esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Energy-Efficient Routing Protocol for Wireless Sensor Networks
## 4 Simulation Results
### Performance for Large Sensor Field  
To further demonstrate that the performance of the proposed IEE-LEACH protocol is still prominent in large scale, we construct a WSN with randomly deployed 500 sensor nodes in a \(500~{}\text{m}\times 500~{}\text{m}\) sensor field. It is clearly from Figure 12 that, compared with the existing routing protocols, the network lifetime is enhanced by the proposed IEE-LEACH protocol as the number of rounds increases. Figures 13 and 14 display the chart view of residual energy and total energy consumption in the network. All nodes in the existing LEACH, EE-LEACH, SEEN and LEACH-XMP protocols are dead after 1328, 1534, 1731 and 2724 rounds, respectively. However, all nodes in the proposed IEE-LEACH are dead after 2692 rounds. It can be seen that the new proposed scheme which considers the initial energy of nodes, residual energy of nodes, total energy of the network, and average energy of all nodes increases the network lifetime by about 5% to 75% compared with different routing protocols. Therefore, it is apparently observed that the proposed IEE-LEACH algorithm performs equally well in case of large network size and is scalable enough for large network areas.  
As indicated in Tables 3 and 4, among the different routing protocols, our protocol can achieve longer network lifetime and lower energy consumption than the others in various scale sensor fields, which is of great significance for diverse practical applications.  
Figure 11: Comparison of total energy consumption of the network.  
Figure 12: Comparison of life cycles for a large sensor field.  
Figure 14: Comparison of total energy consumption of the network for a large sensor field.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Protocol** & **Network Lifetime** & **Residual Energy** & **Total Energy Consumption** \\ \hline LEACH & 1361 & 1328 & 1411 \\ EE-LEACH & 1719 & 1534 & 1589 \\ SEEN & 1812 & 1731 & 1826 \\ LEACH-XMP & 3313 & 2724 & 2785 \\ IEE-LEACH & 3491 & 2692 & 2793 \\ \hline \end{tabular}
\end{table}
Table 4: A performance comparison between different protocols in a 500 m \(\times\) 500 m sensor field.  
Figure 13: Comparison of residual energy of nodes for a large sensor field.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Protocol** & **Lifetime** & **Residual Energy** & **Total Energy Consumption** \\ \hline LEACH & 1678 & 2267 & 2267 \\ EE-LEACH & 2326 & 3053 & 3114 \\ SEEN & 2416 & 2889 & 2990 \\ LEACH-C & 1836 & 2422 & 2723 \\ O-LEACH & 2178 & 2972 & 2972 \\ LEACH-M & 1933 & 2492 & 2775 \\ IEE-LEACH & 2636 & 3242 & 3242 \\ \hline \end{tabular}
\end{table}
Table 3: A performance comparison between different protocols in a 100 m \(\times\) 100 m sensor field.
## 4 Experiments
### Camera Calibration
#### 4.1.1 Calibration Method  
The calibration process adopted in this study is based on [PERSON]'s method, a widely used planar pattern calibration technique. A standard black-and-white checkerboard with known grid size was used as the calibration target. The intrinsic parameters--including the focal lengths \((f_{x},f_{y})\), principal point \((c_{x},c_{y})\), and lens distortion coefficients (radial: \(k_{1},k_{2},k_{3}\); tangential: \(p_{1},p_{2}\))--were estimated by minimizing the reprojection error of the detected corner points.  
To ensure robustness and accuracy, multiple images were captured with the checkerboard placed at various positions and orientations within the camera's field of view. This multi-view coverage enhances the numerical stability of parameter estimation and reduces the sensitivity to noise or local distortion.  
The extrinsic parameters, which describe the rigid-body transformation from the camera coordinate frame to the reference world frame (in this case, the manipulator base), were simultaneously estimated based on the known spatial geometry of the checkerboard corners and their corresponding image projections.
