esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Named Data Networking and Its Role in IoT Smart Environments
### NDN in a Nutshell
#### 2.1.3 Control Plane  
The NDN control plane is in charge of the routing decisions and includes the _Routing Information Base_ (RIB) and the _Strategy Choice Table_ (SCT). RIB entries are originated by manual configuration from administrators, applications, and routing protocols and are used to update the FIB. The SCT, instead, identifies the forwarding strategy per namespace; it can be updated by management protocols or administrators, and it is supposed to be quite stable. The forwarding strategy is responsible for deciding if it should, on which face to, and when to forward NDN packets.
## 2 Materials and Methods
### Polarization Feature Extraction
#### 2.2.1 Huynen Decomposition  
When the absolute phase, amplitude, and backscatter conditions are ignored, the Huynen target parameters can indicate the polarization capabilities of the target. These parameters are closely linked to the structural characteristics of the target. According to matrix decomposition theory, an asymmetric matrix can be diagonalized through singular value decomposition:  
\[\mathbf{S}_{d}=\mathbf{U}_{S}^{T}\cdot\mathbf{S}\cdot\mathbf{U}_{E} \tag{3}\]  
where the column vectors of \(\mathbf{U}_{S}\) and \(\mathbf{U}_{E}\) correspond to the eigenvectors of the Hermitian matrices \(\mathbf{S}\mathbf{S}^{+}\) and \(\mathbf{S}^{+}\mathbf{S}\).  
Equation (2) can be further expressed as follows:  
\[\mathbf{S}=\mathbf{U}_{S}^{*}\cdot\mathbf{S}_{d}\cdot\mathbf{U}_{E}^{\perp} \tag{4}\]  
\[\mathbf{S}=\mu\mathrm{e}^{\mathrm{i}\mathbf{k}}\left[\mathrm{e}^{-\mathrm{i}\mathbf{s}_{ \mathrm{i}}\sigma_{3}}\right]\left[\mathrm{e}^{-\mathrm{i}\tau_{\mathrm{i}} \sigma_{2}}\right]\left[\mathrm{e}^{\mathrm{i}\delta\tau_{1}}\right]\left[ \mathrm{e}^{-\mathrm{i}\eta}\sigma_{2}\right]\left[\mathrm{e}^{\mathrm{i} \delta\tau_{3}}\right] \tag{5}\]  
Figure 5: Component decomposition.  
where \(\sigma_{1}\), \(\sigma_{2}\), and \(\sigma_{3}\) are in the Pauli basis, while \(\mu\) and \(k\) relate to the absolute magnitude and absolute phase, which are independent of the target's electromagnetic scattering mechanisms and as such are not considered further. The parameters \(\theta_{s}\), \(\theta_{i}\), \(\tau_{s}\), \(\tau_{i}\), \(v\), and \(\gamma\) represent the Huynen parameters for bistatic radar targets. For monostatic radar targets, the Huynen parameters are transformed into four parameters: \(\theta\), \(\tau\), \(v\), and \(\gamma\).  
In [39], the authors utilized the Huynen target parameters to construct a four-dimensional feature vector \([|\tau_{i}-\tau_{s}|,|\tau_{i}+\tau_{s}|,|v|,\gamma]\) for target classification. This feature vector has demonstrated superior robustness and a clearer physical interpretation. Therefore, in this study we also employ this polarization feature vector.
# Impact of Sentence Representation Matching in Neural Machine Translation
###### Abstract  
Most neural machine translation models are implemented as a conditional language model framework composed of encoder and decoder models. This framework learns complex and long-distant dependencies, but its deep structure causes inefficiency in training. Matching vector representations of source and target sentences improves the inefficiency by shortening the depth from parameters to costs and generalizes NMTs with a different perspective to cross-entropy loss. In this paper, we propose matching methods to derive the cost based on constant word-embedding vectors of source and target sentences. To find the best method, we analyze the impact of the methods with varying structures, distance metrics, and model capacity in a French to English translation task. An optimally configured method is applied to English translation tasks from and to French, Spanish, and German. In the tasks, the method showed performance improvement by 3.23 BLEU at maximum, with an improvement of 0.71 on average. We evaluated the robustness of this method to various embedding distributions and models, such as conventional gated structures and transformer networks, and empirical results showed that it has a higher chance to improve performance in those models.  
recurrent neural network; machine translation; similarity; sentence representation; guiding pressure +
Footnote †: journal: _Sciencedence_  
designed for guiding training of a model with constant word vectors. To obtain a more optimized structure and deeper understanding about the guiding pressure generated by the method, we analyze its impact in the framework with varying structures, distance metrics, and model capacity determined by layer dimensions. The best model derived from the analysis is applied to practical translation tasks of English from and to the French, Spanish, German languages. Then, we analyze its robustness to various embedding distributions and model structures as long-short term memory in recurrent neural networks and transformer networks.  
