esa-sceva/satcom-chunk-collection huggingface dataset:
# Depth Estimation of a Deformable Object via a Monocular Camera
## 1 Introduction
trained an unsupervised deep neural network with binocular stereo data to address the ground truth depth data deficient problem in traditional depth estimation  
Figure 1: The tool used in a human–robot cooperation scenario for screwing a bolt. Since the human and the robot together screw the cross tool, the human arm might be obstacles to the robot during the bolt screwing process. Figure 2: 3D reconstruction for training a cooperative robot, which provides training samples and allows the robot to learn to avoid obstacles in a virtual environment. methods. A novel training loss was proposed for the deep convolutional neural network to perform single image depth estimation with a high quality [14]. Inspired from the concept of autoencoders, [PERSON] et al. trained the first convolutional neural network end-to-end from scratch for single view depth estimation in an unsupervised manner [15]. Deep learning methods are powerful tools for regressions. However, they usually need expensive platforms to run the algorithm and they usually need many training samples related to the target object for accurate depth estimation. Different from the aforementioned previous approaches, the proposed method in this paper estimates depth information by means of 3D reconstruction. The first thing we need to do is to circle a camera around the target object and get its reconstruction at the very beginning. Afterwards, only a single monocular image is required to accurately estimate the depth information of the object no matter how it moves or becomes deformable in front of the camera. The proposed method is therefore more applicable in the scenarios where the target object is not rigid and where accurate depth information is necessary. With the point cloud of a target object (in a certain static pose) reconstructed beforehand, the proposed method can estimate the pose and reconstruct the point cloud of a target object (in its other poses) by a single input RGB image. This work can be used on not only humans and humanoid objects, but on other deformable objects as well. The remainder of this paper is structured as follows. Sections 2-4 individually introduce the three modules of the proposed approach specifically. Section 5 introduces the whole flow chart of the proposed method. Experimental evaluations are provided on a NAO robot and a human in Section 6. Section 7 concludes the contributions of this paper.
###### Abstract  
Multimedia services will play a prominent role in the next generation of internet. With increasing real time requirements, internet technology has to provide Quality of Service (QoS) for various kinds of real time streaming services. When the bandwidth required exceeds the available network resources, network paths can get congested, which results in a delay in packet delivery and packet loss. This situation leads to the design of new strategies for congestion avoidance and control. One of the popular and appropriate congestion control mechanisms that is useful in transmitting multimedia applications in the transport layer is TCP Friendly Rate Control Protocol (TFRC). However, TFRC still suffers from packet loss and delay due to long distance heavy traffic and network fluctuations. This paper introduces a number of key concerns like enhanced Round Trip Time (RTT) and Retransmission Time Out (RTO) calculations, Enhanced Average Loss Interval (ALI) methods and improved Time to Live (TTL) features are applied to TFRC to enhance the performance of TFRC over wired networks.  
congestion control; TCP; RTT; RTO; TFRC
# Wiener Filter and Deep Neural Networks: A Well-Balanced Pair for Speech Enhancement
## 6 Results and Discussion
### Objective Quality Metrics  
This section presents the results of the objective quality metrics: PESQ, STOI, COVL, and CBAK. The black line is the reference that corresponds to the noisy speech, the blue line represents the statistical-based _omlsa_ method, and the red line is related to the _proposal_. For comparison purposes, we used three state-of-the-art DL-based SE methods _segan_[40] and two _deepxi_ versions: resnet 1.1c [22] and mhanet 1.1c [23].  
Figure 5 shows the results for objective quality metrics. Similar to the behavior shown by the spectrograms below, either the _proposal_, as all methods for comparison, achieved improvement of the objective quality concerning the noisy speech and also the statistical-based _omlsa_. _Segan_ obtained the most moderate results, except for intelligibility (STOI), where the performance is very similar for all methods evaluated. The method proposed and _deepxi_ achieved the best quality, with very similar values indeed. _deepxi-mhanet_-1.1c achieved better values for PESQ and COVL. These metrics target the overall signal quality, so it is expected that both agree on the behavior. Note that the architecture behind this method employs multi-head attention (MHA), which is the most powerful architecture among all methods, so this result is expected to some extent. From the point of view of CBAK, _deepxi_ methods and the _proposal_ have almost the same background-noise intrusiveness, with a slight improvement of the proposal for \(SNR>10\) dB. Note that in general, the difference between _deepxi_ and _proposal_ is slight. So, when listening to the signals enhanced, that difference among objective quality metrics is very hard to perceive in the practice.  
