esa-sceva/satcom-chunk-collection huggingface dataset:
# GPU@SAT DevKit: Empowering Edge Computing Development Onboard Satellites in the Space-IoT Era
###### Abstract  
Advancements in technology have driven the miniaturization of embedded systems, making them more cost-effective and energy-efficient for wireless applications. As a result, the number of connectable devices in Internet of Things (IoT) networks has increased significantly, creating the challenge of linking them effectively and economically. The space industry has long recognized this challenge and invested in satellite infrastructure for IoT networks, exploiting the potential of edge computing technologies. In this context, it is of critical importance to enhance the onboard computing capabilities of satellites and develop enabling technologies for their advancement. This is necessary to ensure that satellites are able to connect devices while reducing latency, bandwidth utilization, and development costs, and improving privacy and security measures. This paper presents the GPU@SAT DevKit: an ecosystem for testing a high-performance, general-purpose accelerator designed for FPGAs and suitable for edge computing tasks on satellites. This ecosystem provides a streamlined way to exploit GPGPU processing in space, enabling faster development times and more efficient resource use. Designed for FPGAs and tailored to edge computing tasks, the GPU@SAT accelerator mimics the parallel architecture of a GPU, allowing developers to leverage its capabilities while maintaining flexibility. Its compatibility with OpenCL simplifies the development process, enabling faster deployment of satellite-based applications. The DevKit was implemented and tested on a Zynq UltraScale+ MPSoC evaluation board from Xilinx, integrating the GPU@SAT IP core with the system's embedded processor. A client/server approach is used to run applications, allowing users to easily configure and execute kernels through a simple XML document. This intuitive interface provides end-users with the ability to run and evaluate kernel performance and functionality without dealing with the underlying complexities of the accelerator itself. By making the GPU@SAT IP core more accessible, the DevKit significantly reduces development time and lowers the barrier to entry for satellite-based edge computing solutions. The DevKit was also compared with other onboard processing solutions, demonstrating similar performance.  
GPGPU; FPGA; deep learning; edge computing Article
## 4 LTC Architecture
### PoT Gateway
#### 4.2.3 PoT Gateway as an IP-PBX Server  
The PoT gateway is equipped with an open-source IP-PBX server through the installation of a bare-metal Asterisk [26] software on top of the Raspberry Pi OS [27] (previously called Raspbian). The embedded Asterisk server to the PoT gateway can provide VoIP communication and the LTC capability to home and small business domains or as a seamless integral part of the existing UC solution within a large enterprise to enrich it with the LTC capability. In the latter case, the PoT gateway is interfaced to the existing UC through the use of phone technologies, namely, SIP (Session Initiation Protocol) trunking [28]. SIP trunking is a virtual connection that leverages the existing TCP/IP network infrastructure, ultimately the Internet, to provide for voice and video communications in UC solutions. SIP trunking eliminates the use of a dedicated physical connection between the communicating UCs, which reduces the cost and eradicates wasted resources. Additionally, since SIP trunking is a virtual connection, adding lines and modifying services are seamless and dynamic, which enhances the LTC system scalability to serve multi-site companies, as illustrated in Figure 4.
## 4 Online and Local File Modes
### Manual Online Mode Change  
To manually change a synchronized file to the online mode, the client starts by selecting a list of files to change. In the CM file synchronization framework, when a client calls the manual online mode change service, it passes a list of files selected by the user as an argument. The sequence diagram in Figure 3 shows the subsequent online mode change process. Since file mode change is also a kind of synchronization process, if file synchronization is currently in progress, the file mode change request does not proceed. If synchronization is not currently in progress, the file mode can be changed, and the client changes the current state to synchronization in progress to prevent conflicts with subsequent separate synchronization requests. The client also stops the directory-monitoring task so that it does not detect file change events. After stopping the synchronization service, the client sends a list of files requested for the online mode change (CMFileSynEventOnlineModeList) to the server and performs the server's confirmation procedure. If the number of files to change mode exceeds the maximum size of the event to be transmitted, the client divides the request list into multiple events and transmits them. The server checks whether the original file of the requested file exists or not. When receiving a response event (CMFileSynEventOnlineModeListAck) transmitted by the server after confirmation, the client changes the checked files to the online mode. To change to the online mode, the client truncates the file size to zero and maintains only the file attributes, which frees up a space equivalent to this file size in the local storage. The client-side file synchronization framework maintains an online mode file list to manage online mode file information. When all requested files are changed to the online mode, the client notifies the server that the online mode change has ended with a CMFileSynEventEndOnlineMode event. When the server sends a response event (CMFileSynEventEndOnlineModeAck) to it, the client finally changes the current state to the synchronization complete status and restarts the directory-monitoring task.  