The contributions of this paper are that it:  
* proposes a sentence representation matching method and loss functions for training;
* provides the impact analysis of structural factors to control guiding pressure;
* provides a good-performing setting in many models, embeddings and translation tasks.  
Section 2 shows related and background works first, then Section 3 explains motivations and details of the proposed method and proposes the factors that affect the guiding pressure to test variants of the method. Section 4 shows the experimental setting on data and model for analysis. Section 5 describes their results and provides a discussion. Sections 6 and 7 present the conclusion and discuss future work.
## 4 Experimentation and Results
### Computation Time and Running Time  
In retail settings, where real-time consumer behavior tracking requires high throughput and low latency, YOLOv5 + DeepSORT's computational efficiency is crucial to its real-time implementation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Execution** & **Accuracy** & **Standard Deviation** \\ \hline
1 & 84.67\% & 1.25\% \\
2 & 85.10\% & 1.20\% \\
3 & 84.90\% & 1.30\% \\
4 & 85.20\% & 1.18\% \\
5 & 84.80\% & 1.22\% \\
6 & 85.00\% & 1.15\% \\
7 & 84.75\% & 1.27\% \\
8 & 85.05\% & 1.21\% \\
9 & 84.85\% & 1.23\% \\
10 & 85.10\% & 1.19\% \\ Mean & 84.91\% & 1.23\% \\ \hline \hline \end{tabular}
\end{table}
Table 12: Results of YOLOv5 model across multiple executions.  
Figure 15: Graph of accuracy and standard deviation over multiple executions.
# Performance of Time Reversal Based Underwater Target Detection in Shallow Water
## 3 Target and Clutter Channel Modeling
### Clutter Channel Modeling  
Since the clutters, where scattering occurs, are randomly distributed underwater, the Rayleigh distribution is applied based on the central limit theorem (CLT), assuming that there are sufficient scatters in the resolution cell. However, recent advances in technology have made it possible for high-resolution active sonar systems to reduce the number of reverberant components that affect the given resolution cell. Therefore, if there are not many scatters in the resolution cell, the CLT cannot be applied, and non-Rayleigh distribution is applied. Furthermore, since the simulation in this paper assumes a shallow water environment of 100 m depth, the width of the resolution cell is not large, and non-Rayleigh distribution is applied considering finite scatters. [PERSON] and [PERSON] derived the reverberation signal using the K-distribution instead of the Rayleigh distribution under this assumption ([PERSON] and [PERSON], 2010). A time-varying finite impulse response filter was developed for the clutter channel in shallow water ([PERSON] et al., 2016).
# Difficulties and Challenges of Anomaly Detection in Smart Cities: A Laboratory Analysis
## 5 Anomaly Detection
### Attack Detection with Machine Learning
#### 5.2.2 Training Phase  
The two techniques, OC-SVM and isolation forest are machine learning algorithms that can be trained with labeled data from just a single class: the attack-free period in our case (Figure 2). This is necessary in our scenario because labelling data from every possible attack or network malfunction is not possible, especially in the case of unknown attacks or abnormal network behaviours. Thus, with these techniques, we have generated models that are able to predict whether new data belong to the same class as the training data. As the training data are attack-free, any data that does not belong to this class can be considered an anomaly. Obviously, these models must be retrained periodically, since smart city WSNs evolve over time.
## 4 Speech Emotion Recognition Datasets
### English Language Datasets  
#### 4.1.1. Ravdess  
Ravdess (Ryerson Audio-Visual Database of Emotional Speech and Song) [25], is an advanced dataset designed for research on emotion recognition and vocal expression. It contains recordings of both speech and singing that are intended to reflect emotions such as joy, sadness, fear, anger, surprise, disgust, calm, and neutral. The collection consists of recordings from 24 professional actors, evenly divided between women and men, who perform the same phrases in different emotional states and perform singing fragments expressing the same emotions. The main goal of the database is to support research on emotion perception through the analysis of audio and visual signals. The Ravdess dataset consists of 1440 recordings of both audio and video. The speech recordings last about 4 s, while singing lasts about 12 s. Various formats are available: audio-only or audio-visual, allowing for the analysis of both vocal and visual expressions of emotion. An important feature of Ravdess is that emotions are expressed in different degrees of intensity-each of them occurs in both low- and high-intensity versions, which allows for detailed studies of the nuances of emotional expression.
