esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Modulation Formats for 400 Gbps Optical Networks and AI-Based Format Recognition
###### Abstract  
The integration of communication and sensing (ICAS) in optical networks is an inevitable trend in building intelligent, multi-scenario, application-converged communication systems. However, due to the impact of nonlinear effects, co-fiber transmission of sensing signals and communication signals can cause interference to the communication signals, leading to an increased bit error rate (BER). This paper proposes a noncoherent solution based on the alternate polarization chirped return-to-zero frequency shift keying (Apol-CRZ-FSK) modulation format to realize a \(4\times 100\) Gbps dense wavelength division multiplexing (DWDM) optical network. Simulation results show that compared to traditional modulation formats, such as chirped return-to-zero frequency shift keying (CRZ-FSK) and differential quadrature phase shift keying (DQPSK), this solution demonstrates superior resistance to nonlinear effects, enabling longer transmission distances and better transmission performance. Moreover, to meet the transmission requirements and signal sensing and recognition needs in future optical networks, this study employs the Inception-ResNet-v2 convolutional neural network model to identify three modulation formats. Compared with six deep learning methods including AlexNet, ResNet50, GoogleNet, SqueezeNet, Inception-v4, and Xception, it achieves the highest performance. This research provides a low-cost, low-complexity, and high-performance solution for signal transmission and signal recognition in high-speed optical networks designed for integrated communication and sensing.  
modulation format; optical networks; convolutional neural network (CNN); modulation format identification (MFI); integration of communication and sensing (ICAS)  
2024272911224/12/  
data to be transmitted to widely distributed multi-level computational nodes via optical communication networks. Combined with technologies such as artificial intelligence, this enables customized feature extraction, deep computation, intelligent recognition, and information fusion, forming a highly efficient and mutually enhancing architecture system characterized by high-bandwidth, low-latency communication; real-time status sensing; and on-demand computational resource scheduling [2].  
Currently, the application scenarios of optical network ICAS technology mainly include the operation and maintenance of optical fiber resources, smart cities, and pipeline and structural monitoring, as well as geological and environmental monitoring.
## 4 Discussion
The presence of positively charged species at the oil/water interface induces repulsion with the positively charged surface of the carbonate rock, increasing its water-wet behavior. Finally, chemical reaction between the rock and the carbonated brine can also play a part in the observed wettability alteration. Calcite, which is the major mineral constituent of the coquina rock samples [70], reacts with the carbonic acid formed by dissolution of CO\({}_{2}\). This reaction increases the concentration of Ca\({}^{2+}\), as described in the literature [33]. Furthermore, the potential determining ion Ca\({}^{2+}\) was reported to weaken the interaction between positively charged surfaces of carbonates, such as chalk [71; 72], and calcite [33], and the negatively charged compounds in the oil. As a result, the water-wet behavior is increased after CO\({}_{2}\) dissolution in the aqueous phase. Digital rock evaluations of carbonate rocks were presented in recent publications [73; 74; 75; 76]. However, the coquina rock considered in this work presents a wide pore size distribution ranging from nanometers to hundreds of microns, as shown in Figure 4. To the best of our knowledge, this is the first study including the impact of carbonated brine on the imbibition relative permeabilities based on numerical experiments for a highly complex carbonate rock sample with a wide range of pore sizes. The curves obtained in this work (Figure 8) indicate the positive effect of carbonated brine injection on relative permeabilities. A shift in the crossover point toward higher water saturation indicates the increase in wettability toward water for carbonated brine injection [77]. This is also supported by the reduction in the endpoint relative permeability to water observed for the carbonated brine [78]. As a result, the obtained curves for carbonate brine injection illustrate an increased flow capacity (i.e., greater relative permeability) of the oil phase for the majority of saturation values. Relative permeability curves play a critical role in oil recovery and EOR [79]. By applying appropriate upscaling techniques and considering mobility effects, capillary limit fractional flow curves can be obtained to estimate the flow of oil and brine under no macroscopic sweeping effects [80]. The shift in wettability previously discussed indicates an increase in oil displacement by carbonated brine injection, suggesting its potential for EOR applications.