When a client deletes an online mode file, the CM file synchronization framework handles it in the same way as the deletion of an existing local mode file during the synchro  
Figure 3: Online mode change process.  
nization process. Since the client must first change to the local mode to access an online mode file other than a deletion event, the server ignores change events other than the deletion for an online mode file.
# Nano-HPLC-HRMS Analysis to Evaluate Leptin Level in Milk Samples: A Pilot Study
## 1 Introduction
Infant formula products are classified in two categories: powdered and liquid formulas. These latter are offered as three formulations: ready to use, concentrated liquid, and powder. The processing techniques of raw materials (i.e., bovine milk) used to obtain infant formulas are largely described in the literature [11; 21]. Briefly, for liquid infant formula, five steps are required to process raw materials and gain a final product perfectly proper for infant fed, growing and health. These steps are recombination and standardization, emulsification, second standardization, sterilization, and finally aseptic storage and filling. This process leads to a complete isolation and adaption of raw materials ingredients (proteins, carbohydrates, and lipids as the main classes) taking as reference the human milk. Since leptin is secreted by adipocytes and expressed mainly on their surface, when bovine milk is defatted to obtain infant formula preparation, it can be assumed leptin is removed with the lipids (adipocytes) because of its lipophilicity [22]. The aim of the study was the development of an analytical method suitable for the trace analysis and determination of lipophilic protein leptin in different milk samples. At the present time, because of great attention to the environment, it is significant to reduce the use of organic solvents and of radioactive assays. The developed method was based on the use of the technique nano-High Pressure Liquid Chromatography coupled through a nano-ESI source to High Resolution Mass Spectrometry (nano-HPLC- HRMS) which consumed for the chromatographic separation a reduced amount of organic solvents since the flow of the nano system was 300 nL/min. High Resolution Mass Spectrometry is demonstrated to be a powerful tool to analyze protein contents in milk [23; 24; 25; 26]. Proteomics research and studies in milk (and in general in food) are essential to control safety, quality, and transformation of the food. The proteome profile of milk is characterized by a huge number of caseins (80%) and as other protein components as lactoglobulins, lactalbumins, serum albumin, and immunoglobulins (20%) for a total concentration of 35 g/L of proteins [27]. Furthermore, others minor compounds are present in milk, such as free amino acids, urea, creatinine, mineral salts, and hormones, such as leptin during lactation period. Among these massive quantity of compound, as previously reported, leptin has low concentration in whole milk [16; 17]. Therefore, an enrichment of this molecule in milk samples is mandatory. Besides many protocols, we selected an immunoaffinity process [28; 29] to purify milk samples and isolate leptin with the use of specific antibodies. To our knowledge, this is the first study that provides a quantification of leptin in milk samples, both maternal, bovine, and liquid infant formulas, using an analytical method based on immunoaffinity purification and nano-HPLC-HRMS analysis.
