esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The rapid development of computer vision technology increasingly affects and even changes people's lives, and it is widely used in the field of manufacturing industries. As an important branch of computer vision, three-dimensional (3D) visual imaging and detection has become a research hotspot in recent years with its high efficiency and precision. Therefore, it can meet various application requirements of automated production lines [1, 2]. In the field of additive manufacturing, additive process quality testing is a major scientific problem [3, 4, 5]. European and American countries have made progress in the research of nondestructive testing of the additive manufacturing process [6, 7, 8, 9], such as QMmeltpool 3D [10] from GE in the United States, PrintRite3D [11] from Sigma Labs, EDSTATE ExposureOT [12] from EOS in Germany, and other products. Since additive manufacturing mainly focuses on application research, research on the online detection method of additive manufacturing has obviously fallen behind [13]. The traditional detection methods are mostly offline with separated manufacturing and detection processes.  
Optical 3D imaging has become an important and helpful detection tool in the field of monitoring additive manufacturing process because of its non-contact, high speed, and high efficiency [14, 15, 16, 17]. In various methods, the structured light measurement method (also known as the fringe projection method) is suitable for the measurement of a diffuse reflection surface. It has obvious technical advantages for the measurement of additive manufacturing parts with a rough surface [18; 19; 20; 21], and, therefore, it has been introduced to realize the online detection for the 3D printing process in this paper. Through the real-time monitoring of the additive parts' changing morphology, the internal quality changes and defect information of each printing layer in the forming process can be obtained. This will provide some useful analysis and build an effective bridge between the process parameters of the additive manufacturing system and the quality of printed metal parts.  
Although structured light 3D imaging technology has been widely used to date, there are still some technical difficulties that have not been addressed, particularly when it is applied in the additive manufacturing process with high temperature and strong emitted light [22; 23; 24]. Based on this, according to the working environment of metal parts' additive manufacturing, a binocular measurement system based on UV light source projection was proposed and built, as outlined in this paper. Firstly, the feasibility of the selected system hardware was tested in the actual measurement environment, and then the accuracy of the developed system was assessed. In the actual experiment, the phase information guided the binocular vision system to match the corresponding points, and the additive manufacturing process of metal parts was reconstructed, which proves the feasibility of the built measurement system.
## 5 Results
### Single-Cycle Tensioning of Strands
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Strand**} & \multicolumn{8}{c}{**Tension Force in Strand \(\boldsymbol{Z_{i}}\) * [kN]**} \\ \cline{2-13}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline
**1** & 131.26 & 127.633 & 124.201 & 120.948 & 117.862 & 114.929 & 112.139 & 109.481 & 106.946 & 104.526 & 102.213 & 100 \\
**2** & & 127.633 & 124.201 & 120.948 & 117.862 & 114.929 & 112.139 & 109.481 & 106.946 & 104.526 & 102.213 & 100 \\
**3** & & & 124.201 & 120.948 & 117.862 & 114.929 & 112.139 & 109.481 & 106.946 & 104.526 & 102.213 & 100 \\
**4** & & & & 120.948 & 117.862 & 114.929 & 112.139 & 109.481 & 106.946 & 104.526 & 102.213 & 100 \\
**5** & & & & & 117.862 & 114.929 & 112.139 & 109.481 & 106.946 & 104.526 & 102.213 & 100 \\
**6** & & & & & & 114.929 & 112.139 & 109.481 & 106.946 & 104.526 & 102.213 & 100 \\
**7** & & & & & & & & 112.139 & 109.481 & 106.946 & 104.526 & 102.213 & 100 \\
**8** & & & & & & & & & 109.481 & 106.946 & 104.526 & 102.213 & 100 \\
**9** & & & & & & & & & & 106.946 & 104.526 & 102.213 & 100 \\
**10** & & & & & & & & & & & 104.526 & 102.213 & 100 \\
**11** & & & & & & & & & & & & 102.213 & 100 \\
**12** & & & & & & & & & & & & & 100 \\ \hline \hline
**Zki [kN] ** & **131.26** & **255.266** & **372.603** & **483.792** & **589.31** & **689.574** & **784.973** & **875.848** & **962.514** & **1045.26** & **1124.34** & **1200** \\ \hline \hline
**\(\boldsymbol{\varepsilon_{ki}}\) [cm] *** & 0.6563 & 1.2763 & 1.8630 & 2.4190 & 2.9465 & 3.4479 & 3.9249 & 4.3792 & 4.8126 & 5.2263 & 5.6217 & 6.000 \\ \hline \hline \end{tabular}  
* number of tensioned strands (i); ** tension force in cable after stressing the i-th strand; *** cable shortening after stressing the i-th strand.  
