esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel ICT Framework for Sustainable Development Goals
## 2 Background of the Problem and Our Contribution
### Brief Insights from the SDGs-Related Literature
Strictly speaking, the ICT framework we propose here endorses several novel features (see Figure 2) by aiming at the following:  
1. to combine three disciplines: governance science, sustainability science, and data science, all wrapped together with the concept of digital democracy, and to act as an interactive process reflecting the interactive character of governance;
2. to increase awareness and understanding of SDGs, through which the societies are steered towards collectively agreed objectives, leading to governance of SDGs on both local and global levels;
3. to integrate data from several sources at different spatial scales (local, region, state, national, and international), temporal scales (daily, monthly, annual) and decision making scales (individual, group, institution), to infer knowledge and to transform data into understandable knowledge;
4. to emphasize various governance theories (adaptive governance, network governance, collaborative governance, and accountable governance) and suggest two novel: fair and trustworthy governance, and to ensure actions across many levels and involving actors from multiple sectors;
5. to allow two-way communications between government and people and effective citizen engagement in addressing SDGs; and
6. to provide means for ensuring true impacts on SDGs by motivating citizens to participate in addressing SDGs and by enabling those in positions of power to endorse the framework. The term 'infer knowledge' (or more general term knowledge discovery) is used here narrowly and refers to knowledge extraction from structured and unstructured sources. For the epistemological questions related to the creation and dissemination of knowledge in particular areas of inquiry, we refer the reader to Stanford Encyclopedia of Philosophy (2019). In information science, relationships among data, information, knowledge, and sometimes wisdom are represented hierarchically (2019). However, some scholars have questioned this formulation of the knowledge hierarchy by arguing that wisdom includes also values, ethics, and effectiveness (2019; 2019).
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 1 Introduction
### Problem Motivation
#### 1.2.5 Insufficient Automation and Real-Time Monitoring  
Without the integration of real-time monitoring tools, key logistical and environmental parameters such as temperature, humidity, location, and transit times are often delayed or not tracked at all [10]. This is particularly critical during the storage and transport phases where poor handling conditions can degrade bean quality. For instance, prolonged exposure to high humidity during shipping can cause mold growth; however, without sensors or alerts such conditions may go unnoticed. Furthermore, the absence of automated event recording means that stakeholders cannot intervene proactively, leading to both quality losses and accountability gaps.
# DB-Net: Detecting Vehicle Smoke with Deep Block Networks
## 5 Experiments
### Implementation Details and Evaluation Metrics  
Implementation details. All the experiments were performed using the Pytorch toolbox ([[https://pytorch.org/](https://pytorch.org/)]([https://pytorch.org/](https://pytorch.org/)), access on 1 July 2022 ) on a Linux server with two GeForce RTX 3090 GPUs. Runtime evaluation was performed on a single GeForce RTX 3090 GPU. We report an average of 100 frames for the frames per second (FPS) measurement. In the training process, we first resized images into 960 \(\times\) 540 and randomly cropped the image into 896 \(\times\) 448 size for data augmentation. Moreover, we used stochastic gradient descent (SGD) with batch size 16, momentum 0.9, weight decay 5 \(\times\) 10\({}^{-4}\), and the initial learning rate was set to 1 \(\times\) 10\({}^{-3}\) in training. The total training epochs number was 50. The frames and images were resized to 960 \(\times\) 540 resolution in all experiments.  
Evaluation Metrics. The evaluation experiments were conducted on LaSSoV-video and PoVSSeg test sets, which are a video dataset and an image dataset, respectively. In the image dataset, the performance was measured using mean Intersection-over-Union (mIoU), which is:  
\[mIoU=\frac{1}{N}\sum_{i=1}^{N}\frac{P_{i}\cap G_{i}}{P_{i}\cup G_{i}}, \tag{6}\]  
where \(N\) is the total number of samples, and \(P\) and \(G\) represent prediction and ground truth, respectively. A greater mIoU value indicates a higher level of block recognition accuracy on the image for the model. On the LaSSoV-video dataset, the recall and precision are used to evaluate the method on frame level, which is formulated as:  
\[Precision=\frac{TP}{TP+FP},\quad Recall=\frac{TP}{TP+FN}, \tag{7}\]  
where \(TP\), \(FP\), and \(FN\) denote true-positive, false-positive, and false-negative, respectively. Based on the _Recall_ and _Precision_, the \(F1\) score is also an important evaluation metric for method comparison, which is formulated as follows:  
\[F1=\frac{2\times Precision\times Recall}{Precision+Recall}. \tag{8}\]
## 5 Results and Discussion
### The Comparative Performance of PRDFS and BRDFS in Detecting Malicious Nodes
#### 5.4.1 Major Findings  
* PRDFS outperforms BRDFS in terms of demand forecasting accuracy by 15% or more (Figure 13).
