esa-sceva/satcom-chunk-collection huggingface dataset:
# Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach
## 4 Measurement of PPV
### Conversion to Peak Particle Velocity (PPV)  
This section details the methodology for processing ground vibration data acquired from quarry operations, utilizing Origin 2021 spreadsheet software. Raw data, in CSV format, was initially imported into Origin, presenting as voltage values. A sampling rate set at 10 kHz allowed for time series creation at 0.0001-s intervals, subsequently enabling the generation of graphs for time-dependent voltage, acceleration, and velocity. Figure 5a exemplifies a time-voltage graph for Z-axis ground vibrations, captured via a tri-axial micro-vibration detector.  
Figure 4: Various settings and data points: (**a**) measuring points for vibration data; (**b**) amplifier settings; (**c**) memory settings; (**d**) trigger settings.  
Utilizing the detector's calibration coefficient of 2, voltage readings were transformed into acceleration units, depicted in a time-acceleration graph in Figure 5b. Velocity metrics were calculated by integrating the acceleration figures, an example of which is provided in Figure 5c, identifying the peak amplitude as the \"Peak Particle Velocity\" (PPV).  
In instances of observed irregularities in the time-velocity graph, noise reduction was critical. This was accomplished with Origin's Fast Fourier Transform (FFT) filter, particularly the band-pass type. A preliminary FFT analysis, converting time-velocity data to a frequency-amplitude graph, identified the significant frequency bands in the vibration data, essential for effective noise filtration. Figure 5d displays an FFT spectrum, highlighting a dominant 20-40 Hz frequency range, confirming these as the main contributors to blast-related ground vibrations. This insight directed the subsequent filtration of extraneous frequencies from the time-velocity data, ensuring an accurate PPV determination.
# Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications
## 2 Problem Statement
### Impacts of Subset Size on Accuracy and Integrity Performances
#### 2.2.2 Integrity Performance Improved by Subset Size Growth  
A similar scenario assumption way is used for the integrity performance analyses in different subset sizes. The ideal minimal HPL geometry is obtained when each satellite at the low elevation bottom circle has the same slope value, which means they are equally spaced on the low elevation bottom of the visible area under independent and identical measurement error assumption. The practical minimal HPL is gained through brute force searching all the possible subsets in the practical satellite distribution scene in which the pseudo-range measurement errors are known to differ. The results are also shown in Figure 2a and Figure 2b, separately.  
A similar tendency can be observed in the ideal scene that the HPL value reduced by considering more satellites in the subset becomes unnoticeable as the subset size increases. What is more, an abnormal tendency is noticed in Figure 2b when the subset size reaches 18 and more. The minimal HPL value begins to increase not reduce even when more satellites are introduced. (Similar anomaly is also observed for VPLs in [23; 25].) This conflicts with the intuition that the more satellites are considered, the better performance can be achieved, and further emphasize the importance of satellite selection in the RAIM process.
## 4 Performance Evaluation
### The Size of the Executable File  
Because WR-Store uses Windows native structures and APIs, it is inherently lightweight. To verify this, we measure the sizes of the executable files of the key-value stores. Table 6 shows the result. The executables of the other key-value stores contain the large size of source codes and even third-party libraries such as zlib, snappy, and lz4 to support full-featured key-value stores.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Sets** & **Total Number of Objects** & **Average of Key Size** & **Average of Value Size** & **Total Size of Data Set** \\ \hline _KVData1_ & 10,000 & 3.89 & 52.44 & 560 KB \\ _KVData2_ & 100,000 & 4.89 & 52.53 & 5.7 MB \\ _KVData3_ & 1,000,000 & 5.89 & 52.54 & 58 MB \\ _KVData4_ & 10,000,000 & 6.89 & 52.50 & 589 MB \\ _ID-Geo_ & 2,656,660 & 19.00 & 28.34 & 122 MB \\ _ID-Hashtag_ & 172,958 & 19.00 & 25.31 & 7.4 MB \\ _ID-Tweet_ & 1,556,300 & 19.00 & 135.54 & 234 MB \\ _User-Followers_ & 140,438 & 12.27 & 5.06 & 2.5 MB \\ \hline \hline \end{tabular}
\end{table}
Table 4: Characteristics of the data sets.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Data Sets** & **Key** & **Value** \\ \hline _ID-Geo_ & 1020235969885134848 & (\(-\)115.223125, 36.232915) \\ _ID-Hashtag_ & 1020253494912135168 & \#OrangeCounty \#I4 \#Orlando \#traffic \\ _ID-Tweet_ & 102031594135923074 & A Chocolate man with a pretty smile \\ _User-Followers_ & 429997348 & 1016 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The samples of real data sets.  