In conclusion, the performance of the _proposal_ is very similar to the method _deepxi-resnet_-1.1c._ However, the formulation of the _proposal_ is better suited for a deep learning solution because when targeting the estimation of the Wiener filter directly, it avoids practical issues, such as slow convergence. Additionally, as the estimated values are between 0 and 1, the same Wiener filter can be used as the spectral probability of the speech presence (SPP). This allows for employing the OMLSA-based speech estimator, instead of the LSA-based speech estimator used in both _deepxi_ methods evaluated. Previous results of Section 6.1 indicated that a suitable selection of _Gmin_ compensates for the distortion loss of the OMLSA-based speech estimator and outperforms the LSA-based speech estimator.  
Figure 4: Spectrograms of two audio examples. (**a**) Speech with noise from a call center (SNR = 5 dB), (**b**) speech with noise of baby crying (SNR = 5 dB).
## 4 Results and Discussion
### Multi-Objective Optimization Results of Outdoor Environmental Performance
#### 4.1.2 Analysis of Optimization Results  
(1) Outdoor Environmental Performance Optimization Results  
Figure 8a-c display the distribution of feasible solutions and Pareto solutions for the three objective functions. As shown, in all three objective functions, the Pareto solutions were optimized compared to the feasible solutions, indicating that urban spatial form optimization holds great potential for improving outdoor environmental performance.  
Figure 8a shows that the median and mean values for the feasible solutions of the objective function UTCI-S are 31.8 \({}^{\circ}\)C, with a distribution range of 31.59-31.92 \({}^{\circ}\)C, and there are many outliers, indicating a dispersed data distribution. In contrast, the median of the Pareto solutions for UTCI-S is 31.20 \({}^{\circ}\)C, with a distribution range of 31.10-31.38 \({}^{\circ}\)C, and the  
Figure 8: Boxplots of data distribution for feasible solutions and Pareto solutions. (**a**) Data distribution for feasible solutions and Pareto solutions of UTCI-S. (**b**) Data distribution for feasible solutions and Pareto solutions of UTCI-W. (**c**) Data distribution for feasible solutions and Pareto solutions of SHs. The points in the figure represent the discrete data points in the three - group data, and the lines are the markers of the median.  
data distribution is more concentrated. The Pareto solutions can create a more comfortable summer outdoor thermal environment compared to the feasible solutions.  
Figure 8b shows that the median value for the UTCI-W feasible solutions is \(-\)8.25 \({}^{\circ}\)C, with a distribution range of \(-\)8.45 to \(-\)8.13 \({}^{\circ}\)C, and there are many outliers, indicating a dispersed data distribution. In contrast, the median of the Pareto solutions for UTCI-W is \(-\)8.05 \({}^{\circ}\)C, with a distribution range of \(-\)8.14 to \(-\)7.95 \({}^{\circ}\)C, and the data distribution is more concentrated. The Pareto solutions can create a more comfortable winter outdoor thermal environment compared to the feasible solutions.  
Figure 8c shows that the distribution range for the SH objective function in the feasible solutions is 5.70-7.25 h, with many outliers, indicating a dispersed data distribution. In contrast, the distribution range for the Pareto solutions for SHs is 5.90-6.74 h. Although the extreme values of the Pareto solutions are lower than those of the feasible solutions, the Pareto solutions exhibit a more concentrated data distribution, resulting in better overall sunlight performance.
