esa-sceva/satcom-chunk-collection huggingface dataset:
# Integration of Freeplay-Induced Limit Cycles Based On a State Space Iterating Scheme
## 2 State Space Equations and the Henon-RK45 Method
### Time Integrations Based on the Runge-Kutta and Henon Methods  
Time integration using a combined fourth-fifth-order Runge-Kutta and the Henon method (Henon-RK45) was proposed by [PERSON] [7] and is widely adopted when solving a set of state space equations with non-smooth nonlinearities. For simplicity, the state space Equation (1) are expressed as \(\dot{\mathbf{x}}=\mathbf{f}(\mathbf{x})\). The Henon-RK45 method integrates the equations using an ordinary fourth-fifth-order Runge-Kutta scheme (RK45) initially until any freeplay boundary is crossed by \(x_{k}(t)\). Assume that the latest time instance before crossing is \(t_{\text{break}}\), and the states at that time are \(\mathbf{x_{\text{break}}}\). Then, the Henon method moves the freeplay state \(x_{k}\) to the about-crossing boundary precisely via the following steps: (1) Change the integrating variable from time \(t\) to the freeplay state \(x_{k}\); that is,  
\[\frac{\text{d}}{\text{d}x_{k}}\left[\begin{array}{c}\mathbf{x}\\ t\end{array}\right]=\left[\begin{array}{c}\frac{\text{d}\mathbf{x}}{\text{d}t }\cdot\frac{\text{d}t}{\text{d}x_{k}}\\ 1\cdot\frac{\text{d}t}{\text{d}x_{k}}\end{array}\right]=\left[\begin{array}[] {c}\dot{\mathbf{x}}/\dot{x}_{k}\\ 1/\dot{x}_{k}\end{array}\right]=\left[\begin{array}{c}\mathbf{f}(\mathbf{x}) /\dot{x}_{k}\\ 1/\dot{x}_{k}\end{array}\right] \tag{3}\]  
Thus, a new set of state space equations can be constructed as below:  
\[\frac{\text{d}}{\text{d}x_{k}}\mathbf{y}=\mathbf{f}^{*}(\mathbf{y}) \tag{4}\]  
where \(\mathbf{y}=\left[\mathbf{x}^{T},\ t\right]^{T}\) is the new vector of the states. (2) Integrate Equation (4) using RK45 from \(\mathbf{y}_{0}=\left[\mathbf{x}_{\text{break}}^{T},\ t_{\text{break}}\right]^ {T}\) as \(x_{k}\) is moved from \(x_{k,\text{break}}\) to the about-crossing boundary, where \(\mathbf{y}\) becomes \(\mathbf{y}_{c}=\left[\mathbf{x}_{c}^{T},\ t_{e}\right]^{T}\). (3) Resume the integration of \(\mathbf{x}(t)\) using the RK45 scheme from \(\mathbf{x}_{e}(t_{e})\) until the next crossing event occurs, and then repeat steps (1) to (3) until the system responses are obtained within a sufficiently long time range.
# Implementation of an IoT Based Radar Sensor Network for Wastewater Management
###### Abstract  
Critical wastewater events such as sewer main blockages or overflows are often not detected until after the fact. These events can be costly, from both an environmental impact and monetary standpoint. A standalone, portable radar device allowing non-invasive benchmarking of sewer pumping station (SPS) pumps is presented. Further, by configuring and deploying a complete Low Power Wide Area Network (LPWAN), Shoalhaven Water (SW) now has the opportunity to create \"Internet of Things\" (IoT)-capable devices that offer freedom from the reliance on mobile network providers, whilst avoiding congestion on the existing Supervisory Control and Data Acquisition (SCADA) telemetry backbone. This network infrastructure allows for devices capable of real-time monitoring to alert of any system failures, providing an effective tool to proactively capture the current state of the sewer network between the much larger SPSs. This paper presents novel solutions to improve the current wastewater network management procedures employed by SW. This paper also offers a complete review of wastewater monitoring networks and is one of the first to offer robust testing of Long Range Wide Area Network (LoRaWAN) network capabilities in Australia. The paper also provides a comprehensive summary of the LoRa protocol and all its functions. It was found that a LPWAN, utilising the LoRaWAN protocol and deployed appropriately within a geographic area, can attain maximum transmission distances of 20 km within an urban environment and up to 35 km line of sight.  
