esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Traditional algorithms and single predictive models often face challenges such as limited prediction accuracy and insufficient modeling capabilities for complex time-series data in fault prediction tasks. To address these issues, this paper proposes a combined prediction model based on an improved temporal convolutional network (TCN) and bidirectional long short-term memory (BiLSTM), referred to as the TCN-BiLSTM model. This model aims to enhance the reliability and accuracy of time-series fault prediction. It is designed to handle continuous processes but can also be applied to batch and hybrid processes due to its flexible architecture. First, preprocessed industrial operation data are fed into the model, and hyperparameter optimization is conducted using the Optuna framework to improve training efficiency and generalization capability. Then, the model employs an improved TCN layer and a BiLSTM layer for feature extraction and learning. The TCN layer incorporates batch normalization, an optimized activation function (Leaky ReLU), and a dropout mechanism to enhance its ability to capture multi-scale temporal features. The BiLSTM layer further leverages its bidirectional learning mechanism to model the long-term dependencies in the data, enabling effective predictions of complex fault patterns. Finally, the model outputs the prediction results after iterative optimization. To evaluate the performance of the proposed model, simulation experiments were conducted to compare the TCN-BiLSTM model with mainstream prediction methods such as CNN, RNN, BiLSTM, and A-BiLSTM. The experimental results indicate that the TCN-BiLSTM model outperforms the comparison models in terms of prediction accuracy during both the modeling and forecasting stages, providing a feasible solution for time-series fault prediction.  
time-series fault; TCN; BiLSTM; fault prediction; hyperparameter optimization +
Footnote †: journal: Applied sciences
## 6 RF Feature Classifiers
### ML-Based Classification
#### 6.2.3 CNN Classifier  
Convolutional neural networks (CNN), the most frequently encountered category of deep learning classifiers, have been widely applied in image identification and pattern recognition. Recently, CNN classifier has also started to be considered as a promising method for the radio identification and RFF [86; 87]. A general CNN consists of a combination of convolutional layers, pooling layers, and fully connected layers. This works as the following:  
1. The convolutional layer applies a convolution operation between the input signal matrix and a filter (or kernel) (the input signals here are the signals that come to the convolutional layer; the input does not necessarily mean the input data to the beginning of neural networks). For example, Figure 16 considers a \(5\times 5\) 'input' and a \(3\times 3\) filter, the red rectangle selects the same size of data as the filter, then the selected data have a convolution operation with the filter. The red rectangle moves after each convolution operation until all the 'input' data experience the convolution operation with the filter.
2. The pooling layer will reduce the number of parameters; it is essentially a sampling method. The common pooling methods are max pooling, average pooling, and sum pooling. Here, we provide an example of max pooling in Figure 17. Max pooling: it chooses the largest number in the selected data.
3. The fully connected layer is the actual neural network, by using the activation function, such as the sigmoid (or logistic function), we are able to label the outputs. A common fully connected layer is made of three parts, the input layer, the hidden layer(s)  
Figure 15: Comparisons between the PCA results in spectrogram images of GNSS (**left**) and spoofer (**right**). The values in the colour bar represent the amplitude levels of the PCA coefficients.  
(also refers to neurons), and the output layer. Figure 18 gives an example of a fully connected neural network. The fully connected neural network can be composed of multiple layers of fully connected neurons. Each layer can be followed by an activation function, such as a relu, sigmoid, or logistic function. The output layer, the last layer of the neural network, commonly uses a sigmoid activation function to assign the probability to each possible class. Figure 18 gives an example of a fully connected neural network.
