esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### PCM-Incorporated Building Model
#### 3.2.2 PCM Type Evaluation  
Investigating the most suitable PCM type for building application includes evaluating three commercially available PCM products that are pre-defined in the DesignBuilder software database, namely, BioPCM(r), InfiniteR PCM, and WinCo EnerCiel PCM. The annual energy simulation results for the building after incorporating the specified PCM products underscore the energy-saving potential of incorporating these PCM products into building envelopes. In comparing the baseline building model with extruded polystyrene insulation to the building after incorporating three commercially available PCM products, significant reductions in electrical energy consumption were observed. For BioPCM(r) M91/Q23, the PCM-incorporated building demonstrated a 34.38% reduction in heating and a 23.33% reduction in cooling, resulting in an overall 18.23% reduction in total end-use energy. InfiniteR PCM23 showed even more substantial reductions, with a 37.21% decrease in heating and a 14.74% decrease in cooling, contributing to a total end-use energy reduction of 16.81%. WinCo EnerCiel PCM 23 exhibited an 11.58% reduction in heating and an 8.89% reduction in cooling, leading to a 6.43% decrease in total end-use energy.  
As directly observed from the simulation results, the building incorporated with BioPCM(r) M91/Q23 reached the highest energy-saving rate compared with the baseline simulation results and the other PCM types. The total energy-saving rate achieved by incorporating a single layer of BioPCM(r) M91/Q23 to the external wall of the proposed building model is 18.23%, compared with energy-saving rates of 16.81% and 6.43% for InfiniteR PCM23 and WinCo EnerCiel PCM 23, respectively. Figure 20 shows the HVAC energy comparison chart for the tested types of PCM.
# MZAP--Mobile Application for Basketball Match Tracking and Digitalization of Endgame Reports
## 5 MZAP Android Application--Match Report Preview and Digital Signing
### Match Report Preview  
To preview a match report, an image containing the tracked match data, QR code, and timestamp is rendered on the canvas, saved to local storage, and then displayed using the MZAPViewImageActivity. Depending on the selected language of the application, different template bases are used, as illustrated in Figure 21.  
The QR code embedded in the match report contains a JSON object with three fields:  
* Match UUID: This identifier can be utilized for various purposes, such as accessing detailed match statistics on the internet.
* Timestamp: This indicates when the match report was generated.
* Language Indicator: This field specifies the language of the generated report.  
As outlined in previous chapters, the UUID is generated using the Java UUID Generator (JUG) library [37], while the QR code is created using the ZXing Core library [40]. The PDF file generation and its subsequent locking and encryption are accomplished using the standard Android PdfDocument and PdfBox-Android [39] libraries, respectively. To create the PDF match report, methods that generate a preview of the match report are used to render an image of the match report on the canvas of the first page. If any match irregularities are reported, a second page is created, and the details of the irregularities, along with the officials' names and signatures, are displayed on its canvas.
# Exploring Channel Properties to Improve Singing Voice Detection with Convolutional Neural Networks
## 3 Proposed CNN Architecture
### Baseline Architecture  
In this study, we used the CNN architecture from [22] as the baseline, where the CNN consists of four CNN layers and three dense layers, as shown in Figure 8. Each CNN layer can extract the features by \"observing\" the upper layer feature maps. Theoretically, the deeper the stack of the CNN network, the more accurate the extracted features. This network is relatively shallower because the experimental datasets are relatively smaller. The 3 \(\times\) 3 convolutional kernel is applied to each of the four CNN layers, followed by a Leaky ReLU activation function. The receptive field of 3 \(\times\) 3 is relatively smaller, but the stack of two 3\(\times\)3 layers has an effective receptive field of 5 \(\times\) 5. The stacked smaller receptive field layers can make the decision function more discriminative and decrease the number of parameters [30]. Because rectifier nonlinearities can improve neural network acoustic models, Leaky ReLU is slightly better than ReLU [31]. Therefore, Leaky ReLU was employed in the architecture. The two non-overlapping 3 \(\times\) 3 max pooling stages follow the two CNN stacks, respectively, to reduce the image size and to avoid over-fitting to some extent. The two dropouts also play the role of mitigating over-fitting, whereas the dense layers transform feature maps into feature vectors for classification.  
