esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Time Series Forecasting for Energy Reduction
### Deep Learning for Time Series
#### 2.4.2 Long Short-Term Memory Neural Networks  
LSTMs, introduced by [20], are a type of recurrent networks designed to avoid the long-term dependency problem, performing better with long sequences. Instead of simply having a single layer, there are four layers interconnected in what is called an LSTM cell. The cell is composed of different gates. Gates are a mechanism to control the passing of information through the cell and which information or part of a sequence must be remembered or forgotten. This is achieved by combining a sigmoid layer and a point-wise multiplication.  
Figure 5 represents the structure of an LSTM. The first part is the forget gate layer \(f_{t}\). The forget gate decides which weights to remove from previous time steps. It takes the input \(x_{t}\) and the previous hidden state \(h_{t-1}\) and outputs a value between 0 and 1 for each one of the cell states \(C_{t}\) using the Sigmoid function \(\sigma\). A value of 0 represents the information that must be deleted. The next step is the input gate layer \(i_{t}\). This layer decides the new information that will be stored in the cell state. The output from the gate is multiplied by the output of a hyperbolic tangent (_tanh_) layer that generates the output cell state \(\bar{C}_{t}\). The result of this is added to the old output state \(C_{t-1}\) to update it with the new information. The final part of the cell is the output gate layer \(o_{t}\), which will be a filtered version of the current computed cell state.  
An LSTM can be defined by Equations (8)-(13) [21], where \(W\) and \(b\) are parameters learned by the network.  
\[f_{t} =\sigma(W_{f}[h_{t-1},x_{t}]+b_{f}) \tag{8}\] \[i_{t} =\sigma(W_{i}[h_{t-1},x_{t}]+b_{i}) \tag{9}\]  
Figure 4: Folded and Unfolded RNN.  
Figure 5: Structure of an LSTM cell.  
\[\widetilde{C}_{t}=tanh(W_{c}[h_{t-1},x_{t}]+b_{c}) \tag{10}\] \[C_{t}=f_{t}*C_{t-1}+i_{t}*\widetilde{C}_{t}\] (11) \[o_{t}=\sigma(W_{o}[h_{t-1},x_{t}]+b_{o})\] (12) \[h_{t}=o_{t}*tanh(C_{t}), \tag{13}\]
# Bootstrap-CURE: A Novel Clustering Approach for Sensor Data--An Application to 3D Printing Industry
## 3 State-of-the-Art
### Data Science Applications in Sensor Analysis
#### 3.1.1 Anomaly Detection  
A majority of works in recent times focus on using sensor data to detect anomalies in real time or offline data. This itself is a fairly general framework with applications in a wide variety of domains, ranging from healthcare, autonomous vehicles, printing, internet-of-things, etc. The literature can further be subdivided based on the kind of algorithms used for the modeling. Deep learning-based methods are most popular in this category [10], sometimes in their classical supervised version [11; 12], and sometimes in an unsupervised deep learning approach, such as Ref. [13] applying autoencoders to detect anomalies in an aircraft system, and [PERSON] et al. [14] and [PERSON] et al. [15] finding anomalies in 3D printing processes. Although deep learning techniques generally have high accuracy, there is a high variety of architectures depending on each concrete application. This implies that the sensor data behave differently as per the application field. In addition, the inability of neural network models, in general, to explain anomalies is a big industrial challenge for research purposes.  
Although relatively fewer, there are still several studies using other machine learning methods, most of them hybrid approaches combining different techniques: Ref. [16], where hybrid random forest with wavelet transform is developed to recognize vehicle steering mode and further detect abnormal behavior; some references based on the combination of support vector machines with other techniques, such as deep belief networks [17] and DBScan [18]; or statistical applications, such as kernel PCA [19]. These works propose a hybrid scheme of using a previously unsupervised method to identify anomalies and build a predictive model on top of using SVM. Although SVM provides more explainable predictions than deep learning, their learning times are not scalable to big-data-based real industrial scenarios yet.
