response (S) is defined as the relative resistance change:  
\[\text{S}=[(\text{R}-\text{R}_{0})/\text{R}_{0}]*100\%,\]  
were R and R\({}_{0}\) are the resistance of nanostructures in the presence of ammonia and in air, respectively. Sensitivity was defined as the slope of S with respect to the ammonia concentration for each nanostructure. The response time and recovery time were calculated as the time required for a sensor to reach 90% of the total response of the signal.
# Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities
## 3 Background
### Gradient Boosting  
A gradient boosting model [25] can be trained using the following steps:  
First, initialize the model with a constant value:  
\[F_{0}(x)=\arg\min_{\gamma}\sum_{i=1}^{n}L(y_{i},\gamma)\]  
where \(L\) is the loss function, and \(y_{i}\) are the true values.  
For each iteration \(m=1\) to \(M\), where \(M\) is the total number of iterations:  
Compute the residuals (pseudo-residuals):  
\[r_{im}=-\bigg{[}\frac{\partial L(y_{i},F(x_{i})}{\partial F(x_{i})}\bigg{]}_{ F=F_{m-1}}\]  
Fit a new base learner (e.g., a decision tree) \(h_{m}(x)\) to the residuals:  
\[h_{m}(x)=\arg\min_{h}\sum_{i=1}^{n}(r_{im}-h(x_{i}))^{2}\]  
Update the model:  
\[F_{m}(x)=F_{m-1}(x)+vh_{m}(x)\]  
where \(\
u\) is the learning rate, controlling the contribution of each tree.  
The final model after \(M\) iterations is as follows:  
\[F_{M}(x)=F_{0}(x)+\sum_{m=1}^{M}vh_{m}(x)\]Gradient boosting iteratively adds weak learners to the model, focusing on reducing the errors made by the previous models. This approach leverages the power of gradient descent optimization to minimize the loss function and improve predictive accuracy.
## Questions concerning the Relationship between CLIL and Digital/Computer Technology
### How Have New Digital Technologies Impacted CLIL Learning?
#### 2.3.3 Language Competence Improvement  
The study of [PERSON] et al. [18] finds that learners demonstrate pre- and post-test improvements in speaking, reading comprehension, vocabulary, and grammar through intelligent tutoring technologies in a CLIL class. For example, by providing appropriate feedback and instruction to students from the technological expert models: computer-assisted pronunciation training (CAPT), especially automatic speech recognition (ASR), can facilitate pronunciation development and provide feedback as effectively as the teacher. Students can be less nervous about pronouncing words in front of classmates and teachers when practicing alone [19]. In addition, compared with the traditional school context in which true interactions are scarce, digital technology provides a way to communicate with native speakers via online resources, supporting [PERSON] and [PERSON]'s [20] assertion that the best way to learn a language is through interaction with native speakers, real interaction, and the use of the natural language in real communicative situations. Consequently, technology use can enhance authenticity using videos and internet communication [4]. It delivers dynamic language instruction with a virtual environment of video and audio materials and realistic conversation possibilities with native speakers, enhancing students' language proficiency.
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 7 Conclusions  
This paper considers the coexistence of eMBB and URLLC services in the uplink of an analog C-RAN architecture from an information theoretic perspective. The rate expressions for URLLC and eMBB users under Orthogonal and Non-Orthogonal Multiple Access (OMA and NOMA, respectively) have been derived considering Analog Radio-over-Copper (A-RoC) as a sample scenario, although the proposed model can be easily adapted to other analog fronthaul technologies. For eMBB signals, performance have been evaluated in terms of information rate, while for URLLC we also took into account worst-case access latency and reliability. In case of NOMA, different decoding strategies have been considered in order to mitigate the impact of URLLC transmission on eMBB information rate. In particular, the performance achieved by puncturing, considered for 5G standardization, have been compared with those achieved by Treating URLLC Interference as Noise (TIN), and by Successive URLLC Interference Cancellation (SIC).  
The analysis showed that NOMA allows for higher eMBB information rates with respect to OMA, while guaranteeing a reliable low-rate URLLC communication with minimal access latency. Furthermore, numerical results demonstrated that, differently from the digital C-RAN architecture based on limited-capacity fronthaul links, for analog C-RAN, TIN always outperforms puncturing, and SIC achieves the best performance at the price of an higher decoder complexity.  
