esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper introduces RSoANU, a dataset of real multichannel room impulse responses (RIRs) obtained in a recording studio. Compared to the current publicly available datasets, RSoANU distinguishes itself by featuring RIRs captured using both a 32-channel spherical microphone array (mh acoustics em32 Eigenmike) and a B-format soundfield microphone array (Rode NT-SF1). The studio incorporates variable wall panels in felt and wood options, with measurements conducted for two configurations: all panels set to wood or felt. Three source positions that emulate typical performance locations were considered. RIRs were collected over a planar receiver grid spanning the room, with the microphone array centered at a height of 1.7 m. The paper includes an analysis of acoustic parameters derived from the dataset, revealing notable distinctions between felt and wood panel environments. felt panels exhibit faster decay, higher clarity, and superior definition in mid-to-high frequencies. The analysis across the receiver grid emphasizes the impact of room geometry and source-receiver positions on reverberation time and clarity. The study also notes spatial variations in parameters obtained from the two microphone arrays, suggesting potential for future research into their specific capabilities for room acoustic characterization.  
recording studio acoustics; ambisonics; room impulse response; virtual reality; dataset +
Footnote †: journal: Applied sciences
# Solving a Simple Geduldspiele Cube with a Robotic Gripper via Sim-to-Real Transfer
## 2 Approach to Solving Geduldspiele Cubes via Sim-to-Real Transfer
### System Architecture for Sim-to-Real Transfer to Solve the Geduldspiele Cubes
#### 2.4.2 Defining State, Action, and Algorithm to Virtually Train the Robotic Agent  
Let \(x_{rel},y_{rel}\) be the relative position of an iron ball with respect to a hole position (also the goal of the geduldspiele cube problem) \((x_{g},y_{g})\), thus  
\[x_{rel}:=x-x_{g}\quad\text{and}\quad y_{rel}:=y-y_{g}. \tag{22}\]  
From (7), (10) and (11), we can obtain an insight to define a state vector: the state should involve the generalized coordinate elements as well as the linear and angular velocities along with them. For time step \(t\), we define the state vector \(s_{t}\in\mathbb{R}^{8\times 1}\) to be  
\[s_{t}=[x_{rel,t},\dot{x}_{t},\dot{\theta}_{x,t},\dot{\theta}_{x,t},y_{rel,t}, \dot{y}_{t},\theta_{y,t},\dot{\theta}_{y,t}]^{T}, \tag{23}\]  
Accordingly, the action \(a_{t}\in\mathbb{R}^{2\times 1}\) can be defined as  
\[a_{t}=[\bar{\theta}_{x},\bar{\theta}_{y}]^{T}\quad\text{where}\quad\bar{ \theta}_{x},\bar{\theta}_{y}\in[-0.5^{\circ},+0.5^{\circ}], \tag{24}\]  
which are related to two torques, \(\tau_{x}\) and \(\tau_{y}\), proportionally.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Symbol** & **Value** & **Unit** \\ \hline Ball mass & \(m_{b}\) & \(1.057\times 10^{-3}\) & kg \\ \hline Ball radius & \(r_{b}\) & \(3.175\times 10^{-3}\) & m \\ \hline The moment of inertia & & & \\ of the ball & \(l_{b}\) & \(4.262\times 10^{-9}\) & kg \(\cdot\) m\({}^{2}\) \\ \hline Plate mass & \(l\) & \(10\times 10^{-3}\) & kg \\ \hline Plate length & \(l_{x}\) and \(l_{y}\) & \(\begin{array}{c}35.73\times 10^{-3}\text{ and}\\ 51.14\times 10^{-3}\end{array}\) & m \\ \hline The moment of inertia & & & \\ of the plate & & \(1.064\times 10^{-9}\text{ and}\\ \end{array}\) & kg \(\cdot\) m\({}^{2}\) \\ \hline Coefficient of friction & \(\mu\) &
\begin{tabular}[]{c} 0.3604 \\ Identified \\ (see Section 2.4.1) \\ \end{tabular} & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The definition of variables and parameters.  