In contrast, WR-Store only uses the Windows built-in structures and native APIs. As a result, the size of executable file for WR-store is quite small comparable to them of the other key-value stores. Specifically, WR-Store reduces the size of the executable file by 153.73 times compared to RocksDB, by 72.69 times compared to BerkeleyDB, and by 17.77 times compared to LevelDB.
# Thermal, Multispectral, and RGB Vision Systems Analysis for Victim Detection in SAR Robotics
###### Abstract  
Technological advancements have facilitated the development of sophisticated vision systems, integrating optical sensors with artificial vision and machine learning techniques to create applications in different fields of robotics. One such field is Search and Rescue (SAR) robotics, which has historically played a significant role in assisting brigades following post-disaster events, particularly in exploration phases and, crucially, in victim identification. The importance of employing these systems in victim identification lies in their functionality under challenging conditions, enabling the capture of information across different light spectrum ranges (RGB, Thermal, Multispectral). This article proposes an innovative comparative analysis that scrutinizes the advantages and limitations of three sensor types in victim detection. It explores contemporary developments in the state-of-the-art and proposes new metrics addressing critical aspects, such as functionality in specific scenarios and the analysis of environmental disturbances. For the indoor and outdoor testing phase, a quadrupedal robot has been equipped with these cameras. The primary findings highlight the individual contributions of each sensor, particularly emphasizing the efficacy of the infrared spectrum for the thermal camera and the Near Infrared and Red Edge bands for the multispectral camera. Ultimately, following system evaluations, detection precisions exceeding 92% and 86%, respectively, were achieved.  
search and rescue; vision sensors; thermal image; multispectral image; RGB image; quadruped robot +
Footnote †: journal: Applied  
Article
# Models to Identify Small Brain White Matter Hyperintensity Lesions
## 3 Materials and Methods
### Dataset  
The experiments were conducted using public and private datasets. The public dataset was collected from the WMH segmentation MICCAI challenge, which is publicly available and downloadable after registration on the challenge's web page [75]. A private Hospital in Ecuador provided the private dataset. Table 1 summarizes the details of the datasets.  
Table 1 refers to the number of volumes and the number of slices collected from all MRI series. In this project, we train the algorithms with slices of each stack of images. Consequently, the total number of images to use in the training and validation process is 2850.  
Out of this, 70% of the images are used for training, 20% for validation, and 10% for testing. The selection was performed randomly, ensuring that each split contained a representative sample of lesion types and sizes. Also, it uses custom feature engineering. Figure 2 represents examples of slice images of the datasets used.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Origin Places**} & \multirow{2}{*}{**Scanner**} & \multicolumn{2}{c}{**FLAIR (Voxel Size, mm3)**} & \multirow{2}{*}{**TR/TE (ms)**} & \multicolumn{2}{c}{**Quantity**} \\ \cline{3-5}  & & & & **Volumes** & & **Slices Images** \\ \hline \multirow{3}{*}{Public} & Singapore & 3 TSiemens Trio Tim & 1.00 \(\times\) 1.00 \(\times\) 3.00 & 9000/82 & 30 & 550 \\ \cline{2-6}  & Utrecht & 3T Philips Achieva & 0.98 \(\times\) 0.98 \(\times\) 1.20 & 11,000/125 & 30 & 536 \\ \cline{2-6}  & Amsterdam & 3T GE Signa HDxt & 0.98 \(\times\) 0.98 \(\times\) 1.20 & 8000/126 & 50 & 1564 \\ \hline Private & HUTPL—EC & 1.5 Philips Achieva & 0.89 \(\times\) 0.89 \(\times\) 6.00 & 11,000/140 & 80 & 200 \\ \hline \multicolumn{6}{c}{Total patients’ volumes studies} & \multicolumn{2}{c}{190} \\ \hline \multicolumn{6}{c}{Total images slices} & \multicolumn{2}{c}{2850} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the data used in this project.  