\end{table} 
Table 3: Applied forces in the strands, stay cable force, and cable deformation for the process of single-cycle tensioning. Figure 10: Tension force in cable as a function of number of tensioned strands.
## 2 Ultrasonic Fatigue Device and Data Acquisition System Development
In addition to the amplitude and mean value of vibration shown in the graphical user interface (GUI) in Figure 2, the real vibration waves during the test were also recorded, as shown in Figure 3. The points were detected directly from the sensor probe, and the line was a sinus-fitted curve. Good agreement indicated the steady vibration during the test. Amplitude on the top and bottom were nearly the same, and the opposite phase proved the fully reversed tension-compression mode (\(R=-1\)) of the specimen. Mean value of the wave is the relative distance between the probe and detected surface, which was set to zero before the test. Position of the specimen moved down slightly due to self-heating phenomenon, corresponding to the mean value increase. The difference in mean values between the top and bottom probes allowed us to assess the elongation of the specimen. This value should be the same as the thermal expansion of the specimen. In addition to the maximum temperature shown in the GUI, the temperature distribution of the specimen surface was also recorded by infrared camera. As mentioned before, elongation of the specimen should be the same as thermal expansion, which was calculated by integration over the specimen. Results of verification in four specific cases are listed in Table 1. The meaning of time corresponds to Figure 2. The difference between thermal expansion and elongation was less than 8%. Good agreement indicated that the infrared camera and laser sensors worked well together during the test. Moreover, elongation of the specimen remained at about 0.1 mm during the test, while the length of the ultrasonic fatigue specimen was tens of millimeters, in general, regardless of the type of metal. The influence of elongation on resonance was negligible. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **300 s** & **500 s** & **1000 s** & **5000 s** \\ \hline Thermal expansion (\(\mu\)m) & 85.50 & 104.3 & 117.3 & 121.2 \\ Elongation (\(\mu\)m) & 86.59 & 96.44 & 110.1 & 113.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Elongation verification of Inconel 718 with self-heating at 20 kHz. Figure 3: Original vibration wave obtained from laser sensor (\(R=-1,f=20\) kHz).
## 3 Results
### Influence of Tissue Thickness Underneath the Measured Surface on Measured Spectrum
#### 3.3.2 Estimated Penetration Depth
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Tissue Type**} & \multirow{2}{*}{
\begin{tabular}{c} **Percentage in** \\ **H \& E Section** \\ \end{tabular} } & \multicolumn{4}{c}{**Scattering**} & \multicolumn{4}{c}{**Absorption**} \\ \cline{3-8}  & & \(\mathbf{h_{s,800}^{\prime}}\) & \(\mathbf{f_{mie}\) (\%)} & \(\mathbf{b_{mie}}\) & \(\mathbf{StO_{2}\) (\%)} & \(\mathbf{Blood\) (\%)} & \(\mathbf{Fat\) (\%)} & \(\mathbf{H_{2}O\) (\%)} \\ \hline IC & 29 & \(2.9\pm 0.9\) & \(100\pm 14\) & \(0.80\pm 0.76\) & \(45\pm 6\) & \(8.1\pm 0.7\) & \(31\pm 12\) & \(69\pm 12\) \\ IC & 71 & \(6.1\pm 1.2\) & \(100\pm 5\) & \(0.40\pm 0.37\) & \(55\pm 3\) & \(6.4\pm 1.0\) & \(36\pm 7\) & \(64\pm 7\) \\ IC & 78.5 & \(4.6\pm 1.3\) & \(100\pm 9\) & \(0.70\pm 0.64\) & \(24\pm 6\) & \(5.6\pm 1.6\) & \(35\pm 11\) & \(65\pm 11\) \\ Connective & 98 & \(24.9\pm 6.9\) & \(100\pm 8\) & \(0.82\pm 0.78\) & \(93\pm 3\) & \(2.7\pm 1.0\) & \(34\pm 15\) & \(66\pm 15\) \\ Connective \({}^{\ddagger}\) & 92 & \(25.6\pm 14.8\) & \(100\pm 73\) & \(2.20\pm 2.33\) & \(87\pm 8\) & \(4.8\pm 3.6\) & \(37\pm 24\) & \(63\pm 24\) \\ Connective \({}^{\ddagger}\) & 93 & \(5.4\pm 1.2\) & \(100\pm 4\) & \(0.20\pm 0.55\) & \(33\pm 6\) & \(3.6\pm 1.2\) & \(17\pm 16\) & \(83\pm 16\) \\ Connective & 93 & \(10.1\pm 1.8\) & \(100\pm 4\) & \(0.50\pm 0.46\) & \(75\pm 3\) & \(1.5\pm 0.3\) & \(43\pm 8\) & \(57\pm 8\) \\ Connective \({}^{\ddagger}\) & 91 & \(11.0\pm 2.1\) & \(100\pm 4\) & \(0.35\pm 0.50\) & \(70\pm 3\) & \(4.6\pm 2.9\) & \(5\pm 1\) & \(95\pm 1\) \\ Adipose & 95 & \(15.0\pm 3.0\) & \(100\pm 6\) & \(0.59\pm 0.53\) & \(78\pm 3\) & \(4.6\pm 0.9\) & \(90\pm 3\) & \(10\pm 3\) \\ Adipose & 96 & \(6.1\pm 1.1\) & \(100\pm 6\) & \(0.54\pm 0.52\) & \(64\pm 4\) & \(3.7\pm 0.6\) & \(89\pm 2\) & \(11\pm 2\) \\ Adipose & 94 & \(10.2\pm 3.7\) & \(100\pm 19\) & \(1.34\pm 1.09\) & \(78\pm 6\) & \(4.4\pm 1.6\) & \(91\pm 5\) & \(9\pm 5\) \\ Adipose & 92 & \(10.9\pm 3.8\) & \(100\pm 23\) & \(1.49\pm 1.19\) & \(75\pm 6\) & \(6.7\pm 2.4\) & \(84\pm 8\) & \(16\pm 8\) \\ Adipose & 92 & \(11.1\pm 2.6\) & \(100\pm 10\) & \(1.00\pm 0.68\) & \(70\pm 4\) & \(3.4\pm 0.8\) & \(92\pm 2\) & \(9\pm 2\) \\ Adipose & 92 & \(5.7\pm 0.8\) & \(100\pm 8\) & \(0.45\pm 0.37\) & \(73\pm 3\) & \(3.2\pm 2.6\) & \(91\pm 1\) & \(9\pm 1\) \\ Adipose \({}^{\ddagger}\) & 92 & \(20.3\pm 13.4\) & \(100\pm 70\) & \(2.10\pm 2.28\) & \(84\pm 10\) & \(8.6\pm 5.8\) & \(87\pm 10\) & \(13\pm 10\) \\ Adipose & 93 & \(7.2\pm 1.1\) & \(100\pm 5\) & \(0.54\pm 0.47\) & \(71\pm 3\) & \(2.6\pm 0.4\) & \(92\pm 1\) & \(8\pm 1\) \\ \hline \hline \multicolumn{8}{l}{\({}^{\ddagger}\) The fitted parameters of this measurement were not included in the estimation of the diffuse reflectance and penetration depth because the confidence interval of \(StO_{2}\) was higher than the fitted value.} \\ \end{tabular}
\end{table} 
Table 3: Estimated fit parameters from lumpectomy measurements (fitted value \(\pm\) confidence interval).
# Deep Spatio-Temporal Graph Attention Network for Street-Level 110 Call Incident Prediction
## 5 Experiment
### Dataset Preparation  
In practical applications, although historical 110 Call incidents data are more abundant than crime data, these data remain sparse. To mitigate the potential negative effects of imbalanced datasets on the model, it is crucial to appropriately balance the training data. Undersampling was used to balance the dataset, focusing the model on incident records without being overwhelmed by the large number of non-incident samples. This approach avoids the increased computational complexity and resource demands of oversampling techniques, while stratified sampling was employed to minimize the information loss by maintaining a representative distribution of non-incident data across locations and times. Both types of samples were then combined to create a balanced dataset. The dataset was shuffled to remove any bias before being split into training (60%), validation (20%), and testing (20%) subsets. Stratified sampling was used to maintain proportional representation of categories across all the subsets and to account for the dataset's hourly temporal characteristics. Multiple rounds of partitioning ensured consistent results and minimized any single partition's influence on the model performance.