## 3 Results
### Effect of Wind Wave Spectra on NRCS Simulation
The scattering pdfs shown in Figure 6a,b were obtained from multiple images, and Figure 6c,d show the scattering profile of a single image. The ranges of the scattering distributions of the Sentinel-1 data are larger than those simulated by the conventional TSM based on the four individual wind wave spectra and two composite wind spectra (Figure 6a,b). Furthermore, the peak values of the pdfs for the Sentinel-1 NRCS are always smaller than those simulated with different inputs of wave spectra. The NRCS corresponding to the peak of the scattering pdf of the Sentinel-1 data is well predicted by the A spectrum at the wind speed of 10 m/s and incidence angle of 32\({}^{\circ}\) and is consistent with the two composite wind spectra at the wind speed of 10 m/s and incidence angle of 45\({}^{\circ}\) (Figure 6a,b). In Figure 6c,d, we can see that the fluctuation range of the Sentinel-1 scattering intensity is in high agreement with the simulated results. Furthermore, the D spectrum obviously underestimates the NRCS of Sentinel-1 at the incidence angles of 32\({}^{\circ}\) and 45\({}^{\circ}\) when the wind speed is 10 m/s. Figure 6: Examples of probability density functions of scattering and scattering profile predicted by the conventional TSM based on six wave spectra and their comparison with the Sentinel-1 data for the wind speed of 10 m/s at the incidence angle of 32\({}^{\circ}\) and 45\({}^{\circ}\) in the upwind direction. Curves for the different wind wave spectra are the same as those in Figure 4, and dark curves indicate Sentinel-1 data. **(a)** Pdfs for NRCSs at a 10 m/s wind speed and 32\({}^{\circ}\) incidence angle; **(b)** pdfs for NRCSs at a 10 m/s wind speed and 45\({}^{\circ}\) incidence angle; **(c)** profiles of NRCSs at a 10 m/s wind speed and 32\({}^{\circ}\) incidence angle; **(d)** profiles of NRCSs at a 10 m/s wind speed and 45\({}^{\circ}\) incidence angle. Overall, the comprehensive analysis of the performance of NRCS simulation using the four individual wind wave spectra and two composite wind spectra reveals that the A spectrum has a better applicability for NRCS simulation at different incidence angles and wind speeds under VV and HH polarizations based on TSM, except that the combined spectra perform slightly better at high incidence angles. This conclusion is similar to that found in [44], which mentioned that the A spectrum that was developed for the EM scattering model has a high accuracy in the full wavenumber and wide wind speed range among the numerous spectra. However, in [39], the A + H18 composite wind wave spectrum has a better applicability than single wave spectra, such as the A spectrum and E spectrum. This might be because the influences of the spectra on the NRCS prediction were analyzed based on the advanced integral equation model in [39]. In the subsequent experiments, we used the A spectrum and cosine spreading function for the sea surface construction and scattering calculation.
# Modelling of ML-Enablers in 5G Radio Access Network-Conceptual Proposal of Computational Framework
## 3 Applying AI/ML into 5G RAN: Proposed Methodology
### Data Selection Model  
The availability of the data is considered the key to train any ML model. The most desirable way for handling data collection is to use measurements reports from the user devices. The MDT feature, considered as the functionality of enabling a variety of radio measurements and performance characteristics collection, can provide the data for 5G traffic evaluation, along with the data for RAN optimization.  
Consequently, to adapt data collection for an ML algorithm for a joint evaluation of RAN optimization and 5G traffic, the 5G RAN parametrizing input data is proposed to trigger the MDT configuration procedure to collect any combination of the A and B dataset:  
\[Input\ Data\in A\cup B \tag{6}\]  
In one exemplary scenario, when the operator decides a coverage optimization is desirable, it will instruct any device to collect data on the radio signal level on a network-centric basis, i.e., \(RSRP/RSRQ/SINR\) in a unit area (e.g., cell). However, in the case the 5G-NR system performance is further deemed to be optimized in a user-centric manner, the particular user-specific performance indicator should be monitored (as per Table 1). For such user-centric observability, where the user's performance represents involvement in different types of 5G traffic, combined requirements need to be reliably fulfilled. For instance, \(eMBB\) rate maximization and \(\textit{URLLC}\) latency minimization. The input data for such a mixed scenario (i.e., for RAN coverage optimization for the user with \(eMBB\) and \(\textit{URLLC}\) service) can be denoted as follows:  
\[Input\ Data=a_{MBB}\cup a_{\textit{URLLC}}\cup b_{\textit{Coverage}} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Coverage Optimization** & **Mobility Optimization** & \(QoS\) **Optimization** \\ \hline \(RSRP\), \(RSRQ\), \(SINR\) & \(RSRP\), \(RSRQ\), \(SINR\) & Latency \\  & Interruption time & Throughput \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key performance indicators per RAN Optimization use case.