# Enabling Autonomous Agents for Mobile Wireless Sensor Networks
## 1 Introduction  
Wireless sensor networks (henceforth _WSN_) comprise a wide field of study. In a WSN, scattered battery-operated sensing elements transfer measured information wirelessly to data collection points called sinks. Traditionally, researchers have proposed several optimization techniques to enhance the weaknesses of the WSNs, that is, network lifespan and coverage [1, 2, 3]. Benefiting from the downfall in price and a considerable growth in popularity of the robotics systems, robots were introduced to cover the deficiencies present in the WSN [4, 5]. The addition of mobile robots (Mobile WSNs) permits the replacement of sensing nodes that are about to run out of battery or are found to be damaged, resulting in an increased resiliency compared to static WSN. Moreover, the mobile robots can help to distribute the traffic load each node forwards by providing routing support or by redistributing nodes, which can lead to a more uniform battery depletion across the network [6]. Even recent trends, such as AI and ML, are merging with WSN [7, 8, 9, 10]. However, despite the interest of the scientific community, several research challenges still need to be faced. These challenges are mostly related with practical implementations, mainly due to the lack of realistic studies or implementations that can actually be deployed [11]. This lack of field testing often leads to unrealistic assumptions regarding the collaboration methodologies employed in mobile WSNs, including the information required for cooperation and the algorithms that facilitate such collaboration. To address this, a software package is provided whose aim is to study different sensor deployment and maintenance strategies. This software package employs the globallyknown robotics software ecosystem, ROS 2 [12, 13]. ROS 2 software packages allow a seamlessly deployment of distributed applications across networked machines or to be run locally using simulators; therefore, algorithm and protocol validation can be achieved before deploying the software on actual hardware.  
This paper is structured as follows: Section 2 provides a comprehensive review of the state of the art regarding mobile WSN and current deployments. In Section 3, our contribution is presented. Sections 4 and 5 provide a relation between the different phases a MWSN suffers and the strategies and implementation followed in this article. Next, in Section 5.5, an overview of the algorithms that are put to test is provided. Results are presented and discussed in Sections 6 and 7, respectively.
# Optimizing Session-Aware Recommenders: A Deep Dive into GRU-Based Latent Interaction Integration
## 4 Experimental Results
### Evaluation Metrics and Parameter Settings  
In our experiment, we adopted evaluation metrics such as Recall@K, MRR@K, and NDCG@K. For Recall@K, we considered top-K predictions with K values set at 5, 10, and 20. The experimental settings for each dataset were optimized as follows: For Amazon, the window ratio was 65 with an equal preference ratio of 0.5/0.5 for long-term and short-term preferences, iterating 100 times at a learning rate of 0.001. Steam's settings included a window ratio of four, a preference ratio of 0.2/0.8, 200 iterations, and a learning rate of 0.001. For MovieLens 1M, the window ratio was 30 with a preference ratio of 0.8/0.2, iterating 200 times at a learning rate of 0.01. The attention-gate was used for feature fusion, and a fixed ratio was used for term fusion. Common hyperparameters were an embedding/GRU hidden unit size of 80, one inner/outer GRU layer, a batch size of 100, and a dropout rate of 0.8. We used the Adam optimizer and cross entropy loss function.  
The feature fusion method utilizes the attention-gate, and the term fusion method employs a fixed ratio. In our study, we set the embedding/GRU hidden unit size to 80 as a common parameter. This choice was informed by our findings that increasing the embedding size beyond 80 does not significantly improve performance. This approach also considers the training cost of the model. Other hyperparameters, set as common parameters, include inner/outer GRU layers of 1, a batch size of 100, and a dropout rate of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Description** & **Amazon** & **MovieLens 1M** & **Steam** \\ \hline Number of users & 9733 & 1196 & 6330 \\ Number of items & 46,959 & 3328 & 4332 \\ Number of actions & 700,960 & 158,498 & 49,164 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of datasets after pre-processing.  
0.8. The experimental optimizer is the Adam optimizer, and the loss function used is the cross entropy loss function.
## 4 Case Study and Discussion
### Simulation Environment
#### 4.1.3 Prices of the REC  
Prices of the REC selected in the case studies were historical data of the reference prices publicly available as shown by Table 6 below. The reference prices were publicized one year after the subject date; therefore, the reference price publicized in the year 2019 and the exchange rate on the subject date were considered in the simulations.
## 3 Results and Discussion
### Antifungal Activity against Plant Pathogenic Fungi
Concerning anti _Fusarium_ activity, _Spirulina_ (S1) had a significantly (\(p<0.05\)) lower inhibition zone compared to Amphbotericin B (Table 2). Interestingly, the extract S3 revealed a strong (\(p<0.05\)) antifungal activity against all tested species, and the distance of the inhibitory zone varied from 18.25-24.75 mm (Table 2). The _Fusarium graminearum_ ISPAVE 271, was the most susceptible with an inhibition zone of 24.75 mm followed by _Fusarium oxysporum_ CTM10402, _Fusarium culmorum_ ISPAVE 21w, _Alternaria alternata_ CTM 10230 and _Aspergillus niger_ CTM 10099. S3 showed a strong activity against mycotoxicien and phytopathogenic fungi, notably against _F. graminearum_, _F. oxysporum_ and _F. culmorum_ producing type B trichothecene mycotoxins, nivalenol (NIV), deoxynivalenol (DON) and their acetylated derivatives. However, moderate activity was found against _Aspergillus niger_ the main ochratoxin A (OTA) producer (Table 2).