In this architecture, the CNN is only fed with a simple log-mel spectrogram, without feature engineering, data augmentation, preprocessing, or postprocessing. This is desirable because the CNN, rather than feature engineering, is required to learn the features.
# Investigation of Static and Kinematic Surveying Performance of Handheld GNSS Receiver +
## 2 Materials & Methods
### Kinematic Field Measurement  
In order to assess the kinematic performance of the Garmin handheld GNSS receiver, a kinematic measurement campaign was carried out in the stadium located on the ITU Campus on 29 July 2023 (GPS Day of Year: 210). At the beginning of the measurements, data was collected for approximately 30 min without movement (as static), and then measurements were continued by walking in the stadium for about 30 min. In order to get the kinematic performance of the Garmin receiver, simultaneous measurements were carried out with a geodetic-grade GNSS receiver (to obtain known coordinates of each measured epoch for use in comparison) that is connected to the same measurement pole as the Garmin receiver (Figure 2a,b). In this way, GNSS data was collected by the Garmin GPSMAP 66 sr and geodetic-grade receivers under almost the same conditions, which allows for precise assessment of the Garmin receiver. In the kinematic campaign, GPS, GLONASS, and Galileo observations were collected at a 5-s sampling rate, and the GNSS raw data was logged in RINEX format into the receiver's internal memory. The  
Figure 1: (**a**) Handheld receiver (GPSMAP 66 sr) used in the study; (**b**) Static measurement; (**c**) Number of satellites; (**d**) PDOP values; (**e**) Signal-to-Noise (SNR) values; (**f**) Multipath values.  
tracked number of GNSS satellites and Position Dilution of Precision (PDOP) values for the kinematic measurement campaign were given in Figure 2c,d. Signal-to-noise ratio (SNR) and site-dependent multipath plots were created with RTKLIB v.2.4.3-b34 Manual and in-house software and were given in Figure 2e,f.
## 5 Simulation Experiments
### Mobile Node Utilization Analysis  
Analyze the connection between mobile node utilization and the quantity of mobile nodes in a target region of \(10\times 10\times 10\) m\({}^{3}\) and \(100\times 100\times 100\) m\({}^{3}\) for the DHD-MEPO. By setting the proportion of mobile nodes to 20%, 40%, and 60% of the total quantity of nodes in hybrid WSNs in different monitoring regions, the mobile node utilization of the DHD-MEPO is observed for a different quantity of working rounds. The analysis of Figure 16 shows that at the beginning when the algorithm works for 40 rounds, the utilization rate of mobile nodes is not high, especially when mobile nodes account for 60% of the total quantity of nodes. Even after 200 rounds of the algorithm, the proportion of mobile nodes  
Figure 14: Plot of the quantity of working rounds versus the nodes’ overall energy consumption: (a) Relationship map within the area \(10\times 10\times 10\) m\({}^{3}\); (b) Relationship map within the area \(100\times 100\times 100\) m\({}^{3}\).  
effectively repairing holes is less than 50%, so if a large amount of mobile nodes is thrown in hybrid WSNs, not only are the resources of mobile nodes not effectively used, i.e., most nodes will act as static nodes responsible for receiving and sending messages, but there will also be an increase in the network's cost. After the comparative analysis of the mobile node ratio in Figure 16, we chose to deploy 15 mobile nodes initially in the experimental area of \(10\times 10\times 10\) m\({}^{3}\) and 30 mobile nodes initially in the area of \(100\times 100\times 100\) m\({}^{3}\), which can ensure that the mobile nodes are used effectively as fully as possible and can improve the energy efficiency of the nodes.
## 3 Results and Discussion
### Compressive Strength and Dry Density  
Figure 4 presents the test results for the compressive strength and dry density analyses of the 3 DPFC. The specific parameters of the 3 DPFC can be found in Table 2, with curing ages of 7 days, 28 days, and 60 days.  
As shown in Figure 4, from left to right, the dry densities of the 3 DPFC samples at 28 days are 1170 kg/m\({}^{3}\), 972 kg/ m\({}^{3}\), 964 kg/ m\({}^{3}\), 839 kg/ m\({}^{3}\), 845 kg/ m\({}^{3}\), and 675 kg/ m\({}^{3}\), respectively. Furthermore, it can be observed from Figure 4 that the compressive strength  
Figure 4: Compressive strength and dry density analyses of the 3 DPFC samples.  
