esa-sceva/satcom-chunk-collection huggingface dataset:
# Spectral Envelope Transformation in Singing Voice for Advanced Pitch Shifting
## 5 Evaluation Methodology
### Objective Evaluation Methodology  
In order to assess the naturalness of the pitch-shifted audio samples generated, some objective measures were defined. The measures were aimed at assessing the similarity between the notes obtained after a pitch shifting process (of an octave) and the corresponding notes naturally sung in the same target pitch. The similarity measures were obtained for three pitch shifting schemes: the Flex Pitch plugin of Logic Pro X, the PSOLA algorithm and our system. We call our measure normalized similarity (NS), and it is defined as:  
\[\text{NS}=\frac{1}{1+E[|X-Y|]} \tag{20}\]  
Figure 9: Magnitude of the compensation filter for pitch shifting an \(F3\) note (172.6 Hz) of a male singer to be pitch shifted a fifth above.  
which is a normalized inverse of the mean absolute difference of two variables \(X\) and \(Y\), where \(X\) and \(Y\) are the normalized magnitudes of the original and transformed samples, respectively, of the different parameters to measure, namely: the magnitude and phase of spectral peaks and the central frequency and bandwidth of formants. In our case, the variables are normalized by the maximum of their spectrum. Specifically, the parameters considered to be evaluated by using the NS statistic are:  
* Magnitude and phase of the 10 first spectral peaks.
* Central frequency of the three first formants (\(F1\), \(F2\) and \(F3\)).
* Bandwidth of the three first formants (\(B1\), \(B2\) and \(B3\)).  
Observe that NS (Equation (20)) ranges between zero and one. The lower the value of NS, the lower the similarity between the variables compared.  
Recall that the ultimate purpose of the evaluation is to provide an objective comparison regarding the goodness of the spectral envelope transformation. Note that all of the spectral envelopes of the original samples and their transformed versions were estimated by using the optimization method detailed in Section 2.2.
## 1 Introduction
The researchers claim that the K-medoids technique performed the most accurate results. The publication of [PERSON] et al. (2019) [23] introduced a grouping model for reference Indian offices with the use of data collected from 230 buildings constructed in last decade. Four scenarios were developed according to energy consumption levels of those buildings. Moreover, the paper of [PERSON] et al. (2021) [24] refers to a bottom-up model based on real-reference buildings as the first urban energy demand study concerning educational building stock in Quito, Ecuador, and Barcelona, Spain. Finally, [PERSON] et al. (2022) [25] highlighted significant discrepancies in the North American Reference Building Approach and proposed the discontinuation of this method. Based on the foregoing, we infer that the technical/scientific community has used several criteria to define samples for RB characterization. However, the criteria for building-stock energy performance may be summarized as being based on three factors: the climatic zone (region), geometry (footprint shape, dimensions), and thermophysical characteristics of the structures (U-values of enclosure). Thus, the main target of this paper is to classify the residential buildings of the Mediterranean area, based on the aforementioned criteria.
# Effects of Transport Network Slicing on 5G Applications
###### Abstract
As such, we utilize SimTalk, an IoT application traffic emulator based on the IoT application development platform IoTalkTM [15], to accommodate actual IoT applications and emulate their traffic patterns to create large-scale traffic. We then examine how the proposed transport network slicing mechanism affects the performance of these applications using the NCTU testbed (described in Section 2). This study's major contribution lies in evaluating the effects of transport network slicing on 5G applications by utilizing the traffic flows from both actual applications and their emulated counterparts. Our study indicates that applications with different traffic characteristics need different Committed Information Rate (CIR) ratios. The CIR ratio is the CIR setting for a P4 meter in physical switch hardware over the aggregated data rate of applications of the same type. A low CIR ratio will cause an application to experience performance degradation because the P4 switches will dispatch application packets to the low-priority queue if the packet arrival rate is higher than the CIR setting for the applications. In general, applications with TCP or UDP elephant flows would require a higher CIR ratio than applications with TCP mice flows. With the proper CIR settings for the P4 meters, the proposed transport network slicing mechanism can enforce the committed rates and fulfill the latency and reliability requirements for 5G MIoT, CloT, and MBB applications in both TCP and UDP. This article is organized as follows. Section 2 introduces the main components in the NCTU testbed. We then describe the 5G applications for network slicing in Section 3. Section 4 describes a P4-based slice and bandwidth management mechanism. Section 5 provides an overview of the SimTalk architecture. Section 6 investigates how transport network slicing improves the performance of 5G applications. Finally, we summarize the findings in our study and point out future work directions in Section 7.