As work in progress, the theoretical model can be extended to account for fading channels or geometric mmWave-link channel models.  
Similarly, a frequency-dependent cable channel can be considered by making the cable crosstalk coefficient \(\gamma\) increase with cable frequency [22]. Another interesting research direction is to consider the case in which the BBU has no knowledge about the incoming signal, i.e., it is not able to detect the URLLC transmissions, so that it is impossible for the BBU to choose the proper metric for joint signal decoding [43; 44]. Finally, the overall system can be extended to the case of multiple users per-cell, where both ENs and users are equipped with multiple antennas.  
Conceptualization, A.M., O.S. and U.S.; Formal analysis, A.M., O.S. and U.S.; Methodology, A.M.; Software, A.M.; Supervision, O.S. and U.S.; Validation, R.K.; Writing--original draft, A.M.; Writing--review & editing, A.M., R.K., O.S. and U.S.  
R.K. and O.S. have received funding from the European Research Council (ERC) under the European Union Horizon 2020 Research and Innovation Programme (Grant Agreement No. 725731).  
The authors declare no conflict of interest.  
Figure 13: URLLC and eMBB per-UE rates as a function of access latency \(L_{U}\) for OMA and NOMA with puncturing.
## 4 Experiments
### Datasets
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**Classification Accuracy (K = 10)**} & \multirow{2}{*}{**Average Improvement**} \\ \cline{2-2} \cline{5-8}  & **CAV** & **CAC** & **SVM** & **kNN** & **DNN-1** & **DNN-2** & **Naïve Bayes** \\ \hline Analcatdata boxing2 & 0.662 & 0.600 & **0.770** & 0.693 & 0.565 & 0.657 & 0.715 \\ Breast w & **0.980** & 0.975 & 0.976 & 0.977 & 0.974 & 0.974 & 0.969 \\ Congressional voting & **0.971** & **0.971** & 0.924 & 0.943 & 0.955 & 0.956 & 0.953 \\ Ecoli & 0.850 & **0.921** & 0.122 & 0.553 & 0.574 & 0.738 & 0.544 \\ Hayes-Roth & 0.786 & **0.788** & 0.310 & 0.663 & 0.622 & 0.685 & 0.742 \\ Mammographic mass & 0.823 & **0.825** & 0.813 & 0.794 & 0.808 & 0.797 & 0.803 \\ Monk’s problems 2 & 0.552 & 0.516 & **0.812** & 0.797 & 0.657 & 0.775 & 0.783 \\ Pima indiv2 diabetes & **0.784** & 0.732 & 0.538 & 0.562 & 0.562 & 0.571 & 0.633 \\ Prm synth & 0.817 & 0.789 & 0.842 & 0.833 & 0.822 & 0.816 & **0.845** \\ Sonar & 0.773 & 0.748 & **0.854** & 0.774 & 0.785 & 0.806 & 0.700 \\ Soybean small & **1.000** & **1.000** & **1.000** & 0.980 & **1.000** & **1.000** & **1.000** \\ SPECTheart & **0.730** & 0.529 & 0.590 & 0.651 & 0.709 & 0.709 & 0.590 \\ Tae & **0.569** & 0.551 & 0.464 & 0.474 & 0.445 & 0.327 & 0.501 \\ Thyroid disease & 0.914 & 0.927 & 0.600 & 0.932 & **0.971** & 0.911 & 0.953 \\ Wisconsin & **0.980** & 0.978 & 0.977 & 0.980 & 0.976 & 0.976 & 0.970 \\ \hline
**Average** & **0.813** & 0.790 & 0.706 & 0.774 & 0.762 & 0.780 & 0.780 \\ \hline
**Improvement** & & 0.023 & 0.107 & 0.039 & 0.051 & 0.033 & 0.033 & 0.057 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The F1 of the proposed CAV compared with CAC, SVM, k-NN, DNN-1, DNN-2 and naïve Bayes (the bold number indicates the maximum value). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**Classification Accuracy (K = 10)**} & \multirow{2}{*}{**Average Improvement**} \\ \cline{2-2} \cline{5-8}  & **CAV** & **CAC** & **SVM** & **kNN** & **DNN-1** & **DNN-2** & **Naïve Bayes** \\ \hline Analcatdata boxing2 & 0.720 & 0.750 & **0.873** & 0.747 & 0.521 & 0.648 & 0.761 \\ Breast w & 0.993 & **0.994** & 0.972 & 0.974 & 0.965 & 0.965 & 0.952 \\ Congressional voting & **0.983** & 0.977 & 0.887 & 0.927 & 0.944 & 0.960 & 0.960 \\ Ecoli & **0.880** & 0.723 & 0.088 & 0.558 & 0.959 & 0.739 & 0.547 \\ Hayes-Roth & **0.835** & 0.830 & 0.366 & 0.637 & 0.631 & 0.693 & 0.751 \\ Mammographic mass & 0.815 & 0.803 & **0.873** & 0.814 & 0.772 & 0.757 & 0.811 \\ Monk’s