esa-sceva/satcom-chunk-collection huggingface dataset:
# Focusing 3D Small Objects with Object Matching Set Abstraction
## 2 Related Work
### Small Object Detection  
Detecting small objects is a crucial yet challenging problem in both 2D and 3D object detection. Currently, general-purpose object detection makes it difficult to achieve satisfactory results in detecting small objects. This challenge arises primarily from the limited representations and inherent algorithmic bias toward the detection of normal-sized objects. For these purposes, a series of strategies for small object detection is employed to alleviate information loss and prediction bias. The strategies can be divided into sample-oriented strategy, feature enhancement strategy, and weighting strategy [16]. The first strategy aims to alleviate the sample scarcity issue by raising the sampling probability of small objects [17; 18; 19]. The second strategy employs the low-level learned representation or super-resolution to strengthen the corresponding features [20; 21; 22]. The third strategy utilizes an attention mechanism to rectify the prediction bias [23; 24; 25].  
In recent years, small object detection in the 3D field has also garnered significant attention. Semantic information is injected into the sampling process by adding a segmentation head [26; 27]. Hence, the sampling probability for the foreground is enhanced, boosting the detection performance. Motivated by these works, we specifically add another segmentation head for small objects to increase the corresponding probability. PSA-Det3D presents a pillar query to sample the key points [28]. Compared with the ball query, the pillar is more consistent with the geometric characteristics of the objects and can reduce the introduction of noise. This method only considers the \(x\) and \(y\) axes when the distance is computed, leading to more unrelated neighboring points in the \(z\) axis. Therefore, when calculating distances, we consider the additional dimension of the \(z\) axis and propose the cube query strategy. Globally, our approach integrates a sample-oriented strategy and weighting strategy to enhance the detection performance.
# Identification of the Surface Cracks of Concrete Based on ResNet-18 Depth Residual Network
###### Abstract
Initially, a database comprising 2000 authentic images depicting the state of concrete surfaces was meticulously curated and manually annotated for the purpose of training deep learning models. The second step involved establishing a crack recognition model based on the ResNet-18 grid, followed by conducting model training and parameter optimization. Subsequently, the model's prediction accuracy was analyzed by evaluating its recognition performance on both the training set and test set. Furthermore, this study also examined the accuracy of identifying concrete cracks in images that are affected by environmental noise interference and real bridge scenarios.
# An Acoustic Feature-Based Deep Learning Model for Automatic Thai Vowel Pronunciation Recognition
## 3 Related Works
### Acoustic Features  
Mel spectrograms (MS) were converted from the raw speech signal (16 kHz) and then applied to the speech command recognition (SCR) task [46]. MS images with the feature size of 125 \(\times\) 80 \(\times\) 1 were used as acoustic features. The light interior search network (LIS-Net) model was applied to the SCR task using the Google Speech Command dataset. The experiment showed a significant improvement in accuracy on all 12, 20, and 35 commands with a fast prediction time and a small total number of parameters. The results of the LIS-Net model achieved 97% accuracy. The model consists of the input layer, followed by the light interior search block (LIS-Block) and a classification block. Each LIS-Block was composed of stacks of several LIS-Cores enclosed by two convolutional layers, followed by the batch normalization (BN) and activation layers. The Adam optimizerwas used in the model. [PERSON] et al. developed a structure combining three classifiers, namely, a DNN, a CNN, and recurrent neural network (RNN). A corpus recorded by 10 actors and containing 5 sessions was used [47]. The model was used to recognize four emotions, namely, angry, happy, neutral, and sad. At the frame level, low-level descriptors (LLDs) were transferred to the RNN to obtain the LLD-RNN model. At the segment level, MS was transferred to a CNN to obtain the MS-CNN model. At the utterance level, the outputs of high-level statistical functions (HSFs) were transferred to DNN to obtain the HSF-DNN model. A multitask learning strategy was applied in the three models to obtain generalized features. The classification of discrete emotional categories and the regression of continuous emotional attributes were simultaneously performed. Finally, a confidence-based fusion strategy was used to combine diverse classifiers in recognizing different emotional states. The model with the fusion strategy achieved a weighted accuracy of 57.1% and an unweighted accuracy of 58.3%. Speech emotion classification [48] with two datasets (i.e., Interactive Emotional Dyadic Motion Capture (IEMOCAP) and Emotional Tagged Corpus on Lakorn (EMOLA)) was categorized into four classes, namely, anger, happiness, neutral, and sadness. The experimental results showed that each emotion used different features. The Mel frequency cepstral coefficient (MFCC) with zero-crossing rate worked well for the anger and happiness emotional classes with 81.95% and 69.86% accuracy, respectively. Recently, MFCC has also operated as an input in neonatal bowel sound detection [49]. The CNN and a Laplace hidden semi-Markov model (HSMM) were presented. The abdominal sounds from 49 newborn samples in the tertiary neonatal intensive care unit (NICU) were applied. The peristalsis (P) and no peristalsis (NP) classes were used. The totals of P and NP were 14,410 and 1991, respectively. The results were presented with accuracy and area under curve (AUC) scores being 89.81% and 83.96%, respectively.
