esa-sceva/satcom-chunk-collection huggingface dataset:
# Big-Delay Estimation for Speech Separation in Assisted Living Environments
## 4 TF Masking
### Elevato-Dulet
It is in the range \(-\pi\leq\Omega\leq+\pi\). This procedure is iterated over all the available parameter pairs, \((\alpha_{j},\hat{\delta}_{j})\), to compute the TF mask,  
\[\mathbf{M}[k,\tau]= \begin{cases}1,&\text{if }\mathbf{J}[k,\tau]=\mathbf{S}_{j} \\ 0,&\text{otherwise},\end{cases}  \tag{38}\]  
where \(\mathbf{M}[k,\tau]\in\{1,0\}\in\mathbb{R}_{+}\). Sources are demixed by combining the mask and the MLE estimate using  
\[\hat{\mathbf{S}}_{j}[k,\tau]=\mathbf{M}[k,\tau]\left(\frac{\mathbf{X}_{1}[k, \tau]+\hat{\alpha}_{j}e^{-j\hat{\delta}_{j}\omega}\mathbf{X}_{2}[k,\tau]}{1+ \hat{\alpha}_{j}^{2}}\right), \tag{39}\]  
where \(\hat{\mathbf{S}}_{j}[k,\tau]\) is the recovered target speech utterance. This illustrates that Elevato-DUET can demix the target speaker experiencing a large relative delay. The quality of any TF-mask, evaluated using the metric Preserved-Signal Ratio (PSR), is computed using  
\[\text{PSR}:=\frac{\left|\left|\mathbf{M}[k,\tau]\mathbf{S}_{j}[k,\tau]\right| \right|^{2}}{\left|\left|\mathbf{S}_{j}[k,\tau]\right|\right|^{2}}, \tag{40}\]  
and is defined as a measure of how well it preserves the energy of, \(s_{j}[n]\). In addition, the Source-to-Interference Ratio (SIR) is given as \[\text{SIR}=\frac{\left\|\mathbf{M}[k,\tau]\mathbf{S}_{j}[k,\tau]\right\|^{2}}{ \left\|\sum_{p=1}^{J}\mathbf{S}_{p}[k,\tau]\right\|^{2}},\text{ if }p\
eq j \tag{41}\]  
where \(\sum_{p=1}^{J}\mathbf{S}_{p}[k,\tau]\) is the sum of the interference. The WDO is calculated as  
\[WDO=\text{PSR}-\frac{\text{PSR}}{\text{SIR}}. \tag{42}\]  
WDO has a maximum value of 1 and is in the range \(0<\text{WDO}<1\). A WDO value that is less than 0 implies that the mask rejects the target source energy and that the voice is barely intelligible. The PSR and SIR are measured in dB. In Equation (33), observe that the \(\text{WDO}\approx 1\) when the \(\text{SIR}\approx\infty\) signifying good separation performance. The mask, \(\mathbf{M}[k,\tau]\), preserves the energy of the speech of interest while suppressing the interference. A WDO of approximately 0 means that either the PSR is approximately zero or that the SIR is approximately 1. The former implies the mask rejects the spectral energy of the target speaker, and the latter means \(\left\|\mathbf{S}_{j}[k,\tau]\right\|^{2}=\left\|\sum_{p=1}^{J}\mathbf{S}_{p} [k,\tau]\right\|^{2}\), that is, the mask results in the source having equal energy as the interference. In practical demixing scenarios, the ideal WDO cannot be achieved in a strict sense. WDO is considered in an approximate sense. The greatest obtainable WDO drops to less than 1 as the number of sources in the mixture increases. Calculating the PSR of Elevato-AdRess is not as straightforward as Elevato-DUET because the mask \(\mathbf{M}[k,\tau]\) is not derived in the form of a binary matrix. Instead, it looks to extract the spectral energies that are localized on the frequency-azimuth plane in Figure 3, and then to create an augmented TF representation. Applying the Inverse TF (ITF) transform gives us the estimated utterance.