## 3 Observations
### Digisonde Measurements  
3.1.1. Overall Storm-Time Condition in the Ionosphere  
Figure 2 illustrates the Digisonde observations of the F1- and F2-layers with their monthly median values, highlighting G-condition, the \"fade-out\" and auroral F- and Es-layer intervals. Following the SSC, the F2-layer's electron density drastically decreased at both stations until 04:50 UT at SO and 04:07 UT at VT on 11 May, when the G-condition emerged. At SO, G-condition intervals were observed at 04:50-07:05 UT, 14:30-14:50 UT, and 16:10 UT (marked by magenta bars on Figure 2), while at VT, they occurred at 04:07:30-08:00 UT, 14:15-15:00 UT, and 16:07:30 UT.  
These intervals were followed by peculiar total \"fade-out\" periods, where all ionogram traces disappeared. At SO, the exceptional \"fade-outs\" occurred from 07:10-14:10 UT and 15:05-16:05 UT, while at VT, they spanned 08:07:30-14:07:30 UT and 15:07:30-16:00 UT. Notably, the \"fade-out\" began one hour later at VT than at SO, though their end times coincided. Before the \"fade-out,\" the F2-layer rose to an extreme altitude (h'F2 > 800 km at VT and >1000 km at SO), while the F1-layer rapidly descended (below 250 km), see Figure 2b,d. An unusually fast recovery in ionospheric electron density was observed on 12 May.  
To quantify the storm-time changes in respect to the quiet day level in percentage value, the so-called deltafoF1, deltafoF2, and deltahF1 and deltahF2 parameters were calculated using the well-known equation from previous studies, e.g., [47; 48]:  
Figure 2: Digisonde F1- and F2-layer measurements during the Mother’s Day storm at Sopron (panel (**a,b**)) and San Vito (panel (**c,d**)). F1-layer data: turquoise line for foF1 and h’F1; F2-layer data: dark blue line for foF2 and h’F2. Gray dashed lines represent median values. Auroral layers (green bars), G-condition (magenta bars) and fade-outs (black bars) are shown, with the altitude range of the three Swarm satellites (A, B and C) marked by a yellow horizontal bar.  
\[\text{deltafoF2}=\left(\frac{\text{foF2}_{\text{storm}}-\text{foF2}_{\text{quiet}}}{ \text{foF2}_{\text{quiet}}}\right)\ *\ 100\% \tag{2}\]  
On Figure A1 the calculated deltafoF1, deltafoF2, deltahF1, and deltahF2 parameters are shown. In Appendix A section the detailed description of the results can be found. In conclusion, the height values in percentage in Figure A1b,d show quite the same pattern as in Figure 2b,d in the virtual height data.  
Section 3.1.3 below describes in detail the nighttime uplift phases on 10/11 May.
## Appendix C An Example How to Construct the Heat Flow Baseline  
It is assumed that the reader is familiar with the results presented above, especially with the procedure for how the cure and post-cure data was collected for each type of experiment, as described in Appendix B.  
As mentioned in Section 2.1 the baseline is give by the heat flow during steady state conditions [15]. When a temperature ramp is applied this is, of course, not a steady state, however, this statement means that no chemical reactions, phase changes or anything else takes place in the sample. Since the sample reaches full conversion during the cure cycle, it can reasonably be assumed that the post-cycle happens under steady state conditions. Figure 11 shows the raw heat flow data for the cure (black and blue lines) and post-cure (orange and purple lines) runs of the dynamic experiment with a temperature ramp of 10 K/min.  
For the first ten minutes, the sample was held at 278 K to establish the same starting point for both runs, as described in Appendix B.1. The steep decline/nicline when the temperature ramp starts/ends lasts approximately 30 s and due to the fact that the DSC instrument and the sample need some time to establish equilibrium after the previous (temperature) state changed from isothermal to dynamic (and _vice versa_) [15]. However, in these sections the curves have basically the same slope (and almost the same values), further confirming that the temperature properties of the uncured and fully cured sample are the same, probably due to the high amount of fillers (cf. Sections 5.2 and 7.2).  
The discrepancy in the heat flow values during the temperature ramp between the cure and post-cure run is obvious, confirming that the sample did not reach full conversion before it reached 423 K. If only a straight line, between start- and endpoint of the cure run ramp data, would have been used (as often is done in the literature) the total heat and all subsequent calculations would have been wrong. Since the sample did not reach full cure during the ramp, heat is released during the isothermal phase afterwards, resulting in higher heat flow values for the cure run, while the post-cure run equilibrates within approximately 30 s to a straight line.  
Subtracting the post-cure data from the cure data leads to the baseline corrected dataset which can be used for the proposed method described in this article.  
We performed an additional correction since each experiment had a different steady state value, very slightly different from zero. Hence, we determined the mean heat flow value when the sample reached steady state towards the end of the experiment and subtracted this value from the baseline corrected data to arrive at the red curve in Figure A1.  
