esa-sceva/satcom-chunk-collection huggingface dataset:
# Measurement of Fish Morphological Features through Image Processing and Deep Learning Techniques
## 2 Materials and Methods
### BMA and SCIA Deep Learning Approaches
New datasets were formed from the BMA or SCIA masks, along with the annotated landmarks. These datasets were used for the training of a VGG16 CNN using Keras and Tensorflow. The VGG16 CNN was trained to generate the 8 pairs of coordinates of the fish landmarks shown in Figure 13. The VGG16 CNN architecture that was already trained on ImageNet [25] was employed in the transfer learning method that followed. The fully connected head layer of the VGG16 model was removed. The weights in all other layers were frozen, and a new fully connected layer head was constructed with 8 pairs of landmark coordinates as the output. During the additional training process followed to train the new head layer, the estimated landmarks were compared with the ground truth landmarks annotated on the SCIA image. The weights of the new head layer were determined using mean squared error (MSE) loss and the Adam optimizer. The regression loss of the training and the validation set for the BMA and the SCIA datasets are drawn in Figure 14a,b, respectively. Fifty (50) epochs were sufficient for flattening the regression loss curves; thus, the training could be terminated after 50 epochs  
Figure 13: The landmarks used in the deep learning approach. for the specific dataset to avoid overfitting. The cosine similarity was also employed in Figure 14c,d in order to measure the alignment of the predicted positions with the true landmark positions. The cosine similarity of two non-zero vectors \(V_{1}\) and \(V_{2}\) was estimated as \(V_{1}\cdot V_{2}/(\|V_{1}\|\|V_{2}\|)\), where \".\" denotes the dot product. Cosine similarity values +1, \(-1\) and 0 corresponded to the fully aligned, opposite or orthogonal vectors, respectively. The VGG16 CNN was used for object detection and landmark annotation on a test image [32]. The fish contour was determined from the binary mask that was generated by the Mask R-CNN. The annotated landmarks were used to measure the fish dimensions (length and height) and to delimit the fish parts of interest (i.e., the caudal fin, the spiny and soft dorsal fins, the anal and pelvic fins and the mouth). The aforementioned procedure (training and testing) was implemented in Python. The distances were calculated, and the contour, the landmarks and the colored fish parts were available in the output image of this approach, as shown in Figure 15.  
Figure 14: Training and validation bounding box regression loss as a function of the number of epochs for BMA (**a**) and SCIA (**b**), with cosine similarity as a function of the number of epochs for BMA (**c**) and SCIA (**d**). Figure 15: An example of a BMA mask (**a**) and the input image with annotated landmarks and colored fish parts that is generated as the output of the deep learning approach (**b**).
## 3 Proposed Method
### Spectro-Temporal Descriptors  
Based on TFRs, we further employ local descriptors to characterize spectro-temporal patterns in a 2D fashion. Several local descriptors are evaluated, such as Higher-order Local Auto-Correlation (HLAC), Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). By using descriptors, we convert raw TFRs to mid-level local structure-based representations which facilitate ASC. In Figure 2, the feature extracted by using local descriptors is noted by \(D(n,k)\), where \(n\) is time index and \(k\) is feature dimension.
# Machine Learning Detection of Cloud Services Abuse as C&C Infrastructure
## 2 Background
### Threat Model
#### 2.3.2 Abuse of Cloud and Legitimate Services as C&C Channels  
The next step in the threat model is the use of CLS as C&C communication channels as presented in Figure 4. This is achieved by the attacker using cloud services or other legitimate services to communicate with infected devices and issue commands. The C&C communication channels are typically hidden and encrypted, making them difficult to detect. The following steps outline the abuse of CLS as C&C channels  
1. The botmaster issues command to the bots in the botnet through the cloud or legitimate service;
2. The bots continuously monitor the designated cloud or legitimate service for new commands from the botmaster;
3. The bots then execute the command, which conducts a range of malicious activities, which can range from data leaks to denial of service attacks to the distribution of additional malware through the utilization of the cloud or legitimate services as a communication channel.