## 4 Results: Proof of Concept
### Identity Management Implementation
#### 4.1.2 Issuance of Verifiable Credentials (VCs)  
In this phase, a trusted entity, called the issuer, generates a Verifiable Credential (VC) for the Raspberry Pi that represents the IoT device (subject). The VC contains key identifying attributes such as firmware version, model, serial number, and MAC address, which are encapsulated within the credential structure. To ensure robust security, a revocation mechanism using a bitmap index is integrated to efficiently identify and revoke compromised credentials. Figure 2 illustrates the data flow in the proposed system, where IoT devices send cryptographically signed Decentralized Identifiers (DIDs) or Verifiable Credentials (VCs) to the IOTA node for validation. The node responds with validation feedback and forwards the validated data to the Tangle for decentralized storage. The data are cryptographically signed by the IoT device prior to transmission, and the Tangle ensures secure, tamper-proof, and scalable storage. The system's processes highlight the roles and interactions of each component in the setup. As shown in the figure, the SSI ecosystem includes three primary roles: issuer, holder, and verifier. The issuer is responsible for creating and issuing a Verifiable Credential to a specific subject (holder). In this implementation, a DID document (DDo) is generated for the issuer to establish a cryptographic identity that enables secure interactions with the network and the generation of VCs associated with the Raspberry Pi's identity. The DDo generated for the issuer is shown in the Listing 2. ```
1 DDOissuer:{
2\"doc\":{
3\"id\":'did:iota:9 FJX2 qCszEvXsb5 jTWWXBpD3 LtMyMH3 wGfHsaalNpc3s\",
4\"capabilityInvocation\":{
5{
6\"id\":\"did:iota:9 FJX2 qCsz2 EvXsb5 JTWWXBpD3 LtMyMH3 wGfHsaNpc3s#sign-0\",
7\"controller\":
8\"did:iota:9 FJX2 qCszEvXsb5 JTWWXBpD3 LtMyMH3 wGfHsaNpyc3s\",
9\"type\":\"Ed25519 VerificationKey2018\",
10\"publicKeyMultibase\":\"2B6 SwwUp5 UUMZJ5 PjkncRBnnb712 wGLdwjjwa1V7179\"
11
12},
13\"meta\":{
14\"created\":\"2023-03-31T11:13:02Z\",
15\"updated\":\"2023-03-31T11:13:02Z\"
16},
17\"proof\":{
18\"type\":\"JcsEd25519 Signature2020\",
19\"verificationMethod\":\"#sign-0\",
20\"signatureValue\":\"34 bvaqRabb5 XgjKL4Y12 LTZ6J8 JBEHHwgggla5 dWenSEA2 MXS8 KTYYXXXXEKFH9#Tg2 KJJ2f2 egbbw90 Gf9 aKm=
21
22} }
```  
Once the issuer's DID document (DDo) is created, it must be updated with a verification method to enable the generation of a Verifiable Credential, as shown in the Listing 3. This involves generating a new key pair and signing the updated document with the previously generated private key. This process uses the _capabilityInvocation_ relationship that authorizes updates to the document. Figure 2: Simulation diagram of the interactions between the different roles.
# Performance Model for Video Service in 5G Networks
###### Abstract  
Network slicing allows operators to sell customized slices to various tenants at different prices. To provide better-performing and cost-efficient services, network slicing is looking to intelligent resource management approaches to be aligned to users' activities per slice. In this article, we propose a radio access network (RAN) slicing design methodology for quality of service (QoS) provisioning, for differentiated services in a 5G network. A performance model is constructed for each service using machine learning (ML)-based approaches, optimized using interference coordination approaches, and used to facilitate service level agreement (SLA) mapping to the radio resource. The optimal bandwidth allocation is dynamically adjusted based on instantaneous network load conditions. We investigate the application of machine learning in solving the radio resource slicing problem and demonstrate the advantage of machine learning through extensive simulations. A case study is presented to demonstrate the effectiveness of the proposed radio resource slicing approach.  
interference coordination (IC); network slicing; 5G; quality of service (QoS); massive multiple input and multiple output (MIMO) +
Footnote †: journal:  
_Future Internet_**2020**, _12_, 99; doi:10.3390/f12060099
## 5 Ordered Pair Bat Algorithm for Optimizing the Scheduling Problem
### Velocity and Position Updating Strategy  
In the standard BA, the method of updating velocity and position based on linear function relationships is only suitable for solving continuity problems and cannot be directly used for discrete problems such as continuous casting and hot rolling in steel mills. Therefore, in this paper, a new set of discrete relations will be investigated to update the bat velocity and position.  
