esa-sceva/satcom-chunk-collection huggingface dataset:
# QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques
## 6 Discussion
Therefore, the final output is more robust since it was tuned by multiple previous models. For that reason, we can see that GBT, which is a boosting technique as well, outperforms both RF and ET in all the experiments. The second reason is that both XGBT and LGBM are designed in a more regularized way to control over-fitting. In more specific terms, XGBT uses the elastic regularization technique, i.e., it uses both L1 and L2 regularization, whereas LGBM uses Gradient-Based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB) for performance boosting. For that purpose, we can see that both perform better than GBT. These reasons also are contributing points to the situation that the performance remains robust and comparatively higher than other competing algorithms, even if a relatively less number of packets (input features) are used. This can be observed in Figure 5.  
As an overall impression, among five ensemble techniques experimented with for QUIC traffic classification in this work, LGBM and XGBT show promising performance in terms of all metrics. In extreme feature requirement situations, classification accuracy lies above 99%, and, in less-feature requirement situations, accuracy is above an acceptable range such as 91%. Moreover, they are less sensitive to feature requirements and faster than all other algorithms. Therefore, it is safe to mention that LGBM and XGBT are the right choices for deploying a real-world online application for multi-class QUIC traffic classification because they can be trained using a small number of features as 15 packets.
# SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots
## 1 Introduction
### The Need for Semantic Interoperability in the Cooperation of Underwater Robots  
In order to cooperatively carry out maritime operations, it is vital that vehicles should be able to collect information and share it among themselves, and with a command and control entity as well, without ambiguous meaning. A variety of information could be obtained from different data producers, such as sensors, vehicles, and external sources (e.g., weather forecast system). Substantial difficulties arise when attempting to exchange information between vehicles. In particular, data heterogeneity obstructs effective information exchange between vehicles to a great extent. Information, pertained to heterogeneous data producers, may be described in different formats, such as JavaScript Object Notation (JSON), depending on their type and involved manufacturer. In addition, even when different vehicles use the same terminology, sometimes it is interpreted with different meanings. For instance, the term Position is used to represent a local frame georeferenced location in robot A while robot B uses it to express its angular coordinates. Therefore, in order to tackle data heterogeneity, semantic interoperability must be guaranteed in the SWARMs system to enable transparent information sharing. It is a necessity to enable machine computable logic, inference, knowledge discovery, and data federation between computer systems (e.g., vehicles, and SWARMs platform components). Information exchanged must be explicitly modeled with rich semantics so that data heterogeneity can be abstracted and unambiguously understood by all involved robotic vehicles.
## Abstracts
### S8. Oral Session 8: Machine Learning  
Session Chair: [PERSON]  
S8.1. Conditioning a Recurrent Neural Network to Synthesize Musical Instrument Transients  
[PERSON] and [PERSON]  
A recurrent neural network (RNN) is trained to predict sound samples based on audio input augmented by control parameter information for pitch, volume, and instrument identification. During the generative phase following training, audio input is taken from the output of the previous time step, and the parameters are externally controlled, allowing the network to be played as a musical instrument. Building on an architecture developed in previous work, we focus on the learning and synthesis of transients--the temporal response of the network during the short time (tens of milliseconds) following the onset and offset of a control signal. We find that the network learns the particular transient characteristics of two different synthetic instruments, and furthermore shows some ability to interpolate between the characteristics of the instruments used in training in response to novel parameter settings. We also study the behavior of the units in hidden layers of the RNN using various visualization techniques and find a variety of volume-specific response characteristics.  
Predicting Perceived Dissonance of Piano Chords Using a ChordClass Invariant CNN and Deep Layered Learning  
[PERSON], [PERSON], and [PERSON]  
This paper presents a convolutional neural network (CNN) able to predict the perceived dissonance of piano chords. Ratings of dissonance for short audio excerpts were combined from two different datasets and groups of listeners. The CNN uses two branches in a directed acyclic graph (DAG). The first branch receives input from a pitch estimation algorithm, restructured into a pitch chroma. The second branch analyses interactions between close partials, known to affect our perception of dissonance and roughness. The analysis is pitch invariant in both branches, facilitated by convolution across log-frequency and octave wide max-pooling. Ensemble learning was used to improve the accuracy of the predictions. The coefficient of determination (\(R2\)) between ratings and predictions is close to 0.7 in a cross-validation test of the combined dataset. The system significantly outperforms recent computational models. An ablation study tested the impact of the pitch chroma and partial analysis branches separately, concluding that the deep layered learning approach with a pitch chroma was driving the high performance.
