esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Intelligent Recognition Method of Ground Penetrating Radar Images Based on SAHI
## 2 Methodology
### Improvement of YOLOv5 Object Detection Method
#### 2.2.2 SAHI  
The pre-trained model used in YOLOv5 is trained on the COCO dataset. The resolution of the COCO dataset is mainly 640 \(\times\) 480, so the input size of YOLOv5 is set to 640 \(\times\) 640, whereas most of the datasets used in this thesis have a resolution of 1024 \(\times\) 256, and if the size of the original image is inputted directly, the YOLO model will compress the image to the input size, thus resulting in pixel loss and reducing the detection target detail information. Therefore, the slicing-based, slicing-aided hyper inference (SAHI) open-source framework proposed by [PERSON] [15] in 2022 at the inference stage is adopted, and Figure 4 shows the schematic diagram of SAHI.  
SAHI can be divided into two parts: full inference and aided inference. Full inference feeds the original image into the model to detect large targets. The SAHI part slices the original image I into 1 M \(\times\) N overlapping slices \(\rm P_{1}^{I}\), \(\rm P_{2}^{I}\),..., \(\rm P_{1}^{I}\), and then adjusts the size of the slices while maintaining the aspect ratio, and feeds each of the overlapping slices into the inference model for prediction separately and independently. Finally, the results of the full inference and SAHI calculations are jointly inputted into the NMS to remove the low-confidence detection frames of the same target and are converted to the original image size. Compared to directly inputting the original image, this method can produce a larger pixel area for the target object in the image, retaining the features of the target as much as possible, and avoiding the loss of features due to too few target pixel points in the original image.  
In this paper, SAHI is not only applied to the inference stage of YOLOv5 but also to the training stage. However, unlike the inference stage, in the training stage, only the original image needs to be sliced into multiple overlapping slices and fed into the model for training without merging the slices to the original size. The overlapping slices are obtained by slicing the original image using SAHI according to the set size of the slices and the overlap rate between the slices, and then the sliced dataset is obtained, which is used for model training. This allows the model to focus more on the target, and can improve target recognition.  
In this paper, the slicing size is set to 640 \(\times\) original height, and the overlap rate between slices is set to 0.4, which ensures that the target exists completely in at least one slice. Finally, the dataset for training is obtained, and the results of a certain original image and slicing results in the dataset are shown in Figures 5 and 6.  
Figure 4: The principle of SAHI.
# Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields
## 2 WRF-CFD Coupled Small-Scale Wind Field Simulation  
### WRF Model Settings  
In this study, WRF version 4.3.3 was used for simulation. The start and end times for the WRF model were 00:00 on 15 March 2022 and 12:00 on 31 March 2022, i.e., a duration of 16 days and a half. The simulated domain of the model is shown in Figure 1, with the centre located at Hong Kong International Airport (113.911\({}^{\circ}\) E, 22.314\({}^{\circ}\) N). The inputs for the WRF model were from the NCEP GFS 0.25 degree Global Forecast Grids Historical Archive. The NCEP GFS offers meteorological reanalysis resources with a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) which are updated every 6 h.  
Figure 1: Partitioning of Weather Research and Forecasting model simulated domain (with Hong Kong International Airport as the centre).  
The WRF model adopts a 3-layer one-way nested grid configuration. The first 12 h of each nested domain is the spin-up time. The valid simulation time of the WRF model is regarded from the 12 th hour. The time interval of the d01 and d02 nested domains is one hour, while the time interval of the d03 nested field is ten minutes. Correspondingly, as the simulation results, 385 meteorological sets are outputted for the d01 and d02 domains, each, while 2305 meteorological sets are outputted for the d03 domain. The parameters for the nested grids are shown in Table 1. The dx and dy represent the grid spacing in the x and y directions, respectively. The vertical grid was divided into 60 layers using a vertically stretched grid approach, with 12 layers below 1 km and the 1 st layer approximately 10 m above the ground. The model used the GMTED2010 elevation dataset with a resolution of 30 s for the topography data. The simulated domain was projected using the Mercator projection scheme. The selection of parameterization schemes for physical processes was as follows: WSM-5 for microphysics, RRTMG for longwave radiation, RRTMG for shortwave radiation, YSU for planetary boundary layer, and NOAH for land surface processes. Due to the high horizontal resolution of the model, the cumulus parameterization scheme was not used [22].