The improved algorithm velocity can be described as the distance difference between the current optimal bat position \(x_{*}\) and the position \(x_{i}\) of bat \(i\), and this functional relationship is constructed using the concept of the ordered pair. Ultimately, the velocity of the discrete problem is expressed as  
\[v_{i}^{t}=\text{Ceil}\Big{[}rand\times Ordered\Big{(}x_{i}^{t-1},x_{*}^{t-1} \Big{)}\Big{]} \tag{35}\]  
where \(Ordered\Big{(}x_{i}^{t-1},x_{*}^{t-1}\Big{)}\) represents the number of different ordered pairs \(k\) between \(x_{i}^{t-1}\) and \(x_{*}^{t-1}\). Then, a random number between 0 and 1 is selected by the _rand_ function and multiplied by \(k\) and rounded by the _ceil_ function. That is, the velocity \(v_{i}^{t}\) is used to represent a random integer between 1 and \(Ordered\Big{(}x_{i}^{t-1},x_{*}^{t-1}\Big{)}\), to represent the velocity of a new bat individual.  
After generating the velocity \(v_{i}^{t}\), the bat's position \(x_{i}^{t-1}\) is transformed by \(v_{i}^{t}\) times _2-opt_ transformation and the optimal value is selected to obtain \(x_{i}^{t}\). In this way, an iterative update is completed. This is expressed as Equation (36):  
\[x_{i}^{t}=\phi\Big{(}v_{i}^{t},x_{i}^{t-1}\Big{)} \tag{36}\]  
Therefore, the improved bat algorithm pseudo-code can be obtained as follows Algorithm 1:  
```
1 Define the objective function \(f(\vec{x})\);
2 Initialize the bat population \(X=x_{1}\),\(x_{2}\),...,\(x_{n}\);
3 for each bat \(x_{i}\) in the population
4 Initialize the pulse rate \(r_{i}\in[\textbf{0},\textbf{0},\textbf{0}]\), loudness \(\textbf{A}_{i}\in[\textbf{0},\textbf{7},\textbf{1}]\);
5 Initialize the bat position \(x_{i}\) by _2-opt_;
6 endfor
```  
**Algorithm 1**Pseudo-code of the ordered pair improved bat algorithm  
Figure 3: The concept of ordered pairs. (**A**) The optimum rolling sequence (**B**) A random rolling sequence.  
The flowchart of using the ordered pair bat algorithm to solve the continuous casting-hot rolling problem is shown in Figure 4.
# Short-Term Photovoltaic Power Generation Based on MVMD Feature Extraction and Informer Model
## 5 Case Study  
To verify the effectiveness of the short-term PV power generation method based on MVMD feature extraction and the Informer neural network model, this paper uses historical PV power generation and weather data from the PV station in Shengzhou, Zhejiang Province, China, as experimental data. The historical PV power and meteorological data cover the entire year from 1 January 2023, 00:00 to 31 December 2023, 23:55, with a sampling frequency of 15 min per point and power data measured in MW. The historical PV output data is provided by the State Grid Zhejiang Shengzhou Power Supply Company, and the weather data comes from the European Centre for Medium-Range Weather Forecasts (ECMWF).