###### Abstract  
Autonomous drone navigation presents challenges for users unfamiliar with manual flight controls, increasing the risk of collisions. This research addresses this issue by developing a multifunctional drone control system that integrates hand gesture recognition, obstacle avoidance, and 3D mapping to improve accessibility and safety. The system utilizes Google's MediaPipe Hands software library, which employs machine learning to track 21 key landmarks of the user's hand, enabling gesture-based control of the drone. Each recognized gesture is mapped to a flight command, eliminating the need for a traditional controller. The obstacle avoidance system, utilizing the Flow Deck V2 and Multi-Ranger Deck, detects objects within a safety threshold and autonomously moves the drone by a predefined avoidance distance away to prevent collisions. A mapping system continuously logs the drone's flight path and detects obstacles, enabling 3D visualization of drone's trajectory after the drone landing. Also, an AI-Deck streams live video, enabling navigation beyond the user's direct line of sight. Experimental validation with the Crazyflie drone demonstrates seamless integration of these systems, providing a beginner-friendly experience where users can fly drones safely without prior expertise. This research enhances human-drone interaction, making drone technology more accessible for education, training, and intuitive navigation.  
gesture-based drone control; autonomous obstacle avoidance; human-drone interaction; 3D mapping and navigation Article
## 4 Results and Discussion
### Angular and Axial Dependence  
The properties of AE signals were investigated as a function of tangential angle at \(\eta\) = 1 cm. Seven AE sensors were mounted at a 30\({}^{\circ}\) interval from \(\delta\) = 0\({}^{\circ}\) to 180\({}^{\circ}\). The mean value of the amplitude was calculated from over 70 k hits. Figure 5a shows the mean amplitudes with \(\sigma\) values generated from D0.2P4\(\eta\)1\(\omega\)0 and D1.2P4\(\eta\)1\(\omega\)0. For D0.2P4\(\eta\)1\(\omega\)0, the amplitude of the AE signals is 0.58 (\(\sigma\) = 0.087) mV at \(\vartheta\) = 0\({}^{\circ}\). As the angle increased, the amplitude decreased, reaching a minimum between 60\({}^{\circ}\) and 90\({}^{\circ}\). Above this angle, the amplitude increased, reaching a maximum at \(\sim\)120\({}^{\circ}\) and a second minimum at \(\sim\)150\({}^{\circ}\). At \(\mathrm{p}_{0}\) = 2 bar, similar angular-dependence features were observed for D0.2-D0.5, while different features showing the minimum at \(\sim\)120\({}^{\circ}\) were found for D0.5 and D1.2 (Figure A2).  
The axial dependence of the AE amplitude was also investigated using D0.2-D1.2 LSs at \(\vartheta\) = 0\({}^{\circ}\). Seven AE sensors were mounted at downstream locations aligned with the LS along the z axis on the cylinder surface. Figure 5b shows the observed results for D0.2P4\(\omega\)0 and D1.2P4\(\omega\)0. For D0.2P4\(\omega\)0 LS, the baseline subtracted (BS) amplitude was 0.27 mV at \(\eta\) = 1 cm. As \(\eta\) increased to 3 cm, the BS amplitude decreased sharply to 0.124 mV. Above \(\eta\) = 3 cm, the BS amplitude slowly decreased with increasing \(\eta\). It can be seen that the mean amplitudes of the AE signals generated from D0.2P4\(\omega\)0 LS clearly exhibit exponential-decay  
Figure 4: Two types of peak-frequency distributions, observed in (**a**) most tests, and (**b**) only D1.2P4.  
characteristics. For D1.2P400 LS, some of the data fluctuated to some extent; however, it is satisfied the exponential-decay characteristics within the standard deviation.
