esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Backgrounds
### Dynamic Time Warping  
DTW is an algorithm used to obtain an effective alignment between two discrete signals. Let \(\text{X}=X_{1},X_{2},\dots,X_{N}\) and \(\text{Y}=Y_{1},Y_{2},\dots,Y_{M}\) be two time-dependent sequences. DTW seeks to find a non-linear warping of X and Y, such that the points with high similarities \((X_{i},Y_{j})\) are aligned together. That is, the best alignment maximizes the sum of the similarities of the alignment pairs. Thus, DTW can be thought of as a mapping function \(f:X\times Y\rightarrow\ R\), where for each pair \((x_{n},y_{m})\), there is an associated cost, which is also known as a local distance measure. The goal is to find the best alignment that is equivalent to  
\[min\sum_{i=1}^{N}\sum_{j=1}^{M}f(x_{i},y_{i}), \tag{3}\]  
where \(f(x_{i},y_{i})\) is inversely proportional to the similarity between \(x_{i}\) and \(y_{i}\).  
Note that the warping path must satisfy the following three conditions: boundary condition, monotonicity condition, and step size condition. The boundary condition assesses whether the first elements of X and Y and the last elements of X and Y are aligned together. The monotonicity condition implies that for any ordered sequence \(X_{i},X_{j}\), where \(X_{i},X_{j}\) is aligned to element \(Y_{i},Y_{j}\) respectively, the order in the X sequence shall be maintained in the corresponding alignment in Y. Finally, the step size condition ensures that the alignment is bijective or that it has a one-to-one correspondence. That is, all the index pairs in a warping path p are pairwise distinct.
## 3 Materials and Methods
### Methods
#### 3.2.4. Experimentation and Testing  
A proof of concept (PoC) was implemented to verify the functionality of the identity system on Raspberry Pi. Steps included  
* DID Verification: Testing the ability of external verifiers to validate the Raspberry Pi's DID on the Tangle;
* VC Validation: Assessing the process of issuing, presenting, and verifying VCs in various scenarios;
* Data Exchange in Real-Time: Monitoring the performance and security of data exchanged over IOTA Streams, focusing on latency, encryption effectiveness, and resource consumption on the Raspberry Pi.  
The results from each testing phase are documented to assess the feasibility and efficiency of the proposed decentralized identity model for IoT.
# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
## 3 Results and Discussions
### Short-Term AI Prediction  
Linear regression serves as a fundamental tool for short-term energy consumption forecasting across various building types, as demonstrated by the examples of the LUCIA, FUHEM, and EII buildings. By establishing a linear relationship between historical energy consumption data and time or other relevant variables, this statistical method allows for the projection of future energy demands, typically for a period of up to three years [36].  
Figure 8a illustrates the HVAC energy consumption forecast for the LUCIA building, designed as a Nearly Zero Energy Building (nZEB). The blue bars depict historical energy consumption data, showcasing fluctuations in HVAC usage. In contrast, the red line forecasts energy consumption for heating, expected to increase during colder periods, while the green line forecasts cooling energy needs, projected to rise during warmer months. Additionally, a linear regression helps to represent the trend in energy consumption, with a marked maximum point indicating the peak value expected over three years. The maximum peak after 3 years in heating is around 800 kWh/m\({}^{2}\) and for cooling, it is around 300 kWh/m\({}^{2}\). Figure 8b illustrates the heating energy consumption forecast for the FUHEM building over the next three years. The x-axis, representing time in hours, spans from 0 to approximately 300 h, with a scale from 0 to 70 kWh/m\({}^{2}\). The maximum peak after 3 years in heating is around 70 kWh/m\({}^{2}\). These figures show the forecast of energy consumption for the EII building cooling and heating demand for the next three years. Figure 8c shows some fluctuations and a peak at approximately 70 kWh/m\({}^{2}\). The green line of the prediction shows an upward trend in cooling energy consumption, which means that when approaching warmer months, the demand increases. In Figure 8d, on the right-hand side, one can observe significant peaks close to 120 kwh/m\({}^{2}\). The forecast for heating energy consumption provided by the red line is steadily increasing, considering the rising demands during the colder periods. These graphs together point out the need for effective forecasting for both the cooling and heating energy management in EII.  