## 3 Results
### Description of the Selected Soil Improvement Techniques
#### 3.2.4 Sustainable Stabilization (Chemical, Physical-Mechanical, and Biological Mechanism of Action)
Recent research promotes biological methods for stabilizing expansive soils, considering them economical, feasible, and environmentally friendly compared to traditional alternatives [54]. These approaches leverage plant extracts, organic residues, and, crucially, microorganisms to modify soil properties, for example, by decreasing the Cation Exchange Capacity (CEC) [54]. One notable technique is MICP, particularly through biostimulation, which is based on the activity of microorganisms of native soil bacteria capable of precipitating CaCO\({}_{3}\)[36]. An interesting finding is that the success of this technique in clays with variable plasticity depends on the optimization of stimulation (\"mellowing\") and curing times [36]. Bioenzyme-based soil stabilizers also promise significant improvements [55], although the exact mechanism of action remains a matter of debate among researchers [56]. It is hypothesized that enzymes are adsorbed by clays, reducing their affinity for water [55]. Microstructural evidence using \(\mu\)-CT shows reduced small pores in treatedsoils, suggesting a densification mechanism [56]. The use of natural biopolymers presents benefits. Xanthan gum, for example, has demonstrated superior mechanical results to lime, and its mechanism could involve direct bonding with clay particles [57]. Furthermore, research is exploring the potential of waste materials. Biochar derived from biomass, such as _Prosopis julifora_, has been evaluated as a modifier, showing increased shear strength and decreased free swelling [58]. Agro-biogenic waste fibers, such as wool and banana peel, improve key geotechnical parameters and reduce swelling [59]. Incorporating agro-industrial waste such as vinasse has proven viable for stabilizing cohesive soils, achieving significant increases in CBR [40].
## 5 Discussion
### VI SOS Detection and Evaluation against GPP-Sos
This shows that differences in the point to pixel scale mismatch between the two EO data sets used (500 m and 0.05\({}^{\circ}\) spatial resolution) and flux observations does not play a major role in explaining the low correlations obtained in the EO SOS evaluation. Uncertainty could also be related to that GPP SOS is a photosynthesis based estimate of phenology, whereas the vegetation indices are LAI-related [75]. For deciduous broad-leaved forests, it has been suggested that an earlier increase in the EVI signal in relation to GPP was caused by leafs burst earlier than the photosynthetic capacity [75]. For evergreen forests, needles or leaves are always present and it is instead temperature that drives the SOS of GPP [76]. Another source of uncertainty could be the GPP based estimates of SOS. Eddy covariance towers measures net ecosystem exchange of CO\({}_{2}\), a flux that is comprised of two different exchange processes: GPP and ecosystem respiration. At the beginning of the growing season, ecosystem respiration is the dominant flux process, and to disentangle the GPP signal can be difficult [77], and only when CO\({}_{2}\) uptake by the vegetation (GPP) starts to be the dominant flux process can the partitioning between the two processes be less problematic. The point in time when partitioning between GPP and ecosystem respiration can differ between years and sites, and thereby add uncertainty in the SOS estimates. A final plausible explanation could be that, as opposed to photosynthesis (GPP measured from flux towers), greenness indices are not reduced down to zero in evergreen needleleaf forest in dormant periods. Therefore, NDVI/EVI for such biomes do not provide an unambiguous estimate on when actual photosynthesis starts or ends as discussed in [43], in which used sun-induced chlorophyll fluorescence (SIF) from GOME-2 was used as a direct proxy of photosynthetic activity to monitor seasonality.
