esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Safety Signs Using Computer Vision Based on Deep Learning
## 4 Experiments
### Experimental Environment and Evaluation Index  
We implemented this experiment using a PC-equipped Intel UHD Graphics 620 CPU with 32 G memory, the used software is Python 3.7, the deep learning framework we used is Pytorch. The system runs under Windows 10 64-bit. The Adam optimizer was used to optimize the weight parameters of the model during the training process. The initial learning rate of the training model was 0.0001, and the momentum was 0.937.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Safety Signs** & **Original Number** & **Augmented Number** \\ \hline Wearing protective gloves & 48 & 239 \\ Wearing safety helmet & 47 & 187 \\ Wearing dustproof mask & 49 & 195 \\ Warning electric shock & 57 & 227 \\ Warning poisoning & 44 & 176 \\ Emergency exit & 54 & 215 \\ No climbing & 52 & 207 \\ No smoking & 47 & 188 \\ No fireworks & 53 & 213 \\ Emergency shelter & 38 & 153 \\ Total & 489 & 2000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset for object detection of safety signs.  
Figure 5: Data augmentation for safety sign images.  
The accuracy evaluation indicators mainly included Precision, Recall, AP, and mAP (see Equations (5)-(8)). In addition, the speed evaluation indicator was FPS, representing how many images are recognized per second.  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{6}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{7}\]  
\[AP=\int_{0}^{1}P(R)dR \tag{8}\]  
\[mAP=\frac{\sum\limits_{i=1}^{C}AP_{i}}{C} \tag{9}\]  
where TP is the number of samples where positive samples are correctly identified as positive samples; FP is the number of samples where negative samples are incorrectly identified as positive samples; FN is the number of samples where positive samples are incorrectly identified as negative samples; Precision is used to measure the accuracy of the positive samples found by the algorithm; Recall is used to measure the ability of the algorithm to find samples in the data set; P(R) is the variation curve of precision with recall. C represents the total number of categories, and AP\(i\) represents the AP value of class \(i\).  
In this paper, the following experiments were considered (see Table 2) based on the YOLOV4-tiny model:  
1. Three attention mechanisms, SENet, CBAM, and ECANet, were compared to investigate the influence of different attention mechanisms on detection accuracy and speed.
2. The Soft-NMS algorithm was introduced to replace the previous traditional NMS algorithm;
3. The Focal Loss algorithm was introduced.
4. The traditional YOLOV4-tiny model and the Faster-RCNN algorithm were compared to validate the improved models.
# Measuring Ocean Surface Current in the Kuroshio Region Using Gaofen-3 SAR Data
## 5 Conclusions  
The ocean surface current field affects global climate change and human activities, and its speed and direction information is one of the most valuable parameters in ocean research. At present, using the Doppler centroid anomaly to process a single SAR image is a commonly used method of remote sensing current measurement, and the SAR data of different systems have retrieved relatively satisfactory ocean surface radial velocities in the Gulf Stream, Agulhas Stream and other sea areas. The Kuroshio Current is also one of the largest western boundary currents in the world, but there are no more relevant experimental reports on the application of SAR data current measurement. Gaofen-3 SAR data have played an important role in retrieving ocean waves, ocean winds and other ocean parameters, but its ability to measure ocean currents needs to be verified. Therefore, this paper uses four available Gaofen-3 SAR datasets to carry out the ocean surface current measurement experiment in the Kuroshio region.  
In order to derive the ocean surface current vectors, a Doppler-based current measurement method combining sub-aperture processing and the least squares technology is suggested. Instead of prior wind information, the method considers the influence of wave motion in the equations, and then estimates the current field directly. The ocean current results derived from Gaofen-3 SAR data are compared with those from ocean model data. It is found that the current velocity and direction both show good agreement. The velocity accuracy is within 0.1 m/s and the direction accuracy is within 10\({}^{\circ}\), which can meet the application requirements and demonstrate the current measurement capability of Gaofen-3.  