## 2 Discussion
### The Janus Kinase (JAK)-Signal Transducer and Activator of Transcription (STAT) Pathway  
The Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway exists downstream of cytokine receptor binding. Upon cytokine binding, JAK proteins are activated, which, in turn, activates STAT proteins via tyrosine phosphorylation [17]. The genes associated with psoriasis that encode players in the JAK-STAT pathway are STAT4 and TYK2. STAT4, like all proteins in the STAT family, is phosphorylated and translocated to the cell nucleus, where it acts as a transcription activator in response to cytokines. STAT transduces IL12, IL23, and IFN type I signals in T lymphocytes and regulates the differentiation of Th cells. TYK2 encodes a member of the JAK protein family that promulgatescytokine signals by phosphorylating receptor subunits. A component of the IFN I and II signaling pathways may play a role in antiviral immunity [4].  
SNP variants in several members of the JAK, STAT, and TYK gene families were implicated in psoriasis. For JAK2, the rs34536443 SNP was associated with psoriasis susceptibility. This variant involves the C allele and is thought to influence JAK2 signaling, which plays a role in immune cell activation. In the case of STAT3, the rs744166 SNP was linked to increased risk of psoriasis. This variant is characterized by the G allele and is believed to affect STAT3 function, a transcription factor involved in immune responses. Additionally, the rs34536443 SNP in TYK2 was associated with psoriasis susceptibility. This variant involves the G allele and is thought to impact TYK2 signaling, which is involved in cytokine signaling pathways. Additional research may aid in better understanding the functional consequences and detailed molecular mechanisms by which these SNP variants influence the manifestation and progression of psoriasis [9; 10; 17].
###### Abstract
In such studies, behavioral indicators including the number of questions asked by the lecture, the number of logins to the portal, the number of lectures taken, and the number of times participated in online discussions are incorporated as features to measure SE [16]. In another study, student engagement (affective state) is computed using voice recognition or physiological pressure sensors and heart rate; however, speech recognition is not recommended for affective state computation [17; 18; 19]. In the latest research, end-to-end approaches are used to detect the level of engagement of students in an online class environment through their head movement or eye gaze by using a webcam [20; 21; 22]. In subsequent work, a hybrid convolutional architecture is proposed to detect students' affective states using cues such as hand gestures, facial emotions, and body postures from an asynchronous learning environment. In this work, not only emotional but behavioral patterns are also analyzed but the trained model only provides the aggregate result of all student's states in each frame. Effective SE necessitates being computed at the individual level which may subsequently be aggregated depending upon the requirements. However, this research is conducted in a constrained environment in which students are aware of the experiment [23]. To date, there is no such method proposed that can detect SE in the offline classroom without a constrained environment, and if available, it only considered limited key features.
# RSVQ-Diffusion Model for Text-to-Remote-Sensing Image Generation
## 3 Methods
### RSVQ-Diffusion Model
#### 3.1.3 Loss Function  
The loss function is constructed based on variational inference and reconstruction error, and it primarily comprises three components: the KL (Kullback-Leibler) divergence loss, the decoder negative log-likelihood loss, and the auxiliary loss.  
Specifically, the KL divergence is an asymmetric measure used to quantify the difference between two probability distributions. It measures the \"information loss\" incurred when using distribution \(p\) to approximate distribution \(q\). The calculation is detailed in Equation (1).  
\[\text{KL}_{\text{loss}}=\sum_{t}q(x_{0}|x_{t})log\frac{q(x_{0}|x_{t})}{p(x_{t} |x_{0})} \tag{1}\]  
where \(q(x_{0}|x_{t})\) represents the posterior distribution of the real data, and \(p(x_{t}|x_{0})\) represents the posterior distribution predicted by the model.  
