esa-sceva/satcom-chunk-collection huggingface dataset:
# Psychoacoustic Approaches for Harmonic Music Mixing +
## 3 Consonance-Based Mixing
### Data Collection and Pre-Processing  
We first explain the necessary pre-processing steps that allow the subsequent measurement of consonance between two pieces of music. For the purpose of this paper, we make several simplifications concerning the properties of the musical audio content we intend to mix.  
Given that one of our aims is to compare consonance-based mixing to key-based matching methods in DJ-mixing software (see Section 4), we currently only consider electronic music (e.g., house music), which is both harmonically stable and typically has a fixed tempo. We collected a set of 30 tracks of recent electronic music for which we manually annotated the tempo and beat locations and isolated short regions within each track lasting precisely 16 beats (_i.e._, four complete bars). In order to focus entirely on the issue of harmonic alignment without the need to address temporal alignment, we force the tempo of each excerpt to be exactly 120 beats per minute. For this beat quantization process, we use the open source pitch-shifting and time-stretching library, Rubber Band [24], to implement any necessary tempo changes. Accordingly, our database of musical excerpts consists of a set of 8 s (_i.e._, 500 ms per beat) mono.wav files sampled at 44.1 kHz with 16-bit resolution. Further details concerning this dataset are in Section 4.1.  
To provide an initial set of frequencies and amplitudes, we use a sinusoidal model, namely the \"Spectral Modeling Synthesis Tools\" Python software package by [PERSON] [25; 26], with which we extract sinusoids using the default window size and hop sizes of 4001 and 256 samples, respectively. In order to focus on the harmonic structure present in the musical input, we extract the \(I=20\) partials with the highest amplitude under 5 kHz.  
For our chosen genre of electronic music and our assembled dataset, we observed that the harmonic structure remained largely constant over the duration of each \(1/16\)th note (_i.e._, 125 ms). Therefore, to strike a balance between temporal resolution and computational complexity, we summarize the frequencies and amplitudes by taking the frame-wise median over the duration of each \(1/16\)th note. Thus, for each excerpt, we obtain a set of frequencies and amplitudes, \(f_{\gamma,i}\) and \(M_{\gamma,i}\), where \(i\) indicates the partial number (up to \(I=20\)) and \(\gamma\) each \(1/16\)th note frame (up to \(\Gamma=64\)). An overview of the extraction of sinusoids and temporal averaging is shown in Figure 4. In Section 4.2, we examine the effect of this choice of parameters.
## 8 Conclusions  
This paper has covered the strategic aspects of PLC systems applications over the electricity grid for the purpose of Smart Metering. The history, strong aspects and the constraints of PLC systems have been shown, in order to understand how PLC may fit in the Smart Metering networks. The paper rationale is the need to further evolve these PLC systems, their design and their architecture on field, to make them capable of offering the functionality and performance needed for the Smart Grid.  
The system decisions to make PLC systems adapt to each Smart Metering scenario have been elaborated in Section 5. The first aspect that needs to be considered is the communications architecture to be developed in the MV and the LV grid segments, where the throughput requirements are an important factor to be taken into consideration, together with the isolation level of the PLC systems used in the connection points of MV and LV segments, _i.e._, the SSs. Once the architecture has been decided, the PLC signal injection at the SSs, and the capabilities of the PLC system chosen will condition the performance results obtained. To conclude the chapter, and being the NB PLC systems the most prevalent ones for Smart Metering networks, specific low level aspects recommendations are provided on the MAC layer, being packet size, FEC strategies and automatic retransmission the most important ones.  
Section 6 includes the deployment guidelines for NB PLC systems. These guidelines are to be applied in both the SSs (injection of PLC signal from the masters of the system) and at the different points of the LV grid if signal repetition due to low SNR is needed. Signal injection strategy is heavily affected by the multi-transformer scenario described in the chapter, and by the nature of the signal injection (single- or three-phase). These guidelines will manage to obtain the performance results needed. In order to check the real performance obtained on field, a PLC network assessment procedure is described, together with the interpretation of the results.  