Finally, the influencing factors of the experimental process and the applicable conditions of the method are discussed. System parameters, such as incidence angle and resolution, sea conditions, and changes in the processing process, will all have an impact on the current measurement results. The proposed method is suitable for SAR data with medium incidence angle, high resolution and uniform sea surface scattering. The data pro cessing procedures under more complex sea conditions need further research and analysis in future work.  
Conceptualization, Y.L. and J.C.; methodology, Y.L.; software, Y.L. and K.S.; validation, Y.L. and J.C.; formal analysis, Y.L., Y.Z. and X.Y.; investigation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, J.C. and Y.L.; supervision, J.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Spatial Enhanced Detection Algorithm
### Energy-Weighting Coefficient  
In the process of the uncertainty measurement, the uncertainty of the local pixel component consistency is calculated. However, the calculation of the variation entropy does not strengthen the energy difference between different regions. Here, background estimation is performed by Gaussian filtering, and the residual signal is obtained.  
The energy-weighting factor is designed based on local energy differences. Based on the uncertainty graph, the energy information contained in the target is strengthened and the target signal is improved. Considering that the weak target signal has a two-dimensional Gaussian shape, the background signal can be smoothed by Gaussian filtering:  
\[I_{gaus}(i,j)=\sum_{x=-1}^{1}\sum_{y=-1}^{1}GK(x,y)I(i+x,j+y) \tag{38}\]  
where \(I\) is the raw image data. \(I_{gaus}\) is the result of the Gaussian filtering of the original image. \(GK\) is the Gaussian template, here set as \(GK=\left[1/16,\,2/16,\,1/16,\,2/16,\,4/16,\,2/16,\,1/16,\,2/16,\,1/16\right]\).  
After the Gaussian template matching convolution, the residual difference between the original image and the Gaussian filtering image can be obtained:  
Figure 5: Entropy operator curves proposed under binary information sources.  
\[I_{res}(i,j)=I(i,j)-I_{gaus}(i,j) \tag{39}\]  
The target center energy decreases due to the accumulation of neighboring pixel energy, and the pixel energy increases due to the accumulation of neighboring pixel energy. Using the same sliding window as the component consistency evaluation process, the local energy difference in the residual image can be calculated as the signal energy weighting:  
\[EW_{ij}=\max\{0,I_{res}(i,j)-I_{b}(i,j)\} \tag{40}\]  
\(I_{b}(i,j)\) is the residual mean of \(I_{res}(i,j)\) in the surrounding eight neighborhood locations. After calculating the LUM and the weighting factor, the ELUM can be defined as  
\[ELUM(i,j)=EW(i,j)\times LUM(i,j) \tag{41}\]
# The MADAG Strategy for Fault Location Techniques
## 3 The Proposed MADAG Strategy  
In this section, we describe the proposed strategy in detail by presenting an example. Our strategy, Minimal Aggregate of the Diversity of All Groups' Elements (MADAG), prioritizes test cases and selects the appropriate ones, thereby applying them to the SBFL technique to locate the fault.
## 3 Results and Discussion
### Correlation between Phenolic Compounds and Antioxidant Potential
#### 3.4.2 Lignans and Stilbenes  
Four lignans were all detected in fermented coffee samples, enterolactone (compound **4**), 7-oxomatairesinol (compound **5**), schisandrol B (compound **6**), and schisantherin A (compound **7**). Lignans are classified as phytoestrogens due to their bioactive benefits for humans, potentially influencing estrogenic activities by acting as agonists/antagonists of estrogen receptors. Additionally, their antioxidant properties may help reduce the risk  
Figure 1: (**a**) Edward’s Venn diagram of phenolic compounds in fermented and unfermented coffee beans; (**b**) Edward’s Venn diagram of phenolic compounds in fermented/unfermented Geisha and Bourbon; the number in the figure represents how many phenolic compounds that have been identified in the samples using LC-ESI-QTOF-MS/MS.  