## 2 Background and Related Work
### Internet of Things/Industrial Internet of Things  
IoT is a paradigm that offers the benefits of the Internet to anything connected to the network, from watches to vehicles, and even clothing and household appliances. Although the IoT was initially conceived to use Radio Frequency Identification (RFID) [18], they were applied in other contexts due to the limitations of the identification and tracking labels' range. Over the years, they have incorporated other technologies such as WSNs and Bluetooth, which, used individually or together, enhance the capabilities of the IoT, with WSNs being one of the most widely used technologies in deploying IoT-based products and services [19].  
The potential of the IoT can be developed in many applications. Since 2008, the Beecham Research company has generated a map of the IoT world, in which nine key sectors are defined, among which IoT applications for coal mining stand out within the industrial sector [20]. The IoT in underground mining has been developing for a few years, mainly in monitoring environmental conditions [21; 22; 23], gas monitoring [24; 25; 26; 27; 28], preventing and managing disasters, and identifying and locating miners [29; 30; 31; 32].  
Alternatively, the Industrial Internet of Things (IIoT) refers to adopting IoT architecture and services in the industry, considering differences in the usage scenario, technical requirements, technology used, and implementation strategies. The IIoT is associated with concepts such as Industry 4.0, Cyber-physical Systems (CPS), control systems and industrial automation (Industrial Automation and Control Systems (IACS)), supervision, and control and acquisition Supervisory Control And Data Acquisition (SCADA) [33]. The IIoT is machine-oriented, typically used in mission-critical scenarios, and has many applications (e.g., predicting equipment failures, increasing production performance, and enhancing efficiency, safety, and working conditions).  
Various technologies converge in IIoT systems, that is, a heterogeneous system that requires flexible architectures. Different architectures have been proposed in the literature. These architectures are varied in terms of functionalities and technical terminology. The basic model is a three-tier architecture comprising edge, platform, and enterprise layers [34] as follows:  
* The edge tier is responsible for perceiving the environment through edge nodes and communicating it using the proximity network.
* The platform tier processes and analyzes data between tiers, relays, and forward control commands between the enterprise and edge tiers.
* The enterprise tier is responsible for domain-specific applications and providing services to customers.
## 2 Materials and Methods
### Methods  
#### 2.2.1 Data Processing  
All results obtained using the SMPS and PASS3 instruments as well as the acidity values and concentrations of NOx and NH3 presented in this work are averages of measurements conducted over periods of around 30 min. Molecular speciation of toluene SOA was performed by analysing gas and aerosol samples drawn from the GO:PAM flow tube outlet at a rate of 4 L per minute by the FIGAERO-HR-ToF-CIMS, operating in particle collection mode. The FIGAERO-HR-ToF-CIMS operated in this mode in 30-min intervals; results obtained using other instruments when the FIGAERO-HR-ToF-CIMS was not operating in particle collection mode were discarded. Also excluded from the analysis were measurements of the SOA mass and bulk properties obtained in cases where the NOx concentration was initially very low or negligible, for example because the NOx ramp MFC valve failed to open.
## 1 Introduction
The integration of remote sensing data with machine learning techniques enables the estimation of air pollution at high spatial resolution, adding a temporal dimension to these estimates [10,14]. The use of ensemble learning algorithms, such as XGBoost, support vector machines, and generalized additive models, to establish regression models based on environmental monitoring data, including meteorological factors and pollution levels, can provide timely warnings of respiratory disease risks [15]. Furthermore, understanding the relationship between air pollution and mortality and developing predictive models for respiratory disease-related deaths due to air pollution is critical for protecting vulnerable populations. A thorough environmental assessment is required to diminish contamination within a catchment scale [16]. Such a comprehensive strategy should incorporate the complexities of hydrological, economic, and ecological elements [17]. An integrated approach must consider the intricate web of interactions between environmental, social, and economic factors, recognizing that pollution does not exist in isolation but rather as a symptom of broader systemic issues [18]. This perspective acknowledges that effective pollution management requires addressing the root causes of environmental degradation, which often lie in unsustainable consumption patterns, inadequate regulatory frameworks, and inequitable distribution of resources. Finally, the integration of traffic control technologies with artificial intelligence is a relevant research topic in urban ecology as well, which is also addressed in [19]. This paper proposes the use of Adaptive Neural Functional Inference Systems (ANFIS) to model and analyze vehicle emissions data and its impact on air quality. The results show that the combination of traffic management and AI technologies can effectively assess and improve air quality in urban areas. The novelty of the study lies in the complex modeling of interrelated atmospheric fields (velocity, temperature, and concentration), taking into account specific urban factors; analysis of the vertical structure of parameter distribution at different altitudinal slices;quantitative assessment of the impact of urban heat islands and industrial zones on the distribution of pollutants; as well as in the development of a methodology for visualizing the results with accurate numerical characteristics, ensuring reproducibility and practical applicability for environmental. The aim of the study is to develop and apply a numerical model to estimate the distribution of key atmospheric indicators, which are air velocity, temperature, and pollutant concentrations, in an urban environment considering different altitude slices.