The paper ends with the challenges that PLC systems face in their evolution to fit the needs of Smart Grid networks. On one side, the need to further optimize existing PLC system is highlighted. On the other, the interaction between PLC systems and the grid is shown as a factor that needs to be used both to improve PLC systems performance, and to obtain a better knowledge of the grid itself. As a final idea, the combination of PLC and non-PLC technologies is suggested as a possibility to pave the way towards Smart Grids.
###### Abstract
Compared to bagging, boost, and support vector machine models, the AGB model using the random forest algorithm obtained higher values for the mean Pearson coefficient and the symmetric index of agreement on the Loess Plateau [27]. Additionally, some studies compared the effectiveness of various machine learning algorithms for modeling grassland biomass and found that the model accuracy of the AGB model using the random forest algorithm was more reliable [28; 29]. It provides stronger generalization capability than traditional linear regression and achieves higher accuracy and reliability in different regions and conditions, making it become a very promising estimation method. In addition, previous studies have combined multiple variables to develop high-precision AGB models using random forest algorithms for the Loess Plateau and the Sanjiangyuan area [27; 30]. However, there have been only limited studies on the prediction of grassland AGB and its spatiotemporal variations in different types of grasslands in Xinjiang at a regional scale. Due to Xinjiang's vast grasslands, diverse topographical features, and unique climate, the distribution of grasslands is highly uneven across the region. Therefore, it is necessary to conduct an analysis based on different types of grasslands. In this study, we created an estimation model for grassland AGB in Xinjiang by combining ground-truthed grassland AGB data, considering climate, vegetation index, and other factors, and explored the spatial and temporal variations in grassland AGB and its relationship with climatic factors. This study aimed to (1) establish a suitable AGB model for the Xinjiang grasslands based on the random forest algorithm; (2) analyze the spatial and temporal changes in grassland AGB in Xinjiang from 2000 to 2022; and (3) explore the impact of climate change on the spatiotemporal dynamics of grassland AGB in Xinjiang. The results can provide technical support for accurately estimating grassland AGB, understanding the status of grassland resources, and informing the sustainable development of grasslands in Xinjiang.
## 2 Related Work
Considering the change in source code files of OSS, Singh et al. [33] proposed an entropy-based multi-release RM of OSS. In addition, considering fault dependence in multi-release open-source software, Chatterjee et al. [34] proposed an RM with an ID and changing point. By studying fault introduction phenomena of OSS, Gandhi et al. [35] established a corresponding RM. Diwakar and Aggarwal [36] proposed an RM for OSS that considers ID. By studying changes in fault-decreasing factors and testing coverage, Tandon et al. [37] built a multi-release RM for OSS. Saraf et al. [38] proposed an integrated method to build a multi-release OSS RM based on a variety of testing and debugging environments. Pradhan et al. [39] proposed two single-release software RMs and one multi-release software RM while considering the fault reduction factor with a generalized inflection S-shaped distribution. In order to optimize the parameter estimation of OSS RMs, Yang et al. [40; 41] used the expectation maximization algorithm to compute the likelihood function of software RMs. Additionally, Yang et al. [42] used the expectation least squares and expectation maximization algorithms to estimate OSS RM parameters while considering the processes of FD and correction. Xiao et al. [43] used an artificial neutral network to build a software RM and predicted the number of the software's remaining faults while considering FD, correction, and testing efforts. Considering the problem of resource allocation in the process of software testing, a multi-objective optimization approach was put forth by Rani and Mahapatra [44] to address the issue of allocating resources for software testing. In the above-mentioned OSS RMs, although some models consider the random change in FD, they do not consider the random change introduced by faults. Moreover, fault introduction in OSSs includes not only the fault introduced during debugging, but also the new fault introduced when faults from the changes in software functions, features, and components are removed following the release of the new OSS version. Therefore, the introduced faults show irregular changes. In other words, in the OSS testing and debugging processes, the behavior of introducing faults is random. In this paper, we use SDE to simulate the random change in fault introduction, which is more consistent with the actual situation of fault introduction in the processes of OSS testing and debugging.