## 2 Materials and Methods
### Network Design: Boundary Specific U-Network (BSU-Net)
#### 2.1.1 BSU-Pooling Layer  
BSU-net has three components. The first is the advanced pooling process. Conventional max-pooling layer used in conventional U-net discards rest of the pixels in a calculation field except for one pixel with maximum value. This process contributes to the efficiency of feature extraction; however, the loss of the information contained in the discarded pixels during the pooling process results in an inaccurate estimation of boundaries of target object in detailed segmentation tasks. Therefore, there is a need for an advanced pooling layer scheme that can minimize the loss of information while increasing the efficiency of feature extraction. The proposed BSU-pooling layer shown in Figure 3c uses both a max-pooling layer that increases the efficiency of feature extraction and convolutional layers that compute the neighboring information without discarding it. In this case, the stride of the convolutional layers is set to 2, so that down-sampling effect as in the max-pooling layer is possible. Furthermore, the inputs of the layer are preserved through multiple paths: a path passing through  
Figure 3: Whole structure of the proposed network. (**a**) Structure of the boundary specific U-network (BSU-net). (**b**) Structure of residual block. (**c**) Structure of BSU-pooling layer.  
\(3\times 3\) convolutional layer and a path passing through \(1\times 1\) convolutional layer and another subsequent \(3\times 3\) convolutional layer (Figure 3c).
## 5 The Striations Interference Phenomenon and Its Formation Mechanism
### The Phenomenon of the Stripe Density and Its Generation Mechanism  
Figures 3 and 4 show the simulation striations for the monostatic configuration and the bistatic configuration. The striations became denser from low frequency to high frequency. The striations were sparse at 400\(-\)800 Hz and denser at the 4\(-\)8 kHz frequency range. When the frequency was in the range of 40\(-\)80 kHz, the striations were denser than those at 4\(-\)8 kHz. The density of the striations increased with the frequency. Figures 7 and 8 show the simulation striations and the experimental striations for the monostatic configuration. It can also be observed that the striations in the 20\(-\)40 kHz were denser than those in the 40\(-\)80 kHz frequency range.  
In order to explain this, we took the sound propagation process from the target to the receiver. The number of modes N excited by the signal was related to the frequency f, the depth of the environment H, and the underwater sound speed c. The relationship among them can be expressed as:  
\[\mathrm{N=}\frac{2\mathrm{H}\mathrm{H}}{\mathrm{c}}. \tag{7}\]  
The number of the modes is proportional to the frequency of the signal. In the same environment, the number of the modes of the high-frequency part is more than that of the low-frequency part, so the interference pattern of the high-frequency part is more complicated. For the simulation scenarios in Section 3, the maximum numbers of modes excited by the source at 400\(-\)800 Hz, 4\(-\)8 kHz, and 40\(-\)80 kHz frequency bands were about 53, 533, and 5333, respectively. Similar to simulation experiments, the maximum numbers of modes excited by the source at 20\(-\)40 kHz and 40\(-\)80 kHz frequency bands were about 746 and 1493, respectively, for the monostatic experimental configuration in Section 4. In comparison, the number of modes excited by signals at high-frequency bands was larger than that of signals at low-frequency bands. The mutual interference between the modes was more obvious, and the interference striations were denser.  
In both Sections 3 and 4, the striations were wide in the high part and narrow in the low part at one frequency band. For example, in Figure 4a, the simulation striations interference length in the range of 400\(-\)500 Hz was shorter than that in the range of 600\(-\)700 Hz. In Figure 7b, the experimental striations interference length in the range of 20\(-\)27 kHz was shorter than that in the range of 27\(-\)40 kHz. According to Equation (4), the oscillation period of a sound field with a second-order mode is written as:  
\[\mathrm{L=}\frac{2\pi}{\mathrm{k_{m}-k_{n}}}. \tag{8}\]  
This oscillation period is the interference length, and the interference length between modes is inversely proportional to the wavenumber difference. According to the definition of the horizontal wavenumbers, the wavenumber difference between two adjacent modes at the high-frequency part is smaller than the low-frequency part. The corresponding striations interference length is also longer.