# Measurement Grid Optimization for OTA Testing of 5G Smart Watches
## 5 Conclusions  
The objective of this research is to determine the optimal measurement grids for OTA testing of 5G smart watches, balancing accuracy with test time. The effect of various coarse grids on OTA performance, specifically the accuracy of TRP/TIS calculation, is analyzed quantitatively using reference patterns from a 5G commercial smart watch with very fine resolution across different bands. The related MU terms are then evaluated and determined quantitatively based on statistical analysis. Based on the STDs comparison, the reasonable MU limits for measurement grid configurations are recommended as the minimum number of grid points with STDs no more than 0.10 dB and 0.20 dB for TRP and TIS testing, respectively. Consequently, the TIS and TRP grids could be reduced from 30/30 (62 points) to 45/45 (26 points) and from 15/15 (266 points) to 30/30 (62 points), respectively, achieving over 60% and 75% reduction in grid points. Given that the overall test time is proportional to the total number of grid points utilized, significant reductions in test time can be achieved. Moreover, reducing the number of grid points could also reduce and even eliminate the repositioning-related MU, thereby realizing an overall MU similar to or potentially even improved over that of using the standard grids. The feasibility of  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Deltas**} & \multicolumn{3}{c}{**Sin Theta**} & \multicolumn{3}{c}{**Clenshaw–Curtis**} \\ \cline{2-7}  & **15/15** & **30/30** & **15/15** & **15/30** & **30/30** & **30/60** & **45/45** \\ \hline Max & 0.1 & 0.27 & 0 & 0.06 & 0.26 & 0.36 & 0.82 \\ Min & \(-\)0.02 & \(-\)0.4 & 0 & \(-\)0.03 & \(-\)0.5 & \(-\)0.6 & \(-\)0.36 \\ Delta (max–min) & 0.12 & 0.67 & 0 & 0.09 & 0.76 & 0.96 & 1.18 \\ Meas. STDs & 0.02 & 0.11 & 0 & 0.06 & 0.11 & 0.24 & 0.21 \\ Simulated STDs & - & - & 0.0138 & 0.0516 & 0.0862 & 0.1835 & 0.1768 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between measured and simulated STDs [dB].  
the proposed MU analysis and the recommended grids is proven by experiments. Above all, the proposed analysis is applicable to measurement grid determination for any type of DUTs with specified radiation behavior.  
Conceptualization, M.Q.; Methodology, M.Q.; Software, X.A.; Validation, F.L.; Investigation, F.L.; Resources, S.S.; Writing--original draft, X.A.; Writing--review & editing, X.A.; Supervision, S.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2022 YFF0604804).  
Not applicable.  
Dataset available on request from the authors.  
The authors declare no conflicts of interest.
# Active Thermography for the Detection of Sub-Surface Defects on a Curved and Coated GFRP-Structure
###### Abstract  
Initial defects, for example, those occurring during the production of a rotor blade, encourage early damages such as rain erosion at the leading edge of wind turbine rotor blades. To investigate the potential that initial defects have for early damage, long-pulse thermography as a non-destructive and contactless measurement technique is applied to a strongly curved and coated test specimen for the first time. This specimen is similar in structural size and design to a rotor blade leading edge and introduced with sub-surface defects whose diameters range between 2 mm and 3.5 mm at depths between 1.5 mm and 2.5 mm below the surface. On the curved and coated test specimen, sub-surface defects with a depth-to-diameter ratio of up to 1.04 are successfully detected. In particular, defects are also detectable when being observed from a non-perpendicular viewing angle, where the intensity of the defects decreases with increasing viewing angle due to the strong surface curvature. In conclusion, long-pulse thermography is suitable for the detection of sub-surface defects on coated and curved components and is therefore a promising technique for the on-site application during inspection of rotor blade leading edges.  
active thermography; sub-surface defects; defect visualization; automatic defect detection; leading edge; wind turbine rotor blade; coated and curved surface; GFRP-structure
## 2 Theoretical Background and Mathematical Models
### Indirect LEMP Drop  
An indirect LEMP discharge does not directly strike any portion of a power system. However, it generates an induced overvoltage that propagates throughout the network. This type of LEMP discharge can cause interruptions in low-insulation lines (low-protection transmission lines). Although indirect pulses produce smaller inductive voltages compared to direct strikes, they often affect the performance of overhead lines. For example, indirect pulses on medium-voltage lines are the primary source of recorded faults. In studies on  
Figure 1: Direct drop of LEMPs on ETLs (h: the height of the transmission line, d\({}_{1,2}\): the distance between insulators, h\({}_{1,2}\): console height, L\({}_{\text{in}}\): conductor length).  
indirect LEMPs, two variants of the dipole technique are commonly employed: a continuity equation technique and a technique based on moving and accelerating charges. These techniques are used in the literature to evaluate electromagnetic fields after the spatial and temporal distribution of the current is given. This study employed the technique based on moving and accelerating charges to express the electromagnetic fields analytically.
