esa-sceva/satcom-chunk-collection huggingface dataset:
# Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption
## 2 Related Works
### 3 DMOP Watermarking Algorithm Based on 3D Point Cloud  
The 3 DMOP watermark algorithm based on point clouds embeds watermarks by modifying the coordinates of the 3D point cloud. [PERSON] et al. (2009) modified the integral invariants of some vertices by shifting the vertices at the vertex and its adjacent positions to embed the watermarking [23]. However, this algorithm is a semi-fragile watermarking, which can usually be used for data integrity authentication, but cannot be used for copyright protection of 3D models. [PERSON] et al. (2012) selected the one-dimensional DWT low-frequency signal of the distance between each point in the point cloud model and the center of gravity of the model to embed watermarking [24], which is robust to cropping attacks, but less robust to common rotation and translation attacks. [PERSON] et al. (2016) proposed a 3D point cloud algorithm based on angle quantization exponential modulation [25], which is robust to affine transformation, reordering, low-intensity noise, etc. [PERSON] et al. (2015)transferred the 3D model from a rectangular coordinate system to a spherical coordinate system [26] and used the distance from the vertex to the centroid as the embedding object to complete the watermark embedding, which is robust to some affine attacks. The above two algorithms are robust to translation and rotation attacks, but not robust to cropping attacks. [PERSON] et al. (2018) proposed a 3D model blind watermarking algorithm based on distance mapping mechanism [27], which is robust to common attacks such as translation, cropping, and rotation, but the effect is poor under simplification and noise attacks. [PERSON] et al. embedded watermarking based on the characteristic line ratio of the 3D model, and the algorithm is robust to geometric attacks and noise, but the effect is poor for cropping attacks [28]. In summary, there are still deficiencies in the robustness and applicability of 3D model digital watermarking algorithms.  
In the past, research on oblique photography 3D models focused more on embedding copyright information and the loss of model accuracy during watermark embedding. When embedding watermarks, more attention is paid to embedding watermarks in model vertex coordinate data and model texture data, with less research on embedding watermarks in texture coordinate data [29; 30]; at the same time, the security of texture information is also neglected. 3 DMOP, a special type of geospatial data, have different characteristics with other types of spatial data. In these different characteristics, the most special one is real texture information. A lot of secret information, e.g., advertising boards, car numbers and human portraits, can be conveniently viewed by users, including illegal users. This threatens the security of 3 DMOP greatly. However, traditional security protection methods of 3 DMOP generally ignored this point. Accordingly, there is still much space for security improvement of 3 DMOP.
## 3 Simulation and Analyses
### Micro-Crack Signal Assessment under the Effect of Roughness \(R_{sm}\)  
To investigate the relationship between \(\mathbf{R_{sm}}\) and normalized mean amplitude of the micro-crack signal, further simulations are performed and the results are shown in Figure 9. The width \(W\) and  
Figure 8: (**a**) Normalized mean signal amplitudes of different cracks reduce as back-surface roughness \(\mathbf{R_{a}}\) increases. (**b**) The signal amplitude ratio factor (SARF) reduces as back-surface roughness \(\mathbf{R_{a}}\) increases.  
Figure 7: (**a**) Normalized mean signal amplitudes of different cracks reduce as front-surface roughness \(\mathbf{R_{a}}\) increases. (**b**) The signal amplitude ratio factor (SARF) reduces as front-surface roughness \(\mathbf{R_{a}}\) increases.  
depth \(D\) of the crack are simultaneously set to be 250 \(\upmu\)m, and \(\mathbf{R_{sm}}\) is set as 200 \(\upmu\)m, 300 \(\upmu\)m, 500 \(\upmu\)m, or 700 \(\upmu\)m. It can be seen from Figure 9a that the change trends are similar when \(\mathbf{R_{a}}\) changes from 6.3 \(\upmu\)m to 25 \(\upmu\)m. The mean amplitudes of micro-crack signals grow slowly with an increase in \(\mathbf{R_{sm}}\) with rough front surface due to weaker wave scattering. On the other hand, the influence of back-surface \(\mathbf{R_{sm}}\) on the mean amplitude of the crack signal is slighter than that with front surface, according to Figure 9b. The variation in surface roughness parameter in the horizontal direction has a slight effect on acoustic attenuation. The fluctuation of microstructure height has greater influence on acoustic wave scattering, resulting in more serious distortion of the crack signal.