Figure 7: Training time for AI models: (**a**) LUCIA building HVAC (nZEB), **(b**) FUHEM building (heating), (**c**) EII building (cooling) and (**d**) EII building (heating).
# Validation of the Developed Psychoacoustic Model for Sound Quality Valuation of Washing Machines
## 1 Introduction
[15] published the results of other experiments focused on assessing the sound quality of household appliances. The results of listening tests can be compared with psychoacoustic properties, such as loudness, sharpness, roughness, etc. A common approach is to try to create a model from these properties to estimate nuisance and noise disturbance, which should be consistent with the results of the listening tests. If such a model is successful, it could be used as a system for objective evaluation without the use of questionnaire methods. In the paper [30], the acoustic characteristics of electric vehicles using both psychoacoustic and soundscape methodologies are analyzed by three key psychoacoustic parameters. Through correlation analysis between perceived values and objective parameters, specific sound sources requiring improvement were identified, including vehicle body acoustics, wheel noise, and acceleration-related sounds. Authors in the paper [31] propose an evaluation model based on an optimized bidirectional long short-term memory using a genetic algorithm. These suggest that a genetic algorithm can greatly enhance the model's performance by optimizing initial learnable parameters. The evaluation obtained by the optimized model is very close to the human subjective evaluation. [PERSON], [PERSON] et al. [32] used the objective and subjective evaluation results; furthermore, a sound quality prediction model for heavy commercial vehicles was developed based on a back-propagation artificial neural network. The sound quality prediction model of heavy commercial vehicles can be used to determine the weighting of objective evaluation parameters that contribute to the results of subjective evaluation. The sound quality models in the paper [17] were established for supersonic and standard hairdryers using multiple linear regression between the discomfort and two psychoacoustic metrics: loudness and tonality. He, [PERSON] et al. [33] fitted the evaluation vector into the subjective evaluation results of vehicle interior noise by a deep neural network. The results demonstrate that our proposed method can effectively estimate human subjective perception of vehicle interior noise. Authors [34], based on the calculation of objective parameters, established a multiple linear regression objective evaluation model for sound quality, an objective evaluation model for sound quality based on a back propagation neural network, and an objective evaluation model for sound quality based on a radial basis function neural network. This article aims to verify the developed model for evaluating the sound quality of washing machines based on the sound quality index. This sound quality index considers the results of both the measurement of psychoacoustic parameters and the perception of sound quality by users. This validation will demonstrate the suitability of using the developed psychoacoustic model for evaluating the quality of sounds of automatic washing machines, without conducting questionnaire surveys.
# Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
## 2 Methods  
### Overview  
The methodology of this study proposes the automatic identification of five human activities, including walking, running, walking upstairs, walking downstairs, and standing. Figure 1 shows the flow diagram of the proposed methodology to perform the classification of the different samples with the extrapolation the missing samples before the classification of the data. The method is composed by seven modules, including data acquisition (Section 2.2), data imputation (Section 2.3), denoising (Section 2.4), features integration (Section 2.5), data normalization (Section 2.6), model training and evaluation (Section 2.7), and performance comparison (Section 2.8). These stages are explained in the next sections.