## 7 Predictive Correlation  
On the basis of the proposed hazard map, a predictive correlation for fatigue life of RC decks, based upon site inspected wetting locations, is introduced. The top surface of the RC deck is divided into four regions (see Figure 24), where WR index (Equation (4)) is calculated for each zone. Then, a predictive damage index (D.I.) is introduced by integrating the WR index for the four regions, where each region is assigned to different weight based on its risk impact, as shown in Equation (5). Finally, nonlinear correlation (Equation (6)) is introduced for the remaining fatigue life as a function of the proposed damage index. Figure 25 shows the relation of the damage index and the fatigue life of the studied cases, where the regression coefficient, coefficient of variation (C.O.V), and prediction interval of variance of 95% (P.I.) of the proposed correlation are 0.91, 21%, and 17%, respectively. The proposed correlation offers to analyze the remaining fatigue life of RC deck based on site inspected wetting locations with high accuracy:  
\[\text{D.I.}=0.55\cdot\text{WR}_{1}+0.23\cdot\text{WR}_{2}+0.17\cdot\text{WR}_ {3}+0.05\cdot\text{WR}_{4}, \tag{5}\]  
\[\text{Life}=1.49+1.43\cdot\text{tanh}(-0.36-0.24\cdot\text{D.I.}), \tag{6}\]  
where D.I. is the damage index of water impact, and (\(\text{WR}_{1}\), \(\text{WR}_{2}\), \(\text{WR}_{3}\), \(\text{WR}_{4}\)) are wetting rates for the regions shown in Figure 24 and calculated from Equation (4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Life** & **Normalized** & **Normalization** \\  & **“Million Cycles”** & **“Dry”** & **(0 and 1)** \\ \hline
1 & 171.7 & 0.78 & 0.65 \\
2 & 153.8 & 0.69 & 0.35 \\
3 & 128.3 & 0.58 & 0.00 \\
4 & 174.9 & 0.79 & 0.68 \\
5 & 160.0 & 0.72 & 0.45 \\
6 & 165.7 & 0.75 & 0.55 \\
7 & 193.6 & 0.87 & 0.94 \\
8 & 192.6 & 0.87 & 0.94 \\
9 & 196.5 & 0.89 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Utilized cases for building the hazard map.  
Figure 23: Hazard map for the wetting locations of higher risk.
# An All-Mach Number HLLC-Based Scheme for Multi-Phase Flow with Surface Tension
## 3 Numerical Methodology
### Summary of Algorithm  
On the first time step, \(\alpha_{L/R}\) is reconstructed using Godunov while the primitive variables are reconstructed using MUSCL. Using this initial guess for \(\alpha_{L/R}\), HLLC computes a face-flux \(u_{ff}\) using Equation (33). This face-flux is used in CICSAM to recompute the face value for the volume fraction field. The curvature is then computed. The reconstructed variables are inputs to the HLLC solver where the conservative \(\mathbf{F}_{f}\) and face-flux are computed and stored. It is important to note that in the computation of \(s_{*}\), CICSAM is used for the computation of \(\{\frac{1}{\gamma-1},\frac{\gamma p_{\infty}}{\gamma-1}\}\) for both the left and right state. Hence the convective flux term is now consistent with the VoF flux (the ECC are satisfied). However, for the surface tension term appearing in the contact wave-speed (Equation (32)), MUSCL is used for the left and the right state for alpha. This ensures consistency between the pressure gradient operator and the surface tension source term (the Young-Laplace condition is satisfied). The benefits of this algorithm is that the use of CICSAM ensures that the volume fraction field is always sharp. This allows curvature to be computed accurately. The HLLC-CICSAM algorithm is summarised in Algorithm 1.  