A reward \(R_{t}\) at time step \(t\) is defined by  
\[R_{t}=e^{-c_{1}(s_{nt,t}^{2}+y_{nt,t}^{2})}-c_{2}(\theta_{x,t}^{2}+\theta_{y,t}^{ 2})-c_{3}(\beta_{x,t}^{2}+\beta_{y,t}^{2}) \tag{25}\]  
where \(c_{1},c_{2},c_{3}\) are tuning parameters and were set to 0.001, 0.1, and 0.05, respectively.  
To make the robotic agent learn an optimal policy \(\pi^{*}(a_{t}|s_{t})\), the proximal policy optimization (PPO) is adopted due to its well-known strength against the problems akin to control problems in continuous-time such as MuJoCo [46].
# An NUTSF at Sub-Region for Suppression of Torque Ripple in Switched Reluctance Motors
## 3 Conventional TSF Control Strategy
### TSF Module of Conventional TSF system  
The TSF module of the conventional TSF system is based on the torque sharing function, so the value of the TSF will directly affect the winding current and motor torque ripple. It is very important to set a suitable TSF to improve the performance of the SRM. The sum of the TSF of each phase windings should always be equal to 1 in order to keep the total instantaneous torque at a constant value, so the TSF should satisfy the following general formula  
\[\left\{\begin{array}{l}T_{ref(k)}(\theta)=T_{ref}f_{k}(\theta)\ k =1,2,\cdots,m\\ \sum\limits_{k=1}^{m}f_{k}(\theta)=1\quad 0\leq f_{k}(\theta)\leq 1\end{array}\right. \tag{5}\]  
where \(f_{k}(\theta)\) is the TSF of the \(k\)-th phase windings, \(m\) is the number of phases, \(T_{ref}f_{k}(\theta)\) is the \(k\)-th phase torque reference, and \(T_{ref}\) is the total torque reference.  
Take the exponential TSF as an example. The torque sharing function of the \(k\)-th phase is  
\[f_{k}(\theta)=\begin{cases}0&0\leq\theta<\theta_{on}\\ q_{up}(\theta)=1-e^{\frac{(-(\theta-\theta_{on})^{2}}{\theta_{ov}})}&\theta_{ on}\leq\theta<\theta_{on}+\theta_{ov}\\ 1&\theta_{on}+\theta_{ov}\leq\theta<\theta_{off}\\ q_{down}(\theta)=e^{\frac{(-\theta-\theta_{off})^{2}}{\theta_{ov}})}&\theta_{ off}\leq\theta<\theta_{off}+\theta_{ov}\\ 0&\theta_{off}+\theta_{ov}\leq\theta<\tau_{\tau}\end{cases} \tag{6}\]  
where \(\theta_{on}\) is the turn-on angle, \(\theta_{off}\) is the turn-off angle, \(\theta_{ov}\) is the overlapping angle, \(\tau_{\tau}\) is the rotor pole pitch, \(q_{up}(\theta)\) is the ascending area and \(q_{down}(\theta)\) is the descending area in the TSF.  
An electrical period is composed of two regions. One region, where a single-phase winding is conducted to provide the total torque, is called the single-phase conduction region (SpC); the other region, where two-phase windings are conducted to provide the total torque, is called the two-phase exchange region (TpE). The TSF in the SpC is equal to 1, and the TSF of the outgoing phase decreases and the incoming phase increases in the TpE.  
The waveforms of the TSF in the TpE are generally similar to a cosine, linearity, exponent and cube, as shown in Figure 4. Among the four waveforms, the torque ripple of a linear TSF is larger, followed by the cosine and cubic TSFs, and the exponential torque ripple has the best effect.  
Figure 3: Block diagram of the conventional TSF system.
# Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings
## 3 Uncertainty Estimation in Neural Networks -- Mathematics and Algorithm
### Alignment of the Estimation of the Three Uncertainty Types with the Mathematical Theory
#### 3.4.3 Distributional Uncertainty  
The estimation of distributional uncertainty in our architecture also closely aligns with the mathematical theory.  
We compute the distributional uncertainty estimate as the variance of the data uncertainty estimate. However, we could also interpret the distributional uncertainty term in Equation (2) as the distribution over the embedding distribution. Since we have a probabilistic embedding, it could be interpreted as the distribution over both the feature vector \(\mu\) and the variance vector \(\Sigma^{(D)}\). Following this interpretation, both \(\Sigma^{(M)}\) and \(\Sigma^{(V)}\) should be considered part of the distributional uncertainty estimate.  
In our experiments, we show, that \(\Sigma^{(V)}\) computed as the variance of the variance vector can be considered a good estimate of distributional uncertainty. This hypothesis is supported by the observation that the estimated distributional uncertainty \(\Sigma^{(V)}\) is a very good fit to predict which data are out-of-distribution, which is the described property of distributional uncertainty in literature [7; 26; 27]. In contrast, the estimated uncertainty vector \(\Sigma^{(M)}\), which we consider the model uncertainty estimate, is much less suitable to predict which data are out-of-distribution.
# Truss Structure Optimization with Subset Simulation and Augmented Lagrangian Multiplier Method
## 5 Conclusions  
This paper presented a hybrid algorithm for structural optimization, named ALSSO, which combines subset simulation optimization (SSO) with the dynamic augmented Lagrangian multiplier method (DALMM). The performance of SSO is comparable to other stochastic optimization methods. ALSSO employs DALMM to decompose the original constrained problem into a series of unconstrained optimization sub-problems, and uses SSO to solve each unconstrained optimization sub-problem. By adaptively updating the Lagrangian multipliers and penalty parameters, the proposed method can automatically detect active constraints and provide the globally optimal solution to the problem at hand.  
Four classical truss sizing problems were used to verify the accuracy and robustness of ALSSO. Compared to the results published in the literature, it is found that the proposed method is able to produce equivalent solutions. It also shows a remarkable statistical performance based on 30 independent runs. A potential drawback of ALSSO at present is that its convergence rate will slow down when the search process approaches the active constraints. In the future, we will introduce a local search strategy into ALSSO to further improve its efficiency and apply ALSSO to various real-life problems.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(N\) & **Best** & **Mean** & **Worst** & **SD** & **NSA** \\ \hline \multirow{3}{*}{ALSSO} & 100 & 25,722.22 & 25,938.99 & 26,743.77 & 303.5379 & 89,655 (88,080) \\  & 200 & 25,617.50 & 25,694.73 & 25,842.59 & 57.9310 & 181,068 (173,280) \\  & 500 & 25,569.98 & 25,624.89 & 25,696.47 & 32.3777 & 453,465 (447,600) \\ \hline HPSACO [40] & & 25,156.5 & 25,786.2 & 26,421.6 & 830.5 & 9800 \\ ABC-AP [36] & & 25,533.79 & N/A & N/A & N/A & 1,450,000 \\ SAHB [31] & & 25,491.90 & 25,610.20 & 25,799.30 & 141.85 & 14,185 \\ TLBO [34] & & 25,488.15 & 25,533.14 & 25,563.05 & 27.44 & 28,059 \\ HPSSO [41] & & 25,698.85 & 28,386.72 & N/A & 2403 & 14,406 \\ FPA [38] & & 25,521.81 & 25,543.51 & N/A & 18.13 & 10,685 \\ WEO [39] & & 25,674.83 & 26,613.45 & N/A & 702.80 & 19,410 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of statistical performance in the 200-bar truss problem.  
Figure 9: Convergence curve obtained for the 200-bar truss problem with \(N=500\).  
**Acknowledgments:** The authors are grateful for the supports by the Natural Science Foundation of China (Grant No. U1533109).  