The model also introduces a negative log-likelihood loss, which is used to evaluate the difference between the final generated data predicted by the model and the real data. The decoder's negative log-likelihood loss is detailed in Equation (2).  
\[\text{NLL}_{decoder}=-E_{x\sim p(x)}[logp(x_{t}|x_{0})] \tag{2}\]  
where \(x\) is a sample drawn from the real distribution \(p(x)\); \(x_{t}\) represents the output generated by the decoder, and \(x_{0}\) denotes the real input sample. \(\log p(x_{t}|x_{0})\) indicates the conditional probability of generating the sample \(x_{t}\) given the input \(x_{0}\). The introduction of the negative sign is due to the log-likelihood values typically being negative; taking the negative ensures the loss is positive, and minimizing the NLL is equivalent to maximizing the likelihood.  
For the auxiliary loss function in the model, refer to Equation (3).  
\[\text{KL}_{\text{aux}}=\sum_{t}q(x_{0}|x_{t-1})log\frac{q(x_{0}|x_{t-1})}{p(x_ {t-1}|x_{0})} \tag{3}\]  
where \(q(x_{0}|x_{t-1})\) represents the posterior distribution of generating the real sample \(x_{0}\) given the state \(x_{t-1}\),capturing the feature information at the time step \(x_{t-1}\); and \(p(x_{t-1}|x_{0})\) represents the theoretical model distribution of generating the state \(x_{t-1}\) given the real sample \(x_{0}\), derived from real data.  
Figure 2: Architecture of the RSVQ-Diffusion model.  
These three loss components are combined through a conditional logic across time steps to form the final loss function, as detailed in Equation (4).  
\[Loss=\left\{\begin{array}{l}\frac{NL_{desdes}+KL_{max}}{p_{t}},t=0\\ \frac{KL_{loss}+KL_{max}}{p_{t}},other\end{array}\right. \tag{4}\]  
where \(p_{t}\) represents the weight associated with the time step \(t\), relevant to the model's generative process at time \(t\), used for loss normalization to ensure consistency of the loss across different time steps or sample sizes.
## 3 Machinery Monitoring
### Add-On Sensors for the Collection of Data Related to Machinery Status  
A number of researchers [40; 41] have proposed the analysis of vibration data as a means by which to monitor the status of machines. In this study, we developed a monitoring system based on an inexpensive single chip controller, Arduino, in conjunction with triaxial accelerometers, infrared sensors, and temperature sensors. The proposed system is inexpensive and extensible, making it ideal for SMEs.
# Integration of EMU Overall Design Model Based on Ontology-Knowledge Collaboration
## 3 Integration of EMU Requirement Models  
The requirement model is integral to the entire lifecycle of EMU design. Constructing the requirement model involves stakeholder analysis, requirement collection, requirement identification, and requirement analysis. The design process of the requirement model is illustrated in Figure 2.  
In the process of constructing the requirement model, the initial step involves requirement collection. During this phase, stakeholders, including clients, users, and management, are engaged in communication to comprehend their expectations and needs and to gather user stories, use cases, scenarios, etc. The design team meticulously defines and identifies all stakeholders pertinent to the project, encompassing individuals who directly and indirectly benefit from it, such as end-users, clients, management, and development teams. Face-to-face interaction with stakeholders plays a pivotal role in ensuring precise requirement gathering. Through interviews, the team can delve deeper into stakeholders' expectations, preferences, and concerns, thereby enhancing their understanding of the project's context and background. Following this, requirements are identified, adhering to specific rules. Finally, requirement analysis ensues, wherein the identified requirements are furnished to the architectural design, laying the groundwork for architectural development.
## 4 Experimental Results  
To verify the feasibility of the proposed modified BP algorithm and explore the influence of ionospheric effects on the GNSS-BSAR system, we developed a GNSS-based passive radar experimental platform. Experiments were conducted at three distinct times--morning, midday, and evening--on a specific day to perform SAR imaging for the same scene, as detailed below.