## 2 Full-Band Modeling  
Spectrum decomposition splits the spectrum of musical instrument sounds into a sinusoidal component and a residual as illustrated in Figure 1a. Spectrum decomposition assumes that there are partials only up to a certain cutoff frequency \(f_{c}\), above which there is only noise. Figure 1a represents the spectral peaks as spikes on top of colored noise (wide light grey frequency bands) and \(f_{c}\) as the separation between the sinusoidal and residual components. Therefore, \(f_{c}\) determines the number of sinusoids because only the peaks at the lower frequency end of the spectrum are represented with sinusoids (narrow dark grey bars) and the rest is considered wide-band and stochastic noise existing across the whole range of the spectrum. There is noise between the spectral peaks and at the higher end of the spectrum. In a previous study [17], we showed that the residual from the SM is perceptually different from filtered (colored) white noise. Figure 1a shows that there are spectral peaks left in the residual because the spectral peaks above \(f_{c}\) are buried under the estimation noise floor (and sidelobes). Consequently, the residual from sinusoidal models that rely on spectral decomposition such as the SM is perceptually different from filtered white noise.  
From an acoustic point of view, the physical behavior of musical instruments can be modeled as the interaction between an excitation and a resonator (the body of the instrument) [16]. This excitation is responsible for the oscillatory modes whose amplitudes are shaped by the frequency response of the resonator. The excitation signal commonly contains discontinuities, resulting in wide-band spectra. For instance, the vibration of the need in woodwinds can be approximated by a square wave [49], the friction between the bow and the strings results in an excitation similar to a sawtooth wave [16], the strike in percussion instruments can be approximated by a pulse [2], while the vibration of the lips in brass instruments results in a sequence of pulses [50] (somewhat similar to the glottal excitation, which is also wide band [46]).  
Figure 1b illustrates a full-band harmonic template spanning the entire frequency range, fitting sinusoids to spectral peaks in the vicinity of harmonics of the fundamental frequency \(f_{0}\). The spectrum of musical instruments is known to present deviations from perfect harmonicity [16], but quasi-harmonicity is supported by previous studies [51] that found deviations as small as 1%. In this work, the full-band harmonic template becomes quasi-harmonic after the estimation of parameters via least-squares followed by a frequency correction mechanism (see details in Section 3.1). Therefore, full-band spectral modeling assumes that both the excitation and the instrumental noise are wide band.  
Figure 1: Illustration of the spectral decomposition and full-band modeling paradigms.