# Fast Frequency Response of a Doubly-Fed Induction Generator for System Inertia Support
## 2 Frequency Control of Synchronous Generators
### Primary Frequency Control  
The purpose of the primary frequency control is to stabilize the system frequency to a new steady state and arrest the system frequency decline. Once the speed deviation of governor is detected, it is used as an input control signal to adjust the gate position of the turbine, thereby increasing or decreasing the mechanical input power of conventional synchronous generators for system frequency control [13]. This relationship can be described as in Equation (2).  
\[\Delta P_{pri}=-\frac{K}{\Delta f}, \tag{2}\]  
where \(\Delta P_{pri}\), \(\Delta f\), and \(K\) are the output power of primary frequency control, frequency deviation, and control gain of primary frequency control loop, respectively.  
With the increasing wind power penetration, conventional synchronous generator plants are retired or de-committed; it is difficult to preserve a permissible primary frequency response [15].
## 4 Test and Result  
To validate the proposed filter strategy, short and long baseline tests are conducted and the traditional DD KF (DD-KF) model mentioned in [1] is also used for comparison. The DD-KF is established in Equations (1)-(4) without the 'DF Data-aid AR Stage'. As the noise of original observations is significantly less than those of wide-lane and ionosphere-free combined measurements, thus, the DF Data-aid AR method is not enabled for the short baseline test. The improvements illustrated in the short baseline test are only beneficial from the AMC-KF method. For the long baseline, the DF Data-aid AR stage is enabled to eliminate atmosphere errors; thus, the improvements in the long baseline test are beneficial both from the AMC-KF and the DF Data-aid AR method. All results obtained are based on post-processing performed on an Intel Core i7 2.30 GHz notebook with 16 GB RAM running on Windows 10.  
As Figure 2 shows, the dataset is collected from seven Australia CORS stations (BONE, QCLF, ANGS, STNY, NEWH, GSBN, WBEE) on January 1, 2021, and all formed baselines are elaborated in Table 1. The first six baselines range from 19-60 km and are used for the short baseline test. The last two baselines are formed by (BONE, GSBN, WBEE) and used for long baselines. The sample interval and cut-off elevation for all tests are 30 s and 10\({}^{\circ}\), respectively.  
Figure 1: The procedure of the filter implementation.
# Study on the Absorbing Properties of V-Doped MoS2
## 4 Conclusions  
In this study, V-doped MoS\({}_{2}\) powders were synthesized via the hydrothermal method with filling ratios of 30 wt%, 40 wt%, and 50 wt%. The results indicate that the RL\({}_{min}\) values of the VMS samples with filling ratios of 30 wt% and 40 wt% are below \(-\)10 dB, which corresponds to an absorption efficiency of EM waves as high as 90%. It is notable that the VMS sample with a filling rate of 40 wt% exhibits the best comprehensive absorption performance, with EAB of 4.40 GHz at a thickness of 1.43 mm and RL\({}_{min}\) value of \(-\)32.24 dB at a thickness of 1.78 mm. This outstanding performance is attributed to the sample's excellent attenuation capacity and good impedance matching at a 40 wt% filling ratio. To further investigate the influence of annealing on VMS composites, the VMS samples were annealed at 600 \({}^{\circ}\)C for 2 h. It was found that annealing deteriorated the impedance matching characteristics, leading to a decrease in absorption performance. After further studying the microwave absorption mechanism of the sample, it is found that the attenuation coefficient increases with the increase in the filling rate of the filler. When the filling ratio is 30 wt%, the impedance matching is the largest in the area close to 1, followed by the area close to 1 when the filling ratio is 40 wt%. When the filling ratio is 40 wt%, the impedance matching degree decreases after annealing treatment, and there is no area close to 1. When the filling ratio is further increased to 50 wt%, the impedance matching area close to 1 is basically absent. Therefore, the excellent attenuation capacity and good impedance matching of the VMS sample at a 40 wt% filling ratio are key factors in its superior comprehensive microwave absorption performance.  