# One-Class LSTM Network for Anomalous Network Traffic Detection
## 5 Experimental Results and Discussion
### One-Class Classification on NSL-KDD
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Anomaly Class** & **OC-SVM/ SVDD** & **IF** & **KDE** & **DCAE** & **OC-NN** & **Soft-Bound** & **One-Class** & **OC-LSTM** \\ \hline DoS & 94.11 \(\pm\) 0.02 & 97.12 \(\pm\) 0.22 & 97.19 \(\pm\) 0.00 & 92.92 \(\pm\) 1.76 & 97.11 \(\pm\) 0.24 & 97.37 \(\pm\) 0.70 & 97.44 \(\pm\) 0.67 & **97.85 \(\pm\) 0.23** \\ Probe & 94.34 \(\pm\) 0.07 & **99.20 \(\pm\) 0.13** & 98.11 \(\pm\) 0.00 & 94.81 \(\pm\) 2.02 & 97.39 \(\pm\) 0.16 & 96.44 \(\pm\) 0.99 & 96.53 \(\pm\) 0.44 & 98.55 \(\pm\) 0.15 \\ R2L & 45.95 \(\pm\) 0.28 & 83.80 \(\pm\) 1.20 & 83.68 \(\pm\) 0.03 & 59.19 \(\pm\) 4.57 & 86.97 \(\pm\) 0.70 & **92.92 \(\pm\) 2.22** & 92.39 \(\pm\) 1.86 & 91.86 \(\pm\) 1.33 \\ U2R & 76.82 \(\pm\) 0.17 & 95.51 \(\pm\) 0.64 & 95.41 \(\pm\) 0.01 & 77.29 \(\pm\) 5.53 & 95.77 \(\pm\) 1.00 & 93.00 \(\pm\) 1.78 & 92.50 \(\pm\) 2.05 & **98.01 \(\pm\) 0.34** \\ Total & 83.86 \(\pm\) 0.06 & 94.59 \(\pm\) 0.25 & 94.42 \(\pm\) 0.01 & 84.46 \(\pm\) 4.91 & 94.97 \(\pm\) 0.22 & 96.17 \(\pm\) 0.71 & 96.11 \(\pm\) 0.48 & **96.86 \(\pm\) 0.58** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average AUROC values as percentages with StdDevs per method on NSL-KDD. Figure 4: The detection performance of different models on NSL-KDD for various types of anomalies.
# Fault Detection and VSC-HVDC Network Dynamics Analysis for the Faults in Its Host AC Networks
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sl. No.** & **Detection Scheme** & **Strength** & **Drawback** \\ \hline
1. & Overvoltage- or overcurrent-based & Provides backup protection, simple method &
\begin{tabular}{c} Venerable sensitivity to fault impedance, \\ network configuration, active measurement \\ \end{tabular} \\ \hline
2. & Differential protection-based & Fast tripping, high precision, and sensitivity &
\begin{tabular}{c} Synchronized communication and \\ measurements are required, \\ higher cost constraint \\ \end{tabular} \\ \hline
3. & Data-driven; pattern recognition-based & Accurate and robust, intelligent fault diagnosis and localization &
\begin{tabular}{c} Large dataset required, complex structure, \\ difficult real-time feasibility, longer time \\ due to large amount of data \\ \end{tabular} \\ \hline
4. & FT-based & Fast detection, accurate, venerable sensitivity & Dependency on choice of window length \\ \hline
5. & ST-based &
\begin{tabular}{c} Fast detection and faster \\ calculation, reliability \\ \end{tabular} & Less sensitive to fault condition \\ \hline
6. & STFT-based & Fast detection and calculation &
\begin{tabular}{c} Choice of window length, limited \\ time–frequency resolution, blurry TFRs \\ \end{tabular} \\ \hline
7. & WT-based & Fast, accurate, resistant to noise &
\begin{tabular}{c} Depending on the choice of mother \\ wavelet, blurry TFRs \\ \end{tabular} \\ \hline
8. & HHT-based &
\begin{tabular}{c} Fast and accurate, robust against \\ noisy conditions \\ \end{tabular} & Large computational burden, blurry TFRs \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different fault detection techniques.
# Smart Warehouse Management System: Architecture, Real-Time Implementation and Prototype Design
## 2 Background
### Market Analysis  
Warehouses and inventories are an integral part of the supply chain. All the trades in a supply chain are managed using a warehouse, which could be public, private, bonded, or governmental. All over the world, various multinational companies use different sorts of mechanisms in order to manage their supply chain and trades, either manually or automated. FedEx is one of the top 10 logistic companies in the world, having 127 warehouses all over North America, with an area of 35.4 million square feet. Similarly, DHL and UPS also have a presence all over the world, comprising large warehouses and a huge number of products to manage. Multinational E-commerce traders such as Amazon have warehouses all over the world, Amazon having revenue of up to USD 70 billion in the last quarter of 2020 [23]. The gigantic size of these companies calls for the need for updated and automated warehouse management systems.
