esa-sceva/satcom-chunk-collection huggingface dataset:
# Magnetic Field Effect of Near-Field Radiative Heat Transfer for SiC Nanowires/Plates
## 3 Results and Discussion  
### Spectral Heat Fluxes and Transmission Coefficient  
From Figure 5, for the plate case, two peaks were observed at frequencies around 1.50 \(\times\) 10\({}^{14}\) rad/s and 1.81 \(\times\) 10\({}^{14}\) rad/s under \(B\) = 3 T. Compared to the plate case, a significantly enhanced spectral heat flux between 1.50 \(\times\) 10\({}^{14}\) rad/s and 1.70 \(\times\) 10\({}^{14}\) rad/s was observed, which is known as hyperbolic dispersion (Type I, blue region). Between 1.70 \(\times\) 10\({}^{14}\) rad/s and 1.90 \(\times\) 10\({}^{14}\) rad/s, the peak of spectral heat flux blueshifted toward increasing angular frequency, because the optical constants change due to different material structures. The linear shape of the spectral heat flux of the nanowires was obviously higher than that of the plates, producing violent shocks under the external magnetic field (Type II, yellow region).  
Figure 5 shows that compared to hyperbolic dispersion type II, SiC nanowires exhibit large heat transfer flux in the hyperbolic dispersion type I region. Compared to the SiC plates, the spectral heat flux of the nanowires structure is more intense, especially in the hyperbolic dispersion regions.  
Figure 4: Comparison of the heat transfer coefficient in this work with that in [PERSON] et al. [29].  
Figure 5: Spectral heat fluxes between two aligned SiC nanowires and between two SiC plates with \(f\) = 0.2 at \(d\) = 10 nm.  
Figure 6 displays the contour plots of the transmission coefficient for both SiC nanowires and SiC plates under the magnetic field \(B=3\) T at \(d=10\) nm. Compared to SiC plates, the transmission coefficient of SiC nanowires is higher at very large \(\beta\) values and in the hyperbolic band. This can allow photons on the surface of SiC nanowires to tunnel through the vacuum gap, which is why the heat fluxes of SiC nanowires is obviously higher than that of the plates in the hyperbolic band. Evanescent waves are the main surface mode of structure in this case. For SiC nanowires, because of their dielectric anisotropy, they support the hyperbolic band for \(p\) polarization. Therefore, in the hyperbolic regions, the nanowires can have a larger transmission coefficient than that of SiC plates.
## 5 Examples of Forward-Inverse Modeling
### Atmospheric Input & Output
#### 5.4.1 The OC-SMART Optimal Estimation Approach
[107] applied the OC-SMART algorithm to a MODIS image obtained on 18 April 2014 over a Norwegian coastal area and compared retrieval results with the standard SeaDAS retrievals as shown in Figure 7 with OC-SMART results at the top and SeaDAS results at the bottom. From left to right, the columns are \(\tau_{869}\), \(f_{a}\), CHL, CDOM and \(b_{bp}\), respectively. The first two columns are retrieved aerosol parameters. Our results are very similar to the SeaDAS retrievals, which makes sense because the two algorithms share the same aerosol model. However, the marine parameters show some differences and we should point out that in OC-SMART, we used the GSM bio-optical model when we retrieved the ocean parameters. The SeaDAS CHL retrieval shown in Figure 7 is from the OC4v6 algorithm. SeaDAS failed when using the GSM model due to negative water-leaving radiances resulting from an incorrect atmospheric correction, which is a common issue in traditional atmospheric correction algorithms. The aerosol optical depths was determined at a near infrared (NIR) wavelength (\(\tau_{869}\)) and then extrapolated to shorter wavelengths. Since the water-leaving radiance is only a small fraction of the TOA radiance measured by the satellite instrument, a small error in the aerosol retrieval may cause the water-leaving radiance to become negative. We also found the OC-SMART results to be close to the field measurements. The CHL value was 0.56 [mg/m\({}^{3}\)] while the field measurementsshowed an average of 0.86 \(\pm\) 0.34 [mg/m\({}^{3}\)] in the spring season. The CDOM absorption coefficient was 0.15 [m\({}^{-1}\)] and the field measurements show an average of 0.14 \(\pm\) 0.06 [m\({}^{-1}\)].