## 2 Materials and Methods
### Short-Lag Filtered-Delay Multiply and Sum Beamforming  
F-DMAS beamforming derives from microwave imaging [25] and was recently introduced in ultrasound B-mode imaging by some of the authors [8]. This algorithm is based on the computation of the RX aperture spatial autocorrelation function. In F-DMAS, after focusing, the received RF signals \(s_{n}(t)\) are amplitude-rescaled, by means of the signed square-root operator, and then combinatorially coupled and multiplied. The beamformed output is thus computed as:  
\[y_{DMAS}(t)=\sum_{n=1}^{N-1}\sum_{m=n+1}^{N}sign(s_{n}(t)s_{m}(t))\cdot\sqrt{ |s_{n}(t)s_{m}(t)|}. \tag{7}\]  
\(y_{FDMAS}(t)\) is subsequently obtained by band-pass (BP) filtering \(y_{DMAS}(t)\), in order to pass the second-harmonics that originates after signal cross-multiplications, and to attenuate as much as possible the baseband and higher frequency components which originate from such non-linear operations. After beamforming, the obtained RF image lines are demodulated using the Hilbert transform, normalized, and logarithmically compressed to produce the final B-mode image.  
The F-DMAS formulation in (7) includes in the summation all possible couples among received RF signals, whose spatial lag \(l=m-n\) varies from a minimum of 1 to a maximum \(M\) equal to \(N-1\) (i.e., only auto-product terms are excluded). Since we are interested in studying the relation between SC and F-DMAS in MLT imaging, in this work we present the short-lag F-DMAS formulation, which limits the maximum lag \(M\) considered in the F-DMAS cross-multiplication stage. In particular, we will analyze the quality of images generated by using the following short-lag F-DMAS formulation:  
\[y_{DMAS}(t,M)=\sum_{l=1}^{M}\sum_{n=1}^{N-l}sign(s_{n}(t)s_{n+l}(t))\cdot\sqrt{ |s_{n}(t)s_{n+l}(t)|}, \tag{8}\]  
with \(M=1,2,3,\ldots,N-1\).
## 2 Mobile Fronthaul WDM-PON with RSOA-FCL as a Colorless Source  
As an example of where low-cost, high-speed PONs are expected to emerge in masses, we discuss the MFH application scenario. The MFH is the fiber network interconnecting the base station antenna with the CO. The MFH is an integral part of the cloud radio access network (C-RAN) architecture [22,23]. In the C-RAN, the signals from various base stations are processed at one location. The remote radio head (RRH) is placed close to the antennae.  
The base band units (BBUs), where the digital signal processing takes place, are then located at the central office (CO). Indeed, this scheme requires high data rate links - a MFH - between the RRH and the BBU. As a protocol, C-RAN uses the common public radio interface (CPRI) to transmit a high bitrate digital signal over the MFH [5,24]. The CPRI line rates range from 614.4 Mbit/s for the CPRI option 1 to 10.14 Gbit/s for CPRI option 8 [24]. These line rate requirements of the CPRI are very high compared to the transmitted radio signal bandwidth. This is because, in CPRI, the analog radio signals are sampled at high speed and transmitted as digital signals from the RRH to the BBU. For example, an up-to-10-Gbit/s link is needed to transmit a 20 MHz radio signal [25]. Therefore, a simpler solution would be to transmit signals by WDM-PONs that rely on cost-efficient transmitters, such as self-seeded RSOA-FCLs.  
Figure 2 shows a schematic of a MFH link. The CO consists of a cluster of BBUs. The multiple RRHs may be offset by up to 5 km from the CO [1]. A WDM-PON may now be used to build the MFH that links the multiple RRHs to the BBUs at the CO. The ONUs at the RRHs consist of a downstream (DS) receiver (Rx) and an upstream (US) data transmitter. The transmitter comprises a RSOA, which acts as a directly modulated colorless source. Lasing is obtained through the cavity, which extends from one highly reflective facet of the SOA up to another mirror behind a WGR filter. This way the RSOA source within the mirrors forms a long Fabry-Perot laser cavity [1,5,26,27]. The color dashed lines indicate the lasing cavities.
# Imaging Top of Volcanic Mounds Using Seismic Time- and Depth-Domain Data Processing
###### Abstract  
A seismic survey identified a basalt flow that could consist of cap rock of CO\({}_{2}\) storage beneath saline aquifer sediment in the Southern Continental Shelf of Korea. To determine the precise depth of the basalt flow, specific depth-domain data processing of migration velocity analysis (MVA) was applied to the seismic survey data. The accurate depth measurement of a target structure provides crucial information when storing and stabilizing injected CO\({}_{2}\) beneath basalt cap rock. Strong reflections of seismic amplitude at the volcanic mounds were adjusted from the time domain to the exact depth domain by the iterated velocity using MVA. The confidence of the updated velocity was verified by the horizontal alignment of seismic events sorted according to their common reflection point (CRP). The depth difference in volcanic mounds before and after MVA application ranged from 32.5 to 60 m along the vertical axis, showing the eruption shape on the strong-amplitude contour map in detail. The eruption shape of the top of volcanic mounds was verified with spatial continuity in 3D geological interpretation. The presented results provide suitable information that can be used to locate drilling sites and to prepare CO\({}_{2}\) injection. The geological model obtained from both time- and depth-domain processing can significantly influence the calculation of the storage volume and can be useful for history matching studies.  