# From Google Earth Studio to Hologram: A Pipeline for Architectural Visualization
## 5 Conclusions  
This study proposed a practical workflow for converting publicly accessible 3D geographic data from Google Earth Studio into high-quality digital holographic stereograms. By integrating Earth Studio rendering with CHIMERA holographic printing, we successfully reconstructed realistic holograms of iconic landmarks without the need for physical site visits or manual 3D modeling. This approach is highly scalable and democratizes access to architectural holography, making it viable for museums, educational exhibits, city planning applications, and digital heritage preservation. It offers a cost-effective and flexible alternative to traditional holography pipelines, reducing the dependence on specialized equipment or field data collection.  
A key limitation of this study was the exclusive use of a horizontal viewing arc, which restricted the hologram to a half-parallax. Future extensions of this research will focus on incorporating vertical parallax, automating camera trajectory computations, and integrating AI-based enhancements to further improve the rendition of the final holograms.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15116179/s1](https://www.mdpi.com/article/10.3390/app15116179/s1)]([https://www.mdpi.com/article/10.3390/app15116179/s1](https://www.mdpi.com/article/10.3390/app15116179/s1)), Video S1: image sequences for the Sagrada Familia; Video S2: image sequences for Arc de Triomphe; Video S3: Full-color CHIMERA holograms of Sagrada Familia and Arc de Triomphe.  
Conceptualization, P.G.; methodology, P.G. and T.L.P.D.; software, T.L.P.D.; validation, P.G., L.H. and. S.L.; investigation, P.G., T.L.P.D., J.F.U.A. and O.M.N.; resources, L.H. and S.L.; writing--original draft preparation, P.G., T.L.P.D., J.F.U.A. and O.M.N.; writing--review and editing, P.G. and T.L.P.D.; visualization, P.G. and T.L.P.D.; supervision, P.G.; project administration, P.G.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a Korea Creative Content Agency (KOCCA) grant funded by the Ministry of Culture, Sports, and Tourism (MCST) in 2025 (RS-2024-00401213), MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2025-II201846) supervised by the IITP (Institute for Information and Communications Technology Planning and Evaluation). The present research was conducted through the excellent researcher support project of Kwangwoon University in 2023.  
**Data Availability Statement:** The original contributions presented in the study are included in the article/Supplementary Materials; further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Validation Experiments
### Digital Compensation of Sensor Front-End Impairments  
As said before, energy detection was based on a periodogram. The band of interest was divided into 4 MHz sub-bands that were analyzed sequentially using I/Q downconversion and sampling at the Nyquist rate of \(fs=\) 4 Msps (note that samples are performed in the complex domain). However, before applying this method, several impairments in the analog front-end of the SSM receiver had to be compensated, exploiting the flexibility of the SDR implementation. These impairments included a strong direct current (DC) component as well as a non-flat frequency response caused by the analog hardware components and filters. They were successfully addressed with two additional digital processing blocks: a DC canceling filter and an equalizer. Digitized signals at the output of an analog to digital converter (ADC) usually contain some DC bias. In our case it was significant, and we used the first-order IIR filter \(H(z)=\frac{1-z^{-1}}{1-\rho z^{-1}}\) to remove it [38]. The pole value \(\rho=0.996\) was empirically adjusted for a sufficiently steep frequency response, preventing attenuation in the neighboring components. In order to uniformize the noise power spectral density across the sensed sub-band, the flatness of the frequency response was improved. A complex-valued linear phase FIR filter (equalizer) was designed for this purpose by generalizing the original design for real-valued filters in [39] to allow non-symmetric frequency responses as well as gain specifications at arbitrary frequency points (instead of uniformly spaced ones as in [39]).  
