esa-sceva/satcom-chunk-collection huggingface dataset:
# Large-Eddy Simulation of Droplet Deformation and Fragmentation Under Shock Wave Impact
## 2 Hybrid Eulerian-Lagrangian Formulation
### DPM Method  
The present research employs the hybrid VOF-DPM approach, with VOF capturing the dynamics and deformation of primary droplets and DPM tracking the formation and trajectories of small liquid fragments. The method detects the formation of small sub-droplets by monitoring the breakup of large droplets. This is achieved through the VOF-LES method, which tracks the interface between the water bodies and surrounding air. Practically, when the primary droplets break up, the VOF technique identifies the regions where new sub-droplets are formed. These regions are then treated as discrete particles by the DPM methodology. This way, the DPM algorithm can track the trajectories of the above water fragments by solving the corresponding Lagrangian equations of motion, where the momentum balance considers the various forces acting on the particles, including aerodynamic loads and weight. By solving these equations, the method calculates the position and velocity of each identified sub-droplet at each discrete time step, allowing for a detailed analysis of their movement and interaction within the airflow. Importantly, the DPM algorithm also accounts for the effects of resolved turbulence and recirculation zones, which influence the trajectories of sub-droplets, their dispersion, and further breakup. As the method continuously updates the position and velocity of each sub-droplet, throughout the simulation, the dynamic tracking provides real-time insights into the dispersion patterns and interactions of the particles with the surrounding airflow, while characterizing their statistical distribution, in terms of both size and velocity.
# IoT's Tiny Steps towards 5G: Telco's Perspective
## 4 IoT in 5G Performance Requirements
### Latency  
**Latency** requirements are usually expressed in terms of end-to-end (E2E) latency perceived by the end user [8]. 5G networks should enable \"zero latency\" [103] represented by the millisecond level of E2E latency [16; 26; 27; 213] through significant enhancements and new technology in architecture aspects [6; 28; 214], such as device-to-device (D2D) communication [5]. The IoT applications associated with uMTC monitoring and managing/controlling activities require low latency, as they tend to be real time. Required latency levels depend on the particular IoT application [8; 16; 28], being the narrowest in uMTC managing/controlling activity with a value that should not exceed 1 ms [8]. For example, the tolerable delay for use case mobile health [215] and remote surgery application is in order of sub-milliseconds [213]. For V2X communications, latencies should be ultra-low for some warning signals [8].
# Shape Optimization of an Open Photoacoustic Resonator
## 2 Materials and Methods  
### Experiment  
The experimental setup is illustrated in Figure 1. A distributed feedback quantum cascade laser (DFB-QCL) (Nanoplas, Nanosystems and Technologies GmbH, Gerbrunn, Germany) is selected as the source of radiation. The laser is operated using a driver (Q-MACS SC, neoplas control GmbH, Greifswald, Germany) that integrates a control system for laser current, voltage and temperature. The laser temperature is maintained at 16 \({}^{\circ}\)C and emits pulses of 100 ns width at a wavenumber of 1080 cm\({}^{-1}\) to excite a carbon black sample. Carbon black is selected to ensure a constant and homogeneous absorbance over the surface and a dry atmosphere inside the resonator. The PA signal is detected using a digital micro-electro-mechanical system (MEMS) microphone (Knowles SPH0641 LU4H-1). The microphone has a resonance at around 25 kHz and has a fairly flat response between 35 kHz and 65 kHz. The frequency response plot of the microphone can be found in our previous work ([PERSON] et al., 2016). The microphone output represents a digital pulse density modulated (PDM) signal which is send through a low-pass filter circuit for demodulation. The demodulated signal is fed into the lock-in-amplifier (DSP Lock-in Amplifier model 7265, Signal Recovery, United State of America) which exhibits a time constant of 100 ms.  
Two measurements are performed. A reference measurement is performed on the open T-shaped resonator usually applied for photoacoustic blood glucose measurements [13]. The resonator consists of three interconnected cylinders referred to as the cavity cylinder, absorption cylinder and resonance cylinder, respectively. They form a T-shaped resonator as illustrated in Figure 2 with the dimensions given in Table 1. A detailed description of the resonator can be found in our previous work [22].  
Figure 1: Schematic of the experimental setup.  