volcanic mounds; seismic time and depth processing; MVA; CO\({}_{2}\) storage Article  
Imaging Top of Volcanic Mounds Using Seismic Time- and Depth-Domain Data Processing  
[PERSON], [PERSON], [PERSON] Imaging Top of Volcanic Mounds Using Seismic Time- and Depth-Domain Data Processing. _Appl. Sci._**2021**, _11_, 4244. [[https://doi.org/10.3390/app11094244](https://doi.org/10.3390/app11094244)]([https://doi.org/10.3390/app11094244](https://doi.org/10.3390/app11094244))
## 3 Results
Among the four models, the Synergy-MSM provided better performance and robustness than those of the original MSM. The average CC and NRMSE of the Synergy-MSM (4.0% and 5.2%, respectively) were better than those of the MSM. The LRM with ICA-synergy as the input also showed excellent performance, although the LRM is the simplest model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Ind** & **Mid** & **Thumb** & **Flex-Ext** & **Radial-Ulnar** \\ \hline \multirow{4}{*}{MSM} & CC mean & 0.9014 & 0.8800 & 0.8661 & 0.9377 & 0.9040 \\  & CC std & 0.0535 & 0.0611 & 0.1321 & 0.0471 & 0.0611 \\  & NRMSE mean & 0.1634 & 0.1525 & 0.1894 & 0.1491 & 0.1396 \\  & NRMSE std & 0.0496 & 0.0608 & 0.0474 & 0.0663 & 0.0514 \\ \hline \multirow{4}{*}{Synergy-MSM} & CC mean & 0.9330 & 0.9343 & 0.9041 & 0.9496 & 0.9318 \\  & CC std & 0.0441 & 0.0497 & 0.0691 & 0.0373 & 0.0360 \\  & NRMSE mean & 0.1115 & 0.1037 & 0.1260 & 0.0926 & 0.1018 \\  & NRMSE std & 0.0417 & 0.0325 & 0.0525 & 0.0376 & 0.0385 \\ \hline \multirow{4}{*}{Synergy-LRM} & CC mean & 0.8912 & 0.9059 & 0.8743 & 0.9182 & 0.9126 \\  & CC std & 0.0743 & 0.0560 & 0.0820 & 0.1096 & 0.0561 \\  & NRMSE mean & 0.1115 & 0.1228 & 0.1293 & 0.1047 & 0.1075 \\  & NRMSE std & 0.0237 & 0.0332 & 0.0533 & 0.0359 & 0.0293 \\ \hline \multirow{4}{*}{Synergy-SVR} & CC mean & 0.9302 & 0.9293 & 0.8944 & 0.9408 & 0.9301 \\  & CC std & 0.0567 & 0.0503 & 0.0597 & 0.0404 & 0.0646 \\ \cline{1-1}  & NRMSE mean & 0.1223 & 0.1139 & 0.1543 & 0.0962 & 0.1082 \\ \cline{1-1}  & NRMSE std & 0.0455 & 0.0391 & 0.0803 & 0.0566 & 0.0380 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average statistics for various models. Figure 6: Test results for Subject 5: For (**a**) index finger, and (**b**) middle finger, and (**d**) flexion and extension motions of the wrist, the angle from 0\({}^{\circ}\) to \(-^{\circ}\) corresponds to the flexion motion and 0\({}^{\circ}\) to \(+^{\circ}\) corresponds to the extension motion. For (**c**) the thumb, 0\({}^{\circ}\) to \(+^{\circ}\) corresponds to the flexion motion, and 0\({}^{\circ}\) to \(-^{\circ}\) corresponds to the extension motion. For (**e**) radial and ulnar deviations of the wrist, 0\({}^{\circ}\) to \(+^{\circ}\) corresponds to the radial deviation, 0\({}^{\circ}\) to \(-^{\circ}\) corresponds to the ulnar deviation. The blue dashed line indicates the measurement angle of Perception Neuron. The red line is the estimated value from the Synergy-MSM, the yellow line is the estimated value from the MSM, the purple line is the estimated value from Synergy-LMR, and the green line is the estimated value from Synergy-SVR.
