esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Quantification of Controllability in the Null Space for Redundant Manipulators
## 3 Simulation and Experiment
### Types of Singularity  
Null space quality can identify two types of singularity, as mentioned at Section 2.2. Type 1 singularity is same as the manipulability because the Jacobian of the control point is dominant in this scope. As shown in Figure 3a, the manipulator moves to the singular configuration with the step input. In this case, both manipulability and null space quality can successfully identify a singular state. This type of singularity can be predicted because it can be determined by the manipulability, which has been studied broadly in the past.  
Type 2 singularity appears due to the joint constraint of the robot. Unlike the type 1 singularity, the manipulability remains neutral, while the null space quality decreased with the step input, as shown in Figure 3c. To verify the state, we must obtain the joint vector from the null space projector Jacobian. Three joint bases from the control point's Jacobian and the dot product with the joint bases of null space projector can be acquired, as shown in Figure 3d. Every dot product of joint vectors is almost orthogonal when the null space quality identifies the singular state. That means every joint vector of the Jacobian is constrained by the null space projector. Thus, a new type of singularity that cannot be explained solely by manipulability can be identified.
# Alternative Design of Binary Phase Diffractive Optical Element with Non-\(\pi\) Phase Difference
## 2 Method
A design example of a 3 \(\times\) 3 beam splitter is also given in Figure 2. The lower left corner is the phase distribution of the 0-\(\pi\) DOE designed for the zero-order eliminated target pattern. The lower right corner shows the variation of the diffraction efficiency and uniformity with respect to the phase difference. The whole curve is symmetrically distributed because the symmetry of the Fourier transform for \(\varphi\) and 2\(\pi\)-\(\varphi\) phases. Thereby, it is only necessary to search for the optimal phase difference between 0 and \(\pi\). It can be seen from the figure that as the phase difference decreases from \(\pi\) to zero, the uniformity first increases and then decreases. This is because when the phase difference deviates from \(\pi\), the zero-order light intensity is gradually increased and consequently the uniformity gets better. Thereafter, as the phase difference is further reduced, zero-order intensity  
Figure 2: Schematic diagram of the proposed method. is gradually greater than other orders and therefore the uniformity is weakened. The reconstructed patterns corresponding to the phase difference of 0.7\(\pi\), 0.815\(\pi\), and \(\pi\) are also presented in Figure 2. The efficiency curve shows that the overall diffraction efficiency always increased with the phase difference decreases. This was mainly due to the increase in zero-order efficiency. Ultimately, when the phase difference was equal to 0.815\(\pi\), the uniformity reached a maximum of 99% and the corresponding diffraction efficiency was 74%. This design result is comparable to the one in [22]. However, the proposed method only needs less than one minute of design time, while the original SA-based method needs several hours.
# Deep Learning-Based Sound Source Localization: A Review
## 3 General Principles of Deep Learning-Based Sound Source Localization
### Model Architectures
#### 3.2.1 Lstm  
Long short-term memory (LSTM) networks, a specialized type of recurrent neural network, address the gradient vanishing problem in traditional RNNs through gating mechanisms and enable efficient modeling of long-term temporal dependencies. Following the implementation in [17], the LSTM core comprises memory cells and three gating modules, operating as follows (see Figure 5):  
The LSTM dynamically controls information flow through the following gates:  
Forget Gate: Determines which information to discard from the cell state by outputting weights in the range [0, 1] via a Sigmoid function.  
Input Gate: Regulates the degree to which current input updates the cell state, jointly governed by Sigmoid and Tanh functions.  
Output Gate: Generates the current hidden state output based on the updated cell state.  
The mathematical expressions for each gate are defined as:  
\[\begin{array}{l}f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},i_{t}]+b_{f}\Big{)}\\ x_{t}=\sigma(W_{x}\cdot[h_{t-1},i_{t}]+b_{x})\\ \widehat{g}_{t}=\tanh\big{(}W_{g}\cdot[h_{t-1},i_{t}]+b_{g}\big{)}\\ C_{t}=f_{t}\odot C_{t-1}+x_{t}\odot\widehat{g}_{t}\\ o_{t}=\sigma(W_{o}\cdot[h_{t-1},i_{t}]+b_{o})\\ h_{t}=o_{t}\odot\tanh(C_{t})\end{array} \tag{3}\]  
\(\sigma(\cdot)\) denotes the Sigmoid function, \(\odot\) represents element-wise multiplication, W is the weight matrix, and b is the bias term.  