commuications; Internet of Things; IoT; LoRaWAN; LPWAN; sewer pumping stations; wastewater; wastewater collection network; WCN +
Footnote †: journal: Sensors
## 4 Results
### Experimental Results
#### 4.2.1 Domain Selection Based on a Two-Class Problem
However, selecting only the best combination of closely related highly beneficial sources can significantly minimize the effect of NT and, in turn, increase performance in the TD. In this experiment, the impact of NT on transfer mapping performance is further investigated using a BCI competition IV-a dataset consisting of nine MI subjects. Furthermore, to determine the impact of NT on performance, four domain selection algorithms are evaluated based on the selected beneficial domains for transfer mapping. As such, EMC-MMFT is further compared with DTE, ROD, and LSA-MMFT across individual TDs under a two-class inter-subject classification scenario. In this case, the EMC-MMFT framework maintains and achieves superior classification performances across all the individual TDs compared to the DTE, ROD, and LSA-MMFT algorithms. Consequently, the highest CAs of 97% is recorded across S3 when DTE and LSA-MMFT are employed, respectively, while a CA of 96% is observed when ROD is employed. However, a superior CA of 98% is recorded when S3 is assigned the role of TD, and S4, S5, S8, and S9 are selected as the highly beneficial SDs, as depicted in Table 1.  
Moreover, removing a single less-beneficial domain in each iteration is demonstrated to pose severe implications for the classification performance, with a significant decline in CA being observed across S8 when DTE, ROD, and LSA-MMFT are employed. The highest CA of 53% is observed when ROD is employed, compared to both DTE and LSA-MMFT, while a 44% increase in CA is observed, with a superior CA of 97% being recorded across S8, when EMC-MMFT is employed, as depicted in Figure 8.  
Figure 6: EMC-MMFT performance evaluation based on a two-class inter-subject scenario (our own SSMVEP subjects). From these results one finds that the complexity of a two-class problem has a minimal impact on the classification performance of ROD, DTE, LSA-MMFT, and EMC-MMFT. However, the presence of less-beneficial domains when transfer mapping is applied can significantly deteriorate classification performance as a result of NT. Notably, removing an individual domain when iterating through domains to identify highly beneficial sources is demonstrated to pose severe implications for the prediction rate. Nonetheless, selecting  
Figure 8: EMC-MMFT performance evaluation based on a two-class inter-subject scenario (BCI competition IV-a). Figure 7: EMC-MMFT performance evaluation based on a two-class inter-subject scenario (our own MI subjects). only the optimal combination of highly beneficial sources and discarding less-beneficial domains is proven to significantly improve the prediction rate across individual TDs when EMC-MMFT is employed.
# SP-YOLOv8s: An Improved YOLOv8s Model for Remote Sensing Image Tiny Object Detection
## 1 Introduction
Moreover, downsampling during the image processing may cause a loss of object information, resulting in a sparse representation of object features within the high-level feature map. Consequently, these features are prone to be disregarded or misinterpreted as background, leading to false and missed detection issues. In addition, complex environmental sets introduce further complexities, including object occlusion, small object aggregation, varying light intensities, noise, and diverse object poses, all impacting the accuracy of tiny object detection. To address these challenges, this paper proposes an improved YOLOv8s-based algorithm for tiny object recognition, using YOLOv8s as a basic network. The improved path aggregation network SPANet is also used to reinforce the fusion of feature maps at various scales and reduce the model parameters. In addition, we incorporate the SPD-Conv module to reinforce the network's capability to extract features from tiny objects. The main contributions of this paper are the following:  
A tiny object detection algorithm based on improved YOLOv8s is proposed for complex backgrounds. * Using the SPD-Conv module, the benchmark network YOLOv8s enhances the complex background tiny object feature extraction capability. It can also effectively retain fine-grained feature information and improve network recognition accuracy;
* The SPANet path aggregation network is used to enhance the fusion effect of different scale feature maps, reduce the model parameters, fully fuse the contextual information, and reinforce the stability of the network to complex backgrounds. Overall, this proposed method addresses the challenges associated with detecting tiny objects in remote sensing images, offering improved recognition accuracy and computational efficiency in complex backgrounds.