Figure 13 shows the original non-flat frequency response of the system with the DC component and both defects corrected after the filtering. The steep fall in system response at the band edges is due to the transition band of the analog anti-aliasing filters. In the experimental prototype, the analysis was restricted to 3 MHz per sub-band (\(\pm 1.5\) MHz from center frequency) to avoid band-edge distortion. Thus, a total of \(\frac{3\text{ MHz}}{200\text{ kHz}}=15\) GSM channels per sub-band could be simultaneously processed.
# On the Optimisation of Practical Wireless Indoor and Outdoor Microcells Subject to QoS Constraints
## 5 Simulation Results and Evaluation
### Wireless Indoor Microcells
Specifically, taking into account two specific areas marked as \(\mathbf{A}\) and \(\mathbf{B}\) in Figures 5-7, it can be observed that the received power at these areas is in the range of \([-65,-55]\) dBm when employing the SISO antennas, while the MISO can provide a higher signal strength of \([-55,-45]\) dBm and the MIMO with higher than \(-45\) dBm.
# Monitoring of Time and Cost Variances of Schedule Using Simple Earned Value Method Indicators
## 2 Materials and Methods: Description of the Scheduled Examined
Figure 2: Graphic interpretation of simple parameters of the Earned Schedule (ES) method (description in the text). Source: [19]  
On the basis of simple indicators of the EVM method, a developed time-cost analysis is proposed [19], examining the impact of the project implementation time on the costs incurred and costs on the planned time-by introducing the following assessment parameters:  
* Indicator for monitoring changes in the assumed financial liquidity due to arising time variances from the schedule (T/S): \[\text{T/S=}\frac{\text{TV}}{\text{SV}}\frac{\text{BCWS-ACWP}}{\text{BCWP-BCWS}}\] (6)
* Indicator for monitoring changes in assumed financial liquidity due to arising variances from planned costs (T/C): \[\text{T/C=}\frac{\text{TV}}{\text{CV}}\frac{\text{BCWS-ACWP}}{\text{BCWP-ACWP}}\] (7)  
Their role is to expand the analysis with control indicators to analyze financial liquidity, by taking into account the costs and time variances in the schedule. Equation (6) extends the simple analysis of project liquidity by including the difference of the earned value indicator, corrected by the value of planned costs (BCWS), constituting its budget. In Equation (7), a simple analysis of financial liquidity (difference in the value of planned BCWS costs and costs incurred ACWP) has been expanded to include the planned cost of the actual work done (BCWP earned value index), reduced by the actual cost (ACWP). In the mathematical structure of indicators, it is unacceptable to equate the values of the BCWS, ACWP, and BCWP parameters-at which the equality of the described tools indicate the implementation of works in accordance with the assumed time and cost. These cases do not require taking remedial actions to improve the monitored schedule. The following tests were performed to calculate the assessment parameters entered for the work schedule model, assuming three basic implementation structures--steady, parallel, and sequence execution methods. To carry out the analysis of time and cost variances, the schedule of the implemented construction project, consisting of the construction of a multi-family housing development, planned for a period of seven months, with a budget of over PLN 20 million, was used. Figure 3 shows the obtained three scenarios for the implementation of works. The allocation of the means of production was planned using three basic structures of project implementation-methods of steady, parallel, and sequence execution.
## 2 Network Slicing Concepts  
Network slicing refers to the partitioning of one physical network into multiple virtual networks, each network slice is architected and optimized for a specific application/service. In this context, the Next Generation Mobile Networks (NGMN) defines two main concepts [8]:  
* The Service Instance (SI) is an end-user service or a business service realized in a Network Slice.
* The Network Slice Instance (NSI) as the complete, instantiated logical network that meets specific characteristics required by a Service instance.  