# The Influence of Processing on the Bioactive Compounds of Small Berries
## 5 Health Implications and Allergic Reactions to Small Berries
### Antimicrobial Properties  
Different phytochemicals recovered from berries have been reported to be very effective antimicrobial agents [11; 145]. The most effective compounds were reported to be polyphenols (both flavonoids and phenolic acids), together with organic acids (tartaric, citric, acetic, and salicylic acids) and terpenes (like eugenol and terpineo) [9; 145]. Bacterial growth can be inhibited directly by phytochemicals' action on cell membranes and enzymes (superoxide dismutase, adenosine triphosphatase, and alkaline phosphatase) [11], or indirectly by modulating the expression of virulence factors [145]. The effectiveness of berry extracts was reported against bacteria like _Pseudomonas aeruginosa_, _Staphylococcus aureus_, _Enterococcus faecalis_, _Listeria monocytogenes_, and _Escherichia coli_ in particular [145; 146].
## 4 Simulation Verification of MMC Overcurrent Calculation
The comparative results of the two methods are summarized in Figure 10:3  
As shown in Table 2, the analysis method proposed in this paper enables a comprehensive investigation into the causes of overcurrent anomalies. Factors affecting overcurrent, such as insulation faults, improper design of surge arresters, and stray parameters, have all been taken into account. The field-circuit coupling model developed herein allows for rapid and real-time parameter computation. Furthermore, it enables preemptive risk assessment based on the data from the CF models, as shown in Figure 10:4  
This approach facilitates swift and real-time localization of factors causing overcurrent anomalies and, on this basis, enables the formulation of more rational current suppression strategies. The field-circuit analysis method proposed in this paper offers distinct advantages in both the system design and argumentation phase, as well as in subsequent operational fault analysis. Based on the calculation results from Figure 13, it is evident that the preliminary system parameters, including overcurrent protection strategy and settings, might not \begin{table}
\begin{tabular}{c c c} \hline \hline
**Content** & \begin{tabular}{c} **Conventional Analysis Methods Without** \\ **Field–Circuit Coupling Model** \\ \end{tabular} & \begin{tabular}{c} **Field–Circuit Coupling** \\ **Analysis Method** \\ \end{tabular} \\ \hline \begin{tabular}{c} Overcurrent peak value \\ \end{tabular} & \begin{tabular}{c} The current value is diminished due to the \\ neglect of stray capacitance \\ Accurate \\ \end{tabular} & \begin{tabular}{c} The current value is relatively accurate \\ considering the stray capacitance. \\ Accurate. \\ Accurate. \\ \end{tabular} \\ \begin{tabular}{c} Overcurrent abnormality factor \\ localization \\ \end{tabular} & \begin{tabular}{c} The process necessitates an item-by-item \\ approach for troubleshooting and localization, \\ which is inherently time-consuming \\ \end{tabular} & Real-time computation and localization. \\ Impact of equipment insulation faults & \begin{tabular}{c} Cannot \\ \end{tabular} & Real-time computation and localization. \\ on overcurrent \\ Impact of equipment stray \\ parameters on overcurrent \\ Design of overcurrent suppression \\ strategies & \
\begin{tabular}{c} The process necessitates an item-by-item \\ approach for troubleshooting and localization, \\ which is inherently time-consuming \\ \end{tabular} &
\begin{tabular}{c} Real-time computation and localization. \\ Real-time computation and localization. \\ provide suppression strategies. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison results of two methods for overcurrent analysis. Figure 14: Overcurrent waveform calculated by conventional calculation method. be reasonable. The initial design parameters of the MMC valve structure might also be unreasonable. Therefore, it is necessary to optimize the system design in response to the distortion of the MMC valve current.
## 2 Related Work
### Latency When Interacting with Virtual Objects in Remote MR Collaboration  
Latency is one of the representative factors that affect a user (e.g., less work efficiency, less application satisfaction, etc.) in a human-VO interaction. Therefore, minimizing the end-to-end latency between performing the interaction and when the performed interaction information is delivered to a remote user is one of the main goals of remote MR collaboration research.  