In [17], LSTM is integrated into a denoising autoencoder DAE framework to form an LSTM-DAE network. This network employs unsupervised learning to encode and decode ship-radiated noise, effectively extracting deep temporal features from acoustic signals. The encoder progressively compresses input dimensions through stacked LSTM layers while reconstructing model parameters; the decoder leverages LSTM's temporal modeling capability to recover key signal features and suppress noise interference. Specifically, LSTM encodes and decodes acoustic signals through unsupervised temporal feature extraction, enhancing robustness against complex acoustic variations and providing high-quality feature representations for subsequent classification and localization tasks. By capturing long-term dependencies through memory gate mechanisms, LSTM helps distinguish temporal patterns of different sound sources, indirectly aiding localization. Although primarily used for temporal feature extraction and classification, LSTM's ability to model temporal dynamics, suppress noise, and enhance feature discriminability provides critical support for downstream SSL algorithms.  
Furthermore, LSTM's long-term dependency modeling capability is combined with DLMN to form a collaborative training architecture. Through reinforcement learning strategies, features extracted by LSTM-DAE are further optimized for target classification tasks. Experiments confirm that this synergistic mechanism significantly improves the robustness of underwater acoustic target recognition, particularly excelling in low-SNR environments.
## 3 Materials and Methods
### Data Extraction and Classification  
The final corpus consisted of 100 peer-reviewed publications: 72 studies selected through the systematic PRISMA-based search and 28 studies incorporated via supplementary strategies (see Table 2). Data extraction focused on the following aspects:  
* Technical debt types addressed (e.g., data debt, model debt, algorithm debt, etc.). * Research methodologies employed (e.g., case studies, empirical analysis, theoretical frameworks). * Identified challenges and proposed mitigation strategies. * Application contexts relevant to AI-based competition platforms. A thematic analysis was conducted to classify the extracted studies by drawing on existing technical debt frameworks in AI and ML systems [20; 30; 31], while also extending and consolidating them into a unified typology. This enriched categorization not only integrates prior classifications but also introduces new debt types observed during the review. It served as the foundation for the stakeholder-oriented assessment questionnaire presented in the following section. To ensure the consistency and reliability of the classification process, a subset of 30 papers (representing 30% of the total sample) was independently annotated by both authors with respect to the two most prominent technical debt types discussed in each study. The level of agreement was evaluated using [PERSON]'s kappa, a widely used measure for inter-rater reliability on categorical data. The resulting score of \(\kappa\) = 0.857 is considered almost perfect, indicating a very high level of agreement [113]. (See Table A1 in Appendix B for the full agreement matrix.) Minor disagreements were resolved through discussion before finalizing the thematic synthesis. Each study was assigned one or more technical debt categories based on its primary thematic content and stated contributions. The assignment was informed by and aligned with our unified typology and was based on a close reading of abstracts, objectives, and methodological sections. This ensured a consistent and meaningful categorization across the entire corpus. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Title** & **Author(s)** & **Year** &
\begin{tabular}{c} **Technical Debt** \\ **Type** \\ \end{tabular} \\ \hline
1 & Algorithm Debt: Challenges and Future Paths & [114] &
\begin{tabular}{c} EIO Simon, [PERSON], [PERSON] \\ \end{tabular} & 2023 & Algorithm \\ \hline
2 &
\begin{tabular}{c} Machine Learning Algorithms, Real-World \\ Applications and Research Directions \\ \end{tabular} & [115] & [PERSON] & 2021 & Algorithm \\ \hline
3 & \begin{tabular}{c} Toward understanding deep learning \\ framework bugs \\ \end{tabular} & [116] & \
\begin{tabular}{c} [PERSON], [PERSON], \\ [PERSON], [PERSON], [PERSON] \& 2023 \\ [PERSON] \\ \end{tabular} &
\begin{tabular}{c} 2023 \\ Algorithm \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Studies included in the scoping review.