Hence, network slicing is sharing network infrastructure across different Service Instance(s) to meet network-specific requirements. Depending on the communication layer where a network operator implements network slicing, resource management, the network characteristics or the toolbox used to implement the network slice can change. Some examples of the network characteristics requested by a Service Instance include ultra-low-latency or ultra-reliability, among others [8].  
The network slicing concept provides a framework for broad applicability across various industries. The majority of these scenarios envisioned to suit emerging and diverse business models are based on the Network As A Service (NASS) approach, creating opportunities for intelligent services and a new business ecosystem [9; 10; 11]. One of the main enablers to drive the network slicing is the Fifth-generation (5G) networks realization. Due to the necessity to integrate multiple services with various performance requirements--such as high throughput, low latency, high reliability, high mobility, and high security--into a single physical network infrastructure, and provide each service with a customized logical network. the Network Slicing being the key technology to achieve the aforementioned goals.  
5G expects to support a new generation of customer-tailored applications with diverse requirements regarding capacity, latency, level of mobility, number of users, and user density. Hence, 5G sets the stage for innovation and transformation in customer services and vertical industries, such as the ones described in above. Table 1[12].  
The services described in Table 1 demonstrate highly diversified traffic characteristics and differentiated quality-of-service (QoS) requirements. For example, the mMTC is based on its application in machine-to-machine communication [13]. The actual realization of those services in telecom networks implies the migration of the current heterogeneous IP/MPLS + DWDM transmission networks to more modern and 5G-ready designs, using the Network programmability, Software-Defined Networking (SDN) and Machine Learning (ML) as their main pillars.  
Implementing the network slices has two main alternatives: Hard and Soft. The way the network resources are shared between the services in each alternative is described in the following sections.
## 6 Simulation and Evaluation of Results
### Trust Evaluation  
Figure 4 shows the trust level of IoT nodes (including honest and malicious nodes) with regard to the number of rounds. The experiment assumes that 10% of the IoT nodes are malicious, and the trust threshold is equal to 0.5. According to this figure, when interactions between IoT nodes increase over time, the trust level of honest nodes gradually approaches one. However, the trust level of malicious nodes decreases and makes zero. The experiment shows that, when increasing interactions between IoT nodes, CTSRD can detect the abnormal behavior of malicious nodes and isolate them in the network.
## 2 Background
### Runtime Variability  
Due to the difficulty anticipating all the variability required by SPLs, we can consider using DSPLs. The main challenge for DSPLs is runtime variability, which can be defined as the ability of software to adapt to fluctuations in the needs of the environment, user requirements and evolving resource constraints [4]. Variability is managed by creating runtime variation points [3]. Several approaches express changes in variability as a function of context [22] (e.g., in feature modeling [23]). Other approaches consider reconfiguration management based on machine learning using the MAPE-K control loop, which is an architectural approach to managing system variability through machine learning [24]. Table 2 shows the differences between runtime variability for SPLs and DSPLs.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Software Product Lines** & **Dynamic Software Product Lines** \\ \hline SPLs are used in systems where variability behaves statically. & DSPLs are used in software systems where variability is constantly changing. \\ Variability management provides a description of the possible systems that can be produced. & Variability management provides the definition of various system adaptations at runtime. \\ Market segment identifies the common set of software products to be developed using the SPL approach. & Variation points specified identifies the level of reconfigurations supported by the DSPL. \\ The approach provides a framework for a set of individual software systems with common features. & The approach provides a unique system, which describes the basis for possible adaptations. \\ SPLs has two stages, domain and application engineering. & DSPLs have two life cycles, the engineering stage and runtime variability management. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Differences between SPLs and DSPLs.