## 5 Experimental Results
The proposed model achieves the best performance with an \(R^{2}\) of 0.98 and RMSE of 0.40. Figure 7: Boxplot comparison for the percentage error. The traditional models (KNN, LR, DT, RF, and XGB) have higher variability, while deep learning models (CNN, MM, proposed) are more consistent. The optimized proposed model has the smallest error range, indicating its precision and reliability [28; 30; 31]. The quantitative experimental results are comprehensively presented in Table 2. We evaluated the various models against the mean error, the Z-score, and the Z-score for the worst 30% of predictions. As observed in Table 1, the deep learning-based models, particularly the proposed models, demonstrated superior mean performance with values of 0.24-0.26. This represents a significant improvement over traditional machine learning models, which exhibit higher mean errors of 0.34-0.57. These results highlight the effectiveness of deep learning approaches in reducing average prediction errors in food component analysis. The Z-score measures the standard deviation of a prediction, providing insight into the consistency and reliability of the model's predictions. Lower Z-scores indicate less variability and more reliable predictions. Therefore, the effectiveness of the rejection can be measured by this metric. As a result, traditional models such as KNN, LR, DT, RF, and XGB achieved Z-scores of 0.91, 1.24, 0.97, 0.92, and 0.87, indicating greater variability and less reliability in their predictions. The comparative analysis of deep learning models versus traditional machine learning models reveals that the deep learning models outperform traditional approaches. For instance, CNN, MM, and MM* achieved an average mean error of 0.26 and a Z-score of 0.82; they are superior to those of traditional models. This result highlights the robustness of the deep learning models in maintaining a consistent performance. Moreover, the evaluation of the proposed model shows that it had a mean error of 0.26 and a Z-score of 0.82; the optimized model (proposed*) demonstrated further improvements with a mean error of 0.24 and a Z-score of 0.64. This indicates that the inclusion of an autonomous result rejection mechanism significantly enhances model performance. The Z-score for the worst 30% of predictions is a critical metric that evaluates the model's performance in handling the most challenging cases. It reflects the model's ability to minimize the impact of uncertain predictions. The proposed model reduced the Z-score (worst 30%) from 1.21 to 0.86 after implementing the result rejection method, showing a 40.6% improvement. These results are improvements to the level of the mean Z-score of the existing deep learning models. Additionally, unreasonable predictions are completely eliminated. It achieved a remarkable performance compared to the evaluation result observed in MM* (=1.20). This dramatic reduction in error for the worst predictions confirms the efficacy of our approach in enhancing model reliability and accuracy.
## 3 Review of Methods
### Recurrent Neural Networks  
Since the works of [PERSON], [PERSON], and [PERSON] [76], as well as [PERSON] [77], _Recurrent Neural Networks (RNN)_ and their derivatives, i.e., _Long Short-Term Memory Networks_, have been widely applied in the fields of natural sciences and economics. In contrast to CNNs, which are characterized as neural networks working in a feedforward manner since their outputs depend strictly on the inputs, RNNs have the ability to memorize their latest states, which makes them suitable for the prediction of temporal or ordinal sequences of arbitrary lengths. They consist of interconnected layers of neurons that use the current inputs and the outputs of the previous time steps to compute the current outputs, with shared weights allocated to the inputs and outputs separately using biases and nonlinear functions. By stacking multiple RNN layers, a hierarchy is implemented, which allows for the prediction of more complex time series.  