## 6 The Evaluation of the Network Availability for WDM-PON Protection Schemes
### The Evaluation of the P2 MP WDM-PON Protection Scenarios  
The network availability of P2 MP WDM-PON protection schemes achieves different values for different protection scenarios. The highest network availability is reached by the Type C protection. Other protection scenarios--Type B and Dual-parented Type B--reach similar results. In Figure 7, the network availability of the dual-parented Type B protection is presented for various protection times from Table 4. As can be seen, this network availability is decreasing with higher restoration times.
# Measurement of Head-Related Transfer Functions: A Review
## 7 Discussion
### HRTF Format  
HRTFs should be stored in an adequate format for different applications and research. The storage of measured HRTFs is typically based on particular requirements or purposes in each laboratory. For HRTFs that are only measured at a few positions, each pair of HRTFs can simply be saved in the \"*wav\" format with its specific file name to represent the information of the measurement point. The MAT (Matlab) format shows some advantages compared to the \"*wav\" format when storing a relatively large dataset, since HRTFs can be stored as multi-dimensional matrix forms. The size of the matrix can be used to represent the information of measurement directions.  
With regard to the publication of measured HRTF datasets, a uniform and standardized format to store HRTFs is required, since each laboratory measures HRTFs based on its own standards, e.g., coordinates, number, and distribution of measurement points. The Audio group of Cologne University of Applied Science developed a MIRO (measured impulse response object) format for the storage of acoustic impulse responses under MATLAB [78]. [PERSON] et al. [205] built an HRTF repository to combine different HRTF datasets, namely MARL-NYU format, which simplifies the navigation between different HRTF sets. To enable a simple exchange of directional audio data such as HRTFs/HRIRs, directivities of loudspeakers and microphones, [PERSON] et al. [206] proposed an OpenDAFF format ([[https://blog.rwth-aachen.de/akustik/opendaff-v17-released](https://blog.rwth-aachen.de/akustik/opendaff-v17-released)]([https://blog.rwth-aachen.de/akustik/opendaff-v17-released](https://blog.rwth-aachen.de/akustik/opendaff-v17-released))). Although it is inconvenient to map the measured HRTF datasets in a regular grid on a sphere, this format has been accepted by many researchers. [PERSON] et al. [207] presented an HRTF storage format, namely the spatially oriented format for acoustics (SOFA), which can be used for describing HRTF measurement results including almost all acoustic information regarding the measurement environment and geometrical setups. This format has been standardized by the AES as AES69-2015 ([[http://www.aes.org/publications/standards/search.cfm?docID=99](http://www.aes.org/publications/standards/search.cfm?docID=99)]([http://www.aes.org/publications/standards/search.cfm?docID=99](http://www.aes.org/publications/standards/search.cfm?docID=99))). In comparison with the OpenDAFF format, the SOFA format seems to provide more detailed information about the measurement setups/environments. The SOFA repository has already collected more than 10 publicly available HRTF datasets and can be found in: [[https://www.sofaconventions.org/mediawiki/index.php/Files](https://www.sofaconventions.org/mediawiki/index.php/Files)]([https://www.sofaconventions.org/mediawiki/index.php/Files](https://www.sofaconventions.org/mediawiki/index.php/Files)).