Methodology, writing--review, editing: J.Z. and Q.X.; writing--original draft preparation, data collection: J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by [PERSON](2022)122 and [PERSON] [2023] 2.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} RL\({}_{min}\) & Minimum reflection loss \\ EAB & Effective absorption bandwidth \\ EM & Electromagnetic \\ MAMs & Microwave absorbing materials \\ RL & Reflection loss \\ \end{tabular}
## 3 Related Work and Shortcomings
### Sampling-Based Path Planning for Mobile Manipulators
#### 3.1.1 Using Traditional RRT Algorithms  
Since RRT-based algorithms can be used in high-dimensional spaces, this is the simplest and most direct idea. The mobile manipulator is considered a whole system with high DOFs and the difference between the mobile base and the manipulator is neglected.
# A Momentum-Based Local Face Adversarial Example Generation Algorithm
## 5 Experiments
### Performance Evaluation for Face Recognition Models  
Five mainstream pre-trained face recognition models were used for comparison, namely ResNet50-IR (IR50) [31], ResNet101-IR (IR101) [32], SEResNet50-IR (IR-SE50) [33], MobileFaceNet [8], and ResNet50 [33]. In order to show the success rate of adversarial attacks more intuitively, the metrics were the True Accept Rate (TAR) and False Accept Rate (FAR) [20]. Given a face pair \((x_{1},x_{2})\), let the matched face pair be \(P_{s}\) and the unmatched face pair be \(P_{d}\). Given a threshold, the calculation of \(TAR\) and \(FAR\) is as follows.  
\[TA=\left\{\begin{matrix}(x_{1},\ x_{2})\in P_{s},\\ \text{with }D(f(x_{1}),\ f(x_{2}))<\text{{threshold}}\end{matrix}\right\} \tag{18}\]  
\[FA=\left\{\begin{matrix}(x_{1},\ x_{2})\in P_{d},\\ \text{with }D(f(x_{1}),\ f(x_{2}))<\text{{threshold}}\end{matrix}\right\} \tag{19}\]  
\[TAR=\frac{|TA|}{|P_{s}|} \tag{20}\]  
\[FAR=\frac{|FA|}{|P_{d}|} \tag{21}\]  
where \(|TA|\) is the number of all matched pairs whose distance is less than the threshold; \(|P_{s}|\) is the number of all matched pairs; \(|FA|\) is the number of all unmatched pairs whose distance is less than the threshold; and \(|P_{d}|\) is the number of all unmatched face pairs.  
Different models will have different thresholds that can objectively reflect the success rate of the attack. Accordingly, the threshold was determined according to different values of \(FAR\), and was chosen when \(FAR=1\times 10^{-2}\) or \(1\times 10^{-3}\). We traversed the range of thresholds and used the 10-fold cross-validation method to find the threshold closest to the target \(FAR\).  
As shown in Figure 9, the TPR of each model (i.e., the proportion of correctly predicted matched face pairs to all unmatched face pairs) was maintained above 96.5% when \(FAR=1\times 10^{-6}\). When \(FAR=1\times 10^{-2}\), the TPR reached more than 98.5%. This indicates that the performance of these backbone network models had excellent performance.  
The test results of the five models on the LFW at a FAR of about 0.01 are shown in Table 1. The value of TAR@FAR = 0.01 (i.e., the probability of correctly identifying matching face pairs when the FAR is close to 0.01) was maintained at more than 98.9%.  
Figure 9: The ROC curve of FPR in the range of \(1\times 10^{-6}\) to \(1\).