## 3 Synthesis of Transition-Metal-Sulfide-Based Nanomaterials
### CdS  
[PERSON] et al. investigated CO\({}_{2}\) reduction on nanorod CdS particles [194]. The study showed that the CdS nanorods were stable in the production of CO for over 40 h of reaction with a faradaic efficiency of 95% in aqueous solutions. The authors noted that a current density of 10 mA/cm\({}^{2}\) at an over-potential of \(-\)0.55 V was effective for CO\({}_{2}\) reduction. Additionally, a high selectivity of the CdS nanorods was reported, which was due to the 0002 face with sulfur vacancies [194]. The doping of CdS nanorods with Ag+ ions was investigated by [PERSON] et al. for the electrochemical reduction of CO\({}_{2}\) to CO [195]. The results indicated that the doping of the CdS nanorods with the Ag\({}^{+}\) ions created S vacancies in the nanorods. The CdS-Ag\({}^{+}\) nanorods showed a faradaic efficiency of approximately 87% and a current density of 53.7 mA/cm\({}^{2}\). The Ag-doped CdS nanorods showed approximately double the faradaic efficiency of the CdS nanorods alone and approximately 1.5 times the faradic efficiency of CdS nanorods with S vacancies. Although CdS is a catalytic and photoactive material, there are insufficient data reported on the use of CdS in CO\({}_{2}\)RR. Perhaps this is due to the toxicity of the Cd ion.
# Voice Maps as a Tool for Understanding and Dealing with Variability in the Voice
## 2 Aspects of Variability in Voice
### Reproducibility and Elicitation
Given that most voice metrics depend considerably on SPL and/or \(f_{\text{o}}\),  
Figure 4: Correlation plots of sustained vowel /a/ productions pre- and post-intervention (\(N=58\)) for the same elicitations; (**left panel**) SPL, (**right panel**) \(f_{\text{o}}\), both averaged over the central part of the vowel. Each participant is represented by three markers \(\blacksquare\)\(\bullet\). Figure 3: Means and standard deviations in SPL, by elicitation of sustained /a/ vowels in two cohorts. Round markers: cohort 1, male (\(N=42\)) and female (\(N=50\)) healthy young speakers, from [40]. Square markers: cohort 2, female voice patients (\(N=85\)) at baseline prior to intervention [16]. The mean SPL of elicited productions differed by 6–8 dB per level, with a standard deviation of \(\pm 5\)–7 dB. these very large and random differences in the independent variables, uncorrelated with the intervention, will render pre-post comparisons for effects of intervention practically meaningless, unless the number of observations is increased by orders of magnitude. Also, there is a considerable overlap between the elicitation categories--even _within_ participants, a few of whom produced, for instance, a lower SPL for \"loud\" than for \"comfortable\". A forthcoming retrospective study [41] of a subset of these data showed that the elicitation instructions (soft/comfortable/loud) produced robust and meaningful variations in the phonatory metrics, while any effects of intervention (surgery or therapy) were undetectable. The problem here is that no attempt was made to control for physical SPL and \(f_{\mathrm{o}}\) during elicitation. The resulting excess variability has greater consequences than is typically recognized, as will be further elaborated upon below. In summary, several studies indicate that the phenomenon, which we might call test-retest \"elicitation noise\", is on the order of \(\pm\)5 dB and \(\pm\)2-3 semitones. When this variability is viewed in the context offered by voice maps, we must conclude that it is a problematic aspect of the \"comfortable loudness and pitch\" data collection paradigm.
## 4 Loss Analysis
### Conduction Losses  
When the switching components are in the conduction path for conveying their particular voltage level, conduction losses arise owing to the ohmic losses associated with the dynamic resistance of the switch. Therefore, conduction losses appear due to the power switch (\(R_{S}\)) and diode (\(R_{D}\)) are expressed mathematically as follows [7]:  
\[P_{C}=[P_{C,\;S}+P_{C,\;D}] \tag{7}\]  
where conduction losses (\(P_{C}\)) due to semiconductor switches (\(P_{C,sw}\)) and power diodes (\(P_{C,D}\)) an be expressed as:  
\[P_{C,\;sw}=V_{S}I_{S,avg}+I_{S,rms}^{2}R_{S} \tag{8}\]  
\[P_{C,\;D}=V_{D}I_{D,avg}+I_{D,rms}^{2}R_{D} \tag{9}\]  
where \(V_{S}\),\(V_{D}\) are the conduction state voltage drops of the switch and diode respectively.  
\(I_{S,avg}\) = average current flowing through a switches.  
Figure 4: The proposed 3-\(\phi\) five-level MLI (**a**) Switching scheme for per phase leg; (**b**) Output voltage.  
\(R_{S}\), \(R_{D}\) = on-state resistance of the switch and diode respectively.  