# A Neural Parametric Singing Synthesizer Modeling Timbre and Expression from Natural Songs +
## 1 Introduction  
Many of today's more successful singing synthesizers are based on concatenative methods [1; 2]. That is, they transform and concatenate short waveform units selected from an inventory of recordings of a singer. While such systems are able to achieve good sound quality and naturalness in certain settings, they tend to be limited in terms of flexibility, and can be difficult to extend or significantly improve upon. One notable limitation is that jointly sampling musical and phonetic contexts usually is not feasible, forcing timbre and expression to be modeled disjointly, from separate, specialized corpora. Machine learning approaches, such as statistical parametric methods [3; 4], are much less rigid and do allow for things such as combining data from multiple speakers, model adaptation using small amounts of training data, and joint modeling of timbre and expression from a single corpus of natural songs. However, until recently, these approaches have been unable to match the sound quality of concatenative methods, in particular suffering from oversmoothing in frequency and time.  
Recent advances in generative models for Text-to-Speech Synthesis (TTS) using Deep Neural Networks (DNNs), in particular the WaveNet model [5], showed that model-based approaches can achieve sound quality on-par or even beyond that of concatenative systems. This model's ability to accurately generate raw speech waveform sample-by-sample, clearly shows that oversmoothing is not an issue. Recently, we presented a model for singing synthesis based on the WaveNet model [6], with an important difference being that we model vocoder features rather than raw waveform. Whilea vocoder unavoidably introduces some degradation in sound quality, we consider the degradation introduced by current models to still be the dominant factor. Thus, if we can improve the quality of the generative model, we should be able to achieve a quality closer to the upper bound the vocoder can provide, i.e., round-trip vocoder analysis-synthesis without modification. Additionally, by decomposing the signal into phonetic and pitch components, we are able to conveniently synthesize any melody with any lyrics, and require less training data to sufficiently cover the entire pitch-timbre space.  
Our previously presented system only generated timbrical features, and did not generate features related to musical expression, such as F0 and phonetic timings. Additionally, the corpora used to train the models were specialized recordings similar to those used for building concatenative voices. In this work, we extend our previously presented system to also include F0 and phonetic timing prediction, and train the entire system from a single corpus of natural singing. We feel that this is an important step forward towards capturing all aspects of a singer's voice in a natural setting. Finally, we provide detailed quantitative and qualitative experiments and results.
## 3 Simulation
### Discussions  
Theoretically, the MDPO algorithm works at any satellite trajectory and with any number of satellites that is more than two. Moreover, it can be evolved into a larger lunar global navigation system that is proposed by other studies [7; 8], without any hardware modification. As proven by the simulation, user position error is basically determined by the product of Total GDOP and receiver observation errors according to Equation (31), while systematic errors determine a minimum user position error when the receiver observation error is small. User position error due to systematic errors is mostly derived from the steepness of the DEM function, i.e., \(z_{R\ DEM}\) in Equation (38), and DEM information error, i.e., \(\Delta z_{R\ DEM}\), which changes depending on the selected mission site.  