# A Survey of Machine Learning-Based System Performance Optimization Techniques
## 2 Background
### Machine Learning Based Cache Management  
The speed of a CPU is far ahead of the memory latency. This problem causes a bottleneck in many computer applications [25]. A hierarchical memory system was constructed with the addition of cache memory to overcome between the speed of memory access and the speed of the CPU. Since cache memory is even smaller than main memory capacity, cache memory must be managed efficiently. Therefore, various techniques such as cache replacement, prefetching, etc. were introduced to increase efficiency. Since these traditional techniques are speculative, they should ideally predict future patterns. However, predictors are almost impossible to know the pattern of the future, so one way is to learn memory patterns of the past.
## 5 Modal Analysis of Bridge  
Modal analysis was performed to analyze the natural frequencies and mode shapes of the bridge. Figure 7 shows the mode shapes of the bridge obtained from the modal analysis. The first and second modes appeared in the bending shapes in the transverse direction, and the third mode appeared when bending in the vertical direction. The natural frequency tends to decrease with increasing pier height, as shown in Figure 8. In the first and second modes, the natural frequencies of the bridge significantly decreased with increasing height to 15 m. At a pier height higher than 15 m, little change in the natural frequency was found. The natural frequency of the third mode was almost constant, irrespective of the increase in the pier height. This indicates that the natural frequencies of the first and second modes of the bridge were largely affected by the change in structural configuration, but those of the third mode were not observed.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Pier Height (m)** & **Yield Displ. (\(D_{y}\), mm)** & **Ultimate Displ. (\(D_{y}\), mm)** & **Max Shear Force (F, kN)** \\ \hline
5 & 22 & 91 & 7200 \\
10 & 88 & 215 & 3600 \\
15 & 193 & 367 & 2400 \\
20 & 361 & 548 & 1800 \\
25 & 563 & 720 & 1420 \\ \hline \end{tabular}
\end{table}
Table 4: Summary of the yield and ultimate displacements and the shear force for the piers of a bridge.  
Figure 7: Mode shapes of the bridge: (a) 1 st mode; (b) 2 nd mode; (c) 3 rd mode shapes
# Device Modeling of Quantum Dot-Organic Light Emitting Diodes for High Green Color Purity
## 3 Results and Discussion
### New Device Architecture for Reduction in the Blue Light Leakage  
As shown in Sections 3.1 and 3.2, even if the density of G-QD is increased to 40% and the thickness of the G-QD layer is increased to 10 um it is difficult to completely block the leakage of blue light. In addition, the green color purity due to blue leakage is far below the deep green level of BT2020. Therefore, in order to completely block blue light leakage and improve green color purity, the existing process architecture has limitations, and an additionally supplemented structure must be considered.  
In general, in the TFT-LCD process used in existing LED TVs, red, green, and blue color layers, which are called color filters, are stacked on the TFT pixels to express colors [18]. Introducing such a similar structure to QD-OLED may improve color purity. Therefore, in this study, as shown in Figure 7, we investigated whether the color purity increases by introducing a structure in which a new color layer is stacked on top of the QD layer.
# Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor
## 3 Proposed Target Speech Enhancement Method
### The FBF Spatial Filter  
As shown in Figure 4, the input signals to the FBF spatial filter are the data captured by the \(u\), \(v\), and \(w\)-sensor of the AVS. With the given DOA (\(\theta_{s}\phi_{s}\)), the spatial matched filter (SMF) is employed as the FBF spatial filter, and its output can be described as:  
\[Y_{m}(\tau,\omega)=\mathbf{w}_{m}^{H}\mathbf{X}_{\text{a}\text{r}\text{s}}(\tau,\omega) \tag{40}\]  
where \(\mathbf{w}_{m}^{H}=\mathbf{a}^{H}(\theta_{s}\phi_{s})/\mid\mathbf{a}(\theta_{s}\phi_{s}) \mid\mid^{2}\) is the weight vector of the SMF, and \(\mathbf{a}(\theta_{s}\phi_{s})\) is given in Equation (5). \(\mid^{H}\) denotes the vector/matrix conjugate transposition. Substituting the expressions in Equations (5), (3), and (9)-(11) in Equation (40) yields:  
\[\begin{array}{ll}Y_{m}(\tau,\omega)&=u_{s}X_{u}(\tau,\omega)+v_{s}X_{v}(\tau, \omega)+w_{s}X_{w}(\tau,\omega)\\ &=u_{s}^{2}S(\tau,\omega)+u_{s}N_{tu}(\tau,\omega)+v_{s}^{2}S(\tau,\omega)+v_{ s}N_{tv}(\tau,\omega)+w_{s}^{2}S(\tau,\omega)+w_{s}N_{tv}(\tau,\omega)\\ &=(u_{s}^{2}+v_{s}^{2}+w_{s}^{2})S(\tau,\omega)+N_{tuww}(\tau,\omega)\\ &=S(\tau,\omega)+N_{tuww}(\tau,\omega)\end{array} \tag{41}\]  
where \(N_{tuww}(\tau,\omega)\) is the total noise component given as:  
\[\begin{array}{ll}N_{tuww}(\tau,\omega)&=u_{s}N_{tu}(\tau,\omega)+v_{s}N_{lv }(\tau,\omega)+w_{s}N_{tw}(\tau,\omega)\\ &=u_{s}(u_{i}S_{i}(\tau,\omega)+N_{u}(\tau,\omega))+v_{s}(v_{i}S_{i}(\tau, \omega)+N_{v}(\tau,\omega))\\ &+w_{s}(w_{i}S_{i}(\tau,\omega)+N_{w}(\tau,\omega))\\ &=(u_{s}u_{i}+v_{s}v_{i}+w_{s}w_{i})S_{i}(\tau,\omega)+u_{s}N_{u}(\tau,\omega) +v_{s}N_{v}(\tau,\omega)+w_{s}N_{w}(\tau,\omega)\end{array} \tag{42}\]  
It can been seen that \(N_{tuww}(\tau,\omega)\) in Equation (42) consists of the interferences and background noise captured by directional sensors, while \(Y_{m}(\tau,\omega)\) in Equation (41) is the mix of the desired speech source \(S(\tau,\omega)\) and unwanted component \(N_{tuww}(\tau,\omega)\).
## 2 Materials and Methods  
### Materials and Apparatus  
The tests were conducted in the hydrodynamic towing tank at the Department of Aircraft and Space Vehicles (ETSI Aeronautica y del Espacio), Universidad Politecnica de Madrid. The towing tank dimensions are \(3000\times 420\times 720\) mm, with an effective working width of \(335\) mm, ensuring sufficient space for controlled fluid dynamics experiments. The towing tank was equipped with an engine-driven trolley, which allowed for adjustable speeds based on the experiment's requirements. This setup enabled precise control over the experimental conditions and ensured accurate simulation of different scenarios. The trolley can reach frequencies of \(0\) to \(155\) Hz, as will be discussed in the Section 2.3.  
It is worth noting that the blockage ratio of the prototype adheres to standard guidelines for wind tunnels, ensuring reliable experimental results. Additionally, the height of the model can reach approximately 12 cm when the angle of attack is adjusted, allowing for flexibility in studying varying flow and thermal interaction scenarios.  
The ink delivery system used a Mariotte bottle, 3.2 mm diameter pipes, and various valves. This allowed us to keep the pressure constant along the circuit and ensure a homogeneous distribution of the fluid. The experiment employed a mixture of 28 % cosmetic colorings (environmental friendly) and 72 % water, selected to minimize contamination during the tests.  
To simulate MMRTG heating, a variable-temperature resistor was installed, capable of adjusting between 291 and 307 K (18-34 \({}^{\circ}\)C). A thermostat was integrated to regulate the resistor's temperature, and an additional thermostat was used to control both the cavity and atmospheric temperatures. After preliminary tests, a stable temperature of 301 K (28 \({}^{\circ}\)C) was selected to maintain consistency during the experiments. It should also be emphasized that the water temperature in the towing tank remained constant at approximately 289 K (16 \({}^{\circ}\)C) throughout the tests. This setup ensured precise temperature control during the experiment, allowing for accurate simulation of thermal conditions similar to those expected on Mars.