# Real-Time Simulation and Sensor Performance Evaluation of Space-Based Infrared Point Target Group
## 2 System Design
### COM Introduction  
STK has two methods for secondary development: Connect mode and COM mode [34]. Figure 2 illustrates the operation of the two external interconnection methods of STK. Connect mode was originally designed to operate STK over TCP/IP by sending commands. Although the command design is characterized by readability, customizability, and platform independence, it has significant limitations such as supporting only built-in report graphs, manual parsing of returned data, and poorly designed error messages which make debugging difficult. COM's purpose is to enable building custom solutions using STK. Additionally, it supports programming languages such as C#, Java, and C++, and provides the ability to control and automate STK objects, manage their lifecycle, and access data provider tools. This enables users to integrate STK scenes into their own systems, reducing the difficulty of developing visualization solutions. We took advantage of COM's custom output to reduce the time overhead of interacting with STK. In addition, we temporarily switched to Connect mode to complete settings such as constellation parameters and target property data when COM did not have the corresponding methods.  
Figure 1: EOIR module overview.  
Figure 2: STK external interconnection diagram.
# Reconfigurable Intelligent Surface in Wireless-Powered Interference-Limited Communication Networks
## 1 Introduction
### Related Works  
Recently, various research works have evaluated the performance of RIS in wireless communication systems. Specifically, the performance of a RIS-assisted UAV relaying system was investigated in [12]. Additionally, the performance of an RIS-aided power line communication system was studied in [13] where the RIS was employed to improve system coverage. The authors in [4] evaluated the performance of an RIS-enhanced underwater communication system. The performance of an RIS-aided network over a Nakagami-m fading channel was investigated in [6]. In [14], the performance of an RIS-assisted source multi-user mixed radio frequency/free space optical (FSO) relay network was evaluated with opportunistic user scheduling. Moreover, the performance of the RIS-assisted mobile network under random user mobility was investigated in [15]. The authors in [16] presented the performance of an RIS-enhanced internet of things network over an \(F\)-distribution fading channel. Further, the impact of modulation index techniques on an RIS-based space shift keying wireless network was evaluated in [17]. In [18], the performance analysis of an RIS-assisted non-orthogonal multiple access network was presented. [PERSON] et al. [19] considered RIS in an FSO setup under the influence of atmospheric turbulence and pointing error. The authors in [20,21] studied the security performance in vehicle networks with different modes of RIS. However, in all the aforementioned works, the systems under study did not considered co-channel interference. As a matter of this, in [22], the performance of RIS in an interference-limited RIS-aided network was studied, where the interference links at the system destination were subjected to Rayleigh fading distribution. It is worth noting that the system model in [22] did not consider a wireless-powered communication technique. To the best of authors' knowledge, the RIS in interference-limited networks in context of a wireless-powered communication scenario has not been yet studied in literature.
# An Optimal Online Resource Allocation Algorithm for Energy Harvesting Body Area Networks
## 4 Resource Allocation Algorithm
### Proposed Algorithm  
In this subsection, we present an online resource allocation algorithm in Algorithm 1. The idea of this algorithm is to obtain the optimal harvested energy \(h^{*}(t)\), collecting rate \(c^{*}(t)\) and transmission power \(p^{*}(t)\) according to the above subproblems firstly. Then, the algorithm updates data queues and energy queues according to Equations (5) and (6) in each time slot. There are four parts included in the online resource allocation algorithm. The first part is mainly to solve the sub-problem of battery management to achieve the optimal harvested energy \(h^{*}(t)\) in time slot \(t\). The second part is mainly to solve the sub-problem of collecting rate control to achieve the optimal collecting rate \(c^{*}(t)\). The third part is mainly to solve the sub-problem of transmission power allocation to achieve the optimal transmission power \(p^{*}(t)\). The fourth part is mainly to update the data queue occupancy \(I_{n}(t+1)\) and energy queue occupancy \(J_{n}(t+1)\) based on the results of the previous three parts. Figure 2 shows the three subproblems and the data flows among them.  
Figure 2: Parameters update diagram of the algorithm.  