Figure 1: The flowchart of the proposed methodology: (**a**) the dataset, acquisition, number of images, preprocessing steps, and data augmentation methods applied to the dataset; (**b**) the DL methods, feature extraction, segmentation using UNET and SAM, and classification and detection using YOLOv8 and Detectron2.  
One important fact derived from the observation of Table 1 is the variability of the equipment used to acquire public and private data. Therefore, it is necessary to preprocess the volumes and all images to get better segmentation and classification results.
## 5 Results and Discussion  
The calibration approach described in previous sections is part of the operational processing environment for DLR's multi-channel, airborne F-SAR [8] and DBFSAR [29] instruments. The external calibration of these sensors is an ongoing activity that is carried out several times each year to account for upgrades to the sensor hardware, the fact that the radar hardware as well as the antenna mounts need to be re-installed after the research aircraft has been engaged in other scientific activities and, last but not least, to regularly monitor the system stability throughout periods of scientific SAR data acquisition campaigns.  
The external calibration of these sensors involves dedicated calibration data acquisitions over a calibration field that is permanently installed at the Kaufbeuren airfield in southern Germany. This installation comprises 9 trihedral radar reflectors ranging in leg-length from 0.9 m to 1.5 m, as well as a single dihedral reflector rotated by 22.5\({}^{\circ}\) in the line of sight to produce equally strong returns in the co- and cross-polarizations. The trihedral reflectors are deployed across the sensor swath at roughly equal intervals corresponding to off-nadir angles in the range 32\({}^{\circ}\) to 55\({}^{\circ}\).  
The results presented in the following sub-sections are, unless otherwise noted, based on SAR data acquired as part of the 2019 F-SAR calibration campaign _OP19 AF_ with 10 independent flights from February to June. Each flight comprises roughly 15 SAR acquisitions, in each of which several of the frequency bands X, C, S and L are acquired simultaneously. Almost all of the data acquired are fully polarimetric and some feature polarimetric single-pass interferometry at X- or S-band. The illustrations in Sections 3.2 and 4 are also derived from the raw data collected in this calibration campaign.  
Each of the following four sub-sections highlights a particular type of calibration correction. The results presented aim to show how each correction improves the fidelity of SAR measurements.  
In addition, the results illustrate the types of errors that can be corrected using the approaches developed. Furthermore, the experimental results show that the analyses carried out on range compressed raw data transfer to the focused SAR imagery. They also aim to show that such improvements represent external calibration. The corrections are seen to address systematic errors, rather than merely correcting for transient effects. This is accomplished by showing that results are consistent over multiple independent calibration data acquisitions. In other experiments, calibration corrections are seen to improve the quality of information derived from raw data acquired in the course of scientific measurement campaigns.
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 1 Introduction  
The most significant change in the last decade was the growth of the internet as the main communication medium between individuals. This has influenced humanity significantly and has opened different possibilities to innovate and to improve the quality of existing services as well as value added service provisioning. Among other enhancements, multimedia applications are also evolved and have gained a considerable place. There is considerable research in motion and event detection in multimedia applications like [1; 2; 3; 4] as well as in feature extraction and analysis among multiple tasks in a multimedia application [5]. In the evolution of multimedia applications in Internet Protocol (IP) networks, synchronization has been a big issue [6].  
Multicast synchronization refers to the simultaneous playout of all multimedia streams over all the intended receivers. For example, if there are many loudspeakers in a building, the media stream is required to be played out in such a way that all speakers playout simultaneously and the voice of speakers does not interfere each other. Most of the multimedia applications use Real-TimeTransport Protocol/Real-Time Control Protocol (RTP/RTCP) to transport multimedia data over the internet [7]. IP network introduces loss and/or delay of media packets resulting in synchronization error at receivers that can be perceived by the user. These distortions need to be removed before data is presented to the end user. Inter-stream and intra-stream synchronization [6] in multimedia applications have been addressed by many researchers using RTP/RTCP protocols but none of them addresses synchronization of multimedia data under multicast environment. In this paper, we have presented an algorithm based on RTP/RTCP protocol that achieved synchronized playout of multimedia streams on all ultimate receivers over User Datagram Protocol/Internet Protocol (UDP/IP).  
