esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Sparse Vector in Single-Trace Inversion
This figure illustrates the mathematical construction of the inversion operator **G** through the matrix decomposition process. It reveals how the inverse operation of \((\textbf{WW}^{\mathsf{T}}+\lambda\textbf{Q})^{-1}\) governs the spatial distribution of reflectivity coefficients, independent of the wavelet matrix **W**. tests, we have observed a stable relationship between the vector \(q\) and the positions of the reflectivity. Taking the scenario with an Lp-norm of 0.8 as an example, Figure 3 illustrates the iterative process of the vector \(q\). During the first iteration, due to the influence of seismic data and mathematical sparsity assumptions, the distribution of \(q\) is relatively uniform, with low values near both reflectivity positions (at time positions 33 and 66) and non-reflectivity positions (Figure 4a). As the iterations progress, the values of \(q\) oscillate and change. By the 10 th iteration, the distribution of \(q\) undergoes a significant transformation (Figure 4b), maintaining low values near reflectivity positions while exhibiting higher values near non-reflectivity positions. By the 100 th iteration, the distribution of \(q\) shows two distinct minima, which correspond to the positions of the reflectivity (Figure 4c). Based on this, we can summarize the relationship between the sparse vector \(q\) and the reflectivity positions: low values of \(q\) appear at reflectivity positions, while high values appear at non-reflectivity positions.
## 4 Results and Discussion
### Annual Energy Gain  
For this section, the annual energy gain was analysed. Equation (25) was used for this purpose. The annual energy gain for the \(PV\) module mounting systems under study is shown in Figure 11:  
The following observations can be deduced from Figure 11:  
1. In Scenario 1, an \(AEG\) of approximately 13% was obtained in the theoretical study. This result is in accordance with the results obtained by [56]. In the study under real operating conditions, this parameter decreased to approximately 8.5%.
2. In Scenario 2, an \(AEG\) of approximately 18% was obtained in the theoretical study. This result is consistent with the results obtained by [18]. In the study under real operating conditions, this parameter decreased to approximately 10.5%.
3. The best results were obtained in Scenario 2, both in the theoretical study and in the study under real operating conditions. However, Scenario 1 is the one implemented in a \(PV\) power plant. The biggest difference was obtained in the theoretical study, of approximately 5.5%. In contrast, in the study under real operating conditions, this difference was approximately 2.2%.
## 3 Description of the Modified Method  
Typically, it is accepted that the main problem in deriving the regulator by this (DS) method when dealing with time-delayed processes lies in the approximation of the time delay, while it is overlooked that the very assumption of the desired closed-loop transfer function in the form of (13) or (21) constitutes a certain pitfall. If we analyze the presented tuning methods, it becomes clear that the determined controller settings will not allow for the response defined by these transfer functions. From (21), it would follow that the controller output at the instant when changing the reference is \(y(t=0^{+})=-\theta/\tau c\), which, after all, is not true. Although the used approximations of the exponential term are correct, by using only the first two or three terms, they can provide a relatively good approximation only for law values of the delay, but then, the set values (20) will be only slightly different from those given in Formulas (7) and (8). In general, the longer the dead time, the more significant the effects of omitting the higher order terms will be.  
[PERSON] followed by [PERSON] and all [8] point out this limitation and recommend that the \(\theta/T\) ratio should not exceed \(\lambda\).  
Suppose that the open-loop system contains a significant transport delay; the desired transfer function of the closed-loop shall be equal to  
\[G_{d}(s)=\frac{e^{-\theta s}}{\tau_{c}s+\ e^{-\theta s}} \tag{30}\]  
To avoid the mentioned drawbacks, the tuning of the regulator, which is calculated to provide a non-oscillatory transient in the shortest time possible, will be carried out differently.  
In the first-order system described by (6), the proportional gain of the controller (8) under assumption (7) only affects rise and settling time. In time-delayed first-order systems, an increase in \(K_{p}\) leads to oscillations of the controlled variable, which is characteristic of systems with complex poles. To avoid them and at the same time ensure the shortest settling time, the proportional gain of an open-loop system should be chosen so that the closed-loop system is critically damped. To determine it, the root-locus method was used.
## 4 Conclusions  
The propagation characteristics of the elastic waves propagating in a non-uniform rotating shaft with a transverse crack is studied considering the three individual crack modes in this paper. The influence of crack mode, crack location, crack depth and the rotating speed to propagation characteristics is thoroughly studied. It is found that:  
1. Different crack modes will lead to different propagation characteristics of the elastic waves. Mode III is the most suitable mode for the cracked shafts in this paper and it is of great importance to study the crack mode in a real practice.