of chronic diseases [47]. Compound **4** was detected in this study with [M+H]\({}^{+}\) ion \(m/z\) at 299.1263 and was confirmed as enterolactone via its fragments at \(m/z\) 281, \(m/z\) 187, and \(m/z\) 165. According to [PERSON] et al. [48] and [PERSON] [49], enterolactone (ENL) is a mammalian lignans that has been converted from plant lignans using several bacteria via chemical transformation. Compound **6** was identified as schisandrol B, detected in the positive mode with [M+H]\({}^{+}\) ion at \(m/z\) 417.1892, and exhibiting fragmented ions at \(m/z\) 224, \(m/z\) 193, and \(m/z\) 165. This identification was confirmed via comparison with a previous study by [PERSON] et al. [50]. Compound 7, schisantherin A, is a dibenzo-cyclooctadiene lignin that was identified in this study in a positive ionization mode at \(m/z\) 537.2130 and had product ions at \(m/z\) 519, \(m/z\) 415, \(m/z\) 385, and \(m/z\) 371 due to the loss of H\({}_{2}\)O, C\({}_{6}\)H\({}_{5}\)COOH, C\({}_{6}\)H\({}_{5}\)COOH-CH\({}_{2}\)O, and C\({}_{6}\)H\({}_{5}\)COOH-C\({}_{2}\)H\({}_{4}\)O, as confirmed in a previous study [51]. The anti-inflammatory potential of schisantherin A was also reported, which could downregulate NF-kB and MAPK signaling by reducing the synthesis of inflammatory cytokines in lipopolysaccharide-induced inflammation [52].  
4-hydroxy-3,5,4\({}^{\prime}\)-trimethoxystilbene (compound **8**) was the only stilbene detected in the fermented coffee sample in the positive mode, with [M+H]\({}^{+}\) ion \(m/z\) at 287.1264. It was previously detected in both cherry and blackberries [15; 38]. It was shown to have some anticancer effects according to [PERSON] et al. [53], which could inhibit the protein kinase C and D, induce apoptosis in cells, and decrease the activity of transcription factors (NF-kB and AP-1).
# IoT Blockchain Data Vercity with Data Loss Tolerance
## 2 Materials and Methods
### Overview of IoT with Edge Computing and Blockchain
#### 2.2.2 Evaluation of IoT Blockchain Data Veracity with Data Loss Tolerance  
After the evaluation of the P2P network and secured distributed storage, its data veracity with data loss tolerance is tested, as depicted in Figure 6, and it shows the process flow of data veracity with data loss tolerance verification.  
In the data loss tolerance verification, Raspberry Pi simulated IoT blockchain nodes, performed traversing all nodes one by one, then examined the items stored in the current node against the items stored in entire IoT blockchain. Data loss tolerance is achieved when no item loss is detected. The algorithm is shown in Appendix A Algorithm A3. As stated in the data veracity with data loss tolerance algorithm, the desired result is achieved when the last node survived the verification.  
Figure 5: Process flow of transaction data replication in IoT blockchain.  
Figure 6: Flow diagram of data veracity with data loss tolerance verification.  
The algorithm of verifying data veracity with data loss tolerance is further tested using the LISPICE simulation model. LISPICE simulation allows verification to be done virtually with much more intelligent small computing resources, instead of being limited by the available physical resources. The verification still has to be carried out based on the flow diagram of data veracity with data loss tolerance (Figure 6), the only difference is that the item matching is replaced with capacitance monitoring. Data loss happens when there is a zero-capacitance situation detected. The data loss tolerance is thus achieved if a zero-capacitance situation does not happen.  
LISPICE simulation is set up with a capacitor-cluster (as IoT blockchain node) consisting of 10 capacitors (as transaction stream item) in the capacitor-cluster (see Figures 7 and 8). The number of capacitors can be adjusted according to the simulation requirements, and 10 capacitors is found suffice for the proposed LISPICE simulation. The capacitors are connected in parallel and simulated with real capacitor behavior; the LISPICE simulation parameter settings are listed in Table 6. Verification is carried out on this single capacitor-cluster which holds the transaction data. The simulation deployed a circuit breaker to represent node failure.  