problems 2 & 0.889 & 0.727 & 0.779 & **1.000** & 0.527 & 0.676 & 0.980 \\ Pima indiv2 diabetes & 0.809 & **0.846** & 0.466 & 0.519 & 0.474 & 0.489 & 0.605 \\ Prm synth synth & 0.854 & 0.789 & 0.832 & **0.920** & 0.776 & 0.760 & 0.848 \\ Sonar & 0.744 & 0.786 & 0.814 & 0.722 & 0.773 & **0.835** & 0.804 \\ Soybean small & **1.000** & **1.000** & **1.000** & 0.975 & **1.000** & **1.000** & **1.000** \\ SPECTheart & **0.815** & 0.583 & 0.450 & 0.450 & 0.700 & 0.700
## 1 Introduction
### Active Distribution Network Management
#### 1.1.1 Decentralized Management Schemes  
Active power generation curtailment is the most basic form of voltage control. It is performed by reducing the active power output of the DG unit when the voltage magnitude exceeds predefined limits. Decentralized control, on the other hand, can be achieved by not enforcing DG units to operate at unity power factor, but allowing the unit to manage its reactive power output. This voltage control scheme acts when predefined voltage limits at the DG unit's terminals are overstepped, thus helping to reduce the voltage rise effect. When the voltage values are within admissible limits, the DG unit operates at unity power factor.
## Reference Vector Field of Vehicle Following  
### Spacing Control Strategy of Platoon  
The control objective of the platoon is to ensure the consistency of the vehicles while maintaining the desired distance between adjacent vehicles. Among them, the calculation strategy of the inter-vehicle distance is called the spacing control strategy. The expecting position of the following vehicle, as determined by spacing control strategy at the current moment, is called the balanced point. When extending the one-dimensional platoon control to two-dimensional, the following vehicle is required to track the path of the leading vehicle while maintaining the longitudinal spacing at the same time. There are two central control strategies when performing lateral platoon control, including direct vehicle-following and vehicle path-following. The path-following has some advantages over direct vehicle-following [17]:  
1. In the path-following strategy, the position of the following vehicle can be represented by the distance in the path coordinate system relative to the leading vehicle. The interaction between followers is reduced based on this strategy. Therefore, the control error will not be amplified with the transmission of the platoon, which weakens the importance of platoon stability.
2. The relative heading angle of the preceding vehicle is hard to measure, which means that the following vehicle cannot get enough and proper information for direct vehicle-following.
3. It is much easier for the path-following strategy to modify the feedback input to improve the following performance. The reference path can be replanted according to the surround information and traffic rules.
4. The path-following strategy requires that the path information of leading vehicle should be saved and updated in following vehicle's controller. When compared with the path-following approach, the direct vehicle-following strategy needs less data, so it is more suitable for the sample driving condition, like longitudinal following condition.  
With these advantages, the path following strategy is selected in this paper. To facilitate the analysis of the positional relationship during the two-dimensional movement of the platoon, the path-following strategy is as shown in Figure 1. \(S_{d}\) is the expecting distance between the leading vehicle and balanced point. Taking the closest position of the following vehicle \(i\) as \(p(x_{gi})\), then \(S\) is the distance between \(p(x_{gi})\) and its stable point; \(y_{i}\) denotes the distance between the position of vehicle \(i\) and the point \(p(x_{gi})\).