###### Abstract  
This study aims to apply the ionic liquid-based solidified floating organic drop micro-extraction (IL-SFDME) technique to improve the preconcentration of trace mercury levels, serving as a preparatory step for analysis via graphite furnace atomic absorption spectrometry (GFAAS) without using chelating agents. The process involves separating and preconcentrating mercury ions using IL-SFDME, followed by GFAAS analysis. This study systematically examined several factors affecting micro-extraction efficiency, including pH, the amount of ionic liquid 1-Hexyl-3-methylimidazolium hexafluorophosphate (HMIMPF6), extraction time, stirring rate, and temperature, to determine the optimal conditions. Under these optimized conditions, an enhancement factor of 171 was achieved for a 75 mL sample solution. The calibration curve was linear within the 0.5-10 \(\upmu\)g/L range, with a detection limit (3\(\sigma\)) of 0.12 \(\upmu\)g/L and a quantification limit (10\(\sigma\)) of 0.40 \(\upmu\)g/L. The relative standard deviation (RSD) for 7 replicate measurements of 1 \(\upmu\)g/L mercury was \(\pm\)2.78%. The combination of the ionic liquid and SFODME methods without using a chelating agent is used for the first time for metal extraction. The method was successfully applied to extract and determine mercury in reference water samples and various real water samples, demonstrating notably satisfactory extraction efficiency.  
s +
Footnote †: journal: Applied Sciences  
olidified floating organic drop micro-extraction; graphite furnace atomic absorption spectroscopy; mercury
# Comparison of Modern Deep Learning Models for Speaker Verification
## 4 Models Overview
### WavLM  
WavLM is a new pre-trained model that leverages self-supervised learning within the HuBERT framework, prioritizing both spoken content modeling and speaker identity retention. This model is pre-trained on large-scale unlabeled data and is able to enhance task performance, diminish the need for data labeling and ease the task-specific adaptation process. The WavLM model learns not only the ASR information by masked speech prediction but also the knowledge of non-ASR tasks by speech denoising modeling. The authors optimize the model structure and training data of HuBERT and wav2 vec2. The addition of the gated relative position bias to the Transformer structure as the backbone improves model performance for ASR while keeping almost the same number of parameters and training speed. They also propose an utterance mixing training strategy, where additional overlapped utterances are created unsupervised and incorporated during model training to improve speaker discrimination. The WavLM was pre-trained on a large amount of unlabeled data consisting of 94k h of public audio data (including Libri-Light, GigaSpeech and VoxPopuli datasets). WavLM achieves state-of-the-art performance on the SUPERB benchmark and significant improvements for various speech processing tasks on their representative benchmarks [26].  
The WavLM architecture uses the Transformer model as a backbone. It contains a convolutional feature encoder and a Transformer decoder. To improve the model, a gated relative position bias [38] is encoded based on the offset between the \"key\" and \"query\" in the Transformer self-attention mechanism. Compared with the convolutional relative position embedding in wav2 vec2 and HuBERT, the gates take the content into  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & \begin{tabular}{c} **Speaker Embedding** \\ **Dimension** \\ \end{tabular} & **Training Dataset** & **Architecture** \\ \hline PyAnnote & 512 & VoxCeleb & X-vector with SincNet \\ \hline WavLM & 256 & LibriSpeech & Transformer \\ \hline TitaNet & 192 & \
\begin{tabular}{c} VoxCeleb, NIST SRE, \\ Fisher, LibriSpeech \\ \end{tabular} &
\begin{tabular}{c} ContextNet with \\ channel attention \\ pooling \\ \end{tabular} \\ \hline Ecapa-TDNN & 192 & VoxCeleb & Improved TDNN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the speaker embedding model parameters.  
Figure 1: Generalized speaker embedding model architecture.  
consideration and adaptively adjust the relative position bias by conditioning on the current speech content.