In general, end-to-end latency in remote MR collaboration occurs for the following reasons:  
1. Tracking latency, or the time required to track the user and align the direction;
2. Application latency, or the time required to run the application;
3. Image generation latency, or the time required to generate an image to be shown as a result;
4. Display latency, or the time required to output an image to a see-through type display, such as Hololens2 [21], or a head-mounted display (HMD), such as HTC-Vibe [22];Network latency, or the time required to transmit and receive interaction-related information between users in remote locations.  
In general, a latency of 100 ms or less is known as a threshold that does not affect users, but according to a recent study, it was found that even such a small latency affects users [23]. Therefore, all of the above latencies play a major role in user satisfaction during the interaction. Since most of these latency causes are essential operations for MR, it is difficult to remove the latency in MR applications completely. However, it is possible to partially reduce the latency through various methods, and for this reason, various studies are in progress [10; 11; 12].  
[PERSON] et al. [10] focused on network latency in wireless VR, especially, and found that the use of mmWave communication, mobile edge computing, and pre-caching helps to reduce latency. [PERSON] et al. [11] focused on display latency in AR systems and proposed a low-latency update algorithm to solve the problem. [PERSON] et al. [12] focused on application latency, tracking latency, and image generation latency in mobile AR systems and proposed a low-latency and low-energy mobile AR system to prevent duplicate image calculation and image loading.  
The conventional studies above were related to reasons for latency occurring between performing the human-VO interaction and when the performed interaction information is delivered. In other words, conventional studies focused on reduction latency after finding the performed human-VO interaction. Meanwhile, action detection (such as contact or collision) between a human and a virtual object is required for finding the interaction performed in remote MR collaboration. Defining the time to check whether the interaction is performed according to user's intention in a remote MR collaboration as the interaction latency, conventional studies related to this interaction latency were insufficient. Therefore, it is required to try a new approach to reduce the interaction latency.
## 2 Spectrum Sensing Model  
Now we consider a CR system which is in a hot-spot area, several studies [14; 16] have shown that across a wide spectrum range, the spectrum can be expressed as a union of some narrow-band transmissions. Our main ambition is to find out the signal support, which means the bands of the signal sources reside. In other words, we intend to sense the spectrum of the current situation at sub-Nyquist rates.  
Suppose that the received signal from one spectrum sensor \(x(t)\) consists of \(N_{x}\) uncorrelated stationary transmissions, which can be formulated like this  
\[x(t)=\sum_{i=1}^{N_{x}}\rho_{i}s_{i}(t). \tag{1}\]  
Here, \(s_{i}(t)\) is a zero-mean bandlimited wide-sense stationary (WSS) signal, and \(\rho_{i}\in\{0,1\}\) indicates that whether the \(i\)th signal is active or not.  
Figure 1 depicts a typical single node spectrum sensing model and spectrum support of a multiband signal. There are three RF transmissions with different carriers \(f_{i}\) over the whole spectrum. The Fourier transform of \(x(t)\) is expressed by \(X(f)\). \(f_{nyq}\) denotes the Nyquist rate of \(x(t)\) and \(S_{x}\) is the support of the Fourier transform \(X(f)\). As is assumed in [11], we consider the same scenario: we have no prior knowledge on the carrier frequencies of the spectrally sparse continuous-time signal \(x(t)\) of finite support on \(F_{nyq}=[-f_{nyq}/2,+f_{nyq}/2]\), and we suppose that the maximal number of signals is \(N_{x}\) with maximal bandwidth \(B\). We can see that the support \(S_{x}\) is contained within a union of \(N=2N_{x}\) disjoint bands in \(F_{nyq}\).  
It is well known that the power spectrum of \(x(t)\) is given by  
\[P_{x}(f)=\int_{-\infty}^{\infty}r_{x}(\tau)e^{-2\pi\tau/\tau}d\tau,\quad f\in F _{nyq} \tag{2}\]  
Figure 1: Typical spectrum support of a multiband signal composed of three RF transmissions.  
where \(r_{x}(\tau)=E[x(t)x(t-\tau)]\) represents the autocorrelation function of \(x(t)\) and \(E[\cdot]\) denotes the expectation operation. From Equation (2), we can see that estimating the power spectrum equals to estimating the autocorrelation function \(r_{x}(\tau)\). Obviously, \(P_{x}(f)\) has the same support with \(X(f)\). So sensing the spectrum changes into estimating the power spectrum.