## 3 Methods
### Extracting Key Variables for AGB Estimation  
The variables used could be categorized into 4 classes, namely, spectral information, vegetation index, tasseled cap transformation index, and terrain information. Vegetation indices are simple yet effective metrics for assessing surface vegetation conditions, calculated by leveraging the ratio or difference between specific spectral bands. These indices reflect the spectral characteristics of vegetation, exhibiting high sensitivity to vegetation cover and growth vigor and providing quantitative information on vegetation growth status, spatial distribution, and temporal changes. In order to reduce the impact of soil background and enhance the sensitivity of sparse vegetation areas to vegetation, this study selected vegetation indices such as SAVI and MASVI to improve the impact of the soil background. In this study, pre-processed image data from Google Earth Engine (GEE) were utilized, and all bands were resampled to a spatial resolution of 30 m using the nearest neighbor interpolation method [28]. A total of 26 variables, including NDVI and SAVI, were calculated for model construction, with the results stored in the GEE cloud-based dataset. To ensure consistency in spatial resolution, all characterization variables--such as Sentinel-2 spectral information, vegetation index, tasseled cap transformation index, and terrain information--were standardized to a resolution of 30 m. Variable-specific information is provided in Table 3.
## 5 Conclusions
### Additional Insights and Future Directions  
Comparison with Human Inspection: The software's ability to identify a higher number of defects than human inspection, especially in Class C, indicates its effectiveness in providing consistent and objective defect detection.  
Model Optimization: The study underscores the importance of tailoring machine learning models to the specific constraints of the deployment environment. Future work could focus on optimizing these models for larger datasets and improving hardware configurations to enhance their potential further.  
Expansion and Integration: Potential areas for development include modules for documentation management and integration with other organizational systems, such as safety or environmental management systems, to facilitate holistic process optimization.  
The study demonstrates that integrating advanced SA and ICT technologies into quality control processes can significantly improve defect detection and management. This research provides valuable insights for enhancing quality management practices and operational excellence in the agricultural sector by offering a robust and efficient solution.  
The system possesses the potential for enhancement and extension in various avenues. One direction for advancement includes the development of modules for documentation management, enabling more efficient handling of documentation related to quality control processes. Moreover, interconnecting the system with other organizational systems, such as those dedicated to safety or environmental management, can facilitate holistic management and optimization of operational processes.  
Conceptualisation, M.S. and M.E.; methodology, A.D.; software, N.P.; validation, R.J. and N.P.; formal analysis, A.D.; investigation, R.J.; resources, R.J.; data curation, N.P.; writing--original draft preparation, A.D.; writing--review and editing, M.S. and M.E.; visualization, A.D.; supervision, M.S.; project administration, A.D.; funding acquisition, M.S. and A.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
## 4 Baseline Algorithms for RF Fingerprinting Method
### Baseline 2: Raw Signal-Based RF Fingerprinting  
The second baseline aims to reflect the recent methods of RF fingerprinting based on raw signal processing. It was designed to train raw SF signals directly in the ensemble approaches of the deep learning classifiers described in [7].  
As described at the beginning of Section 4, the SF extraction process was the same as that of the proposed method described in Section 3.1.  
For feature extraction, the SFs were segmented using \(N_{seg.}\) in Equation (24). The core idea of this approach was to train the raw signals in the ensemble classifiers, and the RT and FT were also segmented in this case. The feature vectors of each segment were set to a two-channel I/Q vector \(\mathbf{s}_{Feature|\mathbf{s}_{\text{SF}|n}}\in\mathbb{R}^{N_{\text{SF}}^{ \text{exp}}\times 2}\) such that  
\[\mathbf{s}_{Feature|\mathbf{s}_{\text{SF}|n}}=\left[\begin{array}{c}\text{Re} \Big{(}\mathbf{s}_{\text{SF}|n}\Big{)}\\ \text{Im}\Big{(}\mathbf{s}_{\text{SF}|n}\Big{)}\end{array}\right] \tag{27}\]  
where \(N_{\text{SF}}^{\text{raw}}\) is the size of each segment \(\mathbf{s}_{\text{SF}|n}\).  
For the ensemble classification approach, the base classifier was set to a one-dimensional CNN as an identification network for outdoor data in [4]. After training each base classifier using segmented feature \(\mathbf{s}_{Feature|\mathbf{s}_{\text{SF}|n}}\), classification was performed using an ensemble approach, as in [7]  
\[\widetilde{k}=\underset{cj\in C}{\operatorname{argmax}}\prod_{n\in N_{seg.}}p \Big{(}c_{j};\mathbf{s}_{Feature|\mathbf{s}_{\text{SF}|n}}\Big{)} \tag{28}\]