2. The location of the crack will make the three stopbands fluctuate. The first stopbands vary slightly with the location of the crack. The bandwidth of the second stopband will reach a peak when the crack locates at the middle location 0.5\(L\), while the bandwidth of the third stopband will reach a peak at locations 0.3\(L\) and 0.7\(L\). The change in bandwidth can be helpful to locate the transverse crack.
3. The bandwidths of the stopbands become wider with the increase of the depth of the transverse crack while the central frequencies of the stopbands become lower at the mean time. The central frequency and the bandwidth can be helpful for transverse crack detection.
4. The increase of rotating speed will shift up the three stopbands, while the bandwidths of them remain the same.  
For a real rotating shaft, such as the shaft of a rotating machinery, the results of this paper can provide a reference for detecting and locating the transverse crack in it by studying the central frequencies and the bandwidths of the stopbands. In real applications, the cracks may have mixed mode and the crack orientation may be not transverse, and there might be more than one crack in the shaft, thus, the combination of the three typical modes as well as the multi-crack cases and the crack orientation need to be studied further.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**RPM** & **Stopbands (Hz)** \\ \hline
0 Hz & [0, 50, 270, 440, 735, 1175] \\
10 Hz & [0, 75, 280, 450, 750, 1185] \\
20 Hz & [0, 80, 305, 480, 760, 1210] \\
30 Hz & [0, 95, 325, 500, 790, 1235] \\
40 Hz & [18, 110, 330, 510, 805, 1250] \\
50 Hz & [30, 120, 335, 520, 810, 1275] \\ \hline \hline \end{tabular}
\end{table}
Table 8: Experimental stopbands derived from different rotating speeds.  
**Author Contributions: [PERSON] developed the original concept and wrote the paper. [PERSON] performed the experiments. [PERSON] and [PERSON] conducted experimental data analysis. [PERSON] verified the results.**  
**Acknowledgments: This research was supported by National Natural Science Foundation of China with grant No. 51505430, Public Project of Zhejiang Province with grant No. LGG18E050021, NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization with grand No. U1709210, International S&T Cooperation Program of China with grant No. 2015 DFA71400.**  
**Conflicts of Interest: The authors declare no conflict of interest.**
# QiOi: Performance Isolation for Hyperledger Fabric
## 5 Evaluation
### Support for Different Ordering Services  
First, we evaluate QiOi when the Fabric network runs different types of ordering services. Fabric offers three ordering services, i.e., Solo, Raft, and Kafka, and users can select one of the ordering services depending on their purposes. Therefore, it is important to support the different ordering services in providing performance isolation. For experiments, we run Raft and Kafka, respectively, for the ordering services, as they are considered representative ordering services in Fabric. We run the smallbank chaincode of Caliper and measure the performance and CPU utilization of Fabric and sysbench containers, respectively, when the Fabric components run with the sysbench containers. Note that the number of sysbench containers is 16, and the Kafka-based ordering service consists of three orderers, four Kafka brokers, and three ZooKeepers.  
Figure 8 illustrates the impact of QiOi on the Raft-based Fabric network. In the x-axis, _w/o QiOi_ indicates before applying QiOi, and _w/ QiOi_ indicates after applying QiOi. Figure 7(a) shows that Fabric throughput (left y-axis as bars) increases by 22% while latency (right y-axis as a solid line) decreases by 2.5 times when we apply QiOi. This indicates that QiOi can mitigate performance degradation of Fabric running with sysbench containers. In addition, Figure 7(b) depicts that the average CPU utilization of Fabric (left y-axis as bars) increases by 24% with QiOi. This is because QiOi controls the _CPU share_ of orderers and peers when Fabric performance decreases because of the sysbench containers. Although QiOi improves Fabric performance by increasing the CPU utilization of Fabric components, the average throughput of sysbench containers (right y-axis as a solid line) only decreases by 2%, which means it is negligible. Note that the average CPU utilization of sysbench containers (left y-axis as bars) increases by 11%.  