Figure 2: (**a**): Cross-sectional view of the T-shaped reference resonator (white). The dashed lines represent the cylindrical symmetry axes. (**b**): 3D view of the resonator. The resonator is rotated compared to the resonator depicted in the schematic of the experimental setup (Figure 1).  
PA glucose measurements utilize a resonance at approximately 50 kHz which spans a wide frequency range. Therefore, the measurement of the frequency response of the reference resonator is performed between 46 kHz and 54 kHz with 30 Hz increments.  
Secondly, the frequency response of the optimized resonator is measured. It is performed between 10 kHz to 60 kHz. The dimensions of the optimized resonator are presented in the results section. The average power of the laser is recorded using a power meter (Thorlab S401C, Thorlabs, Newton, NJ, USA) at different pulse repetition rates and used to normalize the measurements to account for laser power fluctuations. For each measurement point, an average of 10 measurements were made.
### Abstract  
Machine learning models based on radiomic features allow us to obtain biomarkers that are capable of modeling the disease and that are able to support the clinical routine. Recent studies have shown that it is fundamental that the computed features are robust and reproducible. Although several initiatives to standardize the definition and extraction process of biomarkers are ongoing, there is a lack of comprehensive guidelines. Therefore, no standardized procedures are available for ROI selection, feature extraction, and processing, with the risk of undermining the effective use of radiomic models in clinical routine. In this study, we aim to assess the impact that the different segmentation methods and the quantization level (defined by means of the number of bins used in the feature-extraction phase) may have on the robustness of the radiomic features. In particular, the robustness of texture features extracted by PyRadiomics, and belonging to five categories--GLCM, GLRLM, GLSZM, GLDM, and NGTDM--was evaluated using the intra-class correlation coefficient (ICC) and mean differences between segmentation raters. In addition to the robustness of each single feature, an overall index for each feature category was quantified. The analysis showed that the level of quantization (i.e., the 'bincount' parameter) plays a key role in defining robust features: in fact, in our study focused on a dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) dataset of 111 breast masses, sets with cardinality varying between 34 and 43 robust features were obtained with 'binCount' values equal to 256 and 32, respectively. Moreover, both manual segmentation methods demonstrated good reliability and agreement, while automated segmentation achieved lower ICC values. Considering the dependence on the quantization level, taking into account only the _intersection subset_ among all the values of 'binCount' could be the best selection strategy. Among radiomic feature categories, GLCM, GLRLM, and GLDM showed the best overall robustness with varying segmentation methods.  
Keywords:robustness analysis; radiomic features; quantization levels; segmentation method agreement; DCE-MRI; breast tumors +
Footnote †: journal: Medical Image Analysis
# Automatic Bridge Crack Detection Using a Convolutional Neural Network
## 2 Materials and Methods
### Proposed Model
#### 2.2.3 ASPP Module  
In deep neural networks, the size of the receptive field can roughly indicate the extent to which context information is used [33]. However, [PERSON] et al. [34] proved that the empirical receptive field of CNN was much smaller than the theoretical receptive field, especially in the higher layers. This greatly limits the ability of CNN to use context information to get accurate predictions. Global average pooling [35] is an effective method to obtain global context information, and has been applied in image classification tasks [29; 36] to solve the above problem.  
However, directly fusing the information obtained by global averaging pooling to form a single vector may result in loss of sub-region context information [33]. He, [PERSON] et al. [37] proposed a layered Spatial Pyramid Pooling (SPP), which can obtain the fusion of information and receptive fields from different sub-regions. Experiments show that multi-scale feature information fusion will bring about the improvement of network accuracy. This is not simply because of the increase in parameters, but because multi-level pooling can effectively deal with object deformation and differences in the spatial layout [38].  
For bridge crack pictures, cracks usually only occupy a small part of the picture. Therefore, in order to accurately identify cracks, it is necessary to effectively utilize context information and accurately extract crack features. Compared with the SPP structure in [37], the atrous convolution used by the ASPP structure avoids the loss of image detail information caused by the down-sampling operation; therefore, it fits better with the need to detect cracks. Consequently, the ASPP module is introduced in the network, and it is part of the DeepLabv2 [39] network proposed by the Google team in 2017. It performs parallel atrous sampling at different sample rates on a given input, which is equivalent to capturing the context of an image in multiple scales, thereby obtaining multi-scale image feature information [32].  