```
Data:\(I(t),J(t),e_{n}(t),\forall n\in N\) Result:\(c^{*}(t),h^{*}(t),p^{*}(t),I(t+1),J(t+1)\). /* Battery Management */ foreach\(n\in N\)do if\(J_{n}(t)<\Omega,\forall n\in N\)then \(h_{n}^{*}(t)=\min{(\Omega-J_{n}(t),e_{n}(t))}\);  end if else \(h_{n}^{*}(t)=0\);  end if end /* Collecting Rate Control */ foreach\(n\in N\)do  Compute \(c_{n}^{*}(t)\) based on Equation (14);  end if /* Transmission Power Allocation */ foreach\(n\in N\)do  Compute \(p_{n}^{*}(t)\) based on Equation (15);  end if /* Update the queue lengths */ foreach\(n\in N\)do  Compute \(I_{n}(t+1)\) based on Equation (5);  Compute \(J_{n}(t+1)\) based on Equation (6);  end
```  
**Algorithm 1**Online Resource Allocation Algorithm.
## 2 Methods
### Time Varying Filtering Based Empirical Mode Decomposition
#### 2.1.3 Judgement of the Residual Signal  
Since the definition of local narrow-band signal is closely related to the instantaneous bandwidth, TVF-EMD has formulated the relative criteria to check the instantaneous narrow-band signal, namely,  
\[\theta(t)\ =\ \frac{B_{Loughlin}(t)}{\varphi_{avg}(t)} \tag{4}\]  
For a given bandwidth \(\xi\) threshold, if \(\theta(t)\leq\xi\), the signal can be viewed as a narrow-band signal. Here, the weighted average instantaneous frequency \(\varphi_{avg}(t)\) and Loughlin instantaneous bandwidth \(B_{L}\) can be calculated by:  
\[\varphi_{avg}(t)\ =\ \frac{{a_{1}}^{2}(t)\varphi_{1}\prime(t)+{a_{2}}^{2}(t) \varphi_{2}\prime(t)}{{a_{1}}^{2}(t)+{a_{2}}^{2}(t)} \tag{5}\]  
\[B_{L}(t)\ =\ \sqrt{\frac{{a_{1}}^{2}(t)+{a_{2}}^{2}(t)}{{a_{1}}^{2}(t)+{a_{2}}^{2}( t)}+\frac{{a_{1}}^{2}(t){a_{2}}^{2}(t){(\varphi_{1}\prime(t)-{\varphi_{2} \prime(t))}}^{2}}{{({a_{1}}^{2}(t)+{a_{2}}^{2}(t))}^{2}}} \tag{6}\]
# Automated Pneumothorax Segmentation with a Spatial Prior Contrast Adapter
## 3 Materials and Methods
### Attention Map Fusion Strategy  
In our study, we have two forms of attention maps, that is, multiple lung segmentation [26] masks and a single probabilistic atlas. These two attention mechanisms have different roles in guiding the segmentation model:  
* **Lung segmentation masks:** These masks are derived from various previous lung segmentations by utilizing the foundation model MedSAM. They provide spatial information on where the lung regions are located, highlighting relevant anatomical structures. Since there are multiple lung segmentation masks, we will treat these as multiple sources of local spatial attention maps.
* **Probabilistic atlas:** The probabilistic atlas is derived from the pneumothorax ground truth in the CANDID-PTX dataset. It provides a probability map indicating the likelihood of a voxel belonging to a pneumothorax region. This map is generated by calculating the probability distribution of pneumothorax occurrence across the available population, guiding the model to focus on regions where pneumothorax is more likely to appear. The probabilistic atlas serves as an anatomical and probabilistic prior, helping the model focus on areas that are more likely to contain pneumothorax, further improving its ability to detect this condition.  
To combine these two sources of attention, we use a weighted fusion strategy. The lung segmentation masks provide local spatial attention focusing on the lung region, and the probabilistic atlas adds a global context for detecting pneumothorax lesions within those regions. Mathematically, the fusion of these two attention maps can be represented as follows:  
\[A_{combined}(x,y)=\sum_{i=1}^{N}\Big{(}A^{i}_{lung}(x,y)\cdot A_{atlas}(x,y) \Big{)}, \tag{1}\]  
where \(A^{i}_{lung}(x,y)\) is the \(i\)-th lung segmentation mask at location \((x,y)\), \(A_{atlas}(x,y)\) is the probabilistic atlas, providing the likelihood of pneumothorax at each location. The sum over \(i\) represents the combined contribution from all lung segmentation masks. The combined attention map \(A_{combined}(x,y)\) is computed by summing the weighted contributions of the lung masks and the probabilistic atlas.