# A Novel Signal Design and Performance Analysis in NavCom Based on LEO Constellation
## 2 Related Surveys
### Satellite-Based Systems
The main peak of the green solid lines’ normalized PSD function is in the center of the receiver, with unnecessary frequency modulation seen for the receiver; the main peak of the black solid lines is split and located on both sides to avoid the aliasing of the main peak and ensure the clarity of the main peak of the spectrum; the main lobe bandwidth of the red solid lines is significantly larger than that of the others, and it has a better code tracking accuracy; the pink solid line is divided into 13 sub–bands with equal bandwidths and pulse-like modulation and is suitable for carrying out the transmission of information. 1.23 MHz. The purpose of this is to effectively avoid the occurrence of frequency aliasing. The signal modulation can be described as SRC (0.2, 1), and the 5 th to 13 th sub-beams are preferred, as they minimize the probability of interference in other systems in the ISM frequency band [42; 43; 44].
# Hybrid Precoding Design for Subarray-Structure-Enabled mmWave URLLC System
## 2 System Model
### Problem Formulation  
Let \(\mathbf{f}\in\mathbb{C}^{N_{\mathrm{RF}}\times 1}\) represent the digital precoding matrix and \(\mathbf{A}\in\mathbb{C}^{N_{\mathrm{t}}\times N_{\mathrm{RF}}}\) represent the analog precoding matrix; let \(N_{\mathrm{x}}=N_{\mathrm{t}}/N_{\mathrm{RF}}\) denote the number of antennas of each subarray. Since phase shifters can only change the phases of the signal, the amplitude of the analog precoding matrix is constant. Specifically, the unit modulus constraint can be expressed by \(|\mathbf{a}_{b}|=1\), where \(\mathbf{a}_{b}\in\mathbb{C}^{N_{\mathrm{x}}\times 1}\) is the analog precoder for the \(b\)-th RF chain.  
Assuming that each signal block has \(M\) symbols, the transmit signal is \(x[m]\), \(m=1,2,\ldots,M\), and the received signal of the \(m\)-th symbol can be denoted as [15]  
\[y[m]=\mathbf{h}^{H}\mathbf{A}\mathbf{f}x[m]+z[m],m=1,2,\ldots,M, \tag{2}\]  
where \(\mathbf{h}\) represents the BS-to-UE link channel matrix, \(z[m]\) represents the additive white Gaussian noise (AWGN) with zero mean and a variance of \(\sigma_{k}^{2}\), where \(z[m]\sim(0,\sigma^{2})\). For the received signal, the signal-to-noise ratio (SNR) is  
\[\gamma=\frac{|\mathbf{h}^{H}\mathbf{A}\mathbf{f}|^{2}}{\sigma^{2}}. \tag{3}\]  
With the short-packet regime, the achievable rate of the signal can be written as [16]  
\[R=\log_{2}(1+\gamma)-\sqrt{\frac{V(\gamma)}{M}}\frac{Q^{-1}(\varepsilon)}{ \ln 2}, \tag{4}\]  
where \(V(\gamma)=1-(1+\gamma)^{-2}\) is the channel dispersion value, \(Q^{-1}\) is the inverse of function \(Q\), and \(\varepsilon\) indicates the packet error rate.  
In this paper, the maximum achievable rate of the signal is taken as the optimization objective, and the optimization problem is established as  
\[\max_{\mathbf{A},\mathbf{f}} R\] (5) such that \[\|\mathbf{A}\mathbf{f}\|^{2}\leq\mathbf{P}_{\mathrm{tot}} \tag{6}\] \[|\mathbf{a}_{b}|=1, \tag{7}\]  
where Equation (6) represents the limitation of the transmit power, and \(P_{\mathrm{tot}}\) represents the total transmit power of the system. As we can see, it is intractable to obtain the global optimal solution of problem (5) because of the nonconvexity of Equation (7); here, we convert the form of Equation (7) in the following part.