Figure 9 shows that QiOi is also effective for a Kafka-based Fabric network. Similar to the result of a Raft-based Fabric network, Fabric throughput increases by 15%, and latency decreases by 37% when we apply QiOi as presented in Figure 8(a). In addition, Figure 8(b) illustrates that performance degradation of sysbench containers is negligible in Kafka as well (decreasing by 2%) and CPU utilization of Fabric and sysbench containers increases by 9% and 6%, respectively. Although Raft and Kafka have some differences in architecture and mechanism, we show that QiOi can offer performance isolation for both of ordering services. This is because QiOi adjusts the _CPU share_ of orderers and peers independent of  
Figure 8: Impact of QiOi on Raft ordering service-based Fabric network.  
the type of ordering services. Note that from the experiments in Section 5.2, we focus on the Raft-based ordering service because it is highly recommended by Hyperledger and known for easier management than Kafka [39].
# A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms
## 2 Methods
### Problem Statement and Proposed Solution Framework
#### 2.1.1 Source Modeling  
In the first stage of the soundscaping framework, the source modeling stage, we need to infer model parameters for the constituent sources in the observed mixture. These constituent sources comprise the background chatter and the speech signal in the conversation. Before the source modeling stage can commence, the user has to record a fragment of both sounds individually. Both speech and chatter fragments are required to last approximately three seconds. This is short enough to impose little burden on the end user, while long enough to obtain relevant information about the acoustic signal. Alternatively, models for common complex acoustic signals, such as speech, can be estimated beforehand on some data sets. In this way, only a fragment of the noise has to be recorded, easing the burden on the user. For each source signal, the model parameters are then estimated through probabilistic inference, based on the recorded fragment. Figure 1 gives an overview of the source modeling stage.  
Nowadays, commercial hearing aids (and other audio devices, such as headphones) come with an accompanying smartphone app to control the settings of the device. From a user experience perspective, we envision that the user has access to a user-friendly app on their mobile device. Here the user can intuitively record sounds for the individual sound models and can enable pretrained models for common sounds like speech through sliders and switches in the app. For creating these recordings, the users can use their mobile phone or a directional microphone for an improved selectivity.  
The inference task corresponding to the source modeling stage for a single source involves calculating the posterior probability of the parameters \(\theta^{k}\) given a recorded fragment \(\hat{s}^{k}\) as input. The source modeling task on the generative model (1) is therefore given by  
\[p(\theta^{k}\mid\hat{s}^{k})\propto p(\theta^{k})\int p(z_{0}^{k})\prod_{n=1}^ {N}p(s_{n}^{k}=s_{n}^{k},\ z_{n}^{k}\mid z_{n-1}^{k},\ \theta^{k})\ \text{d}z^{k}. \tag{2}\]This expression is obtained by applying [PERSON] theorem and by marginalizing over the distributions of all nuisance variables. We assume that \(\hat{s}^{k}\) is directly and solely observed, resulting in the simplified source mixing model corresponding to \(p(x_{n}|s_{n})=\delta(x_{n}-s_{n})\), where \(\delta(\cdot)\) denotes the Dirac delta function. Note that (2) is in principle computable since the individual factors (\(p(\theta^{k})\), \(p(z_{0}^{k})\) and \(p(s_{n}^{k},\ z_{n}^{k}\ |\ z_{n-1}^{k},\ \theta^{k})\)) are readily specified in the generative model (1). The calculation of this equation can be performed using message passing as will be discussed in Section 2.3. The posterior probability of the parameters \(\theta^{k}\) from the first stage is consecutively used as the prior distribution of the parameters \(p(\theta^{k})\) during the second stage as \(p(\theta^{k}\ |\ \hat{s}^{k})\).