Data loss tolerance was subsequently tested on node failure in the IoT blockchain model. LTSPICE simulation is setup with 10 capacitor-clusters instead of one. It creates secured distributed storage and redundancy. Inductance on the single inductor is used as the strength measurement of the entire IoT blockchain, which is the accumulation of all capacitance. The result showed that individual node failure only caused strength reduction but no data loss.
## 2 Quality Assessment of Water Level through Calibration and Validation
#### 2.1.1 Kavaratti, Altimeters' Calibration Site  
The Kavaratti site gives an opportunity to do absolute calibration of altimeters over the tropical region. This calibration site do not show altimeter land contamination since it is in the open ocean, which gives it an added advantage. Kavaratti Island, is located southwest of Indian peninsula, \(\sim\)450 km from the coast, as a platform of scientific observations, it has historic measurements from many Indian scientific institutions (e.g., Survey of India specific to water level). Figure 3 represents the part of Arabian Sea with Lakshadweep Islands at north and Maldives Islands at south. This permanent facility in Kavaratti is situated near the ground tracks of TOPEX/Jason and SARAL/AltiKa missions. The dedicated calibration facility at Kavaratti includes tide gauges, meteorological and oceanographic instruments, and a bottom pressure recorder [10]. This site has long-term sea level measurement at the tide gauge station TG1, which allows for the precise estimation of drift in sea surface height. The site characterization and calibration experiment and its results can be found in [10].  
The absolute calibration of SARAL/AtiKa is carried out for its 23 cycles at this site (Figure 4). The determined absolute bias is \(-\)39.8 mm with standard deviation of 23.6 mm. In this AltiKa calibration exercise we have not used any local high resolution tide models, also the effects of wind and atmospheric pressures, and vertical land motion for the precise local geoid estimation are disregarded in determining the absolute Sea Surface Height (SSH) bias. These results and the good agreement with other in situ calibration sites shows that the location is robust and encourages us to continue observations to carry out the absolute calibration of altimetric SSH. The errors in estimation of absolute SARAL/AltiKa bias over this site will be improved through future field experiments.  
Figure 2: **Left** (this study): water level bias from Crete [8], Kavaratti (Section 2.1.1), Corsica (Section 2.1.2, Lake Issykul (Section 2.1.3), the regional (Section 2.2) and global (Section 2.3) approaches. The mean value of all the determinations is \(-\)54 mm with a standard deviation of 15 mm. **Right**[9]: TOPEX, Jason and Sentinel-3A Sea Surface Height (SSH) biases from in situ calibration sites.
## 5 Results  
### Energy Spectral Density Comparisons of Construction Machinery in Idling, Digging, and Moving Modes  
Typically, there are three single working modes: (1) idling mode, wherein only the engine of the machinery runs without any load, (2) digging mode, wherein the shovel swings owing to loading and unloading while the fuselage remains stationary, and (3) moving mode, wherein the fuselage moves forward and backward. In the pre-experiments, to test whether the frequency band of the construction machinery overlaps that of human respiration (0.2-0.4 Hz), the frequency spectra of the construction machinery at different working modes were analyzed. The energy spectral density (\(ESD\)) can illustrate how the energy of the radar signal is circulated with frequency [31]. It is defined by  
\[ESD=\int_{-\infty}^{\infty}R_{AF}(f)^{2}df, \tag{26}\]  
where \(R_{AF}(f)\) represents the fast Fourier transform of the preprocessed radar signal. The typical \(ESD\) of construction machinery in idling, digging, and moving modes is shown in Figure 6. For the idling and moving modes, most of the energy is concentrated in the frequency ranges of 0-0.2 Hz and 0.4-0.6 Hz, respectively. However, the energy of  
Figure 5: Experimental scenarios.  
the digging mode is mostly distributed in the range of 0.2-0.4 Hz, which overlaps with the frequency band of human respiration. The energy ratios in the frequency range of 0.2-0.4 Hz of the idling, digging, and moving modes in Figure 6 were 6.42%, 71.50%, and 9.49%, respectively. Thus, the digging modes of the construction machinery can induceofrequency interference when searching for humans during a postdisaster rescue. Herein, it was chosen as the classification target.
# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 2 Network Architecture and Project's Main Achievements
### Achievements
#### 2.2.1 The KDN Framework  
We have proposed and evaluated a novel method that combines Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN) to optimise the routing configuration in a KDN-based network scenario. The evaluation shows that the proposed DRL + GNN optimisation architecture outperforms state-of-the-art DRL agents (based on fully connected neural networks) when it is applied over different networks unseen during the training phase. This reveals an unprecedented ability of the proposed DRL + GNN agent to generalise over networks, which is a fundamental feature to achieve practical ML-based solutions for networking. We also extend the evaluation of our DRL + GNN agent to 136 real-world topologies of Internet Topology Zoo, again showing good generalisation capabilities over networks and achieving an important improvement over the application of a classic load-balancing routing policy (21.39% better performance on average).
## 4 Performance Comparison  
To further investigate the vibration isolation and launch velocity performance of the proposed spacecraft in-orbit launch design, this section will compare the proposed design with traditional linear design to demonstrate the advantages of the proposed design in enhancing performance. The mathematical model is validated through MSC Adams 2020 software in Appendix A. In the subsequent simulation calculations, the numerical solutions are solved through the 4 th Runge-Kutta method subjected to the frequency-sweeping base excitations from 0.1 Hz to 5 Hz with a rate of 0.001 Hz/s.
# SDN Architecture for 6 LoWPAN Wireless Sensor Networks
## 4 Implementation of SD6 WSN on the Contiki Platform
### Hardware Architecture
#### 4.2.1 6 LBR in a Linux System Connected to an IEEE 802.15.4 Contiki Mote  
In the first option, a computer with the Linux operating system hosts the implementation of 6 LBR in Contiki (\"native-border-router.c\"). Contiki communication functions (named \(\mu\)IPv6) include IPv6  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Data Area Size (RAM)** & **Code Area Size (Flash)** \\ \hline Class 0, C0 & \(\sim\)10 kbytes & \(\sim\)100 kbytes \\ Class 1 C1 & \(\sim\)10 kbytes & \(\sim\)100 kbytes \\ Class 2, C2 & \(\sim\)50 kbytes & \(\sim\)250 kbytes \\ \hline \hline \end{tabular}
\end{table}
Table 4: Resource-constrained device classes [32].  
Figure 9: Next-hop address determination in Contiki’s “tpip.c” program.  
and 6 LoWPAN at the network layer; ICMPv6, TCP, and UDP at the transport layer; RPL routing; and CoAP at the application layer. An external mote is in charge of physical and MAC layers, usually an SoC that binds to the host through a serial connection. The mote consists of an SoC, which includes an IEEE 802.15.4 radio. Its micro-controller loads Contiki and \"slip-radio.c\", the last being in charge of the serial connection to the host. The MAC and RDC (radio duty cycle) layer drivers are chosen according to the WSN specification. The serial connection, called SLIP (serial line IP), is described by the developers as being IEEE 802.15.4 over a serial line, because IEEE 802.15.4 frames are transmitted through the serial connection.  
The communication between the \(\mu\)IPv6 stack (in \"native-border-router.c\") and IPv6 stack (in Linux) is performed through a tunnel interface (tun0). The command line defines the serial interface (SLIP) that \"native-border-router.c\" uses for communication with \"slip-radio.c\". The Linux host must have an Ethernet interface for IPv6 communication outside 6 LoWPAN. In Figure 10, one can observe two protocol stacks inside the Linux host, uIPv6 within \"native-border-router.c\" and the standard Linux IPv6 stack. The virtual interface (tun0) that provides communication between the two stacks is also depicted. A third communication stack can be seen in the 6 LBR mote and its communication with \"native-border-router.c\" across the serial interface.  
One of the advantages of this option is the possibility of using motes with less processing capacity and memory with the slip-radio function since only the physical, link and RDC (Radio Duty Cycle) layers are present. The layers that require more processing power are handled by the program running on the host computer, which usually has a processor far superior to those found in the motes.