The structure of the ASPP module we used is shown in Figure 6. On the feature map extracted by the first few layers of CNN in Figure 3, the atrous convolutions with rates of 2, 4, and 8 were performed in parallel, and the feature maps extracted for each atrous rate were further processed by a depthwise separable convolution in a separate branch. The multi-scale features were then merged with the globally pooled input feature map to produce the final result. Since the ASPP module detects input feature maps with multi-sample rates, it can capture object and image context information [39] on multiple scales, thus improving the accuracy of the network prediction.
## 5 Experimental Results
### RQ2: By How Much Can HS-Based Parameter Optimization Method Reduce Cost?  
This section shows how predictive models reduce the effort for code inspection compared to random selection inspection.  
Table 8 shows the results of cost effectiveness to answer RQ2. As we explained in the experiment setup, CIR is a performance metric, which is the ratio of reduced lines of code in commit to inspect using the proposed model compared to a random selection. For dataset A, CIR was an average of 35.4% and for dataset B, CIR was an average of 44%, so such efforts can be reduced compared to a random inspection, respectively. In most cases, we concluded that HS-based parameter optimization could reduce efforts for code inspection.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**DT**} & \multicolumn{3}{c}{**DT+BG**} & \multicolumn{3}{c}{**DT+BG+HS**} & \multicolumn{3}{c}{**[PERSON] et al. [14]**} \\ \cline{2-13}  & **PD** & **PF** & **B** & **PD** & **PF** & **B** & **PD** & **PF** & **B** & **PD** & **PF** & **B** \\ \hline
**A1** & 0.33 & 0.08 & 0.51 & 0.50 & 0.06 & 0.63 & 0.83 & 0.15 & 0.82 & 0.54 & 0.05 & 0.67 \\
**A2** & 0.72 & 0.09 & 0.77 & 0.82 & 0.07 & 0.85 & 0.88 & 0.06 & **0.90** & 0.84 & 0.05 & 0.88 \\
**A3** & 0.88 & 0.13 & 0.85 & 0.87 & 0.08 & 0.86 & **0.92** & 0.07 & **0.91** & 0.89 & 0.06 & **0.91** \\
**A4** & 0.87 & 0.09 & 0.86 & 0.90 & 0.08 & 0.89 & **0.95** & 0.08 & **0.93** & 0.94 & 0.07 & **0.93** \\
**A5** & 0.90 & 0.12 & 0.87 & 0.94 & 0.13 & 0.88 & **0.95** & 0.06 & **0.94** & 0.94 & 0.07 & 0.93 \\
**B1** & 0.72 & 0.03 & 0.79 & 0.83 & 0.03 & 0.87 & **0.93** & 0.08 & **0.92** & 0.86 & 0.02 & 0.90 \\
**B2** & 0.83 & 0.08 & 0.86 & 0.92 & 0.04 & 0.94 & **0.94** & 0.05 & **0.95** & 0.93 & 0.03 & **0.95** \\
**B3** & 0.86 & 0.08 & 0.88 & 0.91 & 0.04 & 0.92 & **0.96** & 0.03 & **0.96** & 0.95 & 0.05 & **0.95** \\
**B4** & 0.93 & 0.06 & 0.93 & 0.95 & 0.04 & 0.95 & **0.99** & 0.04 & **0.97** & 0.97 & 0.04 & **0.96** \\
**B5** & 0.91 & 0.10 & 0.90 & 0.94 & 0.04 & 0.94 & **0.98** & 0.04 & **0.97** & 0.97 & 0.05 & **0.96** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experimental results to answer RQ1.