# Energy-Efficient User Pairing for Downlink NOMA in Massive MIMO Networks
## 7 Results
### Performance Evaluation
According to formulas in the complexity analysis section, correlation-based pairing has the highest complexity followed by the Hungarian algorithm,  
Figure 5: The spectral efficiency versus number of BS antennas, with \(U=10\) users, ZF beamforming. Figure 6: Power consumed in user paring in dBm versus user number, with \(A=36\) BS antennas. Figure 7 shows the effect of increasing the number of BS antennas on complexity. The complexity of correlation-based pairing increases linearly with \(A\) while the complexities of the Hungarian algorithm and GS algorithm are constant with respect to \(A\). Figure 8 demonstrates the relation between the number of users and the complexity at a specific number of BS antennas. The complexity of the three approaches is increasing with the increase in the number of users. The highest complexity is for the correlation based pairing, while the lowest is for the GS algorithm.
###### Abstract  
Thermal management at a high heat flux is crucial for electronic devices, and jet impingement cooling is a promising solution. The heat transfer properties of a rectangular-finned heat sink are investigated under angled and multi-impingement jet configurations in this study. Experiments were conducted with three different nozzle diameters, three different heat sink angles, three dimensionless nozzle-to-heat sink distance ratios, and five different velocity values. As a result, the obtained data are presented as Nu-Re graphs, and the impacts of the parameters on heat transfer (HT) are analyzed. It is concluded that the Nusselt number increases with the increasing nozzle diameter and Reynolds number, whereas it decreases with increasing distance between the nozzle and the heat sink. When comparing the angle values under an identical flow velocity, nozzle diameter, and dimensionless h/d distance experimental conditions, it was found that the Nusselt numbers were very close to each other. Under constant heat flux and for all investigated angles, the highest Nusselt number for the rectangular-finned inclined heat sink was observed at a 10\({}^{\circ}\) heat sink inclination, a nozzle diameter of D = 40 mm, a dimensionless distance of h/d = 6, and a flow velocity of 9 m/s. This study deepens the understanding of the heat transfer mechanism of impinging jets and provides an efficient method framework for practical applications.  
forced convection; heat transfer enhancement; multi-nozzle impingement jet Article  
1
## 3 Numerical Tests
### Stontic Flow/Anurysmal Flow
#### 3.3.1 Effect of Degree  
Three hard constraints with different degrees (i.e., \(k\) = 1.0, 2.0 and 3.0) and the same scaling factor (i.e., \(a\) = 1.0) are generated for PINN model construction.  
For stenotic/aneurysmal flow, relative \(L_{2}\)-norm error is adopted to measure the PINN prediction accuracy, where \(u_{i}\) in Equation (24) denotes the FV-based CFD-predicted axial velocity at the \(i\)-th node. The relative \(L_{2}\)-norm errors of axial velocity with different degrees are listed in Table 5. As the pressure at the inlet and outlet is fixed and the geometric parameter \(A\) has a considerable effect on the pressure drop in the flow channel, the Reynolds number, defined with \(v\), \(d\) and \(u_{\text{max}}\) at the inlet of the computational domain, varies in the range of 12.5 to 18.0. For the four examined geometries, both second- and third-degree hard constraints result in smaller errors than the first-degree hard constraint. Compared with the second-degree hard constraint, the third-degree hard constraint results in slightly enhanced prediction accuracy.  
In Figures 7 and 8, the distributions of axial velocity for stenotic/aneurysmal flow are further compared between PINN and FV-based CFD. For the sake of saving space, only the results of stenotic flow with \(A\) of 4.0 \(\times\) 10\({}^{-3}\) and aneurysmal flow with \(A\) of \(-\)2.2 \(\times\) 10\({}^{-2}\) are provided. There are significant deviations between PINN-predicted results with first-degree hard constraints and FV-based CFD results, while the predicted axial velocity distributions by PINNs with second- and third-degree hard constraints exhibit good agreements with the FV-based CFD results, corresponding to the errors shown in Table 5.  
Figure 6: Stenotic flow and aneurysmal flow: (**a**) stenotic flow; (**b**) aneurysmal flow. Here, \(L\) is the channel length, \(d\) is the distance between the channel walls, \(u\) is the local streamwise velocity and \(u_{\text{max}}\) is the velocity at the center line.