## 4 Case Study
### Crack Measurement Tests in Lab
The comprehensive evaluation index of each crack is presented in Table 5, in which the width value of undetected cracks is calculated as 0. The \(AE_{\text{mean}}\) and \(RMSE\) of most cracks are less than 0.1, which indicate the robustness of the measurement system to different crack widths. The large errors occur in cracks nos. 1 and 7; since the crack is too narrow and the camera resolution is not sufficient, the unit pixel length is larger than the crack width. Except for undetected cracks, Table 6 illustrates the impact of different photographing angles and distances on the measurement results. The measurement result of the proposed measurement system is accurate in most positions, and the average absolute error is less than 0.2 mm. The minimum \(R^{2}\) occurs in image 6, image 7, image 19 and image 20, which are at a more than 50\({}^{\circ}\) horizontal angle to the target plane, and the greatest influence on the results is the undetected cracks in the image. Meanwhile, better measurement results appear in image 1 and image 12, because they have less distance and angle to the target plane, resulting in a larger number of pixels occupied by cracks. The test results prove that the measurement system proposed in this study can measure concrete cracks from any angle and distance--the maximum error is less than 0.3 mm and can be reduced to 0.15 mm when photographing closer. Similar to the previous artificial standard cracks, the capturing angle is the factor that has the greatest impact on the measurement accuracy. Tests in a laboratory show that the capturing angle should not be greater than 50\({}^{\circ}\) and the measurement distance should not be larger than 30 m for the equipment under this study. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Item** & **Standard** & **Average** & \(AE_{\text{mean}}\) & \(RMSE\) \\ \hline Crack No. 1 & 0.32 & 0.354 & 0.147 & 0.183 \\ Crack No. 2 & 1.17 & 1.110 & 0.069 & 0.077 \\ Crack No. 3 & 0.60 & 0.626 & 0.064 & 0.071 \\ Crack No. 4 & 1.16 & 1.115 & 0.064 & 0.074 \\ Crack No. 5 & 0.91 & 0.908 & 0.046 & 0.051 \\ Crack No. 6 & 1.53 & 1.527 & 0.057 & 0.064 \\ Crack No. 7 & 0.51 & 0.431 & 0.152 & 0.244 \\ Crack No. 8 & 1.83 & 1.789 & 0.085 & 0.098 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation of the 12 crack width values between measured in 20 images and real values (mm). Figure 22: Measurement results of each crack in each image, and the real widths of 12 cracks. Of these, crack no. 1 is not detected in image 6, image 7 and image 19; crack no. 7 is not detected in image 6, image 7, image 19 and image 20.
## 4 Discussion
### Design  
Currently, the software and interfaces used in the design and fabrication of maxillofacial prosthetics are costly and not specifically tailored for this purpose. This makes the process more complex, requiring significant creativity and limiting accessibility to highly skilled dental professionals or CAD engineers [3]. Among the commercially available software used are Mimics, 3-Matic [38], CMF Pro Plan (Materialise, Leuven, Belgium) [16], ZBrush (Pixologic Inc.) [13,15,16], and DentalCAD (exocad GmbH) [13], which are utilized individually or in various combinations for sculpting components or designing retention systems. A more detailed description of the software used in partially and fully digital workflows for maxillofacial prostheses has been previously published [3].  
An important consideration in fully digital workflows is the impact of file formats on the quality of the final 3D-printed prosthesis. The STL (Standard Tessellation Language) format remains the most widely used for 3D printing due to its ability to provide a triangulated mesh representation of a 3D object [41]. However, STL files do not store color, texture,or material properties, which are essential for achieving realistic aesthetic outcomes in maxillofacial prostheses [42]. The resolution of STL files also affects surface smoothness, with low-resolution files leading to visible faceting, particularly in areas with complex curves and organic shapes such as ears and nasal structures [43].  
For Polylet-based prostheses, VRML files are preferable, as they allow for full-color 3D printing by encoding geometry, color, transparency, and texture mapping within the file. This ensures a more accurate reproduction of skin tones and anatomical details in facial prostheses compared to STL files, which require separate post-processing for color application [40]. Studies have demonstrated that VRML-based workflows combined with photogrammetry color capture enhance pigmentation accuracy [12,40].