# A Ku-Band GaAs Multifunction Transmitter and Receiver Chipset
## 3 Circuit Blocks of the Ku-Band Multifunction Chipset
### Low-Noise Amplifier (LNA)  
A schematic and micrograph of the Ku-band three-stage LNA is shown in Figure 14. The first two stages employ a 2 \(\times\) 50-\(\upmu\)m HEMT (M\({}_{1}\), M\({}_{2}\)) while the last stage uses a 2 \(\times\) 75-\(\upmu\)m (M\({}_{3}\)), considering the tradeoff among the gain, noise figure and DC power consumption. The inductive source degeneration (L\({}_{\rm S}\)) is applied to the first stage for compromising the noise figure, input matching and gain. In Figure 15, the maximum available gain (MAG) and minimum noise figure (NF\({}_{\rm min}\)) are simulated with different L\({}_{\rm S}\) values. With a compromised value of 0.35 nH, the MAG and NF\({}_{\rm min}\) exhibit values of 8.6 dB and 0.74 dB, respectively, at 15 GHz. A 10-\(\Omega\) series resistor (R\({}_{\rm S}\)) is inserted into the output of each stage to improve the stability.  
Figure 14: Schematic and chip micrograph of the low-noise amplifier (LNA).  
Figure 13: Measurement results of the MPA. (**a**) Gain and matching performance, (**b**) output power, power gain and power-added efficiency (PAE) versus input power at 15 GHz.  
Figure 15: Simulated maximum available gain (MAG) and minimum noise figure (NF\({}_{\rm min}\)) of the 2 \(\times\) 50-\(\upmu\)m transistor with various values of L\({}_{\rm S}\).  
The LNA test cut occupies an area of 1.6 \(\times\) 0.9 mm\({}^{2}\), excluding probing pads, as shown in Figure 14. The LNA consumes a DC power of 92 mW, with a drain bias voltage of 2 V. Figure 16 shows the measurement results of the LNA. The measured peak gain and noise figure, shown in Figure 16a, are 27.5 dB and 2.4 dB, respectively, at 15 GHz. In Figure 16b, the measured input matching is below \(-\)10 dB from 13.7 to 28.5 GHz. The output matching is broadband, below \(-\)10 dB, from 3.6 to above 30 GHz, due to the lossy output matching network.
## 4 Results and Discussion  
Based on the numerical model developed and its validity in practical applications, a case study was evaluated in the copper mining industry of Chile. According to the latest energy balance available at the country level for the year 2021 [44], this sector represents 40% of the total energy consumed in the industrial and mining sectors of the country. Hence, in this industrial sector, it is essential to have alternative energy sources (such as CSP) where TES is a viable solution, at least from a technical point of view. Therefore, a correct design of an energy storage tank is essential for implementing it in a case study later. In this study, the numerical model demonstrated that it is valid for this type of industrial application.
###### Abstract  
The main task while developing a mobile robot is to achieve accurate and robust navigation in a given environment. To achieve such a goal, the ability of the robot to localize itself is crucial. In outdoor, namely agricultural environments, this task becomes a real challenge because odometry is not always usable and global navigation satellite systems (GNSS) signals are blocked or significantly degraded. To answer this challenge, this work presents a solution for outdoor localization based on an omnidirectional visual odometry technique fused with a gyroscope and a low cost planar light detection and ranging (LIDAR), that is optimized to run in a low cost graphical processing unit (GPU). This solution, named FAST-FUSION, proposes to the scientific community three core contributions. The first contribution is an extension to the state-of-the-art monocular visual odometry (Libviso2) to work with omnidirectional cameras and single axis gyro to increase the system accuracy. The second contribution, it is an algorithm that considers low cost LIDAR data to estimate the motion scale and solve the limitations of monocular visual odometer systems. Finally, we propose an heterogeneous computing optimization that considers a Raspberry Pi GPU to improve the visual odometry runtime performance in low cost platforms. To test and evaluate FAST-FUSION, we created three open-source datasets in an outdoor environment. Results shows that FAST-FUSION is acceptable to run in real-time in low cost hardware and that outperforms the original Libviso2 approach in terms of time performance and motion estimation accuracy.  