For isothermal measurements only this very last correction would be necessary. However, also for these experiments we subtracted the post cure run from the cure run to have a consistent method for obtaining the data used for the calculations.
###### Abstract  
Various components are involved in the end-to-end path of data transfer. Protecting data integrity from failures in these intermediate components is a key feature of big data transfer tools. Although most of these components provide some degree of data integrity, they are either too expensive or inefficient in recovering corrupted data. This problem highlights the need for application-level end-to-end integrity verification during data transfer. However, the computational, memory, and storage overhead of big data transfer tools can be a significant bottleneck for ensuring data integrity due to the large size of the data. This paper proposes a novel framework for data integrity verification in big data transfer systems using a cross-referencing Bloom filter. This framework has three advantages over state-of-the-art data integrity techniques: lower computation and memory overhead and zero false-positive errors for a limited number of elements. This study evaluates the computation, memory, recovery time, and false-positive overhead for the proposed framework and compares them with state-of-the-art solutions. The evaluation results indicate that the proposed framework is efficient in detecting and recovering from integrity errors while eliminating false positives in the Bloom filter data structure. In addition, we observe negligible computation, memory, and recovery overheads for all workloads.  
data integrity; Bloom filters; probabilistic structures; false-positive errors; distributed systems; high-performance computing +
Footnote †: journal: _sciences_
# Enhanced Path Planning by Repositioning the Starting Point +
## 4 Experimental Evaluation
### Computational Efficiency Analysis
#### 4.2.5 Scaled-Up Experimental Analysis
Station-based consistency analysis reveals minimal variation, with 5 stations showing original 32.7% versus scaled 32.3% SPD improvement, 25 stations demonstrating original 21.8% versus scaled 23.0% SPD improvement, and 100 stations exhibiting original 13.1% versus scaled 16.8% SPD improvement. Drone-based consistency analysis similarly shows acceptable variation ranges, with 2 drones producing original 19.6% versus scaled 17.3% SPD improvement, 5 drones yielding original 22.9% versus scaled 17.7% SPD improvement, and 10 drones achieving original 24.1% versus scaled 18.7% SPD improvement. The minor variations between datasets (typically \(\pm\)2-4%) fall well within expected statistical ranges given the reduced iteration count in the scaled experiment. This consistency validates both the robustness of the geometric median approach and the reliability of the experimental methodology across different sampling intensities. _Operational Implications and Scaling Insights_  
The scaled experimental analysis provides several critical insights for practical implementation. Performance predictability emerges as a key finding, with optimization benefits following predictable patterns across all tested scales, enabling mission planners to estimate potential improvements based on operational parameters. Threshold identification represents another significant discovery, as the 25 to 100 station range represents a critical transition zone where optimization benefits decline most rapidly, suggesting focused analysis for missions in this range. Large fleet viability becomes evident through the increasing effectiveness with larger drone fleets, confirming the approach's suitability for major commercial operations requiring substantial autonomous vehicle coordination. Scalability validation demonstrates that the maintenance of optimization benefits even at the largest tested scales involving 800 stations and 60 drones establishes the approach's practical applicability across industry-relevant operational ranges. These findings establish the geometric median optimization approach as a robust, scalable solution for drone fleet path planning across the full spectrum of anticipated commercial and research applications, from small-scale monitoring missions to large-scale infrastructure and environmental assessment operations.
# Research on Energy Harvesting Mechanism and Low Power Technology in Wireless Sensor Networks
## 3 Methods
### Energy Harvesting System  
The battery is generally used to supply energy for nodes in WSN, but it is necessary that the battery limited energy storage is timely replaced and supplied to ensure the continuous normal operation of the node. In practical application, the real environment is often very complex, which brings great difficulties in laying the power wire or replacing the battery for each node; therefore, it largely limits the universality and flexibility of WSN applications. To solve this problem, we used energy harvesting technology to realize the autonomous power supply of WSN.  
At present, the sources of energy harvesting technology generally include solar energy, electromagnetic radiation energy, mechanical vibration energy, and thermal energy. The solar harvesters can obtain several milliwatts of power per square centimeter, which can meet the application demand of wireless sensor nodes. However, the solar cells can convert energy output by the panel only if the light is sufficient, and a single solar energy cannot guarantee a sustainable and reliable power supply for nodes. The energy harvester based on mechanical vibration has received the attention of scholars at home and abroad, because the vibration energy is widely found in the natural environment. The main methods of vibration-to-electric energy conversion are as follows: piezoelectric, electromagnetic, and electrostatic. Among them, the basic principle of piezoelectric vibration energy harvesting is that under the excitation of the external vibration source, the mechanical deformation of the piezoelectric material causes the movement of its internal electrons to generate electric energy. Piezoelectric has the advantages of simple conversion structure, fast response speed, and high energy density, becoming more and more favored by research institutions and high-tech enterprises. The piezoelectric power generation mechanism relies on the d\({}_{31}\) and d\({}_{33}\) effects of piezoelectric materials. Among them, the cantilever type piezoelectric generator utilizes the d\({}_{31}\) effect, which demonstrates a remarkable power output [32; 33]. Additionally, the cantilever structure is characterized by its simplicity, high flexibility, and ability to achieve a low natural frequency. Currently, numerous studies [34; 35; 36] have been conducted on the vibration generation of piezoelectric cantilever beams, with a primary focus on investigating the impact of piezoelectric structure, size, and parameters on power generation. Considering the cost and reliability of the energy harvesting system, this paper organically combines the piezoelectric technology and the solar technology to ensure the energy supply to the sensor nodes.