## 6 Error Analysis and Correlation Matrix for Aerosol Retrieval  
In the previous section, we focused on the ability to retrieve aerosol properties from CAPI measurements. Another crucial issue to be considered is the source of error in this retrieval. In this section, we discuss the a posteriori error covariances, \(\mathbf{\hat{S}}\), and analyze its sources of a priori uncertainty and measurement noise, respectively, for all of the scenarios described in Section 5. Model parameter uncertainties only related to aerosol profile parameters (half-width, peak-height) are also taken into consideration. The impact of different observation geometries and satellite operational modes are presented. Finally, an error correlation matrix between all of the retrieved parameters is derived, to analyze the correlation between the surface and aerosol parameters.
## 3 Path Tracking Control Based on States Estimation
### Patch Tracking Control Based on State Estimation
\begin{array}{c}\textbf{x}^{a}_{k+1|k}\approx\sum\limits_{i=0}^{2N}W_{i}^{( m)}\textbf{x}^{\star}_{k+1|k}(i)\\ \textbf{P}_{k+1|k}^{a}\approx\sum\limits_{i=0}^{2N}W_{i}^{(c)}\bigg{\{} \textbf{x}^{\star}_{k+1|k}(i)-\textbf{x}^{a}_{k+1|k}\bigg{\}}\bigg{\{}\textbf {x}^{\star}_{k+1|k}(i)-\textbf{x}^{a}_{k+1|k}\bigg{\}}^{T}\end{array} \] (12) where weights \(W_{0}^{(m)}=\lambda/(N+\lambda)\), \(W_{0}^{(c)}=1-\alpha^{2}+\beta\) and \(W_{i}^{(m)}=W_{i}^{(c)}=\frac{1}{2(N+\lambda)}\); \(\beta\) depends on the prior distribution of \(x\), and usually \(\beta=2\) in the case of [PERSON]. We take a set of sigma-points to form the vector matrix \(\textbf{x}_{k+1|k}\) by Equation (11) using \(\textbf{x}^{a}_{k+1|k}\) and \(\textbf{P}_{k+1|k}^{a}\). We substitute \(\textbf{x}_{k+1|k}\) into the observation function of the nonlinear system: \[ \begin{array}{c}\textbf{y}_{k+1|k}=\textbf{h}(\textbf{x}_{k+1|k})\\ \textbf{y}_{k+1|k}\approx\sum\limits_{i=0}^{2N}W_{i}^{(m)}\textbf{y}_{k+1|k}(i) \end{array} \] (13)
* State updating \[ \begin{array}{c}\textbf{P}_{yy}=\sum\limits_{i=0}^{2N}W_{i}^{(c)}\bigg{\{} \textbf{y}_{k+1|k}(i)-\textbf{y}_{k+1|k}\bigg{\}}\bigg{\{}\textbf{y}_{k+1|k}( i)-\textbf{y}_{k+1|k}\bigg{\}}^{T}\\ \textbf{P}_{xy}=\sum\limits_{i=0}^{2N}W_{i}^{(c)}\bigg{\{}\textbf{x}_{k+1|k}(i )-\textbf{x}^{a}_{k+1|k}\bigg{\}}\bigg{\{}\textbf{y}_{k+1|k}(i)-\textbf{y}_{k+ 1|k}\bigg{\}}^{T}\\ \textbf{K}_{k}=\textbf{P}_{xy}\textbf{P}_{yy}-1\\ \textbf{x}^{a}_{k+1}=\textbf{x}^{a}_{k+1|k}+\textbf{K}_{k}(\textbf{y}_{k+1}- \textbf{y}_{k+1|k})\\ \textbf{P}_{k+1}^{a}=\textbf{P}_{k+1|k}^{a}-\textbf{K}_{k}\textbf{P}_{yy} \textbf{K}_{k}^{T}\end{array} \] (14)  
We take the state \(\textbf{S}=(x_{e},y_{e},\theta_{e},\ \omega_{L},\ \omega_{R})\) as the N-dimensional independent variable \(\textbf{x}\); take the pose of the roadheader in the XOY coordination, \((X_{c},Y_{c},X_{c})\), as the observation vector \(\textbf{y}\) in the procedure; with the process noise \(\varepsilon_{k}\) being relevant with the skidding ratio, which varies obeying the uniform distribution with boundary values at [0.1, 0.35]; the measurement noise \(\delta_{k+1}\), which obeys a normal distribution with the mean value of 0 and a standard deviation of 4 cm; and the direction angle deviation \(\theta_{e}\) obeys a normal distribution with a mean value of 0 and a standard deviation of 5\({}^{\circ}\). Given an initial state \(\textbf{S}_{0}\) and a set of pre-designed poses along the expected path, using SVD-Unscented Kalman filtering, the \(\textbf{x}^{a}_{k+1}\) and \(\textbf{y}^{\star}_{k+1|k}\) from Equation (14) would be obtained as the estimated values of the state \(Q=(x_{e},y_{e},\theta_{e},\ \omega_{L},\ \omega_{R})\) and the \((X_{c},Y_{c},X_{c})\) at the moment \(k+1\), respectively. Then, \((x_{e},y_{e},\theta_{e})\) is fed back into Equation (4) for the recommended \(v_{c}\) and \(\omega_{c}\), i.e., the heading speed \(\dot{x}\) and turning angular speed \(\dot{\varphi}\), respectively.