An exemplary structure of an RNN is presented in Figure 6. On the left side, the network architecture is presented using general notation, whereas on the right side, its temporal unrolled (or unfolded) presentation is illustrated, where each column represents the same model at a different point in time. The current input, \(x_{t}\), is required to update the first hidden state, \(h_{t}^{i}\), of node, \(i\), where \(i\) and \(t\) denote the node index and time instance, respectively. This update happens along with the previous state of the same node using the weighting matrices, \(U\) and \(W^{i}\), and a nonlinear activation function for the output. Then, the output of the node is passed to the next hidden state, \(h_{t}^{i+1}\), as input via the weighting matrix, \(V^{i}\). Last but not least, the model's output is obtained using another nonlinear activation function. This leads to a structure of interlinked nodes that are able to memorize temporal patterns, where the number of nodes determines the memorability.  
Despite their enormous potential for the prediction of complex time series, RNNs suffer from two main phenomena known as _unstable gradients_ and _vanishing gradients_, which limit their capabilities. The first phenomenon occurs when a complex task involvesmany layers, which leads to the accumulation of increasingly growing products that cause _exploding gradients_, whereas the second refers to the problem where the cells, due to their limited structure, tend to reduce the weights of the earliest inputs and states.
## 3 Design of a Memory-Efficient Lightweight Object-Detection Model
### Detection Head and Loss Functions  
In this research, a unified detection head architecture is employed, referred to as the coupled head, which integrates three key branches through a single convolutional neural network. This head simultaneously predicts class information, bounding box coordinates, and objectness probability, processing the feature maps output by the FasterNet-16 backbone. The design of the coupled head not only ensures efficient operation but also maximizes feature utilization.  
To refine the model's performance further, the following loss functions are implemented:  
1. Classification loss. The classification loss is computed as follows: \[L_{\text{cls}}=-\frac{1}{N}\sum_{i=1}^{N}\sum_{c=1}^{C}y_{i,c}\log(\hat{y}_{i,c})\] where \(N\) is the number of samples, \(C\) is the number of classes, \(y_{i,c}\) represents the true label of sample \(i\) belonging to class \(c\), and \(\hat{y}_{i,c}\) represents the predicted probability of sample \(i\) belonging to class \(c\).
2. Bounding box regression loss. The bounding box regression loss is defined using the smooth L1 loss function: \[L_{\text{reg}}=\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{4}\text{smooth}_{L_{1}}(t_ {i,j}-\hat{t}_{i,j})\] where \(t_{i,j}\) represents the true bounding box coordinates and \(\hat{t}_{i,j}\) represents the predicted bounding box coordinates. The smooth L1 loss function is defined as: \[\text{smooth}_{L_{1}}(x)=\begin{cases}0.5x^{2}&\text{if }|x|<1\\ |x|-0.5&\text{otherwise}\end{cases}\]  
Figure 6: Multi-scale feature-fusion neck module based on PConv.  
3. Objectness loss. The objectness loss, assessing the likelihood of an object's presence, is calculated by: \[L_{\text{obj}}=-\frac{1}{N}\sum_{i=1}^{N}[y_{i}\log(\hat{p}_{i})+(1-y_{i})\log(1 -\hat{p}_{i})]\] where \(y_{i}\) represents the true label indicating whether sample \(i\) is an object and \(\hat{p}_{i}\) represents the predicted probability that sample \(i\) is an object.
4. Combined loss function. The overall loss, combining the three individual components, is given by: \[L_{\text{total}}=\lambda_{\text{cls}}L_{\text{cls}}+\lambda_{\text{reg}}L_{ \text{reg}}+\lambda_{\text{obj}}L_{\text{obj}}\] where \(\lambda_{\text{cls}}\), \(\lambda_{\text{reg}}\), and \(\lambda_{\text{obj}}\) are the weights for the classification loss, regression loss, and objectness loss, respectively. These weights balance the contributions of each part of the loss to the total loss.  
By defining and strategically combining these specific loss functions, the coupled head model is optimized for effective training and performance across classification, regression, and objectness tasks, significantly boosting the overall efficacy of the object-detection system.