## 3 Experiment
### Quantitative Metrics and User Study
#### 3.8.2 User Preference Study  
To further validate the practical value of the generated layouts, we conducted a user study with 20 participants (10 male, 10 female, average design experience 3 years). Each participant evaluated 50 layout sets containing results from the TGAN, LayoutGAN, and Deep Layout. The evaluation metrics include the following:  
* Aesthetics: visual appeal and style consistency (1-5 scale).
* Functionality: element alignment and readability (1-5 scale).
* Overall preference: percentage of participants selecting the method as \"best layout\". The experimental results are shown in Table 4. What we found was as follows:
* The TGAN achieves significantly higher scores in both aesthetics and functionality (4.5 vs. 3.4) compared to the LayoutGAN.
* A total of 78% of participants selected the TGAN as the preferred method, demonstrating strong alignment with human design intuition.
* Qualitative feedback highlights the TGAN's superior performance in handling complex element relationships (e.g., \"Text-image alignment feels natural\").
# Modeling Hydraulic Fracture Entering Stress Barrier: Theory and Practical Recommendations
###### Abstract  
Numerical modeling of hydraulic fracturing is complicated when a fracture reaches a stress barrier. For high barriers, it may require changing the computational scheme. In view of the strong influence of stress barriers on the final footprint and opening of a hydraulic fracture, for decades, their modeling has been the subject of special investigations. Actually, classical models of propagation within a pay layer with impenetrable boundaries referred to the case of extremely high-stress contrast in neighbor layers. Further improvements tended to account for the fracture growth in these layers by including stress contrasts as input parameters of a model. This tendency resulted in the suggestion and successive enhancements of pseudo-three-dimensional models. All of them have used stress intensity factors (SIFs) to characterize the combined resistance caused by stress contrasts, material strength, and fluid viscosity. Specifically, the SIFs served to formulate the conditions that control the front penetration into a neighbor layer. This key concept presents the background of our research. Despite examples of modeling propagation through barriers, there is no general theory clarifying when and why conventional schemes may become inefficient and how to overcome computational difficulties. This paper presents the theory and practical recommendations following it. We start with the definition of the barrier intensity, which exposes that the barrier strength may change from zero for contrast-free propagation to infinity for channelized propagation. The analysis reveals two types of computational difficulties caused by _spatial discretization_ as follows: (i) _general_, arising for fine grids and aggravated by a barrier, and (ii) _specific_, caused entirely by a strong barrier. The _asymptotic approach_, which avoids spatial discretization, is suggested. It is illustrated by solving _benchmark_ problems for barriers of arbitrary intensity. The analysis distinguishes three typical stages of the fracture penetration into a barrier and provides theoretical values of the Nolte-Smith slope parameter and arrest time as functions of the barrier intensity. Special analysis establishes the accuracy and bounds of the asymptotic approach. It appears that the approach provides physically significant and accurate results for fracture penetration into high, intermediate, and even weak stress barriers. On this basis, simple practical recommendations are given for modeling hydraulic fractures in rocks with stress barriers. The recommendations may be promptly implemented in any program using spatial discretization to model fracture propagation.  
hydraulic fracture; stress barrier; barrier intensity; numerical modeling +
Footnote †: journal: Applied Physics Letters A
## 4 Discussion  
A performance comparison with previous low-noise amplifier studies is shown in Table 1. When \(\mathrm{V_{ni}}\) is the input referred noise and \(\mathrm{I_{tot}}\) is the total consumption current of the operational amplifier, the NEF of the performance comparison can be obtained from Equation (15) [15]. The proposed low-noise chopper-stabilized multi-path operational amplifier achieves a current consumption of 96.7 \(\upmu\)A with a 1.8 V power supply, UGBW of 3.16 MHz, and input referred noise of 11.8 nV/\(\upmu\)Hz, resulting in a NEF of 4.46.  
\[\mathrm{NEF}=\mathrm{V_{ni}}\cdot\sqrt{\frac{2\mathrm{I_{tot}}}{\pi\cdot\mathrm{ U_{T}}\cdot 4\mathrm{kT}\cdot\mathrm{BW}}} \tag{15}\]  
Figure 14: Input referred offset measurement result of the proposed multi-path amplifier.  
Figure 15: Noise measurement results of the proposed multi-path amplifier: (**a**) input referred noise and (**b**) histogram of the measured input referred noise at 200 Hz.  
Because of the multi-path architecture, the proposed multi-path operational amplifier is relatively large in size and power consumption, but has a low input referred noise and wide bandwidth, so its NEF is better than previous studies.