Figure 3: Laboratory test for assessing the buildability of the 3 DPFC samples.  
of the samples at 7 days, 28 days, and 60 days varies with the change in the formulation of the 3D-printed expanded perlite foam concrete. For instance, considering the 28-day compressive strength of the samples, sample EP40S2 and sample EP80S1 exhibit similar dry densities, with the 28-day compressive strength of sample EP40S2 at 10.80 MPa, while sample EP80S1 achieves a compressive strength of 15.05 MPa, indicating an increase of 39%. Similarly, samples EP80S2 and EP100S1, which have comparable dry densities, demonstrate 28-day compressive strengths of 6.49 MPa and 8.21 MPa, respectively, representing an increase of 26.5%. It is noteworthy that within the same density range, samples with higher replacement rates of expanded perlite exhibit significantly higher compressive strengths at 7 days, 28 days, and 60 days of curing compared to those with lower replacement rates. This variation can be explained by differences in foam content among the samples. Samples with lower replacement rates of expanded perlite often require more foam to achieve similar dry densities, while those with higher replacement rates require less foam, thereby enhancing the mechanical performance of the 3 DPFC [41]. Additionally, samples with higher foam content exhibit higher porosity and larger pore sizes, which is a significant contributing factor to their lower compressive strength [42].  
In comparison with previous studies on 3 DFC [26; 41], the 3 DPFC presented in this study demonstrates notably higher compressive strength at similar dry densities.
# Compensating Uncertainties in Force Sensing for Robotic-Assisted Palpation
## 2 Robotic palpation with Force Based Approach  
In this section, a force based robotic palpation approach is introduced. To quantitatively analyze the stiffness of soft tissue with a hard inclusion embedded, [PERSON]'s modulus is used, which considers the probe size and the materials of the environment. The measured force is the main factor in robotic palpation which is usually obtained from the force sensor. However, sensor uncertainties are inevitable. In the robotic palpation process, the probe is often controlled by an operator or moved autonomously to contact the soft tissue, thus the inertial force will be introduced during the dynamic motion of robotic palpation. To obtain an accurate stiffness of the soft tissue for better analysis, it is necessary to compensate for the inertial force measured from the sensor during the dynamic motion.
## 4 Results and Analysis
### Full Passage  
#### 4.2.1 Overall Performance  
The overall performance of the LL impeller with full passage model is shown in Figure 13. In this figure, the experimental data referred to is from Reference [19]. The calculation result of the full passage model can be matched with experiments very well, while the performance of the single passage model is different from that of the experiment. In the single passage model, the volute is ignored so that flowing loss in volute is not considered.  
Figure 12: Relative Mach number distribution on blade to blade plane at 0.85 flowrate.  
Figure 13: Performance verses flow rate.  
It is shown in Figure 14 that the numerical result comparison of Mach number and pressure coefficient distribution between full passage (Figure 14a) and single passage (Figure 14b) at the design point. It is apparent that the flow pattern of real compressor is unsymmetrical because of the unsymmetrical geometrical structure of the solute.  
The circumferential flow situation of mid-surface can be seen in Figure 15. The absolute Mach number (Figure 15a) and pressure distribution (Figure 15b) are evidently asymmetric throughout the compressor because of asymmetric structure of solute. There is an obvious high Mach number region in the impeller near the tongue of solute, and the static pressure of this region is also relatively high. It can be inferred that the tongue of solute has significant impact in the nearby flow.
# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 3 The Modulation Recognition Method Based on Deep Learning
### Application of Combination Neural Network in Modulation Recognition
Finally, the spatial and temporal properties of the signal are fused using GAMP, and the classification is done with a fully connected layer. Experiments show that the attention cooperation framework proposed in this paper can recognize 11 kinds of modulated signals with an accuracy of more than 90% at 0 dB. The recognition accuracy is better than that of VGG, RNN-GRU, GoogleNet, and CLDNN. The authors of [79] proposed a CGDNet network structure composed of shallow convolutional networks, GRU, and DNN networks in their work. Among them, a shallow convolutional network and GRU are used to extract the features of IQ sequence signals, and DNN is used to complete the classification task. In the experiments, the author uses the RadioML2016.10a and RadioML2016.10b datasets to verify the performance of the CGDNet structure. When identifying the RadioML2016.10a modulation dataset, it can achieve a recognition accuracy of more than 90% at 0 dB; when recognizing the RadioML2016.10b modulation dataset, it can achieve a recognition accuracy of more than 90% at 18 dB. The authors of [80] proposed a modulation recognition algorithm based on convolutional long and short-term deep neural networks (CLDNN). The CLDNN network structure consists of four layers of convolution layers, four layers of pooling layers, two layers of LSTM layers, and two layers of fully connected layers. Experiments show that the average recognition rate of the CLDNN model for 11 kinds of modulation signals reaches 90.8%, and it can recognize both QAM16 and QAM64 better. In recent years, modulation identification methods based on combinatorial networks have become more popular, and many scholars have devoted themselves to researching more reliable combinatorial networks for AMR. Combinatorial network-based modulation identification methods over recent years are summarized in Table 3.