## 5 Discussion and Results
### LHV of Syngas, Global Efficiency, and Temperatures Inside the Gasifier  
According to Figure 8 (left), the variation in the inlet air flow rate in Test 2, from 8.5 to 11.5 Nm\({}^{3}\)/h, produced a variation in the LHV of the syngas from 5.3 to 6.5 MJ/Nm\({}^{3}\). Similarly, in Test 1, a variation in the inlet air flow between 9.64 Nm\({}^{3}\)/h and 13.87 Nm\({}^{3}\)/h produced a lower heat value varying between 5.3 and 6.5 MJ/Nm\({}^{3}\). Consequently, for a variation in flow between 8.50 and 11 Nm\({}^{3}\)/h, the LHV of syngas ranged between 5.3 and 6.57 MJ/Nm\({}^{3}\), giving an average value of 5.95 MJ/Nm\({}^{3}\), which is in the order of magnitude of the result obtained by other researchers in similar applications [40].  
Considering the obtained values, the efficiency of conversion of the gasifier, evaluated by Equation (2), is equal to 79.8%. This is a high cold gas efficiency but within the usual range between 75% and 90%, as studied by [PERSON] et al. [41]. Applying Equation (3), the ICM-generator efficiency was 22% while the power production facility was working in a steady state and producing a maximum power of 5 kW. Therefore, the global efficiency of the DGPP is 17.6%. Additionally, the profiles of the reduction and combustion temperatures obtained experimentally are represented in Figure 8 (right). As shown, once a steady state is reached, after 10-15 min, the reduction temperature bounces between 600 and 700 \({}^{\circ}\)C, while the combustion temperature varies between 800 and 900 \({}^{\circ}\)C. Therefore, as stated in Section 4.3, the obtained values are within the ranges studied in the bibliography for this kind of gasifier.  
Figure 8: LHV of syngas and temperatures inside the gasifier.  
Figure 7: Results obtained from Test 2.
# An Equilibrium Strategy-Based Routing Optimization Algorithm for Wireless Sensor Networks
## 1 Introduction  
### Background and Motivation  
With the acceleration of the Internet of Things era, wireless sensor networks (WSNs) have been widely deployed in various application scenarios, such as volcanic eruption monitoring [1; 2], medical care monitoring [3], and smart home monitoring [4]. Therefore, many studies about the topic have been done, including load balancing, security, QoS, congestion control, connectivity, coverage [5; 6; 7; 8; 9; 10; 11], etc. Besides, due to the limited energy storage [12; 13] of battery-powered sensor networks, these applications also have a common requirement for the network lifetime, thus reducing the energy consumption to extend the network lifetime is one of the key tasks of the WSN. However, some nodes undertake excessive data forwarding because of the location factors, and the network is prone to energy hole phenomenon due to uneven energy consumption [14], which resulting in faster node death and lower network lifetime. Therefore, in order to maximize the network lifetime, both energy consumption reducing and load balancing are needed to be considered when making routing decisions.
## 2 Identification Method  
The orthotropic steel bridge deck is directly subjected to wheel loads, and the local effects dominate its structural stresses. Under the support of the diaphragms, the stress status in the U-ribs is similar to that for a multi-span continuous beam; moreover, its stress influence line is very short. When a vehicle passes, the gross weight of the vehicle is related to the total area of the strain response of the U-rib and the transverse position of the vehicle. Firstly, a calibration vehicle is driven through the bridge, the strain response is measured, and the vehicle's speed is evaluated. Then, the calibrated strain response area can be calculated and the transverse position of vehicle can be located. In the following, the unknown vehicle is tested, and the practical strain response area and transverse position can be determined. Based on the calibrated strain response area at corresponding transverse positions, the gross vehicle weight can be identified. The whole flowchart of the identification method is shown in Figure 1.