# In Situ Transformation of Electrospun Nanofibers into Nanofiber-Reinforced Hydrogels
## 3 Results
### Mechanical Characterization  
The results of the mechanical characterization are summarized in Table 2. Tensile tests were only conducted on the dry samples due to the large swelling of the samples in water. The coaxially electrospun scaffolds zeinPEO-GPP and zeinPEOT-GPP were slightly more elastic, i.e., showed lower Young's moduli, and demonstrated lower strengths than the uniaxially electrospun samples G and GPP. The elongation at break was comparable for all tested samples and between 2.5% and 5.2%.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Young’s** & **Tensile** & **Elongation** & **Vapor** & **Hardness, N** & **Cohesiveness, \%** & **Springiness, \%** \\  & **Modulus, kPa** & **Strength, kPa** & **at Break, \%** & **Sorption, \%** & **Hardness, N** & **Cohesiveness, \%** & **Springiness, \%** \\ \hline G & 18.9 \(\pm\) 13.5 & 490.3 \(\pm\) 286.8 & 4.2 \(\pm\) 0.2 & 48.4 & - & - & - \\ \hline GPP & 31.6 \(\pm\) 1.2 & 527.7 \(\pm\) 182.2 & 2.6 \(\pm\) 0.4 & 45.4 & - & - & - \\ \hline zeinPEO-GPP & 4.8 \(\pm\) 4.9 & 131.1 \(\pm\) 126.6 & 5.2 \(\pm\) 2.2 & 33.3 & 0.65 \(\pm\) 0.17 & 65.0 \(\pm\) 8.6 & 99.9 \(\pm\) 1.3 \\ \hline zeinPEOT-GPP & 4.9 \(\pm\) 3.0 & 97.5\(\pm\) 63.9 & 3.2 \(\pm\) 0.4 & 33.9 & 0.46 \(\pm\) 0.17 & 55.6 \(\pm\) 4.3 & 100.3 \(\pm\) 1.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tensile properties, compression behavior and water vapor sorption capacity of the dry nanofiber scaffolds.  
Figure 3: Interactions of nanofiber scaffolds with water. (**a**) Water contact angle (wettability) and (**b**) water uptake, volume change and mass loss after 24 h of incubation of the nanofiber scaffolds in Milli-Q water and their transformation into hydrogels. The data are presented as means with standard deviations of \(p<0.0002\) (***). Key: G, sample containing gelatin; GPP, sample containing gelatin crosslinked by polydopamine and polyethyleneimine; zeinPEO-GPP, sample containing zein and polyethylene oxide in the core and gelatin crosslinked by polydopamine and polyethyleneimine in the shell; zeinPEOT-GPP, sample containing zein, polyethylene oxide and tetracycline hydrochloride in the core and gelatin crosslinked by polydopamine and polyethyleneimine in the shell.  
Texture profile analysis was carried out to determine the compression behavior of the samples, including hardness, springiness and cohesiveness (Table 2). Analysis was only possible for the coaxially electrospun samples zeinPEO-GPP and zeinPEOT-GPP as the uniaxially spun samples G and GPP were too fragile. ZeinPEO-GPP and zeinPEOT-GPP showed good compressibility and ability to spring back to their original height after the first compression (springiness \(\sim\)100%), while only \(\sim\)60% was reached after the second compression (cohesiveness). The hardness was found to be similar for both the drug-loaded and the drug-free samples.
## 3 Segmentation of Arbitrary Signal through a Gaussian Mixture Model  
Notwithstanding the fact that often signal is not Gaussian, cf. the illustration of Figure 1, we can fit observational data-density \(f\) with support in \(m\)-dimensional real space, \(\mathbb{R}^{m}\) - by Gaussians. Consider the case of heavy tailed distributions.  
Heavy tailed probability distributions, examples of which include long memory or \(1/f\) processes (appropriate for financial time series, telecommunications traffic flows, etc.) can be modeled as a generalized Gaussian distribution (GGD, also known as power exponential, \(\alpha\)-Gaussian distribution, or generalized Laplacian distribution):  
\[f(x)=\frac{\beta}{2\alpha\Gamma(1/\beta)}\exp-(\mid x\mid/\alpha)^{\beta}\]  
where  
- scale parameter, \(\alpha\), represents the standard deviation,  
- the gamma function, \(\Gamma(a)=\int_{0}^{\infty}x^{a-1}e^{-x}dx\), and  
- shape parameter, \(\beta\), is the rate of exponential decay, \(\beta>0\).  
A value of \(\beta=2\) gives us a Gaussian distribution. A value of \(\beta=1\) gives a double exponential or Laplace distribution. For \(0<\beta<2\), the distribution is heavy tailed. For \(\beta>2\), the distribution is light tailed.  
Heavy tailed noise can be modeled by a Gaussian mixture model with enough terms [9]. Similarly, in speech and audio processing, low-probability and large-valued noise events can be modeled as Gaussian components in the tail of the distribution. A fit of this fat tail distribution by a Gaussian mixture model is commonly carried out [10]. As in [PERSON] and [PERSON] [10], one can allow Gaussian component PDFs to recombine to provide the clusters which are sought. These authors also found that using priors with heavy tails, rather than using standard Gaussian priors, gave more robust results. But the benefit appears to be very small.  