Figure 6: GDOP history over the course of the simulation period. (a) for 1000 min (closer look), (b) for 15,000 min (overall). Unless both satellites are in view, DOP is not calculated and not shown in the figures.
# A Novel GBSM for Non-Stationary V2V Channels Allowing 3D Velocity Variations
## 4 Time-Variant Parameter Computations  
### Time-Variant Distances  
During the movement of transceivers Tx, Rx, and clusters \(C_{n}^{Tx}\), \(C_{n}^{Rx}\), the location vector for a very short time interval of the transceivers or the clusters can be represented as:  
\[\|\textbf{r}\|_{n}^{i}(t)=\|\textbf{r}\|_{n}^{i}(0)+\int_{0}^{t}\bigg{(}\Big{(} \|\textbf{v}\|_{n}^{i}(0)\Big{)}+\int_{0}^{\tau}\Big{(}\|\textbf{a}\|_{n}^{i} (t^{\prime})\Big{)}dt^{\prime}\bigg{)}d\tau \tag{13}\]  
\[\|\textbf{r}\|_{n}^{\text{C}_{n}^{i}}(t)=\|\textbf{r}\|_{n}^{\text{C}_{n}^{i}} (0)+\int_{0}^{t}\bigg{(}\Big{(}\|\textbf{v}\|_{n}^{\text{C}_{n}^{i}}(0)\Big{)} +\int_{0}^{\tau}\Big{(}\|\textbf{a}\|_{n}^{\text{C}_{n}^{i}}(t^{\prime})\Big{)} dt^{\prime}\bigg{)}d\tau \tag{14}\]where \(\left\|\mathbf{r}\right\|_{n}^{i}(t)\) and \(\left\|\mathbf{r}\right\|_{n}^{C_{n}^{i}}(t)\) denote the 3D relative location of the transceivers and clusters on x-, y-, and z-axes, respectively. Due to the mobility of the clusters and terminals, the distance between the Tx and \(C_{n}^{Tx}\) or between the Rx and \(C_{n}^{Rx}\) can be represented by \(\left\|\mathbf{D}\right\|_{n}^{i}(t)\), where \(i\in\{\text{Tx},\,\text{Rx}\}\), which is based on the correlative movements. In this paper, it can be expressed as:  
\[\left\|\mathbf{D}\right\|_{n}^{i}(t)=\sqrt{\left(\left\|\mathbf{r}_{n,x}^{C_{ n}^{i}}(t)-\mathbf{r}_{n,x}^{i}(t)\right\|\right)^{2}+\left(\left\|\mathbf{C}_{n,y}^ {i}(t)-\mathbf{r}_{n,y}^{i}(t)\right\|\right)^{2}+\left(\left\|\mathbf{r}_{n, z}^{i}(t)-\mathbf{r}_{n,z}^{i}(t)\right\|\right)^{2}}. \tag{15}\]  
According to the location vector of the Tx and \(C_{n}^{Tx}\) or the Rx and \(C_{n}^{Rx}\), the instantaneous location of both the transceivers and clusters can be represented as (16) and (17), respectively, for the x-, y-, and z-axes:  
\[\begin{split}\mathbf{r}_{n,x}^{i}(t)&=\cos\!\left( \varphi_{n}^{i}(t)-\varphi_{v}^{C_{n}^{i}}\right)\cos\alpha_{n}^{i}(t)\\ \mathbf{r}_{n,y}^{i}(t)&=\cos\!\left(\varphi_{n}^{i} (t)-\varphi_{v}^{C_{n}^{i}}\right)\sin\alpha_{n}^{i}(t)\\ \mathbf{r}_{n,z}^{i}(t)&=\sin\alpha_{n}^{i}(t)\\ \mathbf{r}_{n,x}^{C_{n}^{i}}(t)&=\cos\!\left( \varphi_{n}^{C_{n}^{i}}(t)-\varphi_{v}^{i}\right)\cos\alpha_{n}^{C_{n}^{i}}(t) \\ \mathbf{r}_{n,y}^{C_{n}^{i}}(t)&=\cos\!\left( \varphi_{n}^{C_{n}^{i}}(t)-\varphi_{v}^{i}\right)\sin\alpha_{n}^{C_{n}^{i}}(t) \\ \mathbf{r}_{n,z}^{C_{n}^{i}}(t)&=\sin\alpha_{n}^{C_{n} ^{i}}(t)\end{split} \tag{16}\]
## 4 Results
### Experimental Results and Analysis  
#### 4.3.1 Vehicle and License Plate Detection Performance  
The results of the vehicle and license plate detection performance evaluation are presented in Tables 2 and 3, respectively. The training process of YOLOv7 and YOLOv8 is shown in Figure 6. For vehicle detection, YOLOv8 demonstrated overall superior performance compared to YOLOv7. YOLOv8 showed approximately a 4.2% improvement in performance with an mAP of 0.689 compared to YOLOv7's 0.661, and particularly showed about 31.3% improved performance in mAP_s for small objects with 0.474 compared to YOLOv7's 0.361. Additionally, in terms of processing speed, YOLOv8 showed approximately 13.1% faster latency at 14.6 ms compared to YOLOv7's 16.8 ms.  
In license plate detection, YOLOv8 also showed superior performance compared to YOLOv7. YOLOv8 showed approximately 1.7% improvement in performance with an mAP of 0.704 compared to YOLOv7's 0.692 and particularly showed about 131.7% improved performance in mAP_s for small objects with 0.373 compared to YOLOv7's 0.161. In terms of processing speed, YOLOv8 also showed approximately 2.6% faster latency at 15.2 ms compared to YOLOv7's 15.6 ms.