## 4 Technologies to Support Contextualization
### Comparing the Present Situation with Historical Data
#### 4.3.1 Finding Patterns in Dynamic Data  
A technology that can be used to find patterns and relations in large industrial datasets is _machine learning_ (ML) [147]. Based on statistical associations between data, algorithms can make decisions or predictions without being explicitly taught how to do so. In industrial settings, ML has been used for anomaly detection, e.g., [148; 149] and fault diagnosis, e.g., [150; 151]. Anomaly detection addresses the need to support operators in noticing changes. For instance, based on the sound emitted by ball-bearings, algorithms can determine the degree of wear or damage. This is commonly used for predictive maintenance, e.g., [152], but it can also be helpful during operation. For instance, algorithms could detect when the quality of chocolate bars that exit the molding unit starts to deteriorate. Conversely, classification algorithms for fault diagnosis can inform operators when a current situation is similar to a previously encountered fault. For instance, motor current timelines might reveal why a tray packer that puts chocolate packages into boxes produces a fault, because different fault types lead to characteristic changes in these curves. Such changes might be barely detectable for humans when visually inspecting the curves, but machine learning is able to find small effects in noisy datasets.  
A major problem of machine learning is that it is hard to understand what the algorithms are doing. To address this lack of transparency, the concept of _explainable artificial intelligence_ (XAI) has been put forward [153; 154]. For instance, attention mechanisms can indicate what information algorithms have used to generate a solution [155]. Thus, when analyzing images of chocolate exiting the molding unit, the algorithm could indicate what areas of the chocolate it has looked at to determine that its quality is insufficient. Four types of XAI technique can be distinguished [153]. Visualization techniques show model representations to reveal the patterns inside neural units (e.g., tree view visualizations). Knowledge extraction techniques try to extract the knowledge a model has acquired during training. Influence methods estimate how important a feature is for the model by changing this feature and observing its effects on performance. Finally, example-based explanations reveal how a model performs with particular instances of a dataset. Research on the effects of XAI has mainly focused on its potential to enhance trust and compliance with the recommendations provided by algorithms [156; 157]. However, explanations can also foster the generation of mental models about the algorithms [158] and support users in identifying false solutions, leading them to cross-check the ML results more carefully [159].
# Retrieval of Nearshore Bathymetry around Ganquan Island from LiDAR Waveform and QuickBird Image
## 3 Materials and Methods
### Research Area and Data
#### 3.1.3 QuickBird Passive Optical Image  
The QuickBird satellite provides a panchromatic band with 0.61-m spatial resolution and a multi-spectral image with 2.44-m spatial resolution, as shown in Table 2. The passive optical image adopted in this study was the multispectral image (Figure 1b). The image acquisition time was 2:31:52 a.m. (Coordinated Universal Time, UTC) on 20 April 2012.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline Repetition rate of pulse & 33, 50, 70 kHz & Sampling with full waveform & 12-bit \\ Scanning angle & 0 to \(\pm\)25\({}^{\circ}\) & Weight & 53 kg \\ Footprints & 30–60 cm & Positioning system & GNSS \({}^{1}\) \\ Bathymetry depth & \(>\)10 m & Power & 900 W; 28 V \\ Flight height & 300–2500 m & Working temperature & 0–35 \({}^{\circ}\)C \\ Quantitative level & 12-bit & Relative humidity & 0–95\% \\ \hline \hline \end{tabular} \({}^{1}\) Global Navigation Satellite System.  
\end{table}
Table 1: The related parameters of the Aquarius bathymetry system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Way of Imaging** & **Push-Broom Scanning Imaging Mode** \\ \hline Sensor & Panchromatic band & Multispectral \\ \hline Resolution & 0.61 m & 2.44 m \\ \hline \hline \multirow{3}{*}{Wavelength} & \multirow{3}{*}{450–900 nm} & Blue: 450–520 nm \\ \cline{3-3}  & & Green: 520–660 nm \\ \cline{3-3}  & & Red: 630–690 nm \\ \cline{3-3}  & & Near Infrared: 760–900 nm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Image parameters of QuickBird.  
Figure 2: Two typical waveform datasets of light detection and ranging (LiDAR). The horizontal scale is the time in nanoseconds, and the vertical scale is the relative amplitude of the waveform. the vertical yellow line is the start of each return waveform section. The vertical red line is the location of each return pulse.