Gaussian mixture modeling of heavy tailed noise distributions, e.g. genuine signal and flicker or pink noise constituting a heavy tail in the density, is therefore feasible. A solution is provided by a weighted sum of Gaussian densities often with decreasing weights corresponding to increasing variances. Mixing proportions for small (tight) variance components are large (e.g., 0.15 to 0.3) whereas very large variance components have small mixing proportions.  
Figures 2 and 3 illustrate long-tailed behavior and show how marginal density Gaussian model fitting works in practice. The ordinates give frequencies. See further discussion in [11; 12].
# Interactive Part Segmentation Using Edge Images
## 6 Conclusions  
We have shown how to improve the IOU accuracy of part segmentation using a proposed model trained with edge images and user interaction. Through the experiment, we found edge images generated by edge detectors suitable for part segmentation. In addition, the proposed model trained with edge images has higher IOU accuracy of part segmentation compared to the existing segmentation model trained with color images. In future research, we plan to study a deep-learning model suitable for edge images so that object or part segmentation can be performed more accurately. We hope that our research will be utilized in various fields as well as in the interactive segmentation model.  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/app112110106/s1](https://www.mdpi.com/article/10.3390/app112110106/s1)]([https://www.mdpi.com/article/10.3390/app112110106/s1](https://www.mdpi.com/article/10.3390/app112110106/s1)): the proposed model 1 detail.xlsx; the details of proposed model 1; the proposed model 2 detail.xlsx; and the details of proposed model 2.  
**Author Contributions:** Conceptualization, J.-Y.O.; methodology, J.-Y.O.; software, J.-Y.O.; validation, J.-Y.O.; writing--original draft preparation, J.-Y.O.; writing--review and editing, J.-M.P.; supervision, J.-M.P.; project administration, J.-M.P.; funding acquisition, J.-M.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Korea Institute of Science and Technology (KIST) Institutional Program under Project 2E31081.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class [Part]** & **Train [Left Side]** & **Car [Front Side]** \\ \hline
**Model** & **IOG** & **Proposed Model 1** & **IOG** & **Proposed Model 1** \\ \hline Input & & & \\ Image & & & \\ Ground & & & \\ Truth & & & \\ Mask and & & & \\ Interaction & & & \\ Estimated & Mask & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: The estimated masks of parts from the IOG model and proposed model 1.
# Application of a Novel High-Order WENO Scheme in LES Simulations
## 3 Application Cases
### Turbulent Boundary Layer Simulation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \(T_{\infty}\)/K & \(T_{\infty}\)/K & \(M_{int}\) & \(\theta\)/mm & \(Re_{\theta}\) & \(\delta\)/mm & \(\delta^{*}\)/mm & \(C_{f}\) \\ \hline [PERSON] et al. [50] & 108.1 & 307 & 2.9 & 0.43 & 2400 & 6.7 & 2.36 & 0.00225 \\ \hline [PERSON] et al. [51] & 108.1 & 307 & 2.9 & 0.38 & 2300 & 6.4 & 1.8 & 0.00217 \\ \hline [PERSON] et al. [56] & 108.1 & 307 & 2.9 & 0.41 & 2300 & 6.5 & 2.06 & 0.00256 \\ \hline [PERSON] \& [PERSON] [53] & 108.1 & 307 & 2.9 & 0.50 & 2400 & 6.7 & 2.85 & 0.00203 \\ \hline Present LES & 108.1 & 307 & 2.9 & 0.47 & 2400 & 6.5 & 2.37 & 0.00209 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of key parameters of the TBL on the inflow plane. \begin{table}
\begin{tabular}{c c c c} \hline  & **Calculation Domain Size** & **Grid Resolution** & **Cells** \\  & \(L_{x}\times L_{y}\times L_{z}l\delta^{3}\) & \(\Delta x^{+}\times\Delta y^{+}\times\Delta z^{+}\) & \(N_{x}\times N_{y}\times N_{z}\) \\ \hline DNS [55] & 8.3 \(\times\) 2.0 \(\times\) 8.2 & 7.5 \(\times\) 4.3 \(\times\) 0.2 & 410 \(\times\) 160 \(\times\) 112 \\ Present LES & 8.62 \(\times\) 2.15 \(\times\) 5.23 & 8.2 \(\times\) 5.3 \(\times\) 0.46 & 311 \(\times\) 120 \(\times\) 112 \\ \hline \end{tabular}
\end{table} 
Table 3: Grid details and comparison. Figure 12: Mean velocity profiles obtained via Van Driest transformed. Figure 13: Reynolds stress profiles at the recycling plane [53].