## 12 Focus on Transport Mode  
The focused search for MCDA and \"Transport mode\", returned 14 papers (with 1 paper overlapping with the broad civil engineering search) and most relevant to these keywords, for example [114] gave a simple conceptualization of the decision problem. However, one paper on MCDA and route selection was also returned because it was related to mode choice. That paper used AHP with nine criteria (Accident Prone Areas, Bus Lanes, Road Side Car Park, Bicycle Parks, Road Grade, Signalization, Traffic Capacity, Connected Bike Lane and Separated Bike Lane) and weights from a questionnaire survey of 460 cyclists in Isparta City in Turkey to select safe cycling routes that integrated with the public transport system [125]. The use of MCDA in group decision making in the transportation area has been reviewed by [126] with a special focus on selecting transport modes in supply chain management. The AHP (or ANP), ELECTRE and TOPSIS methods dominated their list. However, the transport mode topic is wider than supply chains. For instance, [127] analyzed the satisfaction of visitors to Valparaiso with their choice of transport mode using the fuzzy TOPSIS method with 16 criteria. Tourists who chose bus transport were most satisfied and those who chose rental cars were least satisfied. In Turkey, a variety of weighted sum approaches (SAW) were applied to the choice of mode for transport to airports, with a case study applied to Istanbul, in which underground metro was the preferred choice [128]. Twenty-four factors that influenced the transport choice of walking in a city were integrated into a GIS-based multi-criteria planning system and demonstrated for the European cities of Bologna and Porto [129]. Scenarios of pedestrian transport in Ramallah were prioritized according to 13 criteria using group decision making with stakeholders and the AHP method [130]. The AHP method was also used to prioritize the provision of advanced public transport mode choice in Korean cities [131]. Prioritization of light rail options in Belgium, [130], also used the AHP method with 13 criteria in four main groups (land use, social, environmental and economic). Interestingly, this study distinguished between criteria relating to the transport provider, local government stakeholders and regional government stakeholders including having separate weighting of the criteria for each group. The availability and practicality of sustainable energy-based options may influence choice of transport modes. Thus, networks of electric vehicle charging stations may influence this choice and deciding on their locations is important. This issue was addressed in the city of Howra using fuzzy number formulations of AHP, TOPSIS and COPRAS using a total of thirteen criteria divided into four main groups (economic, environmental, traffic, and social) [132] and a sensitivity analysis in which the top ranking alternatives did not change, but the order of the lower ranking alternatives did change. The main papers are summarized in Table 11.
## 4 Results and Discussion  
Figure 10 shows images taken from the actual testbed using an ordinary camera and a thermal camera. The image histogram was plotted using the pixel values vs. the number of pixels. The histogram of the solar cell image taken from the ordinary camera exhibits a good bimodal distribution. One peak represents the object pixels, while the other peak represents the background. This is good for selecting the threshold of an image. Conversely, the histogram of the thermal solar cell image shows a good distribution of pixels over the entire intensity range. The histogram also shows most pixel values clustered in a small area, whereas the top half of the intensity values are occupied by only a few pixels. The more the pixels are evenly distributed over the entire intensity range, the easier the image can be transformed. Thus, a flat output is obtained.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyper-Parameters** & **CNN** & **CNN-SVM** \\ \hline Batch size & 128 & 128 \\ Dropout rate & 0.5 & 0.5 \\ Learning rate & \(1\times 10^{-3}\) & \(1\times 10^{-3}\) \\ Steps & 10,000 & 10,000 \\ SVMC & N/A & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hyper-parameters used for CNN and CNN-SVM models.  
Figure 9: Confusion matrix for the classification.  
The CDF is more relatively linear in the thermal solar cell image. Therefore, the extracted features can provide more convenient and feasible health monitoring and assessment of solar panels.  
Figure 11 shows the histogram equalization and AHE of the sample solar cell images. It can be seen that the image produced using histogram equalization contains a region that is significantly lighter or darker, the contrast of which is not sufficiently enhanced throughout the image. AHE improves the image by converting each pixel with a transformed function from a neighborhood region.