\(I_{S,rms}\), \(I_{D,avg}\) = root-mean-square and average current through the switch and diode respectively.
## 5 Results
### Evaluation Using Daily Observations from 2000 to 2020
#### 5.1.2 Monthly Time Scale
However, in a few months, higher POD values were recorded for IMERG and PERSIANN than GSMaP. The Performance diagrams indicate that in dry months CHIRPS achieved a higher success ratio (lower FAR) values, while GSMaP was ranked first in the wet months. More general, the performance of all GPPs declined in drier months compared to months with higher precipitation rates and occurrences. Concerning CSI, the best performance was achieved by GSMaP, and ERA5 and IMERG were in the second and third ranks. TMPA and CHIRPS had the lowest CSI values in five and seven months, respectively, suggesting the weakest performance of TMPA. Taking all the metrics of the Performance Diagram into account, GSMaP remained the most accurate GPP in all months, and the ERA5 was second to that, followed by IMERG, PERSIANN, CHIRPS, and TMPA. Figure 8: Performance diagrams for comparing six GPPs using daily observations between 2000 and 2020 in the monthly time scale from (**a**–I) January to December, respectively.
# Ride Comfort Prediction on Urban Road Using Discrete
## 4 Results
### Correlations between DRI and MTVV  
Since the DRI can effectively identify the pavement roughness of short sections, it is possible to predict the short-time ride comfort by using the DRI. Based on the simulation results on the measured 108 urban road sections, the correlations between the DRI and MTVV were developed for ride comfort prediction. During the simulation, we considered three factors: the vehicle speed, the time period, and the combined effect of the left and right wheel paths.
## 3 Theoretical Analysis
### Advantages of the Method  
#### 3.6.1 Effectively Reduce Runtime  
For single-cell replacement, updating the timing in the STA tools is replaced by updating the path weights and cell weights. The delay information of the cell is obtained at the initial stage, and the calculation method using CCS is replaced by an approximate calculation when updating the cell delay. The approximate calculation may bring two cases: (1) a small amount of redundant replacement slightly increase static power consumption; (2) a small amount of path timing is incompletely repaired, which can be solved by adding paths to the path collection. However, the approximate computation can substantially improve the algorithm's feasibility and reduce the operating cost.  
Another way to improve the algorithm's feasibility is to use some of the most critical paths instead of all as constraints. The disadvantage is the existence of sub-critical paths that are not considered, which can also be solved by expanding the path collection.
###### Abstract
Notation: \(\mathbb{E}_{h}(\cdot)\) denotes statistical expectation operation with respect to random variable \(h\), \(\mathbb{C}^{M\times N}\) denotes the space of all \(M\times N\) matrices with complex entries; \((\cdot)^{-1}\) denotes inverse operation; \((\cdot)^{H}\) denotes Hermitian transpose; \((\cdot)^{*}\) denotes complex conjugate; \((\cdot)^{T}\) denotes transpose; \(|\cdot|\) denotes the absolute value of a complex scalar; The distribution of a circularly symmetric complex Gaussian (CSCG) random vector with a mean vector \(\mathbf{x}\) and a covariance matrix \(\sigma^{2}\mathbf{I}\) is denoted by \(\mathcal{CN}(\mathbf{x},\sigma^{2}\mathbf{I})\), and \(\sim\) means \"distributed as\". \(\mathbf{I}_{P}\) is an \(P\times P\) identity matrix.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using On-Policy
#### 3.1.1 Motion Planning  
In [58], the authors propose a navigation approach that goes beyond replicating human behavior. They emphasize instructing robots about behavior to avoid and understanding socially acceptable norms. They replicated human capacity to intuitively recognize violations of social norms as a guiding principle. The authors presented SA-CADRL, which stands for _\"socially aware multi agent collision avoidance with deep reinforcement learning\"_[58]. In this work, an ROS was used as middleware to integrate components and manage hardware. The main innovation of SA-CADRL is its ability to effectively balance time efficiency and social compliance objectives.  
The authors in [58] validated their approach through simulations and real-world experiments to confirm the effectiveness of the approach. The simulation results show that SA-CADRL outperformed traditional methods such as Optimal Reciprocal Collision Avoidance (ORCA) [59] in time efficiency when following social norms. However, SA-CADRL still showed a preference for passing on the right in certain scenarios, suggesting that certain navigation biases may emerge naturally as solutions to symmetrical collision avoidance problems. The real-world deployment shows the potential of SA-CADRL in enabling robots to seamlessly integrate into human-centric spaces without causing disruption or safety concerns. For more information on the hardware and software used, please see Table 4.  