# Maintenance Time Prediction for Predictive Maintenance of Ship Engines
###### Abstract  
Ships carrying large amounts of cargo and passengers are larger and slower than other modes of transportation. They are mostly foreign flagged and operate at sea far from coasts for 20 years or more, incurring more operating costs than construction costs. Therefore, an efficient maintenance system is necessary for stable, economical ship operation. Researchers are attempting to equip ships with predictive maintenance technology, which is used proactively in other modes of transportation to predict the maintenance time of machines through data monitoring and analysis. However, due to the nature of ship operation, data collection is difficult, and most studies focus on fault detection, hindering the application of predictive maintenance to ships. In this study, we developed a maintenance time prediction algorithm using the revision generator engine condition criterion (RGCCV) value and the cylinder exhaust gas temperature, as developed in a previous study for marine generator engines. And through comparison and verification using machine learning, the average mean absolute error (MAE) across all cylinders was 2.916 for the RGCCV-based method and 8.138 for the temperature-based method, demonstrating a 64% improvement. These findings establish a practical foundation for implementing predictive maintenance in ship engines by enabling more reliable and condition-based maintenance.  
ship generator engine; predictive maintenance; anomalous symptoms; maintenance time prediction; machine learning Article
## 1 Introduction  
Receptor-interacting protein kinase 1 (RIPK1) is a crucial regulator of inflammation and cell death. Recent findings indicate that both genetic mutations and non-genetic factors influencing RIPK1 activity can lead to a range of inflammatory and degenerative diseases, highlighting the necessity for precise regulation of RIPK1 function in maintaining human health [1; 2].  
The full-length human RIPK1 is a cytosolic protein consisting of 671 amino acids, with a molecular mass of approximately 76 kDa, sharing 68% identity with its mouse counterpart (Figure 1). Belonging to the RIP kinase family, RIPK1 is one of seven members, each featuring a homologous kinase domain (KD). Besides the common N-terminal kinase domain, RIPK1 possesses a C-terminal death domain (DD), facilitating its dimerization or interaction with other death-domain-containing proteins, such as TNFR1 (tumor necrosis factor receptor 1), TRADD (TNFR1-associated death domain protein), and FADD (Fas-associated death domain) [3]. Additionally, RIPK1 contains a bridging intermediate domain (ID) housing a RIP homotopic interaction motif (RHIM) [4]. The RHIM domain of RIPK1 facilitates its self-polymerization to form amyloid fibers [5]. It also allows interaction with other RHIM-containing proteins, such as RIPK3, ZBP1 (Z-DNA binding protein 1, also known as DAI and DLM-I), and TRIF (TIR-domain-containing adapter-inducing interferon \(\beta\)) [1; 2].  
RIPK1 primarily regulates inflammation through its scaffold function, while its involvement in cell death requires its kinase activity. The regulation of RIPK1 function involves various post-translational modifications, including ubiquitination, phosphorylation, and glycosylation. This review aims to summarize RIPK1's impact on inflammation, cell survival, cell death, development, and disease pathogenesis, with a focus on the role of phosphorylation and dephosphorylation.
# New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
## Mining Closed Frequent Itemsets with Map-Reduce
### Reduce Step
#### 3.2.6 Completeness  
We need to show that the algorithm outputs all the frequent closed itemsets. Assume negatively, considering that \(c=i_{1},\dots,\ i_{n}\) is a closed frequent itemset (that we assume was not produced). Suppose, that \(c\) has no proper subset that is a closed frequent itemset. Therefore, for all items \(i_{j}\in c\), \(sup\big{(}i_{j}\big{)}=sup(c)\) and \(g\big{(}i_{j}\big{)}=g(c)\). Therefore \(h\big{(}i_{j}\big{)}=h(c)=c\). Since \(h\big{(}i_{j}\big{)}=c\), then \(i_{j}\) is a generator of \(c\), and the algorithm will output \(c\) at the first iteration.  
Suppose that \(c\) has one or more proper subsets and each is a closed frequent itemset. We examine the largest one and denote it \(l\). \(l\) is generated by the algorithm because its generator is shorter than the generator of \(c\). We also denote its generator \(g_{l}\), meaning that \(g(l)=g(g_{l})\). Since \(g\) is antitone and since \(g_{l}\subset c\), then \(g(c)\subset g(g_{l})\). What we show next is that if we add one of the items not in \(l\) to \(g_{l}\), we will generate \(c\). Consider an item \(i\), such that \(i\in c\backslash l\). Let \(g_{l}^{\prime}=g_{l}\cup\{i\}\). Therefore, \(g\big{(}g_{l}^{\prime}\big{)}=g(g_{l})\cap g(i)=g(l)\cap g(i)\). Assume that \(g\big{(}g_{l}^{\prime}\big{)}\supset g(c)\). It implies that \(l\prime\) is a generator of a closed itemset \(h\big{(}g_{l}^{\prime}\big{)}\) that is a proper subset of \(c\) in contradiction to \(l\) being the largest closed subset of \(c\), therefore \(g\big{(}g_{l}^{\prime}\big{)}=g(c)\), meaning that \(c\) will be found by the mapper by adding an item to \(g_{l}\) (see lines 3-4 in Algorithm 2. Mapper).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Closed Item Set** & **Generator** & **Support** \\ \hline \(\{a\}\) & \(\{a\}\) & 3 \\ \(\{c,f\}\) & \(\{c\}\) & 4 \\ \(\{e\}\) & \(\{e\}\) & 4 \\ \(\{a,c,d,f\}\) & \(\{a,c\}\) & 2 \\ \(\{a,e\}\) & \(\{a,e\}\) & 2 \\ \(\{c,e,f\}\) & \(\{c,e\}\) & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Closed Frequent Itemsets of D.