## 2 Characteristic Mode-Based Antenna Methodology
Mode 1-3 have an approximately 180\({}^{\circ}\) phase lag between the surface current and the electric field, as represented by the characteristics angle in Figure 5d. The modification to the structure significantly suppressed Mode 4 and 5 at 25.5 and 38 GHz. With the port excitation, dual-resonance can be seen in Figure 5e at 25.5 and 38 GHz with good impedance matching of 17.5 dB and 22.5 dB. It achieved a 25-25.9 GHz and 37.9-38.9 GHz bandwidth. Figure 2: Eigenvector current \(J_{n}\) on the surface of Stage 1 and its respective radiation pattern at (**a**) 25.50 GHz and (**b**) 38 GHz. Figure 4: Eigenvector current \(J_{n}\) on the surface of Stage 2 and its respective radiation pattern at (**a**) 25.50 GHz and (**b**) 38 GHz. Figure 3: CMA of Mode 2–5 and reflection coefficients of the Stage-1 MS antenna. (**a**) Stage-1 antenna (dimensions in mm), (**b**) modal significance, (**c**) eigenvalues, (**d**) characteristic angle, and (**e**) reflection coefficient with port excitation. The eigenvector current of Mode 1 and 2 at 25.5 GHz has an in-phase current resulting in bidirectional radiation, as shown in Figure 6a. At 38 GHz (Figure 6b), Mode 1 has an in-phase current along the vertical edges and an anti-phase current along the horizontal edges, resulting in bidirectional quad radiation. Similarly, in Mode 2, there are in-phase currents along horizontal edges and anti-phase currents along vertical edges. Though Mode 5 is largely capacitive, its surface current impacts the radiation patterns. Figure 5: CMA of Mode 2–5 and reflection coefficients of the Stage-3 MS antenna. (**a**) Stage-1 antenna (dimensions in mm), (**b**) modal significance, (**c**) eigenvalues, (**d**) characteristic angle, and (**e**) reflection coefficient with port excitation. Figure 6: Eigenvector current \(J_{n}\) on the surface of Stage 3 and its respective radiation pattern at (**a**) 25.50 GHz and (**b**) 38 GHz.
## 5 Results
### Morphological Channel Change Analysis  
At the Site 1 bank, despite a monitoring period of five years, the recorded erosion rates were so low and insignificant that no further analysis was carried out. Conversely, at Site 2, the bank erosion rates were significant within the three-year period after riprap removal. The initial state of the channel before the removal of the riprap was mapped for 2019 (Figure 6). The growth of the point bar and downstream bar occurred concomitantly with bank retreat. The sinuosity index increased from 1.43 in 2019 to 1.46 in 2022. On the basis of the width of the active channel each year, the water slope in 2020, and the Q\({}_{2}\) discharge, the specific stream power decreased from 135 W/m\({}^{2}\) in 2019 to 82 W/m\({}^{2}\) in 2020 and to 78 W/m\({}^{2}\) in 2021 and 2022.  
In comparison to the two control reaches, Site 2 showed a singular behaviour of active channel evolution (Figure 7). After the reactivation of bank erosion between 2019 and 2020, the active channel width at Site 2 continued to widen, which was marked by significant bank erosion and important sediment deposition on bars between 2020 and 2021.  
Between 22 November 2021 and 11 August 2022, bathymetric surveys recorded vertical and lateral morphological channel changes (Figure 8). At the two upstream cross-sections, no changes in the bank position were recorded, which contrasted with the main channel where the erosion process dominated, with an erosion area of 26 m\({}^{2}\) on the first cross-section (t1) and an erosion area of 15 m\({}^{2}\) on the second cross-section (t2). The two downstream cross-sections (t3 and t4) recorded bank retreats of \(-\)12 m\({}^{2}\) and \(-\)19 m\({}^{2}\), respectively. At t4, where in-channel accumulation dominated, bank erosion caused an overall difference of \(-\)10 m\({}^{2}\).