# Demonstration of Subretinal Injection Using Common-Path Swept Source OCT Guided Microinjector
###### Abstract  
In this paper, we present the development of a handheld common-path swept source optical coherence tomography (CP-SSOCT) guided microinjector system and demonstrated its utility by precisely injecting fluorescein dye in the subretinal layer of ex vivo bovine eyes. The system enables precise subretinal injection with micron-level injection depth control. This was achieved by using a high-resolution CP-SSOCT distal sensor and signal processing using a graphics-processing unit (GPU), which made a real-time smart motion control algorithm possible. The microinjector performance was first evaluated using a gelatin phantom in terms of its ability for dynamic depth targeting and injection depth. This is followed by using an ex vivo bovine eye model to perform multiple consecutive subretinal injections of fluorescein dye. The results validated the OCT guided injector's ability to precisely guide and lock in the needle tip to the target depth during injection. The ex vivo evaluation tests demonstrate that an OCT-guided injector can consistently guide the injecting needle to the desired depth and is able to maintain the position with 9.38 \(\upmu\)m average root mean square error during the injections.  
Keywords:optical coherence tomography; surgical technology; optical coherence sensor; ophthalmology; robotic surgery +
Footnote †: journal: Applied
## 4 Numerical Simulation
### Results and Discussion
#### 4.2.1 The Scenario with a Single Traffic Wave  
Given these parameters, a speed-reduction traffic wave was generated. Figure 3 shows the experimental simulation results of the scenario with a single traffic wave, where the black line represents the trajectory from the initial values. The vehicle trajectories and vehicle velocity without control are shown in Figure 3a,b. The vehicle trajectories and speed profiles followed the trajectory of the initial values.  
The optimized vehicle trajectories and velocity profiles were compared against those without control, as shown in Figure 3c,d. The trajectories suggested by the optimization model satisfy all constraints in the cost function. By analyzing these profiles, it is observed that the vehicle trajectories and speed profiles became smoother. Moreover, the vehicle velocity was improved within the maximum speed limit, indicating that the optimal control can enhance its fuel economy and traffic efficiency. In Figure 3e, the initial and optimized acceleration profiles are denoted by the blue and red lines, respectively. Based on the acceleration trajectories, it can be observed that optimized acceleration values were relatively small, and they eventually stabilized at zero after 485 s. This demonstrated that JAD optimal control can effectively smooth both high deceleration and acceleration rates, thereby improving the comfort of the human driver.  
To better analyze the impact of the optimal control method on traffic congestion, the traffic throughput and safety indicators for different methods were compared in the same scenario. The results of the comparison of performance metrics are reported in Table 1. The results indicated that both the JAD and the proposed optimization control method have improved traffic throughput and reduced the risk of rear-end collisions compared to the no-control case. The performance of the proposed optimal control strategy was superior to that of the previous JAD strategy. Specifically, compared with JAD, the proposed optimization strategy resulted in improvements of 6.05% and 1.66% in the safety indicators TET and  
Figure 3: The results of the scenario with a single stop-and-go wave: (**a**) The vehicle trajectories without control; (**b**) The velocity trajectories without control; (**c**) The optimized vehicle trajectories; (**d**) The optimized velocity trajectories; (**e**) The acceleration profiles. (The black line represents the initial trajectory without control for (**a–d**). For (**e**), the blue and red lines correspond to the initial and optimized acceleration profiles, respectively).  
TIT and a traffic throughput improvement of 2.65%. This reason is that the cost function of the optimal method, taking into account traffic stability and safety, effectively reduces acceleration. Therefore, the simulation revealed that the proposed optimal control method can effectively dampen traffic waves and improve traffic performance.
## 2 Materials and Methods
### Data
#### 2.2.2 Remote Sensing Data  
In this research, the remote sensing data utilized comprised the following MODIS products: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) (MOD13A2) [14; 30], and surface reflectance (MOD09A1) [14; 31]. The spatial resolution of the NDVI and EVI products was 1000 m and the temporal resolution was 16 days, while the spatial resolution of the surface reflectance was 500 m and the temporal resolution was 8 days. In order to acquire data with consistent spatial and temporal resolution, the quality control and data completion approaches proposed by [PERSON] et al. [32] and [PERSON] et al. [33] were applied. The surface reflectance data were used to calculate the land surface water index (LSWI) [34].