# An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields
## 3 Methodology
### Model Specification  
#### 3.4.1 Linear Regression (LR)  
A multiple linear regression model was estimated using the ordinary least squares (OLS) method, which minimizes the sum of squared residuals to obtain coefficient estimates. This approach is standard in econometric modeling and does not require hyperparameter tuning [17]. The regression was implemented using the standard LinearRegression class from the Scikit-learn library in Python 3.13.2, which computes the weights analytically based on OLS. No regularization techniques were applied in this baseline model.
## 1 Introduction
This often requires models that are too specific for a certain circuit or making certain assumptions when modeling specific nonlinearities. Therefore such models are not easily transferable to different effects units since expert knowledge of the type of circuit being modeled is always required. Also, musicians tend to prefer analog counterparts because their digital implementations may lack the broad behaviour of the analog reference devices. Recently, deep learning architectures have been explored for black-box modeling of audio effects. In previous works, we explored convolutional neural networks (CNN) to model linear effect units, such as equalization [6]: nonlinear effects with short-term memory, such as distortion, overdrive and amplifier emulation [7]. Furthermore, in [8], the later architecture was extended with recurrent neural networks (RNN) in order to model linear and nonlinear, time-varying and time-invariant audio effects with long temporal dependencies, such ring modulation or multiband compression. Also, in [9], [PERSON] et al. explored variants of the WaveNet architecture [10] in order to model nonlinear effects such as a tube amplifier. In this work, we analyse and compare the deep learning architectures from [7; 8; 9] and we propose a new model based on the combination of the convolutional and dense architectures from [8] with the feedforward WaveNet from [9]. Therefore, we explore whether a latent-space based on WaveNet can learn long temporal dependencies such as those learned by the Bidirectional Long-Short Term Memory (BI-LSTM) layers from [8]. We show the models performing virtual analog modeling of the _Universal Audio vacuum-tube preamplifier 610-B_, the _Universal Audio transistor-based limiter amplifier_ 1176 LN and the rotating _horn_and rotating _woofer_ of a _145 Leslie speaker cabinet_. We measure the performance of the models via perceptually-based objective metrics and through a subjective listening test. We report that convolutional and feedforward WaveNet architectures perform similarly when modeling nonlinear audio effects without memory and with long temporal dependencies, but fail to model time-varying tasks such as the _Leslie speaker_. On the other hand, and across all tasks, the models that incorporate RNNs or WaveNet architectures to explicitly learn long temporal dependencies, tend to outperform (objectively and subjectively) the rest of the models. The paper is structured as follows. In Section 2 we present the relevant literature related to modeling nonlinear and time-varying audio effects and Table 1 summarizes the different approaches. Section 3 provides the description of the different deep learning models and Section 4 the experimental procedures. Sections 5-7, respectively show the obtained results, discussion and conclusions.
# Design of Wireless Sensor Network for Agricultural Greenhouse Based on Improved Zigbee Protocol
## 1 Introduction
Therefore, this research uses Zigbee as the research object; a greenhouse monitoring system based on the improved Zigbee algorithm is designed, which forms the wireless sensor network through various sensor nodes, and improves the network topology and routing algorithm of Zigbee to achieve high-efficiency and the low-consumption monitoring of environmental parameters. Compared with the previous research, this research innovatibly combines the wireless topology structure with the improved Zigbee routing algorithm; it not only optimizes Zigbee itself, but also optimizes the wireless network composed of Zigbee, and is mainly oriented to the vegetable and fruit scenes in greenhouses, making the research more realistic.