## 3 Data Analysis Method  
### Flame Describing Function Model  
The flame transfer function (FTF) is defined as the ratio of the HRR fluctuation and the relative velocity fluctuation at the reference position of inlet of the burner:  
\[FTF(f_{e})=\frac{Q^{\prime}/\overline{Q}}{u^{\prime}/\overline{u}}=G(f_{e})e^{-i \Delta\varphi(f_{e})} \tag{1}\]  
where, \(Q^{\prime}/\overline{Q}\) is the HRR fluctuation, \(u^{\prime}/\overline{u}\) is the normalized velocity fluctuation at the reference position of inlet of the burner (P1). It can be written in the frequency domain as a complex number with a gain \(G\) and a phase-lag \(\Delta\varphi\).  
The FTF can be extended to the FDF by taking into account the excitation amplitude of the relative velocity fluctuation. The FDF is described by Equation (2).  
\[FTF(|u^{\prime}/\overline{u}|,f_{e})=\frac{Q^{\prime}/\overline{Q}}{u^{\prime} /\overline{u}}=G(|u^{\prime}/\overline{u}|,f_{e})e^{-i\Delta\varphi|u^{\prime }/\overline{u}|,f_{e})} \tag{2}\]  
To obtain the FDF, the velocity fluctuations upstream of the burner were measured using the two-microphone method, while the overall HRR of the flame was measured using the PMT. The reference position for the velocity fluctuations was set at the pressure measuring point P1, which was located 73.5 mm away from the top of the bluff body. The analogue signals from the microphones and the PMT were measured simultaneously and transformed into the frequency domain using the fast Fourier transform (FFT). The HRR is derived from the PMT signal using Equation (3):  
\[\frac{Q^{\prime}}{\overline{Q}}=\frac{I_{CH^{*}}-\overline{I_{CH^{*}}}}{ \overline{I_{CH^{*}}}} \tag{3}\]  
where \(I_{CH^{*}}\) represents the analogue signal obtained from the PMT, and \(\overline{I_{CH^{*}}}\) represents the time-averaged analogue signal of the PMT over 1 s.
## 4 Methods (ASMAG Algorithm)
### Statistics
#### 4.4.2 Snow Line Altitude (SLA)  
The uncertainty of the SLA was determined for each dataset and glacier. Uncertainties result from: (1) The pixel size of the images, which range from 30 to 60 m, (2) the slope of the glacier near the SLA ranging between 6.8\({}^{\circ}\) and 26.5\({}^{\circ}\), (3) the vertical accuracy of the DEM (\(\pm\)16 m) [12; 14; 41], and (4) the temporal difference between image acquisition and DEM date. To accommodate for (1) and (2), an elevation bin of 20 m (by adding \(\pm\)10 m to the current SLA) is calculated. The mean slope of this elevation bin is derived in percent and multiplied with the respective grid size. For (3), DEM uncertainty was added using error propagation (root of the quadratic sum of the different independent errors). The impact of (4) is discussed in Section 6.2.2 as this source introduces a systematic bias that has not been corrected. The resulting total uncertainty of all SLAs varied between 16 and 66 m, depending on the scene and the glacier. The resulting mean uncertainty is \(\pm\)19 m, which is taken as a measure of uncertainty for the SLA. It is important to distinguish this uncertainty value from the elevation bias of the SLA in any given year due to glacier surface height changes before and after 2000 (see Section 6.2.2).  
The performance of the computed SLA has been determined for four glaciers (HEF, KWF, VNF and Hochjochferner (HJF); no. 11, 13, 23 and 26 in Figure 1) by a regression analysis comparing SLAs derived by manual assessment with those derived automatically by ASMAG. The manual SLA value was derived visually in the GIS by overlaying the satellite image on the reference DEM and subsequent determination of elevation values along the snow line visible in the image. All values were rounded to the nearest 20 m interval for comparison with the automatic value. The potential of ASMAG to approximate the ELA as derived by field observations (for the three glaciers with direct measurements) in Chapter 5.3, was restricted to years with at least two Landsat images available and years where the ELA was above the highest elevation of a glacier are eliminated. This assures that at least two values of the SLA are available for one summer. Finally, in each year the highest Landsat derived SLA value is selected to provide an approximation of the ELA [12].