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 6 Security Analysis
### Informal Security Analysis
#### 6.1.6 Mutual Authentication  
After obtaining the login request messages \(\{M_{1},M_{2},RID_{i},Q_{M}\}\) from \(MU\), the \(HA\) checks whether \(Q_{M}^{*}\stackrel{{?}}{{=}}Q_{M}\). If this holds, \(HA\) authenticates \(MU\). After obtaining the messages \(\{M_{3},Q_{F}\}\) from \(FA\), the \(HA\) checks whether \(Q_{F}^{*}\stackrel{{?}}{{=}}Q_{F}\). If it is valid, \(HA\) authenticates \(FA\). After obtaining the messages \(\{M_{4},M_{5},Q_{H}\}\) from \(HA\), \(FA\) checks whether \(Q_{H}^{*}\stackrel{{?}}{{=}}Q_{H}\). If this holds, \(FA\) authenticates \(HA\). Finally, \(MU\) checks whether \(Q_{MF}^{*}\stackrel{{?}}{{=}}Q_{MF}\). If this holds, \(MU\) authenticates \(HA\). Consequently, our protocol ensures secure mutual authentication among \(MU\), \(FA\) and \(HA\) because a \(MU_{a}\) does not know the secret parameter of \(MU\) and \(FA\).
## 3 Case Study and Results
### Process of Establishing the IPSO-LSSVM Model  
#### 3.3.1 Selection of the Hyper-Parameters of LSSVM Model  
\(\sigma\) and \(\gamma\) have a significant impact on the accuracy of the LSSVM model. In the following steps, the \(\sigma\) and \(\gamma\) of the model are tuned by the IPSO algorithm.  
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2. Generation of sample sets
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8. Generation of sample sets
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Figure 5: Optimization curve of the function fitness of the model of \(K_{\text{c}}\).  
Figure 6: Optimization curve of the function fitness of the model of \(P_{\text{min}}\).  
Figure 7: Optimization curve of the function fitness of the model of \(P_{\text{max}}\).  
Table 4 shows the optimal \(\gamma\) and \(\sigma\) of the three models, and inputs the optimal hyper-parameters into the LSSVM models to establish the IPSO-LSSVM models.
## 3 Results
### Comprehensive Analysis  
#### 3.2.1 Spatial Pattern Analysis of Ecological Sensitivity  
Based on the results of the above single-factor analysis and the weights of each factor, supported by ArcGIS 10.5 software, the above factors were calculated using the formula (1), and the comprehensive evaluation results map of ecological sensitivity in Tumen River Basin was obtained (as shown in Figure 6a). Then, with 2, 4, 6, and 8 as thresholds, the classification was carried out to obtain the final comprehensive classification results and statistical results of ecological sensitivity, as shown in Figure 6b and Table 7.  
From the evaluation results, we can see that there are no extremely sensitive areas or insensitive areas in the study area. (1) The smallest of the various sensitive areas is the highly sensitive area, with a total area of 133.86 km\({}^{2}\), accounting for 0.59% of the total area, which is mainly concentrated in the lakes, rivers, reservoirs, and other areas within the study area. These areas have a high sensitivity level in several important factor layers, such as land use type (factor weight ranked first), vegetation coverage (factor weight ranked third), and elevation (factor weight ranked fourth), and thus the sensitivity level is also high after the comprehensive evaluation. For this area, attention should be paid to protecting nearby vegetation to conserve water, controlling sewage discharge to reduce pollution, and taking various other measures to strengthen the protection of water resources and enhance the stability of the ecosystem. (2) The moderately sensitive area is the largest, with an area of 12,281.04 km\({}^{2}\), accounting for 54.12% of the total area. The distribution is more concentrated in the middle of the Tumen River Basin. Other areas are also widely distributed around cultivated land and construction land on both sides of the road. These areas are easily disturbed by human activities, and the degree of ecological sensitivity easily increases with the intensification of human activities. Therefore, it is necessary to strengthen environmental protection management and education to reduce the disturbance of the ecosystem by human activities. (3) Slightly sensitive areas cover an area of 10,275.79 km\({}^{2}\), accounting for 45.29% of the total area, mainly distributed in the northern and southern  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensitivity Level** & **Area (km\({}^{2}\))** & **Proportion (\%)** \\ \hline Insensitive & - & - \\ Slightly sensitive & 10,275.79 & 45.29 \\ Moderately sensitive & 12,281.04 & 54.12 \\ Highly sensitive & 133.86 & 0.59 \\ Extremely sensitive & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comprehensive ecological sensitivity analysis results.  
Figure 6: Distribution of ecological sensitivity.  
mountainous areas of the study area. This area has a relatively high vegetation cover and it is far away from areas with frequent human activities, so it has better ecological stability.