## 5 Proposed Model Quality Control in the GALDAM  
### Augmented-Image Quality Checking for Filtering Heavily Bean-Overlapping Images  
The augmented-image quality checking mechanism is designed to verify whether the augmented images can be the qualified in training models \(M_{k}^{DS}\) for the defect-sensitive inspection network. The criteria is that the overall overlapping area of beans in a single bean image shall not be greater than a threshold value, represented by \(\theta^{AIQC}\), since the too high degree of bean overlapping decreases inspection accuracy of the trained model. More precisely, the criteria of reserving an augmented image can be expressed as the following equation:  
\[\sum_{m=1}^{\gamma_{i}-1}\sum_{n=m+1}^{\gamma_{i}}IoU(b_{m}^{ LBIG},b_{n}^{ LBIG}) \begin{array}{l}\leq\theta^{AIQC},\\ >\theta^{AIQC},\end{array}\text{\quad reserve the augmented image.} \tag{6}\]  
where the \(\gamma_{i}\) is defined as number of GDVs in image \(i\) and \(b_{j}^{LBIG}\) is denoted \(j\)-th bean image in LBIG. The IoU (Intersection over Union) [24] means the degree of two overlapped areas \(Area_{i}\) and \(Area_{j}\) is measured, and defined as  
\[IoU(Area_{i},Area_{j})=\frac{|Area_{i}\cap Area_{j}|}{|Area_{i} \cup Area_{j}|}, \tag{7}\]  
where \(|Area_{x}|\) indicates the area size of \(Area_{x}\). Based on this idea, we developed a procedure for checking quality of all augmented bean images.  
The detailed steps of the augmented-image quality checking procedure are described as follows.  
**Step 1:**: Set image counter \(i=0\), bean label set \(L^{AIQC}=\emptyset\), and bean image set \(IMG^{AIQC}=\emptyset\).
**Step 2:**: Determine whether image \(i\) is qualified to be reserved. If the criterion (\(\sum_{i=1}^{\gamma_{i}-1}\sum_{j=+1}^{\gamma_{i}}IoU(b_{k}^{ LBIG},b_{j}^{ LBIG})<\theta^{AIQC}\)) holds, then the \(i\)-th bean image is qualified and reserves its information as the following substeps:  
Add \(L_{i}^{LBIG}\) into \(L^{AIQC}\);  
Add \(IMG_{i}^{LBIG}\) into \(IMG^{AIQC}\);
**Step 3:**: Prepare the next image. If \(i\) is less than the number of training images, then increase the image counter by one, i.e., \(i=i+1\) and go to Step 2.
**Step 4:**: Return the qualified images and associated labels, \((IMG^{AIQC},L^{AIQC})\).  
Figure 10 shows examples of bean-overlapping images that do not pass the augmented-image quality checking criterion. By looking the figure, we can see that those heavily overlapping of beans are not even easily distinguished by human experts, and thus, it is reasonable to discard such images. The formula above, i.e., Equation (6) is designed to appropriately achieve this goal by using the parameter \(\theta_{AIQC}\), which determine the acceptable overlapping degree.
_applied sciences_  
_Article_  
**Automated Detection of Social Conflict Drivers in Civil Infrastructure Projects Using Natural Language Processing**  
**[PERSON], [PERSON], [PERSON] and [PERSON]**\({}^{\star}\)**  
Department of Civil and Environmental Engineering, Yonsei University, 50 Yonsei-ro, Sedaemun-gu, Seoul 03722, Republic of Korea; [EMAIL_ADDRESS] (S.B.); 821 [EMAIL_ADDRESS] (D.N.); [EMAIL_ADDRESS] (J.W.)  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]  
**Abstract:** Early detection and mitigation of social conflict in civil infrastructure projects is essential due to its significant impact on project performance and social governance. Nevertheless, there is no scientific system for monitoring conflict drivers in a timely manner in practice. Furthermore, previous studies of social conflict in the civil engineering and management domains have relied on manual literature reviews and case studies. Although these qualitative approaches have provided context-specific insights, they are limited in their generalizability and broad perspectives. Against this backdrop, this study presents an automated process for detecting conflict drivers from news articles using ChatGPT. The authors collected news articles related to civil infrastructure projects implemented in the Republic of Korea using web crawling. Then, ChatGPT was used to extract conflict-related keyphrases from the article collections and classify the keyphrases into predefined conflict drivers. The result showed a notable performance with a micro average F1-score of 85.7%. Moreover, the authors confirmed the validity of the keyphrase extraction and classification results through two illustrative case studies. The proposed process and methods contribute to facilitating data-driven conflict management. Although this study focused on conflict drivers of public infrastructure projects, other types of information extraction tasks can benefit from the presented framework.  
**Keywords:** civil infrastructure project; conflict driver; keyphrase extraction; keyphrase classification; natural language processing; ChatGPT