mobile robots; visual odometry; sensor fusion; heterogeneous computing
# Recognition of Urdu Handwritten Characters Using Convolutional Neural Network
## 4 Our Dataset  
In research activities related to data science, a data-enrich dataset has a key role in generating the correct results. The precise and established dataset leads to the correct evaluation of the mathematical models that are implemented on the dataset. Furthermore, it is mandatory to have a standard dataset for each data-science domain to achieve the benchmark results. While performing our experimental work, we found that there was no publicly-available standard dataset, as mentioned earlier. In order to bridge this gap, we developed a novel dataset of Urdu handwritten isolated characters and numerals. Our dataset contained 800 images of each of the 38 Urdu character and 10 numerals. The dataset was built by inviting 500 native Urdu speakers from different social groups. Each author was directed to write both the Urdu characters and numerals each in his or her own handwriting in Nastaliq font in a column, as shown in Figure 9. As mentioned earlier, the dataset was not from a single writer; therefore, there was very less chance of overfitting the classification model. It is pertinent to mention that the numeral part of this dataset was also used successfully for visualization in our work [59]. The ground truth and information about the authors of our proposed dataset, e.g., age, gender, hand preference while writing (left hand/right hand or both), physical impairment (if any), and profession, were also recorded in a suitable XML-based repository. After dataset collection, the text pages were scanned on a flatbed scanner at 300 dpi and segmented manually into images of 28 by 28 for each Urdu character and numeral data. As mentioned earlier, the dataset consisted of \(800\times 10=8000\) numeral images and \(800\times 38=30400\) Urdu characters. We planned to increase the number of authors to 1000 later in order to enrich the dataset by adding as many variations of handwriting as possible. Upon completion of the dataset, we will make the dataset publicly available to researchers.  
In the case of Urdu characters, we considered only those characters that have much the shape similarity rather than the number of strokes, as shown in Figure 3. Keeping this observation, we divided the Urdu characters into 12 groups, as shown in Figure 10. It is pertinent to mention that our way of grouping the Urdu characters was different since we grouped the characters based on the shape similarity rather than based on the number of strokes, as reported in [21].  
Figure 10: Grouping of Urdu characters according to shape similarity. Each groups is numbered from right to left.  
Figure 9: A sample of the handwritten Urdu characters is shown in (**a,b**), while (**c**) depicts a sample of handwritten Urdu numerals.
###### Abstract  
Shear wave elastography provides information about the stiffness of thyroid nodules that could be a new indicator of malignancy. The current study aimed to investigate the feasibility of using shear wave elastography (SWE) alone and in conjunction with grey scale ultrasound (GSU) to predict malignancy in 111 solitary thyroid nodules. Malignant thyroid nodules tended to have microcalcification, hypoechogenicity, tall to width ratio \(>\)1, and irregular borders (\(p\) \(<\) 0.05). SWE indices (\(E_{\text{maximum}}\) and \(E_{\text{mean}}\)) of malignant nodules (median \(\pm\) standard error: 85.2 \(\pm\) 8.1 kPa and 26.6 \(\pm\) 2.5 kPa) were significantly higher than those of benign nodules (median \(\pm\) standard error: 50.3 \(\pm\) 3.1 kPa and 20.2 \(\pm\) 1 kPa) (\(p\) \(<\) 0.05). The optimal cut-off of \(E_{\text{maximum}}\) and \(E_{\text{mean}}\) for distinguishing benign and malignant nodules was 67.3 kPa and 23.1 kPa, respectively. Diagnostic performances for GSU + \(E_{\text{maximum}}\), GSU + \(E_{\text{mean}}\), GSU, \(E_{\text{maximum}}\) and \(E_{\text{mean}}\) were: 70.4%, 74.1%, 96.3%, 70.4% and 74.1% for sensitivity, 83.3%, 79.8%, 46.4%, 70.2%, and 66.7% for specificity, and 80.2%, 78.4%, 58.5%, 70.3%, and 68.5% for accuracy, respectively. Our results suggested that combining GSU with SWE (using \(E_{\text{maximum}}\) or \(E_{\text{mean}}\)) increased the overall diagnostic accuracy in distinguishing benign and malignant thyroid nodules.  