## 3 Methodology  
The GL-BME fusion framework blending LEO and GEO satellite observations based on the Bayesian maximum entropy theorem is a pixel-based algorithm to recover the missing values with ensured accuracy in 10-min Himawari-8 AHI AOD images via incorporating discrepancies from different categories of products. As depicted in the flowchart in Figure 2, the GL-BME fusion framework is divided into two stages: (1) data preprocessing, in which multi-source satellite-based AOD products undergo a series of preprocesses, including space/time matching, quality control, and data combination, to ensure the spatiotemporal consistency and accuracy reliability of different AOD products; and (2) GL-BME fusion, in which the quality-assured AODs are blended based on the temporal and spatial autocorrelations to fill the data gaps, including constructing soft data under Gaussian distribution, modeling spatiotemporal covariance, estimating AOD isotropous component via the Bayesian paradigm, and adding global spatiotemporal trends to expected AOD isotropous components. More details on AOD fusion procedures and mathematical mechanisms are described in the following sections.  
Figure 1: Spatial distribution of 22 AERONET (red circles) and MAN (brown triangle) sites.
## 3 Measurement Setup  
The micrograph of the fabricated integrated stacked PA is presented in Figure 5. Including the input and output pads, the dimensions of the PA is 684 um \(\times\) 331 um = 0.225 mm\({}^{2}\). By excluding the probe pads, the active area is only 239 um \(\times\) 331 um = 0.079 mm\({}^{2}\). In Figure 5, HD MIM capacitors are highlighted with red rectangles in the micrograph (C1, C5, C7 and C8), and the HQ MIM capacitor is shown with a blue rectangle (C6).  
A Keysight PNA-X network analyzer was used to measure the proposed PA with Cascade Infinity I40 probes on a Cascade Microtech model 11,000 probe station. The measurement system is large and does not fit into an environmental chamber for temperature dependency testing, for example. In a single-tone power sweep, a measurement pre-amplifier (Caio Wireless CA263-141) was needed due to the fact that the PNA-X was not able to provide enough power to drive the PA into the compression. The power was calibrated at the end of the input cable. Then, the measurement was normalized using a Thru standard that was implemented on chip. The power calibration was repeated for every measured frequency point in single-tone power sweep meas  
Figure 5: Measurement setup for single-tone large signal measurements using PNA-X network analyzer and the micrograph of the stacked PA.  
and output power at the reference planes (see Figure 5) were calculated by subtracting the measured losses of the probe and the Thru.  
Measurements with a modulated signal were performed using a Keysight UXA N9040A signal analyzer. A Keysight arbitrary waveform generator M5502A was used to generate a 3 GPP/NR FR2 OFDM 100 MHz wide 16-QAM signal, which was mixed to mmWave frequencies with a Keysight E8267D signal generator. In order to minimize the EVM error of the test setup, we did not use a pre-amplifier, and therefore the signal generator limited the available input power.
# An Assessment of Agent-Based Modelling Tools for Community-Based Adaptation to Climate Change
## 3 Results
### SeSAM  
SeSAM excels in dynamic community interactions, enabling intricate agent decision-making and supporting learning and adaptation processes for agents [96]. It integrates socio-economic variables and demographic factors, enhancing realism in community-based adaptation scenarios. SeSAM's strength lies in its flexibility, allowing the incorporation of various socio-economic variables through plugins. It considers demographic factors, contributing to a more realistic representation of community dynamics. Despite limited documentation on participatory modelling, SeSAM stands out as being able to handle diverse data types, enhancing scenario complexity and accuracy. Its compatibility with GIS-based data makes it adaptable to different community sizes, from small-scale to larger urban or regional contexts. SeSAM addresses scalability challenges in both larger and smaller community contexts, ensuring efficiency across simulation scales. However, explicit mechanisms for assessing uncertainties, modelling risks, sensitivity analysis, scenario testing, feedback loops, and monitoring strategies are not clearly outlined. In summary, SeSAM is a powerful and user-friendly platform for agent-based modelling in social science domains. While documentation provides insights into modelling strengths, additional details on participatory modelling and uncertainty assessment would enhance its utility in community-based adaptation research.