## 3 Overview of Multihop CA-IoT Architectures and Testbeds  
Agri-IoT technology can automate various farm management processes, such as precision irrigation, chemical application, and remote disease management [4]. This technology ensures remote monitoring, planning, and control of farm processes through battery-powered SNs equipped with sensing (e.g., DHT22 and STEMMA soil moisture sensors), processing (e.g., Raspberry Pi and Arduino Uno), and communication capabilities (e.g., LoRa, ZigBee, BLE, Wi-Fi, NB-IoT, SigFox) [39; 41; 42]. Since these networks are compelled to drive on battery power and are constrained by distance-limited communication technologies, the embedded supervisory routing protocol must adopt a network architecture that supports efficient power optimization, simplified network management, fault tolerance, and self-adaptability [1; 3; 4]. Within this research area, the MCA-IoT approach has emerged as the most suitable candidate to harness these advantages when implemented in custom-built testbeds [9; 10]. In a typical CA-IoT-based network, such as that shown in Figure 6, SNs are grouped into either static or dynamic clusters, each with an optimally selected cluster head (CH) to minimize intra-cluster communication distances among member nodes (MNs). An MN samples the soil moisture/livestock data and transmits it to its CH. A CH aggregates the received readings from its MNs, executes error and data redundancy control checks, and communicates directly or via a relay CH (RCH) to a BS/gateway to increase the event data quality and network lifespan. This approach enhances data quality and network longevity
# Adaptive Noise Cancellation Based on Time Delay Estimation for Low Frequency Communication
## 2 Adaptive Noise Cancellation Based on Time Delay Estimation
### Maximum Correntropy Criterion  
The correntropy, measuring the similarity between two arbitrary scalar random variables \(X\) and \(Y\), is defined by:  
\[V_{\sigma}\left(X,Y\right)=E\left[k_{\sigma}\left(X-Y\right)\right] \tag{7}\]where \(k_{\sigma}\) is a shift-invariant Mercer kernel with kernel size \(\sigma\), and \(E\left[\cdot\right]\) is the mathematic expectation. Using the most popular Gaussian kernel \(G_{\sigma}\) and substituting \(E\left[\cdot\right]\) by the most recent \(L\) samples estimator, the correntropy estimator becomes:  
\[\hat{V}_{\sigma}\left(X-Y\right) = \frac{1}{L}\sum\limits_{i=k-L+1}^{k}G_{\sigma}(x(i)-y\left(i \right)) \tag{8}\] \[= \frac{1}{L}\sum\limits_{i=k-L+1}^{k}\frac{1}{\sqrt{2\pi}\sigma} \exp\left(-\frac{\left(x\left(i\right)-y\left(i\right)\right)^{2}}{2\sigma^{2 }}\right)\]  
In the adaptive filter, the correntropy estimator between the desired signal \(d_{i}\) and the filter output \(y_{i}\) is used as the cost function, then we get the cost function at \(k\) moment:  
\[J\left(k\right)=\frac{1}{\sqrt{2\pi}\sigma L}\sum\limits_{i=k-L+1}^{k}\exp \left(-\frac{\left(d\left(i\right)-y\left(i\right)\right)^{2}}{2\sigma^{2}}\right) \tag{9}\]  
The cost function \(J\left(k\right)\) is maximized using the stochastic gradient and the filter coefficient vector \(\mathbf{W}\) is updated online:  
\[\mathbf{W}(k+1)=\mathbf{W}(k)+\mu\
abla J\left(k\right) \tag{10}\]  
\(J\left(k\right)\) is substituted in (9), then we obtain:  
\[\mathbf{W}(k+1)=\mathbf{W}(k)+\frac{\mu}{\sqrt{2\pi}\sigma^{3}L}\sum\limits_{i =k-L+1}^{k}\left[\exp\left(\frac{-e(i)^{2}}{2\sigma^{2}}\right)e\left(i \right)\mathbf{x}\left(i\right)\right] \tag{11}\]  
where \(e\left(i\right)=d\left(i\right)-\mathbf{W}(k)^{T}\mathbf{x}\left(i\right)\) and \(\mu\) is a positive scalar. As shown in (11), when the errors increase, the weighting term decreases exponentially, and shows robust in impulsive noise environments.  
When MEE criterion is used to train the adaptive filter, the filter coefficient vector \(\mathbf{W_{MEE}}\) is updated online [21]:  
\[\mathbf{W_{MEE}}(k+1)=\mathbf{W_{MEE}}(k)+\frac{\mu}{\sqrt{2\pi}\sigma^{3}L^{ 2}}\sum\limits_{n=k-L+1}^{k}\sum\limits_{i=k-L+1}^{k}\left[\exp\left(\frac{- \left(e(n)-x(i)\right)^{2}}{2\sigma^{2}}\right)\left(e\left(n\right)-e\left(i \right)\right)\left(\mathbf{x}\left(n\right)-\mathbf{x}\left(i\right)\right)\right] \tag{12}\]  
For the most recent L samples, the computational complexity of MCC algorithm is \(O\left(L\right)\). The computation of entropy for L samples involves a double sum over all the samples and the computational complexity of MEE algorithm is \(O\left(L^{2}\right)\)[22]. Therefore, the computational complexity of MEE algorithm is much higher than that of MCC algorithm and MEE algorithm is unsuitable for real-time implementation.