The rest of the paper is organized as follows. Section 2 surveys the literature review. Sections 3 and 4 discuss the problem statement and proposed a solution in detail respectively. Implementation results and their analyses are described in Section 5. Section 6 concludes the paper.
## 3 Hybrid Test
### Analysis of Reduction Factor for Iteration  
In the case studied in Section 3.2, no treatment for overshooting is employed, _i.e._, the reduction factor \(\theta\) takes the value of unity. In this subsection, the effects of different value of \(\theta\) are investigated. The results with \(\theta\) used for the first and first two iterations are shown in Figure 6a,b, respectively. For comparison, the result of \(\theta=0.85\) for all iterations is also included. From Figure 6, it is seen that overshooting is almost eliminated for all \(\theta<1\), and the number of iterations with \(\theta\) used for first or first two iterations is much less than the case in which \(\theta=0.85\) is employed for each iteration. The results also indicate that \(\theta=0.85\), which is used only for the first iteration, is optimal in the sense that the overshooting is effectively suppressed and the iteration times are the least. So the strategy of \(\theta=0.85\) for the first iteration is adopted for the hybrid test in this paper.
# SatScope: A Data-Driven Simulator for Low-Earth-Orbit Satellite Internet
## 4 Simulation Experiment
### Lightweight Comparison  
SatScope leverages network measurement results to model and simulate networks with up to ten thousand user nodes. In experimental settings, the simulation completes within one minute, with an average runtime of 1.2 s for continuous simulations, while maintaining constant memory usage. SatScope's methods are based on open-source libraries and tools, facilitating personalized customization by researchers.  
While STK supports simulations across various scenarios and scales, its high commercial licensing fees and user environment-dependent node support can be limiting. StarPerf, when simulating satellite nodes of similar scale in experimental environments, requires between two to three minutes per simulation, with memory usage increasing rapidly as continuous simulation time extends. Hypatia, which relies on NS-3 for satellite network simulations, is unable to complete a simulation within 24 h under these conditions. StarryNet employs digital twin technology to simulate satellites; however, each user node incurs significant computational and storage costs, thereby restricting the user base to those with adequate budget allocations.
## 4 Discussion
### MyFlex-\(\delta\) vs. Their Own Prosthesis
#### 4.1.3 Step Length  
A prosthetic foot has the desired stiffness, and therefore guarantees greater gait symmetry when the length of the step with the prosthetic leg does not differ much from the length of the step with the healthy leg [71; 72; 116; 117].  
_Prosthetic Leg vs. Healthy Leg_  
As for the comparison between the healthy leg and the prosthetic leg, there were contradictory results, as the prosthetic leg step was shorter than that of the healthy leg for the first two participants, while for the third it was the opposite (Table 7, section: \"Prosthetic Leg Ankle Range of Motion/Healthy Leg Step Length\"). This result was independent of the prosthesis used.  
_MyFlex-\(\delta\) vs. Own Prosthesis_  
As for the step length, Myflex-\(\delta\) had negative outcomes compared to their prosthesis for the first and second participants, while for the third participant there was an improvement since the ratio reported in the second section of Table 7 (\"Prosthetic Leg Ankle Range of Motion/Healthy Leg Step Length\") was closer to 100% when Participant 3 used their own prosthesis (from 117.9% with their own prosthesis to 111.2% with MyFlex-\(\delta\)). The worsening for the first two participants, however, did not go beyond 5% (from 89.3% to 88.1% for Participant 1 and 72.0% to 67.7% for Participant 2). In the third section of Table 7 (\"Step Length Using MyFlex-\(\delta\)/Using their Own Prosthesis\"), we saw how the ratio between the step length made with the prosthetic leg using MyFlex-\(\delta\) and the step length made with the healthy leg using a participant's prosthesis was always less than 100%: this means that all three participants took a longer step with the prosthetic leg when using their prosthesisthan when using Myflex-\(\delta\). The difference, however, was again below 10%; indeed, the ratios were: 99.0% (Participant 1), 94.9% (Participant 2) and 96.7% (Participant 3).