# A Collocation Method for the Numerical Solution of Nonlinear Fractional Dynamical Systems
## 3 Results
### The Linear B-Spline Basis  
Given a finite interval \([0,T]\), the linear B-spline basis  
\[\mathcal{N}_{1h}=\left\{N_{k1h}(t),0\leq k\leq n_{k}\right\},\qquad t\in[0,T]\,,\]  
has just one left-edge boundary function, i.e., \(N_{01h}(t)\), and \(n_{k}\) interior functions, i.e., \(N_{k1h}(t)\), \(1\leq k\leq n_{k}\), where \(n_{k}=T/h\). Obviously, \(T\) and \(h\) must be chosen in order \(n_{k}\in\mathbb{N}\). The explicit expression of the basis functions is well known and is given by  
\[N_{01}(t)=\left\{\begin{array}{ll}(1-t)\,,&0\leq t\leq 1\,,\\ \\ 0\,,&\text{otherwise}\,,\end{array}\right. \tag{19}\]  
\[N_{k1}(t)=B_{1}(t-k+1)\,,\qquad 1\leq k\leq n_{k}\,, \tag{20}\]  
where  
\[B_{1}(t)=\left\{\begin{array}{ll}t\,,&0\leq t\leq 1\,,\\ \\ 2-t\,,&1\leq t\leq 2\,,\\ \\ 0\,,&\text{otherwise}\,.\end{array}\right. \tag{21}\]  
The Caputo derivative of fractional order \(0<\beta<1\) of the basis functions has the following expression  
\[{}_{C}D_{t}^{\beta}\,N_{01}(t)=\frac{1}{\Gamma(2-\beta)}\,\left(t^{1-\beta}+( t-1)_{+}^{1-\beta}\right)\,, \tag{22}\]  
\[{}_{C}D_{t}^{\beta}\,N_{k1}(t)=_{C}D_{t}^{\beta}\,B_{1}(t-k+1)\,,\qquad 1\leq k \leq n_{k}\,, \tag{23}\]  
where  
\[{}_{C}D_{t}^{\beta}\,B_{1}(t)=\frac{1}{\Gamma(2-\beta)}\,\Delta^{2}t_{+}^{1-\beta} \tag{24}\]  
is the Caputo derivative of the linear B-spline.  
The edge function \(N_{01}\) and the linear cardinal B-spline \(B_{1}\) are shown in Figure 2 while their fractional derivative of order \(\beta=0.5\) is shown in Figure 3.  
Figure 2: The left-edge function \(N_{01}\) (blue) and the linear cardinal B-spline \(B_{1}\) (orange).
## 5 Surface Roughness Estimation
### Network Training  
Surface images were collected from the milled spheroidal graphite cast iron 500-7 workpiece. In the experiment, as mentioned in the previous part, 370 images acquired for each condition. Therefore, the dataset contains 3700 records of images in total. Among these 3700 samplers, 370 images are randomly selected from the testing dataset and the rest are used as the training dataset. Some typical samples of input images are displayed in Figure 15.  
As can be seen in Figure 15, throughout the whole image sensing process, noise is added from various sources, which may include fixed pattern noise, dark current noise, shot noise, amplifier noise and quantization noise. These additional irregular noises would strongly influence the generated image gray-scale value [41]. Therefore, the actual camera settings are unstable fluctuated in every single capture process.  
In this research, the employed ResNet is shown in Figure 9. The input shape of the network for each image is a normalized patch. After the convolutional layer (the kernel number is 64) there is a batch normalization layer. The size of the kernels in the first layer was chosen to be \(7\times 7\). Following the first convolutional layer, there is a ReLU. After that, there are one convolutional identity block and two identity blocks. In the final output layer, softmax activation is chosen for the classification to represent the categorical distribution. Adam is a first-order gradient-based algorithm, designed for the optimization of stochastic objective functions with adaptive weight updates based on lower-order moments. In this study, Adam optimizer is used to minimize the categorical entropy.  