## 2 Materials and Methods
### Confocal Raman Microspectroscopy (CRM)  
Confocal Raman Microspectroscopy (CRM) spectra were measured in depth-profiling mode using a Renishaw inVia Raman confocal microscope equipped with a Leica DM2/700 optical microscope and a 532 nm excitation source. Three depth profiles along the sample \(Z\)-axis for samples were measured by displacing the motorized microscope stage vertically relative to the laser focus with a step of 1-2 \(\upmu\)m. The measurements were performed in an extended spectral range of 100-3200 cm\({}^{-1}\) using a grating of 1800 L/mm and a thermoelectrically cooled CCD detector (spectral range 400-1060 nm) with a spectral resolution of 1 cm\({}^{-1}\) per CCD pixel (functional resolution of 3 cm\({}^{-1}\)). The laser power on the sample was 16 mW, with typical 10 s integration times and 1 accumulation. The data spectra were collected with a 100x long-distance objective (HC PL FLUOTAR NA = 0.75; theoretical spot sizes\({}_{532}\) = 0.43 \(\upmu\)m, FWD = 4.7 mm) and processed with Wire5.5 and OriginPro8.5 software. The measurements were performed following the OCT profiles.  
The depth-profile data were obtained by plotting the high intensity of selected bands from the polymers studied as a function of depth and calculating the full width at half-maximum (FWHM)-nominal depth (\(\Delta\))-after Gaussian fitting using OriginPro 2018 (OriginLab, Northampton, MA, USA) software. We calculated the corrections of nominal depths with Matlab R2021a (MathWorks, Natick, MA, USA) software.
## 3 Results and Discussion
### Effect of Fruit- and Tree-Based Flours on the Textural Properties of Wheat Bread
#### 3.2.2 Adhesiveness  
Adhesiveness refers to the tendency of the crumb to adhere to surfaces, primarily affecting the mouthfeel and sensory comfort during consumption. In this study, the control wheat bread exhibited an adhesiveness value of 0.469. The lowest values were recorded for breads enriched with 15% apple flour (-1.462 N) and 10% sweet chestnut flour (-1.308 N), indicating reduced stickiness and enhanced eating comfort. This reduction may be attributed to the presence of soluble fibres and sugars, which help stabilise the crumb structure and limit its stickiness.  
Similar effects were reported by [PERSON] et al. [42] in the context of cookies, where the addition of chestnut flour reduced adhesiveness due to its hydrophilic nature and moisture-retention capacity. Although this study did not concern bread, the mechanical principle may partially apply to the crumb behaviour observed in our experiment.  
In gluten-free breads, [PERSON] et al. [3] found that the inclusion of acorn flour led to lower adhesiveness and cohesiveness compared to standard GFB loaves. This was explained by the reduced starch content and lower water-binding capacity, which resulted in less sticky crumb textures. These findings suggest that acorn flour may similarly contribute to reduced adhesiveness in wheat bread formulations.  
Additionally, [PERSON] et al. [43] observed that the chewiness of gluten-free bread crumb increased with higher levels of chestnut flour from 5% to 20%. While adhesiveness was not directly measured, the rise in chewiness indicates changes in the crumb structure that may also affect the perceived stickiness during eating.
# Regression of Likelihood Probability for Time-Varying MIMO Systems with One-Bit ADCs
## 1 Introduction
### Related Works  
In this subsection, the proposed method is compared with the state-of-the-art methods in terms of scalability and complexity for time-varying channels.  
* The proposed method learns the likelihood probability in time-varying channels. The original approach of learning likelihood probability, initially introduced in [37], efficiently exploited input-output samples from the data detector using reinforcement learning. However, the approach in [37] was designed for time-invariant channels and faces limitations in scalability when applied to time-varying channels. To address this limitation, a channel-based likelihood probability is derived following the methodology outlined in [38]. Additionally, unlike [37], this study introduces a sample-based likelihood leveraging the decoder's output to enhance scalability further.
* The proposed method reduces detection complexity, making it more applicable to practical systems. While a learning method that updates likelihood probability was proposed in [38] based on the on the time-varying channel model, the complexity remains a significant barrier to practical implementation. This study proposes a reduced-complexity algorithm to efficiently update the likelihood probability in time-varying channels. To achieve this, an offline learning method inspired by [39] is developed for time-varying channels. Especially, a regression-based approach is proposed to efficiently integrate channel- and sample-based likelihood probabilities.