# The Non-Linear Impact of Industry 4.0 on Carbon Emissions in China's Logistics Sector
## 2 Methods
### Model Specification  
Carbon emissions and Industry 4.0 development are said to be linked using econometric models that consider changes across the province and within time [10]. A fixed effects estimator was employed to account for unobserved heterogeneity across provinces, which ensured that estimates would reflect the variances caused by the within-province factors over time. To tackle possible reverse causality problems, a dynamic panel regression model was fitted, where lagged dependent variables were used as instruments. In this sense, both direct and mediating effects of Industry 4.0 adoption on carbon emissions are described, resulting in a more precise estimate of the total impact. Moreover, a mediation analysis was conducted to investigate the channels to Industry 4.0 carbon emissions, centering around production efficiency, the scale of operations, and information integration [9; 10; 11].  
The DSR process adopted in this study followed a structured six-step approach:  
1. Problem Identification and Motivation--Identifying the challenges in integrating Industry 4.0 technologies into logistics operations, particularly the high initial carbon emissions during the early stages of adoption;
2. Definition of Solution Objectives--Establishing objectives such as minimizing carbon emissions while improving operational efficiency and scalability;
3. Design and Development--Developing a conceptual framework for integrating advanced IoT, AI, and automation technologies into logistics processes, aiming to optimize resource use and reduce emissions;
4. Demonstration--Implementing the framework in selected logistics hubs and monitoring its impact on carbon emissions and operational efficiency;
5. Evaluation--Using econometric models and dynamic panel data analysis to evaluate the framework's effectiveness in reducing emissions;
6. Communication--Sharing the findings with academic and industry stakeholders to promote the adoption of sustainable logistics practices.  
The experimental design involved deploying this toolkit in multiple logistics hubs, each representing different stages of Industry 4.0 adoption and varying levels of infrastructure readiness. Scenarios included high-tech urban logistics centers with advanced automation and AI systems, as well as rural logistics hubs where technology integration was in its nascent stage. This diversified approach allowed for a comprehensive analysis of the toolkit's effectiveness across different operational environments.
## 2 NR Uplink Channels Overview  
The 3 GPP specifies the frame structure, physical resources, and bandwidth parts of NR signals [8; 9]. In this work, we focus on the analysis of physical uplink channels that correspond to a set of resource element carrying information originating from a higher layer, such as PUCCH and PUSCH, generated using the conventional cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) technique. Uplink transmission is organized into frames of 10 ms in duration, where each frame is subdivided into slots of 0.5 ms. We consider format #0 PUCCH modulated using quadrature phase shift keying (QPSK) modulation and having a length of two consecutive OFDM symbols per slot for the control information, and for PUSCH data part modulated using 64 QAM modulation format.  
Unlike the LTE-A single 15 kHz subcarrier spacing (SCs) option, NR supports scalable SCs. The considered NR parameters for each type of SCs are summarized in Table 1.
## 4 Security Threats
### Negotiation
#### 4.2.5 Spectrum Needs Over-reporting  
Each node participating in a DSA network should compute its spectrum needs by itself (in terms of channel, time, bandwidth, _etc._), taking into account both its node characteristics and traffic demand (_i.e._, traffic type, maximum acceptable delay, jitter, _etc._). However, a selfish node could choose not to conform to this rule and request more resources than it really needs [11]. Motivated either by greed orfear of problems in case of sudden QoS degradation, a node may choose to lie (over-claim), particularly if no cost is associated with resource requests (/reservations). Techniques to provide the right incentives for nodes to always report their true needs are required.