**Author Contributions:** [PERSON] and [PERSON] conceived, designed and coded the algorithm; [PERSON] computed and analyzed the first three examples; [PERSON] computed and analyzed the forth example; [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Advanced Statistical Analysis of 3D Kinect Data: A Comparison of the Classification Methods
## 2 Materials and Methods
### Neural Networks  
Two deep neural network (DNN) structures were trained on the given data. The core of the first DNN structure, which is further referred to as GRU DNN, are three recurrent layers with Gated Recurrent Unit [31] cell architecture (for full pipeline see Figure 3). Given the nature of the data, the main strength of GRU DNNs is the ability to infer temporal patterns and dependencies in the time-series, if any are present.  
The second DNN structure, which is further referred to as CNN DNN, utilises three two-dimensional convolutional layers (for full pipeline see Figure 4). Convolutional layers have a strong history in feature extraction and pattern recognition [32,33], both of which are expected of the data. They also allow for an effective dimensionality reduction and, thus, computational efficiency.  
Both DNN structures have been trained and evaluated on the same data as the statistical methods. The parameters were optimised by minimising cross-entropy loss function using the Adam optimiser. Accuracy and F1-score, tracking both precision and recall, were used to measure performance on the validation data. For more information about performance metrics, we refer the reader to [34].  
To account for imbalance in class distribution of the training data, the loss function was scaled by class weights, which were calculated based on the ratio of samples in each class.  
To counteract overfitting, which was expected given the small size of the dataset, a static dropout rate of 0.5 was applied after the feed-forward layer directly following the core structure (see Figures 3 and 4). The training process was also stopped early if the average value of area under curve (AUC) on the validation data did not improve for 20 subsequent epochs and only the best performing parameters were saved.  
Figure 4: CNN DNN with three 2D convolutional layers (CNL) at its core. FF marks a typical fully-connected (dense) layer. \(n\) is number of filters or hidden units, \(f\) is 2D filter dimensions and \(s\) is stride of the filter for the respective layer.  
Figure 3: GRU DNN with three RNN layers with gated recurrent unit architectures at its core. FF marks a typical fully-connected (dense) layer. \(n\) is number of hidden units in the respective layers.  
The cross-validation results are categorised by the DNN structure type (GRU or CNN) and an indicative number of trainable parameters of the whole structure.
# Presenting a Human Pupil Orbit Model (HPOM) for Eye-Gaze Tracking
## 1 Introduction
The specific contribution of this work is a method that simplifies the gaze direction detection process. The HPOM uses mathematical principles to systematically analyse the shape and attitude of the pupil in 3D space before simplifying it to an estimate in 2D space. In this paper, we demonstrate that HPOMs can be constructed by a two-dimensional simplification method we present by performing experimental simulations. After estimating the HPOM, the gaze direction can simply be detected based on the two-dimensional centre point of the pupil being projected onto the camera.
# Three-Dimensional Drone Cell Placement: Drone Placement for Optimal Coverage
## 5 Experiment and Results  
Two sets of experiments were conducted. Firstly, DC placement using default parameters, and secondly, using tuned parameters. The results of the final experiment were evaluated using the Nemeyi Hypothesis test and a statistical significance value for all environments was calculated to generate a final score for evaluating algorithmic performance. The entire system was implemented in Python, mainly using base python libraries and modules.