## 4 Experiments
### Data Preparation  
The evaluation of the proposed ASD system required a considerably large amount of data, preferably with a high degree of heterogeneity and with respect to natural conversations. Yet as previously noted, no single dataset encompasses all these characteristics and so the testing phase examined data from two sources: The Columbia dataset [15]; and the 20 videos considered acceptable from the AVA-ActiveSpeaker dataset [25]. All of the latter's sequences were already under the 10 second mark and so were left untouched. The former's sequences however were originally 136 and ranged from less than 1 second to several minutes in terms of duration. Consequently and considering the standard 30 fps frame rate of the original video, all instances above the 30 frame mark were segmented into 30 frame long sequences. This was performed in order to obtain a much larger number of instances and take better advantage of all the available data for training and testing. Sequences from both datasets below the 30 frame mark were discarded, as instances shorter than 1 second were considered to be similar to static images in terms of unusefulness for active speaker detection. It is also important to note that each segment during training and testing contained a single label (e.g., \"SPEAKING\"), yet the condition may very well vary throughout (e.g., a \"SPEAKING\" segment may contain small portions of silence or vice-versa) as speech activity is heterogeneous in nature and somewhat hard to classify consistently. Nonetheless, the models were always given approximately the same number of instances for each class, so that distribution was balanced.  
Given how the parsed sequences of AVA-ActiveSpeaker were of varying lengths, masking was also employed in order to enable all available sequences to be accepted by the RN-LSTM. Considering this, training was independent of frame window size, making it arbitrary (within 1 and 10 seconds) for model applications. As for the AVA-ActiveSpeaker sequences pertaining to the third label \"SPEAKING_NOT_AUDIBLE\", these were considered for the visual only portion of the experiments and disregarded for the rest. The overall characteristics of the evaluated data are specified in Table 1.  
Figure 3: Structure overview of the proposed model. From a video, the image sequence of a face is extracted as well as the corresponding audio. A narrowband spectrogram is generated from the audio and progressed through VGGVox. The image sequence is progressed through the RN-LSTM without any preprocessing. The obtained audio and visual embeddings are then classified either separately or merged by concatenation or the SCF layer.
## 4 Discussion
### BMI and Ankle Fractures  
In our study, among younger subjects (60-70 years), 81.5% were overweight, and only 15.6% had osteoporosis. This result is in accordance with the fact that patients sustaining an ankle fracture are usually younger [17] and have a higher body mass index (BMI) [31] compared to other fragility fractures. In the age group of 70 to 80 years, the prevalence of overweight individuals decreased (50%), while the prevalence of osteoporosis remained stable (14.3%). Among patients aged over 80 years, there were no overweight individuals observed, and the prevalence of osteoporosis increased to 40%. These results suggest that excess body weight may have a greater impact as a risk factor in younger individuals compared to bone fragility; as age increases, the contribution of excess weight may decrease in the face of an increase in fragility-related factors.  
In our patient cohort, there appears to be a correlation between a high BMI and anatomically more complex ankle fractures. This relationship may be attributed to the observation that overweight and obese individuals exhibit impaired protective responses during falls, predisposing them to sideways or backward falls [53]. It is demonstrated that, in the obese, the distribution of osteopororotic fractures is different from the typical distribution. Differently from the normal-weight osteopororotic people, whose most frequent fragility fractures are located at the hip or spine, in obese osteopororotic individuals other sites of fractures are prevalent, like ankle fractures [54; 55; 56]. [PERSON] et al. [11] called this particular phenomenon \"obese's fracture site paradox\". Additionally, patients with higher BMIs generate greater forces upon impact during a fall, which correlates with an increased likelihood of sustaining more complex ankle fractures [57].  
Regarding the relationship with the type of trauma underlying the fracture, no BMI-related differences were observed. This result likely reflects the fact that low-energy traumas are more common among patients aged over 80 years in which there were no overweight individuals observed.  
In accordance with the literature, patients with high BMI exhibited significantly worse postoperative outcomes in terms of FADI scores. This result correlates with the fact that these patients often have poor lifestyle habits such as smoking, sedentary behavior, and alcohol consumption. Another aspect recently highlighted is that, especially in obese patients and to a lesser extent in overweight ones, the perception of postoperative pain is significantly greater, possibly due to alterations in cellular pain signaling [58].
# A Real-Time GNSS/PDR Navigation System for Mobile Devices
## 3 Results
### GNSS/PDR Integration  
In this Section, the experimental results of the algorithm both in the best performance and in the power saving modes will be shown. The position results that have been obtained by using only inertial data, and therefore using the PDR method, will be compared with those obtained from GNSS data and the reconstruction of the KF.  