## 4 Methodology
### Autoencoder Implementation  
Autoencoder network architecture was selected for engine feature learning. Autoencoders (AEs) are neural networks trained to reconstruct inputs through a bottleneck layer, forcing the model to learn compressed representations. Variants include the following:  
* Denoising Autoencoders: Robust to noisy inputs.
* Variational Autoencoders (VAEs): Probabilistic latent space modeling.
* Sparse Autoencoders: Enforce sparsity constraints for interpretability.  
AEs have proven to have successful applications in predictive maintenance. They have been used to detect bearing, gear, and turbine anomalies by reconstructing healthy operational data and flagging deviations.
# Pasteurization of Foods with Ultrasound: The Present and the Future
## 6 Quality
For dairy products, ultrasound acts on the fat globules, breaking the fat globule membrane and reducing the size of the globules. This size reduction confers an emulsifying effect on the product achieving an almost perfect homogenization after processing and during storage. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
In recent years, one of the most explored categories in ultrasound research is related to the sensory evaluation of products. In the early stages of the technology, products were not thoroughly evaluated on sensory attributes because of the lack of information regarding toxicological aspects. Besides, former ultrasound research showed some drawbacks in sonicated products' sensory attributes and considered the future of ultrasound in food processing. For example, even though whole milk was whiter and with a perfect emulsion after ultrasound processing, there were some red flags in the aroma and chemical composition of this dairy product after processing. The aroma of milk after sonication was often reported as \"rubberry\" or \"burnt,\" and the deposition of metallic particles from sonotrodes was often cited as one of the main areas for equipment optimization. Milk processing with ultrasound has been widely studied in recent years [1, 15, 23, 60, 61]. The sensory evaluation of sonicated milk used in cheese-making and yogurt production has been documented. The generation of off-flavors in milk because of the effect of cavitation has now been analyzed in detail. These aromas reported as \"rubberry,\" \"burnt,\" \"plastic,\" \"
# Download script/URL (optional)  
download: [[https://ultralytics.com/assets/coco128.zip](https://ultralytics.com/assets/coco128.zip)]([https://ultralytics.com/assets/coco128.zip](https://ultralytics.com/assets/coco128.zip))  
-weights yolov5s.pt:  
Specifies the path to the initial weights file to initialize the YOLOv5 model before training. In this case, it starts with the yolov5s.pt weights, which represent the \"small\" version of the YOLOv5 model.  
-cache:  
This parameter enables caching during data loading. Caching can speed up the training process, especially when using large datasets. Cached data are stored on the disk for faster retrieval during subsequent epochs.  
**Annex B--**Command used for YOLOv5 Detect [15]:  
The command:  
!python/content/yolov5/detect.py --weights/content/best.pt --img 1309 --conf 0.25 -- source/content/tambau_1309.jpg --hide-conf --hide-labels --class 0 --iou 0  
Where:  
--weights/content/best.pt:  
This parameter indicates the path to the model weights file to be used for detection. In this case, the model loaded is best.pt, located in the \"/content\" directory.  
--img 1309:  
Set the size of the input image during detection. In this case, the input images will have dimensions of 1309 \(\times\) 1309 pixels.  
--conf 0.25:  
This parameter sets the confidence threshold to filter detections during inferencing. Only detections with a confidence score above 0.25 will be considered, which is the default score.  
--source/content/tambau_1309.jpg:  
Specifies the path to the source image that will be used for detection. In this case, the file \"tambau_1309.jpg\" located in the directory \"/content\" will be used as input.  
--hide-conf:  
With this parameter, the confidence score of the detections will not be displayed in the output.  
-hide-labels:  
This parameter causes the labels (class names) of detections not to be displayed in the output.  
-class 0:  
Specifics the index of the class you want to detect. In this case, the value \"0\" indicates that only the class with index 0 will be detected. The index of classes is based on the order in which they were defined during training.  
-iou 0:  
Defines the value of the IoU (Intersection over Union) overlap threshold for suppressing non-maximums. A value of 0 disables non maximum suppression.
# An Investigation into Audio-Visual Speech Recognition under a Realistic Home-TV Scenario
## 3 Methods
### Audio-Visual Fusion  
The fusion of audio and visual information also plays an important role in AVSR. The widely used approach is to directly splice the two modal features, as described in our previous system. We also explore the different audio-visual fusion methods to replace the simple embedding concatenation from the encoder and decoder parts, respectively.  