## 2 Materials and Methods
### Data Source and Extraction  
Two authors independently ([PERSON] and [PERSON]) performed the literature search in October 2021. PubMed and Google scholar were accessed. Afterwards, Web of Science and Scopus were also accessed to identify further articles. The following keywords were used in combination: _shoulder, arthroplasty, hemiarthroplasty, total, glenolumeral, osteoarthritis, prosthesis, implant, clinical, functional, outcomes, humeral, head, replacement, surgery, constant, score, index, complication, revision, pain_ (Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Database** & **Terms** & **Results** \\ \hline PubMed: & & \\ \#1 & Shoulder & 92,763 \\ \#2 & Arthroplasty & 104,761 \\ \#3 & Hemiarthroplasty & 3900 \\ \#4 & Anatomic & 310,373 \\ \#5 & Glenohumeral & 7203 \\ \#6 & Osteoarthritis & 99,581 \\ \#7 & Prosthesis & 600,963 \\ \#8 & Implant & 550,839 \\ \#9 & Clinical & 6,068,222 \\ \#10 & Functional & 14,565,742 \\ \#11 & Outcomes & 2,786,291 \\ \#12 & Humeral head replacement & 880 \\ \#13 & Surgery & 5,125,722 \\ \#14 & Constant score & 10,306 \\ \#15 & Index & 1,221,380 \\ \#16 & Complication & 3,528,187 \\ \#17 & Revision & 180,279 \\ \#18 & Pain & 908,693 \\ \hline Google Scholar: & & \\ \#1 & Shoulder & 4,530,000 \\ \#2 & Arthroplasty & 723,000 \\ \#3 & Hemiarthroplasty & 32,000 \\ \#4 & Anatomic & 1,470,000 \\ \#5 & Glenohumeral & 69,700 \\ \#6 & Osteoarthritis & 1,160,000 \\ \#7 & Prosthesis & 1,040,000 \\ \#8 & Implant & 2,420,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Search strategy for the electronic databases.  
The same two authors separately screened the resulting titles from the above searches. If the title and abstract matched the topic, the full text of the article was accessed. A cross-reference of the bibliographies of the full text was also performed. Disagreements were resolved by a third author (N.M.).
## Appendix A Calculation Details of the Marine Predators Algorithm (MPA)
### Application of FADs Effect  
Fish Aggregating Devices (FADs) effects, a component of the Marine Predators Algorithm (MPA), operate as a mechanism that influences the optimization process by attracting prey towards promising regions within the search space. This effect is applied with a probability of 0.2 and is implemented using a binary vector \(\mathbf{U}\).  
The binary vector \(\mathbf{U}\) is constructed by generating random numbers between 0 and 1 for each dimension of the search space. If the generated number is less than 0.2 (the probability of the FADs effect, denoted as \(FADs\)), the corresponding element in the binary vector is set to 0; otherwise, it is set to 1.  
The FAD effect is incorporated into the position update equation as follows:  
\[\mathbf{\gamma}_{k}^{i}=\begin{cases}\mathbf{\gamma}_{k}^{i}+CF[\mathbf{lb}+\mathbf{R}\otimes( \mathbf{ub}-\mathbf{lb})]\otimes\mathbf{U},r\leq FADs\\ \mathbf{\gamma}_{k}^{i}+[FADs(1-r)+r]\big{(}\mathbf{\gamma}_{k}^{r1}-\mathbf{\gamma}_{k}^{ r2}\big{)},r>FADs\end{cases} \tag{11}\]  
where \(\mathbf{lb}\) represents the lower bound of the parameter value range, \(\mathbf{ub}\) denotes the upper bound of the parameter value range; \(r\) is a uniform random number in \([0,1]\); \(r1\) and \(r2\) represent random indices corresponding to elements in the prey matrix.
# Convolutional Neural Networks for Off-Line Writer Identification Based on Simple Graphemes
###### Abstract  
The writer's identification/verification problem has traditionally been solved by analyzing complex biometric sources (text pages, paragraphs, words, signatures, etc.). This implies the need for pre-processing techniques, feature computation and construction of also complex classifiers. A group of simple graphemes (\"S\", \"\(\cap\)\", \"C\", \"\(\sim\)\" and \"U\") has been recently introduced in order to reduce the structural complexity of biometric sources. This paper proposes to analyze the images of simple graphemes by means of Convolutional Neural Networks. In particular, the AlexNet, VGG-16, VGG-19 and ResNet-18 models are considered in the learning transfer mode. The proposed approach has the advantage of directly processing the original images, without using an intermediate representation, and without computing specific descriptors. This allows to dramatically reduce the complexity of the simple grapheme processing chain and having a high hit-rate of writer identification performance.  