In all the figures in this paragraph, the blue line represents the path which is obtained by INS using the DR technique; the orange line represents the interpolation of the GNSS readings, which have been left continuously active in order to choose different GNSS  
Figure 8: Scheme of state machine for multi-rate Kalman filter with compensation of systematic errors.  
update interval to be adopted in the energy saving routines; the green line represents the path reconstructed by the KF with appropriate corrections of systematic errors in the case of energy saving. The carried out tests have been performed by walking at an average pace in an urban environment, so emulating a typical everyday situation. The urban environment is in fact a typical scenario in which there may be disturbances on the Earth magnetic field given by vehicles and buildings. Tall buildings can decrease the accuracy of the GNSS data and a path that is not free from obstacles does not guarantee the stability of the direction of motion and the regularity of the steps. These difficulties represent an excellent scenario on which to test the developed algorithm.
# Co-Design of Mechanical and Vibration Properties of a Star Polygon-Coupled Honeycomb Metamaterial
## 4 Comparison and Analysis of the Bandgap Properties
### Effects of the Configuration Parameters
#### 4.3.2 Size of the Inserted Pins  
According to the previous analysis, altering the size of pins without changing their materials will also have an effect on the vibration modes, and the effect is mainly attributed to two aspects. Firstly, the size of rings should be adjusted to adapt to different sizes of pins, while the overall size of the unit cell remains unchanged, which means that the length of the connecting rods will change together and the frequency of vibration deformation related to the connecting rods will be regulated. Secondly, the relative frequency of vibration deformation between the rods and pins will be adjusted owing to the mass variation of rings. The effect of different sizes of pins on the bandgap distribution is shown in Figure 8, where the pin is made of copper and the radii of the pins are 3 mm, 3.5, mm and 4 mm, respectively. The first bandgaps are indicated in red and the rest in orange.  
As shown in Figure 8, the first bandgaps of the three metamaterials occupy the frequency bands from 963 Hz to 1780 Hz, 964 to 1600 Hz, and 988 Hz to 1310 Hz, respectively, and the elastic waves are restrained within these frequency ranges to isolate vibrations. The lower limit of the first bandgap is determined by the mode shape of G2, which is mainly formed by the interaction between the pins and the outside rings; as the deformation of the pins rarely changes regardless of size alteration, the lower limits of the first bandgaps are almost the same. Furthermore, the dispersion curves of the metamaterials move toward low frequencies as the size of pins increases, resulting in a decrease in the upper limit of the first bandgaps.  
These results indicate that the bandgaps of the proposed metamaterial are able to be tuned by altering the size of pins. Moreover, this method contributes effective instructions for generating bandgaps in required frequency ranges.  
Figure 7: Effect of materials of pins on bandgap of the SPCHM: (**a**) SPCHM with aluminum pins; (**b**) SPCHM with copper pins; (**c**) SPCHM with tungsten pins. The first bandgaps are marked in red and the rest in orange.
## 1 Introduction
[PERSON] et al. [20] simulated the gob flow field distributions under different porosity conditions and confirmed that porosity change directly affected gob flow field. The change characteristics of gob porosity are also affected by the complex distribution and collapse of the overlying strata. Under these influences, it is difficult to obtain the accurate porosity. Numerous studies reveal that numerical simulation is a good tool for studying rock formation failure mechanism and porosity change. [PERSON] et al. [21] comprehensively studied the fracture development law of overlying rock in the working face during cavity by similarity simulation, theoretical analysis, and field measurement. [PERSON] et al. [22] explored the failure characteristics and stress evolution law of overburden strata during cavity using the COMSOL numerical simulation software. By the physical experiments on similar materials and particle flow numerical simulation, [PERSON] et al. [23] established a mathematical model of the tensile deformation of rock formation and the gas-conducting fracture zone. They innovatively measured the porosity using the discrete element Particle Flow Code (PFC) software to accurately divide the gas-enriched area and revealed the evolution law of fracturing expansion-penetration of fracture in the overlying strata of gob. In addition to the PFC and CFD methods, [PERSON] [24] and others used pneumatic control automation systems to identify coal spontaneous combustion heat sources in coal mining faces. Most of the studies on the spontaneous combustion hazardous area in coal seam gob focus on the working faces shorter than 200 m and there are very few studies on the flow fields in the gobs behind ultra-long working faces. With the mechanization of caving, most working faces have been extended to more than 200 m. Due to the strike and long working face in the strike direction, there is a large amount of residual coal left in the large gob, which provides the material conditions for coal spontaneous combustion. In addition, most working faces adopt gob-side entry retaining (GER) technology, which results in large air leakage in the gob and complicated air leakage channels to provide a good oxygen supply environment for the oxidation of residual coal, resulting in a higher risk of spontaneous combustion. Therefore, studying the gob flow field behind an ultra-long working face and clearly defining the hazardous zone of spontaneous combustion in the gob have strong guiding significance for coal fire prevention and extinguishing in similar mines.