## 5 Experiments
### Data Description and Preprocessing  
Our study aims to refine the precision of home appliance sales forecasting via TL. In the experiment, we employ real sales data of home appliance products from the Chinese market. Rich historical sales data from outlets in large cities are selected as the source domain, while small towns or newly established outlets with lower market maturity and scarce data are chosen as the target domain. Specifically, we select two geographically close cities, City A and Town B in the Yangtze River Delta region, as the subjects of our study to ensure a certain degree of similarity in features between the source and target domains, such as geographical location, climate, and weather conditions. This similarity helps the model to better adapt to the characteristics of the target domain.  
The source domain dataset covers 1260 days of air conditioner sales data from City A from 2020 to 2023, while the target domain dataset includes 405 days of air conditioner sales data from Town B from 2022 to 2023. These data provide a solid foundation for subsequent experiments, enabling us to conduct in-depth analysis and forecasting of home appliance sales patterns.  
During the data preprocessing phase, the collected source and target domain datasets are first cleaned and filtered to remove outliers and noise. Missing values in the datasets are smoothed through mean imputation, which helps maintain data integrity and reduces the potential impact of missing data on model training.  
We apply min-max normalization to standardize the data across variables, as shown below:  
\[x_{j}^{\prime}=\frac{x_{j}-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}}, \tag{22}\]  
where \(x_{j}\) and \(x_{j}^{\prime}\) represent the original data and normalized data, respectively, with \(x_{\text{min}}\) and \(x_{\text{max}}\) being the minimum and maximum values in the original sequence.  
To visually present the experimental datasets and analyze the sales changes over time more accurately and meticulously for the two domains, we normalize and align the sales data of the source and target domains on the time axis, as shown in Figure 7. It can be seen that the sales trends from both domains exhibit a degree of similarity, suggesting that, despite variations in data volume and market maturity, there exists some shared patterns in sales fluctuations. This commonality offers robust empirical evidence that is instrumental for the domain adaptation training of the TransTLA model.  
The dataset is partitioned into three distinct subsets: training (70%), validation (10%), and testing (20%). This division is strategically designed to address the limited availability of the target domain samples by assigning a larger training dataset, ensuring appropriate data support for the model's training, optimization, and assessment. To model the sequences, a sliding time window technique is employed, with a fixed window size, such as 7 days, to capture consecutive time intervals. This approach allows for overlapping windows, enabling the model to effectively handle the sequential and evolving nature of sales data.
###### Abstract  
In recent years, multi-temporal imagery from spaceborne sensors has provided a fast and practical means for surveying and assessing changes in terrain surfaces. Owing to the all-weather imaging capability, polarimetric synthetic aperture radar (PolSAR) has become a key tool for change detection. Change detection methods include both unsupervised and supervised methods. Supervised change detection, which needs some human intervention, is generally ineffective and impractical. Due to this limitation, unsupervised methods are widely used in change detection. The traditional unsupervised methods only use a part of the polarization information, and the required thresholding algorithms are independent of the multi-temporal data, which results in the change detection map being ineffective and inaccurate. To solve these problems, a novel method of change detection using a test statistic based on the likelihood ratio test and the improved Kittler and Illingworth (K&[PERSON]) minimum-error thresholding algorithm is introduced in this paper. The test statistic is used to generate the comparison image (CI) of the multi-temporal PolSAR images, and improved K&I using a generalized Gaussian model simulates the distribution of the CI. As a result of these advantages, we can obtain the change detection map using an optimum threshold. The efficiency of the proposed method is demonstrated by the use of multi-temporal PolSAR images acquired by RADARSAT-2 over Wuhan, China. The experimental results show that the proposed method is effective and highly accurate.  
Keywords:change detection; test statistic; improved Kittler and Illingworth (K&I); generalized Gaussian model (GGM); PolSAR; multi-temporal +
Footnote †: journal: Applied Sciences