**Fusion in the Encoder Part.** For the encoder part, we employ the self-attention (SA) operation, which can be expressed as:  
\[\text{Multihead}(\mathbf{Q},\mathbf{K},\mathbf{V})=[\mathbf{H}_{1},\mathbf{H} _{2},\dots,\mathbf{H}_{h}]\mathbf{W}^{O} \tag{4}\]  
\[\mathbf{H}_{i}=\text{Attention}\Big{(}\mathbf{Q}\mathbf{W}_{i}^{Q},\mathbf{K} \mathbf{W}_{i}^{K},\mathbf{V}\mathbf{W}_{i}^{V}\Big{)} \tag{5}\]  
\[\text{Attention}(\mathbf{q},\mathbf{k},\mathbf{v})=\text{softmax}\bigg{(} \frac{\mathbf{q}\mathbf{k}^{\text{T}}}{\sqrt{d_{k}}}\bigg{)}\mathbf{v} \tag{6}\]  
where \(\mathbf{Q}\), \(\mathbf{K}\) and \(\mathbf{V}\) denote the video, audio and audio features, respectively. \([\cdot]\) represents the concatenation operation. \(h\) is the number of attention heads. \(\mathbf{W}^{O}\in\mathbb{R}^{(h\times d_{x})\times d}\) is the final linear parameter matrix. \(\mathbf{W}_{i}^{Q},\mathbf{W}_{i}^{K}\in\mathbb{R}^{d\times d_{k}}\) and \(\mathbf{W}_{i}^{V}\in\mathbb{R}^{d\times d_{x}}\) are the learnable parameter for the \(i\)-th head. \(d\), \(d_{k}\) and \(d_{y}\) denote the dimension of input, key and value, respectively.  
Figure 4: The AVSR system with pre-trained audio–visual representation.  
Figure 5: Exploration of model architectures of audio and visual embedding extractor.  
**Fusion in the Decoder Part.** For the decoder part, we performed the audio-visual fusion based on the transformer [39], as shown in Figure 6. We first used the self-attention for audio and video encoder outputs, and then we fused the two attention outputs as:  
\[\text{out}=[\text{Multihead}_{audio}(\mathbf{Q}_{1},\mathbf{K}_{1},\mathbf{V}_ {1}),\text{Multihead}_{video}(\mathbf{Q}_{2},\mathbf{K}_{2},\mathbf{V}_{2})] \tag{7}\]  
where \(\text{Multihead}_{audio}(\bullet)\) and \(\text{Multihead}_{video}(\bullet)\) are the self-attentions based on the audio and video encoder outputs, respectively. \(\mathbf{Q}_{1},\mathbf{K}_{1}\) and \(\mathbf{V}_{1}\) are the vectors of text embedding, audio encoder output and audio encoder output, respectively. \(\mathbf{Q}_{2},\mathbf{K}_{2}\) and \(\mathbf{V}_{2}\) are the vectors of text embedding, video encoder output and video encoder output, respectively.  
Finally, in order to further improve the overall performance, we also performed the system fusion by averaging the posterior probabilities of various models.
###### Abstract  
Text classifiers are Artificial Intelligence (AI) models used to classify new documents or text vectors into predefined classes. They are typically built using supervised learning algorithms and labelled datasets. Text classifiers produce a predefined class as an output, which also makes them susceptible to adversarial attacks. Text classifiers with high accuracy that are trained using complex deep learning algorithms are equally susceptible to adversarial examples, due to subtle differences that are indiscernible to human experts. Recent work in this space is mostly focused on improving adversarial robustness and adversarial example detection, instead of detecting adversarial attacks. In this paper, we propose a novel approach, explainable AI with integrated gradients (IGs) for the detection of adversarial attacks on text classifiers. This approach uses IGs to unpack model behavior and identify terms that positively and negatively influence the target prediction. Instead of random substitution of words in the input, we select the top \(p\%\) words with the greatest positive and negative influence as substitute candidates using attribution scores obtained from IGs to generate \(k\) samples of transformed inputs by replacing them with synonyms. This approach does not require changes to the model architecture or the training algorithm. The approach was empirically evaluated on three benchmark datasets, IMDB, SST-2, and AG News. Our approach outperforms baseline models on word substitution rate, detection accuracy, and F1 scores while maintaining equivalent detection performance against adversarial attacks.  
adversarial attacks; AI cybersecurity; integrated gradients; text classification; explainable AI