# A Novel Fused Multi-Class Deep Learning Approach for Chronic Wounds Classification
## 4 Experimental Setup and Results
### Explainability  
Due to the patient's health concerns, the performance, trustworthiness, and explainability of the model classifying the medical images is essential. The black-box nature of the deep learning classifiers makes them less adaptable to clinicians/surgeons, since they cannot trust the obscure cause on which these models generate predictions. So, it is necessary to interpret the predictions of such models by visualizing and inspecting the outputs of the model's layers. Therefore, we conducted an analysis to assess the explainability power of the proposed framework for wound classification. For this purpose, we applied the gradient-weighted class activation mapping (Grad-CAM) [29] to the last convolution layer of the proposed model to interpret the model's predicted output via the generation of heatmaps. It is anticipated that the previous convolution layer extracts abstract and salient deep features that influence the final decision. So, the generated heatmaps highlight the critical regions of the wound image sample that endorse the model predictions, since the model focused on those regions to make the final predictions. Such interpretations can enhance the confidence of clinical experts with regard to wound classifier decisions, using a visual explanation. The original wound images and their corresponding heatmaps generated through the Grad-CAM are shown in Figure 12 for the different types of wounds, including Pressure, Diabetic, and Venous wounds. The visual regions with bright colors (red, orange, and yellow) represent more focused pixels, while the blue and green colored areas are less focused by the model.  
Figure 12: Heatmaps Visualization of Wound Images.  
From Figure 12, it can be observed that the SEEN-B4 model identifies the precise location of the different types of wounds as it concentrates on the area where the wound exists in the image. Specifically, our model focuses more accurately on the reddish wound areas, and it is sometimes unable to precisely focus on the area where the eschar or dry crust is present. Overall, our model focuses on the more profound and darker damaged site, wound scar, and surrounding wound tissues. This reveals our fused framework's effective feature selection and learning capability while classifying the multiple types of wounds.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 5 Simulation Study
### Results and Analysis
#### 5.2.4 Comparison of Power Consumption
When the proposed protocol is implemented in the same attack environment, the frequency of node rank decreases, packet loss rate also decreases, and throughput is increased. This proves that the proposal is able to defend against the RPL attacks.
# Privacy-Preserving Handover Optimization Using Federated Learning and LSTM Networks
## 3 Method
### F-LSTM Model  
In F-LSTM, we use 10 clients for federated learning training. Figure 3 shows the architecture of the LSTM model in F-LSTM, with an input data time interval of 10 ms (the same as the time interval of each data in the dataset). The model consists of four LSTM layers with hidden feature output sizes of [120; 50; 50; 50]. To prevent overfitting, we apply Dropout regularization after the output of each LSTM layer. Finally, a dense layer is used for the output.  
To reduce the occurrence of the ping-pong effect, we use the LSTM model to capture the temporal information of RSRP and NR-RSRP. Figure 3 shows the model architecture we use. We input the historical RSRP and NR-RSRP from the dataset into the model, enabling it to predict future RSRP and NR-RSRP values. We represent the historical RSRP and NR-RSRP information from the dataset as follows:  
\[D=\{(RSRP_{i},NRRSRP_{i})\mid i=1,2,\ldots,n\}, \tag{1}\]  
In our dataset, NR-RSRP values have missing entries. We utilize interpolation methods to fill these missing values as follows:  
\[y_{i}=y_{i-1}+(x_{i}-x_{i-1})/(x_{i+1}-x_{i-1}*(y_{i+1}-y_{i-1})), \tag{2}\]  
After filling the missing data, we normalize the entire dataset to a range of 0 to 1. Upon completing all data preprocessing steps, the dataset is ready to be used as input for federated learning training.  
In the framework of federated learning, we provide a system model that allows clients to train the model using their own data. After completing the local training, clients return the model weights to the system for aggregation.  
We employ the FedAvg [19] architecture, with the model aggregation formula as follows:  
\[\forall k,w_{t+1}^{k}\gets w_{t}-\eta*g_{k} \tag{3}\]  
For each client \(k\), the updated weight \(w_{t+1}^{k}\) at time \(t+1\) is computed by subtracting the product of the learning rate \(\eta\) and the gradient \(g_{k}\) from the current global weight \(w_{t}\). This formula ensures that each client's model weights are adjusted based on their local gradients, contributing to the overall aggregation process in the federated learning system.  