All of the experiments in this research were performed under a desktop (operating system: Microsoft Window 7, CPU: Intel Xeon E5-2609 v4 @ 1.7 GHz, GPU: NVIDIA Quadro M2000). Because of the hardware limit, the batch size is set to 5 and 0.003 in learning rate. The performance curves representing the training of the established model are shown in Figure 16. The blue solid ascending curve shows the accuracy rate change during the training process where accuracy is the most intuitive performance measure and it is simply a ratio of correctly predicted observation to the total observations. The red solid descending curve corresponds to the loss function values (cross entropy in each epoch). The results show that the loss function value reaches a stable value after 25 epochs, and the accuracy rate achieves stability after epochs 25 with almost 92%.  
Figure 15: Typical samples of input images (**a**) Uneven texture; (**b**) High contrast; (**c**) Strong noise and (**d**) Insufficient exposure.
## 4 Results
### Experimental Results and Analysis  
As can be seen in Figure 9, the YOLOv5-G model's parameters are calculated using the log data stored during training. The training loss is minimal, and the result value of the train/box_loss is around 0.015, suggesting that the training outcomes are more accurate; the value of train/obj_loss is around 0.022, indicating that the detection accuracy of the target is high. The values of val/box_loss and val/obj_loss were about 0.014 and 0.0194, respectively, showing that the model fitting effect is good. The precision is around 94%, indicating that the accuracy of detecting the target object is relatively high; the recall rate is about 89%, indicating that the accuracy of finding the correct objects is relatively good. The proportions of [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 are approximately 92 and 65, respectively.  
The same dataset and parameters were used to train both the YOLOv5 model and the YOLOv5-G model, and a comparison chart of the two models was created based on the training outcomes, as shown in Figure 10 and in Table 5. In Figure 10, although the recall rate R has dropped by 2.2%, the accuracy rates P, [EMAIL_ADDRESS], and [EMAIL_ADDRESS]:0.95 have all improved compared to the previous ones. Specifically, [EMAIL_ADDRESS]:0.95 has increased by 5.6%. Due to its higher IOU threshold requirement, [EMAIL_ADDRESS]:0.95 is mostly utilized to represent the positioning impact and border regression capability. This demonstrates that improving the model enhances the network's ability to regress to the target boundary. Table 5 shows that YOLOv5-G's parameters, GFLIOP, and size have been decreased, and its detection speed is 1.9 times faster than that of YOLOv5, producing the lightweight effect.
# Parametric Analysis of Outrigger Systems for High-Rise Buildings with Different Geometric Shapes
## 2 Simplified Approach for Outrigger Systems
### Results and Discussion
The findings align with prior research [61; 62; 63; 64; 65], which established that the lateral stiffness of twisted towers is inherently reduced compared to their straight counterparts, with an inverse correlation observed between lateral stiffness and the angle of twist. Consequently, the design of highly twisted configurations necessitates the careful consideration and implementation of alternative or supplementary lateral load-resisting systems to ensure adequate structural performance.
# Analysis of Wind-Induced Vibration Response of Transmission Wire Under Crosswind in Canyon Terrain
## 6 Wind Vibration Characteristics of Wire Under Step Wind Speed Input  
### Wind Vibration Mechanism of Wire Under Step Wind Speed Input  
Figure 11 shows the time-history curves of the inlet wind speed, the pre-line wind speed, the lateral force of the wire, the displacement of the wire in the z-direction, the lift force of the wire, and the displacement of the wire in the y-direction under step condition 2. Figure 12 shows the pressure cloud image around the wire in the central section at different times under condition 2. We selected the time when the wind field started to affect the wire and the time when the wind speed tended to be stable, at which time the pressure changed obviously. This can be seen in Figures 11 and 12.  
Figure 11: Time–history curves of wind speed, stress, and wire displacement.  
Figure 10: Time–history curves of displacement.  
1. The change in air velocity in front of the wire was basically the same as that in the incoming flow. However, affected by the mountain, the peak wind speed around the wire decreased by approximately 40%, and there were obvious local fluctuations;
2. Lift force and lateral force were generated when the air traversed the wire, and the amplitude of the lateral force was much larger than that of the lift force;
3. The variation in the lateral force was consistent with the variation in the air velocity in front of the wire. The wire swung under the action of the lateral force;
4. The lateral force, lift force, y-displacement, and z-displacement entered a periodic decay process and finally re-stabilized after undergoing a transition process under the excitation of the step incoming flow. This transition process ended at the first minimum extreme point of the lateral force (point A in the figure).