# A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
## 3 Methodology
### Active Learning Query Strategy
#### 3.2.2 Query by Committee (QBC)  
QBC is another typical informative-based strategy; it is based on the inconsistency of ensemble learning. In this strategy, a committee is composed by training multiple classifiers on different subsets of instances drawn from the labeled dataset. The fundamental assumption of QBC is that different classifiers should exhibit consistency with the provided labeled data instances. Hence, the query instance is selected based on the unlabeled instance that demonstrates the highest disagreement among the committee members in label prediction.  
There are two ways to construct this committee, namely, boosting and bagging. In query by bagging, a committee of \(m\) classifiers is created by applying bootstrap aggregating, which involves randomly sampling with replacement \(m\) times from the labeled training data. In query by boosting, the random instances are bootstrapped with replacement from available labeled training data.  
There are two kinds of indicators for measuring disagreement, namely, vote entropy and the average Kullback-Leibler (KL) divergence. Vote entropy identifies the instances with the largest entropy among the predicted class labels. Such instances are considered hard samples, and are selected as query instances for labeling. The average KL divergence measure identifies the most informative query as the one with the largest average difference between the label distributions of any one committee member and the consensus [26].
# EEMIP: Energy-Efficient Communication Using Timing Channels and Prioritization in ZigBee
## 3 The Proposed EEMIP Method
### Priority and Time Interval Selection
The end devices synchronise when they can communicate, so there is no wait period for any synchronization (beacon) frame, nor for time slot (in a sense of standard beacon mode ZigBee communication). Therefore, the end devices can spend most of their lifetime in a sleep mode and wake-up only for their dedicated time slots (without wasting power for waiting). If the coordinator identifies that some end device is getting desynchronized, it can re-initiate its time-interval selection procedure, which resynchronizes it again, by resending the Offer message. Because of centralized management by the coordinator, the communication collisions are not really an issue (they occur only for a short period of time when a new device is not yet assigned with a dedicated time interval and not synchronized with others). However, the coordinator, as a single processing node, could get congested by the traffic from too many end devices. In such a case, the priority-based selection of time intervals has other uses, specifically, to ensure that high-priority data are not lost. Some of the low-priority data are sacrificed in order to deal with the congestion. As a result, less retransmissions occurs, since high-priority data are sent more frequently. This increases energy efficiency even more.
# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 4 Answering the RQs
### RQ2--Techniques and Strategies
#### 4.2.3 Right-Sizing  
Although the initial layers of the DNN reduce the size of the data, sometimes the intermediate result generated is still large, or the whole process is too slow. Therefore, a method for rapid inference is necessary. Thus, the network must be trained in a different way to generate good results before reaching the end of the network (early-exit), being able to give a result in real-time, without the need to send it to the cloud, thereby reducing the traffic. This method is known as model right-sizing [34], where the DNN model has different exit points and a shorter branch implies a smaller size and thus a shorter runtime. This mechanism focuses on adjusting its size to the limitation of the existing environment.  
Regardless the number of devices, the right-sizing method aims at optimizing the use of external resources to accelerate computation. As for DNN right-sizing, the emphasis is placed on tailoring the model size to the constraints of the available environment, which requires advanced training techniques to create a new adjusted model from the original one.  
Figure 3: Model Partitioning.  
[PERSON] et al. [2018] propose using Distributed Deep Neural Networks (DDNNs) for fast and localized inference using shallow portions at end devices. It uses DDNNs in the geographical diversity of sensors to improve functions of objects and recognition accuracy and reduce communication costs. Then, it is possible to enhance sensor fusion, system fault tolerance, fast inference and privacy in applications.  
The proposals in [PERSON], [PERSON], and [PERSON] [2017] and [PERSON], [PERSON], and [PERSON] [2018] integrate both mechanisms of DNN partitioning and DNN right-sizing. Suppose the fog nodes are not able to make an accurate inference by themselves. In that case, they will continue to upload the intermediate data of the DNN to the remote cloud servers for further processing. However, if the fog devices or nodes have obtained results that meet their requirements, they can just stop uploading intermediate values and adopt fast inference as the final results.  
[PERSON] et al. [2018] present a system called Boomerang, which is designed with two key concepts aimed at meeting the requirement for inference tasks on the manufacturing process. The first key concept is DNN right-sizing, which is employed to accelerate the execution of inference by exiting DNN execution early at a selected intermediate DNN layer. Another key concept is DNN partition, where the DNN computation is partitioned adaptively and distributed to the IoT devices and the edge server according to the available bandwidth.