The above-mentioned work also points to some of the drawbacks of robot navigation approaches, including strict geometric rules and resource-intensive learning methods. The authors introduce a novel approach that prioritizes adherence to social norms over strict imitation of human behavior. Their approach creates opportunities for robots to navigate challenging environments while honoring social norms and human preferences, promising better human-robot interactions in real-world settings.
## 3 Proposed Method
### Method Overview  
Given a category of interest \(G\) (e.g., _chair_), \(U\) is the 3D shape of an **unseen** instance. \(\textbf{C}^{can}\) is the clean (noise-free) canonical point cloud of \(U\), and \(\textbf{N}^{can}\) is the noisy (noise-corrupted) canonical point cloud of \(U\). The denoiser (downstream task) of CMD-Net targets eliminating corruption from \(\textbf{N}^{can}\) so that \(\textbf{H}^{can}_{u}\), the noise-free canonical point cloud that lies within the convex hull of the unseen instance \(U\), is recovered (see Figure 2). We define a distance function \(d\) between two point clouds \(\mathbf{a}\) and \(\mathbf{b}\) as \(d(\mathbf{a},\mathbf{b}):\mathbb{R}^{M\times 3}\times\mathbb{R}^{M\times 3} \rightarrow\mathbb{R}^{+}\), and the following objective is optimized in the downstream task:  
\[\textbf{H}^{can^{*}}_{u}=\operatorname*{arg\,min}_{\textbf{H}^{can}_{u}}d( \textbf{N}^{can},\textbf{H}^{can}_{u}). \tag{1}\]  
Our method is capable of canonicalizing noise-corrupted clouds under arbitrary rotations, therefore circumventing the requirement of pre-aligned data. The canonical mapping part (auxiliary task) of CMD-Net takes the noise-corrupted rotated point cloud \(\textbf{N}^{rot}\) of \(U\) as input and aims to predict the best network parameters that minimize the following objective:  
\[\textbf{N}^{can^{*}},\textbf{R}^{*}=\operatorname*{arg\,min}_{\textbf{N}^{ can},\textbf{R}}d(\textbf{R}(\textbf{N}^{rot}),\textbf{N}^{can}), \tag{2}\]  
where **R** is the rotation matrix that aligns \(\textbf{N}^{rot}\) to \(\textbf{N}^{can}\). Based on Equations (1) and (2), the complete network intakes an arbitrarily rotated noisy point cloud \(\textbf{N}^{rot}\), first canonicalizes it to obtain \(\textbf{N}^{can}\) (auxiliary task), and then employs denoising (downstream task) to obtain \(\textbf{H}^{can}_{u}\), the noise-free canonical point cloud that lies within the convex hull of the unseen instance \(U\) (see complete network in Figure 2).
# Slow Pyrolysis as a Method of Treating Household Biowaste for Biochar Production
## 4 Conclusions  
This study investigated the properties of three types of biowaste collected in Poland: kitchen biowaste, spring garden biowaste, and autumn garden biowaste. The next stage focused on evaluating the potential of these materials for biochar production via slow pyrolysis. The pyrolysis process was performed in an electrically heated Horizontal Tube Furnace (HTF) in a nitrogen atmosphere at temperatures of 400 \({}^{\circ}\)C, 500 \({}^{\circ}\)C, and 600 \({}^{\circ}\)C. Following pyrolysis, the characteristics of the resulting biochar were analyzed.  