# Novel Approach to Manufacture an AUV Propeller by Additive Manufacturing and Error Analysis
## 3 Results and Discussion
### Error Measurement  
In order to get a better view of air flaws and dimensional accuracy, a comparison of the developed 3D printed model with that of the CAD model was carried out. This was carried out by selecting three sections at a distance of 15 mm (Figure 7) from a minimum to maximum cross-section area. The CAD model was taken as a benchmark. The cross-sectional areas have noticeable variation; however, the perimeter is not significantly different, as shown in Table 2. The highest model (Section 15) shows that the CAD cross-section was 35.5% smaller than the physical part. As the model height was decreased, the CAD model cross-sectional area became larger than the physical part. To investigate these variations, the CAD cross-sectional curve was superimposed onto the physical part, as shown in Figure 8. There were multiple reasons for CAD and physical model deviation. Firstly, the impeller is a thin shape structure, while during deposition there is no side support material. Therefore, when the physical part has many deposition layers, the lower layers expand, allowing a much higher physical cross-sectional area. This observation can be noted from Figure 5, where the sides of the impeller have rough edges when comparing Section 15 with the other two sections. Secondly, it can be safely assumed that the physical model cross-sectional values were low in number, in relation to the nozzle diameter. Near the edges, the thickness was less and thus, accurate deposition of that size was not possible, resulting in the higher area value in the physical part. At the lower height (Section 30, 45), the design had thicker cross-sections and resulted in easier deposition. However, some air gaps were observed in Sections 30 and 45, which was unavoidable in FDM, due to maintenance of the dimensional accuracy during the deposition. These air gaps were minimal and not visible to the naked eye and needed a high-resolution camera to predict their existence. This can be ignored from a design perspective. The air gaps allowed the physical part to shrink, resulting in a smaller physical cross-sectional area, as shown in Figure 8.  
Figure 6: Formation of blade in five stages using cold forging process.  
Figure 7: Manufactured truncated models and reference from the CAD model. The arrows show the quality of side edges of the 3D printed impellers.  
The perimeter of each section was nearly identical. This shows that despite the observation that the cross-sectional layers may have thickened or shrank at random locations in the lateral direction, the overall dimensional accuracy could be maintained to allow such impellers manufactured by 3D printing to have a direct application without further need for any post-manufacturing process.
# RNN-Based Sequence to Sequence Decoder for Run-Length Limited Codes in Visible Light Communication
## 4 Results and Analysis  
Extensive simulations were carried out to assess the performance of the seq2 seq decoder. The VLC model described in Section 2.1 is the focus, and the SNR of the received signal is denoted as \(E_{b}/N_{0}\), with \(E_{b}\) being the energy per transmitted bit and \(N_{0}\) being the variance of the Gaussian noise. For the performance comparison with seq2 seq decoder, the look-up table and MAP decoding were adopted as the benchmark, which are the decoding methods used in the VLC standard and the ideal optimal decoding method, respectively.
## 4 Results and Discussion  
### Methane Adsorption Characteristic  
As shown in Figure 3, the methane adsorption curves had the same characteristics. The volume of methane adsorbed on the coal increased rapidly with the increase in pressure until 3 Mpa. The rate of increase in adsorbed methane gradually slowed down when the pressure lay within the range of 3-5 Mpa. The rate of increase in adsorbed methane was not significant when the pressure exceeded the value of 5 Mpa, and therefore, it was inferred that the system had reached its saturated state at this stage.  
Based on the curve characteristics of the methane adsorption on samples, the Langmuir model was used to describe the isothermal adsorption curves of the samples, as given by Equation (6):  
\[V=\frac{V_{\text{L}}P}{P_{\text{L}}+P}, \tag{6}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Samples** & **Coal Seam** & **Coal Type** & \(M_{\text{ad}}\) (\%)** & \(A_{\text{ad}}\) (\%) & \(V_{\text{ad}}\) (\%) \\ \hline XW1 & C\({}_{17}\) & anthracite & 2.06 & 33.16 & 9.72 \\ XW2 & C\({}_{25}\) & anthracite & 1.16 & 14.46 & 10.32 \\ WH & C\({}_{17}\) & anthracite & 2.35 & 12.21 & 8.94 \\ ZK & C\({}_{25}\) & anthracite & 1.83 & 16.79 & 8.23 \\ \hline \hline \end{tabular} Note: \(M_{\text{ad}}\)—air-dried moisture, \(A_{\text{ad}}\)—air-dried ash, \(V_{\text{ad}}\)—air-dried volatile.  
\end{table}
Table 1: Information on the coal samples used in the current work.  