In the \(t+1\) round of global model training on the server, the global weights \(w_{t+1}\) are updated by computing a weighted average of the locally trained weights from each client. This approach, as depicted in the formula below, helps to reduce the communication cost associated with training the model:  
\[w_{t+1}=\sum_{k=1}^{K}n_{k}/n*w_{t+1}^{k} \tag{4}\]  
where \(K\) represents the total number of clients, \(n_{k}\) denotes the number of data samples held by client \(k\), and \(n\) is the total number of data samples across all clients. This weighted averaging ensures that clients with more data have a proportionally greater influence on the global model update.  
After the global aggregation is completed, the trained system model can be used to predict future RSRP and NR-RSRP values. These predicted values serve as inputs for the dynamic handover algorithm and act as reference values for triggering handovers.  
Figure 3: LSTM Model Structure.
## 3 Results and Discussion
### Environmental Fluctuation Analysis for Cold Chain
#### 3.2.3 CO\({}_{2}\) Level Change with Temperature and Relative Humidity Fluctuation  
The CO\({}_{2}\) level change with temperature and relative humidity fluctuation in the peach and nectarine cold chain is presented in Figure 7. The curve of the CO\({}_{2}\) level is very complex with the energy released by the life activities of fresh fruits and mutual influence of temperature and relative humidity. The curve of CO\({}_{2}\) level and the CO\({}_{2}\) level per kg and volume of fruit over time is showed in Figure 8. As the volume of headspace for peach and nectarine in the loading, unloading process, and display and sale process is difficult to quantitatively estimate, the CO\({}_{2}\) level per kg and volume of fruit over time in these processes are not calculated. Moreover, the curve trend of CO\({}_{2}\) level and the CO\({}_{2}\) level per kg and volume of fruit over time are the same in the segment S1, S3, and S5 (among them, 0.06% CO\({}_{2}\) is equal to the CO\({}_{2}\) level of 600 ppm).  
Figure 6: The absolute humidity and temperature fluctuation in the fresh fruit cold chain.  
Figure 7: The CO\({}_{2}\) level change with temperature and relative humidity fluctuation for the peach and nectarine cold chain.  
The CO2 level experienced rapid changes in segment S1 and S2 from about 0.03% to 0.33%, as the consequence of the influence of the ambient air, temperature, and relative humidity, as well as the respiration of the fresh fruit. During the storage segment S3, the CO2 level changed following a ladder-like fluctuation. The CO2 level declined rapidly in segment S4 and S5 from about 0.33% to 0.03%. During segment S6, after a brief delay the CO2 level experienced a sharp decline due to the effect of temperature and humidity change, then varied with the ambient air. The monitoring data results largely reflect the atmosphere of fresh peach and nectarine cold chain logistics, which could be real-time monitored via the sensor nodes installed. The results show that the system could provide complete and accurate temperature, humidity, and gas monitoring information throughout the cold chain, and was thereby able to provide more effective safety and quality assurance for fresh fruit in the cold chain.
## 4 Si Transporters
### Brief Introduction of Si Transporters in Some Other Plants
#### 4.2.6 Soybean  
[PERSON] et al. [25] cloned two putative Si transporter genes, _GmNIP2-1_ and _GmNIP2-2_, from soybean. Both are expressed in roots and shoots and decrease as Si increases. When the protein encoded by _GmNIP2-2_ is expressed in _Xenopus_ oocytes, it functions in Si transport, thereby confirming the genetic ability of soybean to absorb Si. Using genome-wide analysis of major intrinsic proteins, we identified two influx transporters in soybean and confirmed their irreplaceable roles in the Si uptake of this species. Studies have also shown that Si-influx and efflux transporters act synergistically to regulate the accumulation of Si in soybean.