The main conclusions of this work can be summarized as follows:  
* All biowaste samples had a high moisture content, ranging from 63.51% to 81.53%, indicating the need for drying prior to undergoing slow pyrolysis;
* Property analysis and SEM imaging revealed that the biowaste types are heterogeneous and differ significantly from each other. Therefore, those biowastes should be collected and processed separately;
* The kitchen biowaste properties were found to be similar to those of food waste studied in other regions of the world and are comparable to those of the typical biomasses used for biochar production through slow pyrolysis;
* Both garden biowaste types may have been contaminated with materials such as soil or rocks during collection. This likely contributed to the high ash content observed (17.75% in spring garden biowaste and 43.83% in autumn garden biowaste), leading to significant deviations from both the literature values for similar wastes and from standard biomass feedstocks used in pyrolysis;
* For all three types of biowaste, an increase in the pyrolysis temperature resulted in a decrease in the biochar yield. The highest mass yields recorded were 36.64% for kitchen biowaste, 66.53% for spring garden biowaste, and 66.99% for autumn garden biowaste;
* Kitchen biochar, in comparison to its raw biowaste, showed an increase in carbon content, fixed carbon, and a higher HHV. The SEM images also indicated that higher temperatures led to larger, more porous biochar particles. These properties make kitchen biochar suitable for applications such as carbon sequestration or enhancing soil by improving water and nutrient retention;
* In contrast, both garden-derived biochars had a lower carbon content and HHV than the original garden biowastes. Furthermore, their ash content was over three times higher than that of the kitchen biochar.
# A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8
## 3 Experiment and Results
### Target Detection Experiment
#### 3.2.3 Visualization Analysis  
To intuitively compare the differences between different methods (the original YOLOv8 model, our improved YOLOv8 model, and our improved model with data augmentation using CycleGAN), the target detection results of these methods in the form of images and detection boxes are shown in Figure 11.  
For the images in Rows A and B (Figure 11), the detection accuracies ([EMAIL_ADDRESS]) of Image A(c) and B(c) were highest, then results of Images A(b) and B(b) came second, and results of Image A(a) and B(a) were worst.  
For the images in Row C, the original YOLOv8 model failed to detect the targets (Image C(a)), whereas the detection results in Image C(b) and C(c) were successful. The [EMAIL_ADDRESS] in Image C(c) as 0.89 was higher than that in Image C(b) as 0.70.  
For the images in Row D, Images D(a) and D(b) had false detection, which incorrectly detected the plane targets as ship targets, whereas the detection result in Image D(c) was still correct.  
For the images in Row E, although all three methods did not miss the plane targets, Image D(c) also had a false detection and took the rock target as a plane target.  
For the images in Row F, Image F(a) had a false detection and took the plane as a ship target. Image F(a) missed the target. In Image F(a), the plane target was correctly detected despite a false detection.  
For the small target images in Rows G and H, all three methods correctly detected the targets. The [EMAIL_ADDRESS] in Images G(b) and G(c) as 0.85 and 0.89, respectively, both of which were higher than that in Image G(a) as 0.79. Moreover, the images in Row H had relatively high noise. The [EMAIL_ADDRESS] in Image H(c) as 0.88 was higher than that in Image H(a) as 0.81 and slightly lower than that in Image H(b) as 0.93.  
Figure 10: Precision–recall curves of the improved YOLOv8 on augmented Dataset E.  
Figure 11: Target detection results of the original YOLOv8 model (**a**), our improved YOLOv8 model (**b**), and our improved model with data augmentation using CycleGAN (**c**). (**A–C**): Ship target images. (**D–F**): Plane target images. (**G**,**H**): Small target images.  
The comparison results demonstrated that the proposed detection method that combined CycleGAN and the improved YOLOv8 had higher accuracy and robustness in target detection of side-scan sonar images with only small samples.
# On the Optimisation of Practical Wireless Indoor and Outdoor Microcells Subject to QoS Constraints
## 2 Related Works
Planet can support most wireless access standards in small cells and heterogeneous networks with different advanced technologies and antenna modelling using antenna library provided by the vendor. Considering the practical geographic information system (GIS) in the radio propagation and modelling, deciBel Planer [47] is the first software program that can be fully integrated with Mapinfo providing flexible network planning taking into account GIS data. While most software programs are based on 2D modelling, WaveSight [48] was developed as a complementary module with ray-tracing 3D building databases that can be used to provide the most accurate network coverage analyses in different infrastructures and network layers, e.g., microcells and macrocells. A number of research works have been dedicated to the network planning with various tools and approaches. As a supplement to the state-of-the-art works, this paper investigates the network optimisation subject to both network coverage and QoS requirements with restricted cell site location. Specifically, a new BSCP algorithm is proposed to find the optimal number of cell sites along with their corresponding location among the preset candidate cell positions. In the scope of this work, EDX SignalPro (version 7.4.0 B, EDX Wireless, Eugene, OR, USA) is selected to validate the effectiveness of the proposed algorithm due to its efficiency in providing free-trial planning tools for wide area service prediction, link analysis, point-to point and point-to-multipoint studies [40].