## 3 Results
### CAN Peripheral Interrupt Synchronization  
Tests on residual PWM delays inserted by preliminary synchronization using CAN peripheral interrupt are shown in Figure 6. Tests were performed between pairs of units, whereas one unit was sending the start-up message and both were trying to synchronize their PWM generators based upon the arrival of the message in the CAN buffer. It can be seen that the standard deviations of the three relative delays change monotonically with an increase of TQ.  
The monotonical increasing trend confirms that, with larger TQ, the CAN peripheral can make larger bit-time jumps to synchronize units, resulting in a less precise overall synchronization.  
Different bit rates can be obtained using the same TQ by modifying the phase segments length during CAN initialization. The results in Table 3 show that increasing the bit rate with the same TQ does not result in a better synchronization, so it is clear that synchronization precision depends more on TQ than on the CAN bit rate.
## 4 Proposed Routing Protocol
### Network Assumptions  
1. Node Types and Roles: The underwater subnet includes source nodes, relay nodes, and surface buoy (sink) nodes. The source nodes are fixed, energy-rich, and computation-capable, and they collect environmental data. Relay nodes, cost-effective and energy-limited, forward data to sink nodes. Surface buoys with abundant energy serve as gateways to external networks. Energy heterogeneity exists: source and sink nodes are energy-rich, and relay nodes are energy-constrained.
2. Hardware Capabilities: All nodes feature power control modules and depth sensors. They estimated the path loss via the Received Signal Strength Indicator (RSSI, which measures the power level of a received signal) and adjusted the transmission power accordingly. Depth information aids routing decisions.
3. Communication Model: Relay nodes share uniform transmission power and range. Signal fading is symmetric in the uplink and downlink, allowing link quality estimation via the RSSI. The nodes support full-duplex communication.
4. Network Topology: Relay nodes are randomly deployed, forming a dynamic layered topology. The network was moderately scaled with sufficient density to maintain connectivity. Data flow hierarchically from the source to the sink.
5. Node Behavior: Nodes stop functioning when their energy is exhausted. To reduce the packet loss resulting from node failures or routing voids, each node maintains a dynamic neighbor table that is continuously updated based on recent communication activity, thereby avoiding the selection of unresponsive neighbors as forwarders.
## 2 System Model  
### System Architecture  
The architecture of a UiL content delivery system is illustrated in Figure 1. The system includes three types of entities: central server, BSs, and users. Each user is associated with one BS, while each BS is connected to the central server, which stores a total of \(F\) content files. It is assumed that each BS, once allocated with a fixed amount of radio resources, operates independently in performing the task of content delivery, so that our study can focus on a single BS. The number of users in the considered BS is denoted by \(U\).  
Using existing recommendation algorithms [21; 22; 23; 24], the central server tracks the interest profile of all users in the BS and outputs a \(U\times F\) user interest matrix \(\mathbf{R}\). The elements of \(\mathbf{R}\) is denoted by \(r_{uf}\), which indicates the interest of the \(u\)-th user in the \(f\)-th file. Here, \(u(u=1,2,\ldots,U)\) and \(f(f=1,2,\ldots,F)\) are the indexes for users and content files, respectively. We use \(L_{f}\) to denote the size  
Figure 1: Interactive content retrieval protocol for personalized multi-user content retrieval.  
of the \(f\)-th content file. The file size varies across different content. For simplicity, it is assumed that the user interest profile is independent from the file size. The UiL content delivery system aims to maximize the sum interests of all contents recommended to all users in the cell, under the condition that the recommended contents can be delivered to the user in time with a predefined probability.