# New Insights in the Ion Beam Sputtering Deposition of ZnO-Fluoropolymer Nanocomposites
## 2 Materials and Methods
### Water Contact Angle (WCA) Measurements  
Dynamic WCA measurements were carried out using a Rame-Hart (Succasunna, NY, USA) manual goniometer (model A-100). Contact angles were measured on five double-distilled water drops for each sample, and the average value was calculated. Advancing and receding contact angles were measured according to the sessile drop method. A 1-\(\upmu\)L droplet was firstly deposited on a sample's surface by a micrometer syringe and its volume was increased, forcing the droplet to advance onto the sample until a constant contact angle was observed. This constant contact angle represented the advancing contact angle. Then, the droplet was then progressively shrunk until a radius decrease occurred. The last contact angle value read before radius change was the receding WCA. For all contact angle data, a minimum uncertainty of \(\pm 1^{\circ}\) was assumed.
# Validation of Drifting Buoy Data for Ocean Wave Observation
## 5 Conclusions  
The agreement of the drifting wave buoy-observed wave heights with the GPS observations was better than that of the ERA5 wave heights. This is also true for the period. However, the result that the accuracy of the drifting wave buoy-observed wave periods was better than that of the ERA5 wave periods was not as robust as the result for the wave heights. The sampling variability by the zero-up crossing method (i.e., Figure A1, black and red lines), resolution of the wave parameters (e.g., 0.1 m for \(H_{b}\)), and sampling variability of the wave parameters were considered in the comparison. The difference between the GPS buoy-measured and drifting buoy-measured wave data was associated with the spatial variability. The accuracy of the ERA5 wave height is lower than that of the drifting buoy wave, especially where the ocean current is fast, such as the Kuroshio.  
The drifting buoys would be useful for studying wave-current interactions. The wave prediction model that incorporated the wave-current interactions in the open ocean can be validated by the drifting buoys.  
This study was financially supported by a Grant-in-Aid for Scientific Research (C-2) from the Ministry of Education, Culture, Sports, Science, and Technology of Japan (20K04708).  
Not applicable.  
The GPS buoy wave data were observed by Maritime Bureau, Ministry of Land, Infrastructure, Transport, and Tourism of Japan, and analyzed by Port and Airport Research Institute. The GFD DENNOU Library (available online at [[http://www.gfd-dennou.org/arch/dcl/](http://www.gfd-dennou.org/arch/dcl/)]([http://www.gfd-dennou.org/arch/dcl/](http://www.gfd-dennou.org/arch/dcl/)) (accessed on 30 June 2021) ) was used for drawing the figures. The comments from the four anonymous reviewers were helpful in revising the manuscript.  
The author declares no conflict of interest.
# Power Factor Correction Application Based on Independent Double-Boost Interleaved Converter (IDBIC)
## 2 Converter Topology Analysis  
### General Representation of the Switching States  
The proposed PFC application consists of a novel DC-DC power-stage topology named Independent Double Boost Interleaved Converter (IDBIC). The current topology is based on a patent application in reference [27] that describes its operation in DC-DC systems through paper [28]. The basic PFC converter topology is presented in Figure 1, while the characteristic waveforms during operation are introduced in Figure 2, where the continuous conduction mode (CCM) is exemplified for a duty cycle D larger than 0.5 by means of switching states S1, S4 and S5, respectively, for a duty cycle smaller than 0.5 with the help of switching states S1, S2, S3 and S5.  