Figure 3: Measured methane isotherms at 30 \({}^{\circ}\)C.  
where \(V\) is the methane adsorption volume at equilibrium (cm\({}^{3}\)/g), \(P\) is the equilibrium pressure (Mpa), \(V_{\rm L}\) is the Langmuir volume under the standard state with a pressure of 101 kPa and a temperature of 273.15 K, and \(P_{\rm L}\) is the Langmuir pressure (Mpa).  
The Langmuir constants of the samples are presented in Table 2. In the present study, the Langmuir volume lay within the range of 26.67-34.56 cm\({}^{3}\)/g, whereas the Langmuir pressure lay within the range of 1.10-1.28 Mpa. Fitting had a high correlation coefficient.
## 4 Experimental Test Bed and Measuring Equipment
### Network Topology  
This section describes the architecture of the power line network (PLN) used for real-time data transfer, measurements, and modeling. This PLN topology served as the testing environment for the SD-PLC system developed in this study. The PLN architecture used in this study is important as the noise parameters vary from country to country due to differences in the mains voltage, power line topology, power line frequency bandwidth, distance, position, and time. The variations in the noise parameters can be attributed to the fact that different countries use different power line configurations, which can result in changes in the frequency bandwidths of power lines.  
The CENELEC C-band frequency range is the operating frequency spectrum chosen for the M-QAM SD-PLC transceiver. As the main aim of the power lines was not to transmit high-frequency signals, the PLN thus offers a highly adverse environment for high-frequency signals.  
Figure 7 depicts the used PLN topology and the test conditions.
## 4 Validation of New AI-FEM Multiple Cracks Coalescence Criteria  
### Geometry and Material  
This section validates the plastic zone size-based coalescence criterion and crack growth using AI-FEM by comparing it with experimental results. The validation is performed by comparing the FCG simulation results obtained from AI-FEM with the FCG experimental data from three-point bending tests on plates with multiple cracks conducted by [PERSON] et al. [5]. Before validating the proposed multiple crack coalescence criteria, the crack modeling and SIF calculation module in AI-FEM for plate geometry used in this study were verified by comparing the results with analytical solutions. Specifically, the SIF values along the crack front for a single surface crack in a plate derived from AI-FEM were compared with the SIF solutions presented by [PERSON] et al. [32].  
Figure 5 illustrates the schematic diagram of the plate used in the FCG experiment. In this diagram, \(w\), \(t\), and \(l\) denote the width, thickness, and length of the plate, respectively, while \(v\) indicates the distance between supports. These dimensions are summarized in Table 2. Multiple cracks are located at the center of the plate. The plate material is carbon-manganese 50D steel (BS 4360), with a [PERSON]'s modulus (\(E\)) of 210 GPa, a Poisson's ratio (\(v\)) of 0.3, and a yield strength (\(\sigma_{y}\)) of 350 MPa. The material and properties were applied to the analysis as provided in the experimental literature by [PERSON] et al. [5] to ensure the same conditions as the experiment.  
Figure 4: Schematic diagram of the AI-FEM multiple crack-growth simulation algorithm: (**a**) multiple crack coalescence criteria application process; (**b**) re-entrant region curvature calculation process; (**c**) multiple crack coalescence modeling process.  
Figure 6a depicts the schematic diagram of a single surface crack used to verify the AI-FEM FE model. Here, \(a\) indicates the single crack depth, \(c\) represents half of the single crack length, and \(\theta\) denotes the angle along the crack front. The crack shapes used for verification are summarized in Table 3.  
Figure 6b illustrates the schematic diagram of two surface cracks on the same plane, used for experimental validation of the newly proposed multiple crack coalescence criteria. In this diagram, \(S\) denotes the distance between the two surface cracks, while \(a_{1}\), \(c_{1}\), and \(a_{2}\), \(c_{2}\) represent the depth and half-length of multiple cracks 1 and 2, respectively. The shapes and distances of these cracks are summarized in Table 4.