###### Abstract  
Partial paralysis caused by spinal cord injury (SCI) or stroke are two of the most prevalent forms of physical disability. Through proper gait training, people with incomplete SCI have more potential to retain or regain the ability to walk than those with complete SCI. To help patients who have these disabilities regain the function of walking unassisted, the robotic walking training device (RWTD) has been developed to perform gait rehabilitation. This research plays a pivotal role in advancing medical robotic technology and gait rehabilitation by conducting a comprehensive evaluation and comparison of three lift mechanisms. Specifically, the lift mechanisms are designed to reposition a patient, using the RWTD, from a supine to a vertical position. Addressing a crucial gap in supporting and placing patients in gait rehabilitation devices, design optimization was performed using the engineering design process. This approach utilizes sophisticated techniques, including CAD modeling, motion analysis, structural analysis using finite element analysis, and a Pugh decision matrix. The findings offer valuable insights for optimizing lift mechanisms for the RWTD, contributing to the enhancement of patient-centric care. This research ensures a focus on safety, efficiency, and comfort in the gait rehabilitation process, with broader implications for the evolution of medical robotic devices.  
stroke; spinal cord injury (SCI); rehabilitation; engineering design methodology; center of gravity (CoG); robotic walking training device (RWTD); lift mechanism +
Footnote †: journal: Applied Sciences  
Article Design Optimization of the Lift Mechanism in the Robotic Walking Training Device Using the Engineering Design Methodology
# LoRaWAN Networking in Mobile Scenarios Using a WiFi Mesh of UAV Gateways
## 4 Simulation Model
### Channel Propagation Models  
AtA links are modeled with a Friis propagation model. It is assumed that the monitored area is flat and no obstacles are present in the LoS of the UAVs at any moment. Since UAVs operate several meters above the ground, the LoS assumption is acceptable. Under these conditions, the Friis propagation model represents a reasonable choice [18]. The path loss \(PL\) in dB is therefore computed as follows:  
\[PL=G_{t}+G_{r}-10\cdot\log\left(\frac{(4\pi d)^{2}L}{\lambda^{2}}\right), \tag{17}\]  
where \(G_{t}\) and \(G_{r}\) are antenna gains in dBi, \(\lambda\) is the wavelength in meters, \(d\) is the distance between the transmitter and the receiver in meters and \(L\) is the system loss. \(G_{t}\) and \(G_{r}\) are set to 0 (omnidirectional antennas) and \(L\) is set to 1.  
In the considered scenario, GNs move in an environment rich of vegetation. Under these conditions, the LoS between GNs and UAVs is not guaranteed at all times, and foliage and trunks produce considerable absorption, scattering and diffraction. In this work, the AtG channel is modeled with a Log Distance path loss model described by the following equation:  
\[PL=L_{0}+10\cdot n\cdot log_{10}\left(\frac{d}{d_{0}}\right), \tag{18}\]  
where \(n\) is the path loss exponent, \(d\) is the distance in meters from the receiver, and \(d_{0}\) and \(L_{0}\) are respectively the reference distance and the reference loss for \(d_{0}\). These parameters are estimated from the LoRaWAN RSSI measurements obtained in a forest environment and collected by [PERSON] et al. in [19] (\(L_{0}=32.22\) dbm, \(n=5.2\), \(d_{0}=1\) m). The path loss exponent value abstracts the range limitations imposed by fading and shadowing effects. The authors show that a significant range drop is expected when LoRaWAN is used in environments with thick vegetation, even if high SFs are used. The implemented model gives a worst-case estimation of the LoRaWAN expected range that agrees with the real experimental measurements in [19].
### Advese Impacts of RFI to SAR System
#### 1.2.1 Data Collection Process  
In SAR raw echoes, the presence of RFI will reduce the signal to interference noise ratio (SINR), bury the scattering response of weak target, and distort the dynamic range of the raw echoes. Besides, the receiver is sensitive to saturation by high-power in-band emissions, especially when aimed towards the interfering sources in the main-lobe [6]. Figure 2 compares particular range spectrums of the real measured SAR echoes with and without RFI. The data is recorded by a Chinese L-band airborne SAR system with the bandwidth of 300 MHz. The echoes are linear frequency modulated, as shown in Figure 2a. Compared with Figure 2b, many RFI spikes existed and the power is dynamically varied, which significantly reduce the SINR and altering the spectrum shape.  
Figure 1: Distribution of observed radio frequency interference (RFI) sources from different frequency bands spread over the global area. The circles with different colors corresponding to the RFI sources observed in various frequency bands, as shown in the legend at the upper right corner. Captured from the database of frequency allocations for microwave remote sensing and observed radio frequency interference [4].