Keywords:writer identification; off-line analysis; simple graphemes; convolutional neural networks +
Footnote †: journal: Applied Sciences
# Seismic Resilience in Critical Infrastructures: A Power Station Preparedness Case Study
## 4 Case Study
### Development and Evaluation of Alternative Mitigation Strategies
This method significantly reduces the chance of displacement and structural damage from seismic shaking, thereby minimizing the risk of operational failures. Research indicates that anchoring effectively alleviates the stress and displacement experienced by vital components such as turbines, boilers, propane tanks, water heaters, gas cylinders, and electrical systems--essential for the continuous operation of power stations. Guidelines and case studies from the Federal Emergency Management Agency (FEMA) [47] and the National Institute of Building Sciences [61] highlight how anchoring has significantly improved structural resilience against earthquakes. The efficacy of anchoring is supported by experimental research and evaluations following earthquakes. For instance, retrofitting older structures with anchoring systems is crucial for seismic adaptation in earthquake-susceptible areas such as California and Japan [62; 63]. Results from these areas indicate that anchored buildings sustain less severe damage than those without retrofitting. Additionally, the American Society of Civil Engineers (ASCE) and the Earthquake Engineering Research Institute (EERI) have extensively documented the benefits of anchoring, presenting empirical evidence that effective implementation of anchoring enhances the longevity and stability of critical infrastructures during and after seismic events [64; 65; 66].
# A PSO-Based Uneven Dynamic Clustering Multi-Hop Routing Protocol for Wireless Sensor Networks
## 3 Network and Energy Model
### Network Model  
This paper considers randomly deploying \(n\) sensor nodes in a square area of size \(M\times M\), The square area is represented by symbol \(A\). The assumptions of the network environment are as follows:  
* All sensor nodes are homogeneous and have the same initial energy.
* Each sensor nodes are aware of its own location by using GPS (Global Positioning System) or some other localization mechanisms.
* After all the sensor nodes are deployed, they are fixed.
* All the sensor nodes are aware of their residual energy and have same transmission range.
* Sensor nodes can adjust their own energy consumption based on the distance to the receiver.
* Each node has a unique ID.
* The BS is static and positioned at the boundary of the square area.
# Mapping Server Collaboration Architecture Design with OpenVSLAM for Mobile Devices
## 3 Visual-SLAM-Based Mobile AR System
### FastAPI Server  
SocketIO is a library that enables two-way event-based communication between a browser and a server and provides functions such as stability, automatic reconnection, packet buffering, and multiplexing through web sockets. The client sets a websocket connection, a communication protocol that provides a full-duplex and low-latency channel between the server and the browser and replaces it with HTTP long polling when a websocket cannot be connected [32]. Google's Protobuffer, which stands for protocol buffer, is a useful data structure for serializing structured data and provides SDKs for various programming languages. Data serialization is a process mainly required when developing a communication program for the purpose of wired communication or data storage. To use Protobuffer, you need to install the protocol compiler required to compile the proto file and the Protobuffer runtime for the programming language you want to use.  
Figure 6 is the overall workflow diagram of the FastAPI server system. On the server side, it is more convenient to implement in the Python server rather than using the spatial  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Desktop** & **Mobile Device** \\  & **(Pangolin)** & **(Unity)** \\ \hline CPU Usage (\%) & 99 & 28 \\ Memory Usage (\%) & 23.1 & 8.3 \\ Screen Resolution & 1920 \(\times\) 1080 & 1280 \(\times\) 720 \\ Frame rate (FPS) & Avg. 30 & Avg. 25 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of SLAM visualization performance on desktop and mobile devices.  
Figure 5: The result of SLAM visualization; (**a**) for desktop (Pangolin); (**b**) mobile device (Unity).  
data processing, visualization, and reconstruction library implemented as C++ in Node.js. Therefore, in this paper, in order to apply the OpenVSLAM-based mobile mixed reality system as a plug-in, the Socket.IO communication module was configured in the same way as the existing Node.js event handler and was used in FastAPI. The data collected by mobile is serialized through Protobuffer, and data communication between the mobile application and the SLAM algorithm of the server is implemented using FastAPI server and SocketIO. It receives image and IMU data from the client through Socket.IO, deserializes it with Protobuffer, puts the deserialized data to the input data of the OpenVSLAM algorithm, receives camera pose data, and sends it back to the client through Socket.IO.  
Figure 7 is the overall workflow of the OpenVSLAM-based mobile AR system. Images and IMU data are collected by the user's client's mobile device camera and transmitted to the Python FastAPI server for mapping. On the mapping server, map data and camera pose data are obtained through the OpenVSLAM and transmitted to the Android application of the user client's Unity engine to visualize the global map and location.