```
1 Initialise \(\alpha\) field;
2 Initialise \(\rho\), \(\rho u\) and \(\rho E\) using \(\alpha\);
3 while\(t<t_{end}\)do
4 forl in N nodesdo
5 Compute \(\Delta t\) using Equation (19);
6 Reconstruct face primitive variables: \(\rho_{l}\), \(\mathbf{u}_{l}\) and \(p_{l}\) using EOS;
7 if\(t=0\)then
8 Reconstruct \(\alpha\) using Godunov;
9\(\alpha_{L}=\alpha_{l}\), \(\alpha_{R}=\alpha_{m}\);
10 Reconstruct left and right state (\(\rho_{k,M}\), \(p_{k,M}\), \(\mathbf{u}_{k,M}\)) using Equation (17);
11 Compute the face-flux \(u_{ff}^{n}\) using Equation (33) using the HLLC solver;
12 Using the face-flux \(u_{ff}^{n}\), compute \(\alpha_{f,C}\) using CICSAM;
13 if\(\alpha\) leads to \(c_{k}^{2}=\frac{p_{k}(\frac{1}{7-1}k+1)+\frac{2p_{0}}{7-1}k}{\left(\frac{1}{7- 1}k\right)\rho_{k}}<0\)then
14 Reconstruct \(\alpha_{f}\) using Equation (17);
15 Compute \(\left\{\frac{1}{7-1},\frac{2p_{0}}{7-1}\right\}\) using Equation (30) ;
16 Compute \(\mathbf{F}_{f}\) using Equation (31);
17 Compute curvature using convolution and height functions;
18 Compute surface tension source term using alpha MUSCL;
19 Update the face-flux \(u_{ff}^{n+1}\) using Equation (33) and conserved variables to \(\rho^{n+1}\), \((\rho\mathbf{u})^{n+1}\) and \((\rho E)^{n+1}\) using Equation (18) ;
```  
**Algorithm 1**HLLC-CICSAM
# SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization
## 2 Establishment of Battery Model and Parameter Identification
### Identification and Verification of Battery Model Parameters
#### 2.2.3 Model Accuracy Verification  
After the model parameters have been identified, the accuracy of the model needs to be verified to determine whether the model parameters can be used for SOC estimation and parameter optimization in this paper. The precision of the model is verified by referring to the hybrid pulse power characterization (HPPC) experiment [26]. The simulated initial SOC is set to 0.5. The comparison between the experimentally measured voltage and the model simulation output voltage is shown in Figure 7. To better distinguish the difference, the error is presented in Figure 8.  
As seen from Figures 7 and 8, when the output voltage of the battery changes due to abrupt current changes, the output voltage of the simulation model can better track the measured voltage. The maximum error is 12 mV, which indicates that the model parameters can be used for the online SOC estimation of the Kalman filter and reasonable parameter optimization.  
Figure 8: The voltage response error.  
Figure 7: The voltage response comparison curve.
## 3 Results and Discussion
### Localization of ZnPc and ZnPcBCH\({}_{3}\) in A549 Cells  
To observe the subcellular localization of ZnPc and ZnPcBCH\({}_{3}\) A549 cells were treated with 5 uL of each dye sensitizer at 2 uM concentration for 1 min. Cells were also treated with NucBlue to stain the DNA in the nucleus. The localization of ZnPc and ZnPcBCH\({}_{3}\) in A549 cells, shown in Figure 7, was monitored by fluorescence microscopy. The fluorescence associated with ZnPc appears green and the one for ZnPcBCH\({}_{3}\) appears red. Cell nuclei appeared as blue using NucBlue. These results show accumulation of the both ZnPc and ZnPcBCH\({}_{3}\) in the cytoplasm of A549 cells. In Figure 7, the images in the left column represent the location of ZnPc or ZnPcBCH\({}_{3}\). Images in the middle column reveal the cell's nucleus stained with NucBlue. The right column shows the overlap of the first two images confirming the distinct cytoplasmic localization of ZnPc and ZnPcBCH\({}_{3}\).  
Figure 6: Monitoring the lipophilicity of ZnPc vs. ZnPcBCH\({}_{3}\) through the intensity of the visible spectra in DMSO, DMEM, and FBS media.