The main operating stages of the converter for positive alternation are shown in Figure 3, where the diodes D\({}_{\rm r1}\) and D\({}_{\rm r4}\) are in conduction and five independent switching states (S1-S5) are highlighted. For the negative alternation, the operating stages are the same on the DC stage; the only difference is that the input current flows through the D\({}_{\rm r2}\) and D\({}_{\rm r3}\) diodes.  
Figure 2: Presumptive functioning waveform and switching stages correlations. V\({}_{\text{GS-T1}}\), V\({}_{\text{GS-T2}}\), V\({}_{\text{GS-T3}}\) and V\({}_{\text{GS-T4}}\)—Gate-Source voltage for T\({}_{1}\)–T\({}_{4}\) transistors; V\({}_{\text{DS-T1}}\), V\({}_{\text{DS-T2}}\), V\({}_{\text{DS-T3}}\) and V\({}_{\text{DS-T4}}\)—Brain-Source voltage for T\({}_{1}\)–T\({}_{4}\) transistors; I\({}_{\text{L1}}\) and I\({}_{\text{L2}}\)–I\({}_{\text{L}}\) and L\({}_{\text{2}}\) inductor currents; D—Duty Cycle; CCM—continuous conduction mode; S1–S5—switching stages from Figure 2.  
Figure 1: The electronic schematic of the proposed converter. V\({}_{\text{in}}\)—main input voltage; V\({}_{\text{rec}}\)—rectified voltage; V\({}_{\text{out}}\)—DC output voltage; I\({}_{\text{rec}}\)—rectified current; I\({}_{\text{in}}\)—input current; and I\({}_{\text{L1}}\) and I\({}_{\text{L2}}\)–L\({}_{\text{1}}\) and L\({}_{\text{2}}\) inductor currents.
## 5 The Proposed Routing Method
### Distributed and Lightweight Trust Mechanism (W-Trust)
#### 5.1.2 Indirect Trust  
The indirect trust of the node \(i\) relative to the node \(j\) (i.e., \(T_{ij}^{indirect}\)) means the performance analysis of the node \(j\) by a group of recommender nodes. To calculate this parameter, recommender nodes must be determined accurately. In W-Trust, recommender nodes are a set of common neighbors of the nodes \(i\) and \(j\), so that their weighted trust level is more than \(T_{threshold}\). Suppose two nodes \(i\) and \(j\) have \(p\) common neighbors (i.e., \(Nei=\left\{nei_{1},nei_{2},...,nei_{p}\right\}\)), and their trust level is higher \(T_{threshold}\). In this case, the weighted indirect trust (\(T_{ij}^{Weighted-indirect}\)) is calculated based on Equation (16).  
\[T_{ij}^{Weighted-indirect}(t)=\frac{1}{p}\sum\limits_{r\in Nei}^{p}\left(T_{ir }^{Weighted-direct}\cdot T_{rj}^{Weighted-direct}\right) \tag{16}\]  
where \(T_{ir}^{Weighted-direct}\) indicates the weighted direct trust of the node \(i\) relative to the node \(r\). \(T_{rj}^{Weighted-direct}\) represents the weighted direct trust of the node \(r\) relative to the node \(j\). Furthermore, the node \(r\) is a recommender node with the trust value more than \(T_{threshold}\).
## Appendix A Considered Doppler Spectra  
The following algorithms represent the analytical expressions for each Doppler spectrum type. In each case, \(f_{d}\) denotes the maximum Doppler shift of the associated fading channel system object.  
* \"Jakes\" spectrum \[P_{f}(f)=\frac{1}{\pi f_{d}\sqrt{1-\left(f/f_{d}\right)^{2}}},|f|\leq f_{d},\] (11)
* \"Flat\" spectrum \[P_{f}(f)=\frac{1}{2f_{d}}\] (12)
* \"Bell\" spectrum \[P_{f}(f)=\frac{G_{b}}{1+A\left(\frac{f-f_{d}}{f_{d}}\right)^{2}},|f|\leq f_{d},\] (13) where \[G_{b}=\frac{\sqrt{A}}{\pi f_{d}}.\] (14) The coefficient \(A\) can be specified in the dependent field.
* \"Gaussian\" spectrum \[P_{f}(f)=\frac{1}{\sqrt{2\pi\sigma_{G}^{2}}}\exp\left(-\frac{(f-f_{d})^{2}}{2 \sigma_{G}^{2}}\right),\] (15) where \(\sigma_{G}\) is the normalized standard deviation.