Keywords:ultrasound; thyroid cancer; shear wave elastography +
Footnote †: journal: Applied Sciences
# Packet-Forwarding Algorithm in DTN Based on the Pheromone of Destination Node
## 3 The Model of Pheromone and Community
### Pheromone Model  
The pheromone obtained by the relay node is considered the betweenness centrality between the core node and the current node. The relay node that holds higher pheromone can successfully forward the packet to the core node with higher probability. As shown in Figure 2, \(R_{a}\) is the communication coverage radius of node \(a\), and \(R_{b}\) represents the radius of node \(b\). \(P_{a,b}\) is the pheromone received by node \(b\), which is spread by node \(a\), and \(P_{b,a}\) has the same mean. If both nodes \(a\) and \(b\) enter their radius, then \(P_{a,b}\) and \(P_{b,a}\) will increase with time and begin to decrease when they leave each other.  
**Definition 1**.: (_Pheromone model_): _We suppose \(n\) as the count of connections between nodes \(a\) and \(b\) during time \(T\), \(T_{k}\) as the \(k\)-th connection time, and \(B_{k}\) as the \(k\)-th disconnection time. \(O_{k}\) is the inter-node communication capability at the start of the \(k\)-th connection, and \(Y_{k}\) is the inter-node communication capability at the end of the \(k\)-th connection. Given that only the node enters the communication radius of the core node, the communication capabilities between them will increase. Therefore, \(P_{a,b}\left(k\right)\) is obtained by \(Y_{k}\). \(\alpha\) is the increasing coefficient, and \(\beta\) is the decreasing coefficient. To simplify the computation, we set \(\alpha=\beta=1\). Thus, we can easily obtain \(O_{1}=0\), \(Y_{1}=T_{1}\):_  
\[P_{a,b}\left(k\right)=Y_{k},\quad 1\leq k\leq n \tag{2}\]  
\[O_{k}=\left\{\begin{array}{cc}0,&Y_{k-1}\leq B_{k-1},2\leq k\leq n\\ Y_{k-1}-B_{k-1}*\beta,Y_{k-1}>B_{k-1}*\beta,2\leq k\leq n\\ Y_{k}=O_{k}+T_{k}*\alpha,&1\leq k\leq n\end{array}\right. \tag{3}\]
## 1 Introduction  
Electromagnetic (EM) methods, such as the magnetotelluric (MT) method, the audio magnetotelluric (AMT) method, and controlled source EM, use the EM diffusion over a wide frequency band range to detect the subsurface geo-electrical structure, from shallow near-surface to great depth [1,2]. It has been widely applied in deep tectonic study, oil and gas detection, mineral exploration, geothermal survey, engineering purposes, and environmental monitoring [3, 4, 5, 6, 7].  
Forward modeling and inversion are important to understand the subsurface geo-electric structure for the EM survey [8,9]. However, meaningful interpretation of EM data is strongly dependent on the accuracy and efficiency of the forward modeling [10]. The forward problem can be solved numerically with the integral equation, finite-element, and finite-difference methods [11,12]. For models with small scale anomaly, the integral equation method is more efficient and accurate [13]. However, it faces challenges in handling complex structures [13,14]. The differential equation method (e.g., finite-difference and finite-element methods) can be used to model complex structure EM problems efficiently [15, 16, 17, 18, 19, 20]. Due to the simplicity and efficiency of implementation, the finite-difference method is widely used in the 3D forward modeling [21, 22, 23, 24, 25].  
In the development of an EM forward modeling algorithm, it is common to apply the quasi-static approximation, where the displacement current is neglected and Maxwell's equations are simplified [26]. For simulations based on the electric-field formulation, small air conductivity (typically in the range of \(10^{-4}\)-\(10^{-14}\) S/m) is assigned to avoid ill-conditioning [27, 28, 29]. This particular choice is sometimes very subjective and its impact on the modeling results is rarely examined. Further, since the use of small conductivity for the air layer is a common practice for EM diffusion modeling based on the electric-field formulation, the displacement current can no longer routinely be neglected at all operating frequencies [27]. This means that the use of the quasi-static approximation in the air can be problematic and deserves further investigation, which is the primary goal of this paper.  
In this paper, we investigate the effect of the quasi-static approximation on the finite-difference EM modeling based on the electric-field formulation. This can be realized by comparing the numerical results from modeling based on the quasi-static approximation and considering both the conduction and displacement currents. The investigation is carried out on several synthetic models, with both flat and uneven surfaces, to model the general earth structure.