## 5 Results and Discussion
### Prediction of Tool Wear on PHM2010 Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**C1**} & \multicolumn{2}{c}{**C4**} & \multicolumn{2}{c}{**C6**} \\ \cline{2-7}  & **MAE (\(\mu\)m)** & **RMSE (\(\mu\)m)** & **MAE (\(\mu\)m)** & **RMSE (\(\mu\)m)** & **MAE (\(\mu\)m)** & **RMSE (\(\mu\)m)** \\ \hline CBLSTM (2017) & 7.5 & 10.8 & 6.1 & 7.1 & 8.1 & 9.8 \\ TDConvLSTM (2018) & 6.99 & 8.33 & 6.96 & 8.39 & 7.5 & 10.22 \\ BLSTM-PF (2019) & 9.8 & 11.3 & 9.9 & 12.1 & 15.2 & 16.1 \\ Integrated model (2020) & 5.54 & 7.67 & 5.27 & 7.84 & 6.86 & 8.60 \\ Hybrid-information LSTM (2020) & 6.9 & 10.9 & 6.5 & 10.5 & 9.2 & 12.7 \\ TBNN (2020) & 4.294 & 6.116 & – & – & 7.772 & 9.553 \\ CTNN (2021) & 3.634 & 5.358 & – & – & 7.531 & 9.209 \\ HLLSTM (2022) & 6.6 & 8.0 & 6.0 & 7.5 & 7.1 & 8.8 \\ DCNN-SLSTM (2023) & 6.94 & 8.34 & 6.59 & 9.93 & 7.74 & 10.02 \\ DCNN-McBiLSTM (2024) & 5.16 & 6.42 & 5.45 & 6.98 & 5.03 & 6.37 \\ BiLPReS (2024) & 3.3033 & 4.3411 & 4.5195 & 5.4833 & 6.7973 & 7.3945 \\ Proposed & **2.32** & **2.84** & **3.37** & **4.24** & **4.52** & **5.89** \\ \hline \hline \end{tabular} The bolded values represent the best results, and this convention is also followed in the subsequent tables.  
\end{table} 
Table 4: Performance comparison results of all baselines and the proposed model. Figure 10: The tool wear prediction on the dataset: (**a**) C1; (**b**) C4; and (**c**) C6. and moderate wear samples, the model must extrapolate in the severe wear stage, leading to higher errors. Furthermore, to explore the impact of the signal denoising method used in this paper on the model prediction accuracy, an ablation experiment was designed, using the original signal and the denoised signal to predict wear. The model structure remains unchanged regardless of whether the input is the original signal or the denoised signal. The prediction comparison results are shown in Table 5, and the MAE and RMSE analyses indicate that using denoised signals results in better prediction accuracy. The maximum prediction error of the original signal and denoised signal all appear in the C6 dataset. However, compared to the original signal, using a denoised signal reduced the MAE by 34.1% and RMSE by 36.0%. This demonstrates that the high-frequency noise in the original signal negatively affects the model prediction accuracy and proves the effectiveness of the signal denoising method. The numbers of the training samples, the validation samples, the testing samples, the batch size, and the mean training time per epoch of the STAGCN are illustrated in Table 6. The computation time is moderate and acceptable for tool wear prediction tasks.
# Automatic Medical Image Segmentation with Vision Transformer
## 4 Experiments and Discussions
### Potential Limitations  
Image quality variability can impact the performance of segmentation models. Models trained on high-quality data, especially in the selected public dataset in our work may not generalize to real-world clinical images of varying quality.  
Biases in the training data arising from limited or labeling errors can propagate biases in model predictions, leading to disparities across patient populations. Careful curation for representativeness and techniques like augmentation and adversarial training can help mitigate such biases.  
Models trained on data from specific clinical settings may not generalize well to new clinical settings with different data distributions. Domain adaptation and transfer learning approaches can potentially improve generalization across domains.  
In summary, key limitations around image variability, data bias, and generalization across clinical settings need to be addressed through robust algorithms, careful data curation to ensure representativeness, evaluation on diverse data, and techniques like augmentation, adversarial training, and transfer learning. Addressing these challenges will enable the reliable deployment of automatic segmentation models in clinical practice.