esa-sceva/satcom-chunk-collection huggingface dataset:
# Special Issue \"Microwave Photonics 2018\"
###### Abstract  
The first step of the development of the proposed technique is to design a new technique for the measurement of the microwave photonics. The second step is to design a new technique for the measurement of the microwave photonics.  
The review manuscripts assess recent results on different devices and systems for microwave photonics signal processing and sensing. In [13], [PERSON] et al. provide a thoughtful review on high-speed photodetectors for MWP, covering high-power photodiodes and waveguide photodetectors as well as integrated photodiode-antenna emitters with bandwidths up to 150 GHz. The authors also present interesting results from heterogeneous III-V photodiodes on silicon and Ge-on-Si photodiode arrays in particular for analog applications. Linear cell radar devices and systems are reviewed by [PERSON] and co-workers [14]. By linear cell schemes the authors refer to radar systems consisting of many antenna units that are connected by radio-over-fiber structures to monitor linear-shaped areas. This paper [14] overviews different electro-optic devices, such as optical modulators and photodetectors, which play an important role in millimeter-wave linear cell systems for high-resolution imaging.  
A review of recent results on simultaneous interrogation of multiple fiber Bragg grating sensors by exploiting microwave photonics is performed by [PERSON] et al. [15]. This work describes the use of both microwave photonic filtering and chirped microwave pulse generation and compression for wavelength-to-power mapping, demonstrating high-resolution and high-speed interrogation. The latest developments in microwave photonic devices based on liquid crystal on silicon (LCOS) technology are reviewed by [PERSON] and colleagues [16]. The authors revise three microwave signal processing functionalities, namely notch filters, phase shifters, and couplers, and also present a new multi-function signal processing structure based on amplitude and phase control functions in conjunction with a power splitting function in a commercial LCOS-based optical processor.  
A research team led by [PERSON] gathered recent developments in microwave photonic signal processing and sensing based on optical filtering [17]. This review paper presents single-sideband modulation schemes to eliminate dispersion-induced power fading and provides high-resolution spectral characterization functions as well as single passband MWP filters to eliminate spectral periodicity. It also assesses high-performance sensing approaches based on MWP filters or optoelectronic oscillators.  
Due to their ability to generate ultra-low phase noise signals, optoelectronic oscillators have attracted considerable attention and are becoming one of the most promising microwave signal sources. The last review paper by [PERSON] and co-workers [18] addresses stable and low-spur optoelectronic oscillators and briefly introduces the operation principle and discusses current research on frequency stability and spurious suppression of optoelectronic oscillators.
# Relative Intensity Noise of Hybrid Mode-Locked Bound Soliton Fiber Laser: Theory and Experiment
## 3 Results
One can see that the required steady state lasing gains for the single- and bound-soliton states are very close in the overlap region marked by the blue circle. The laser operation within this region is expected to be not very stable experimentally, due to the co-existence of two states with close steady state lasing gains, since small fluctuations of the laser will easily lead to state jumping under this condition. This explains why, experimentally, we can only operate the laser under the single-soliton state in the lower power level, and under the bound-soliton state in the higher power level. Figure 4: Numerical simulation results for (**a**) single-soliton case at \(g_{0}=2.5\); (**b**) bound-soliton case at \(g_{0}=5\), both within 1000 roundtrips propagation. Figure 5: Numerical simulation results of (**a**) pulse energy; (**b**) pulse width and bound-pulse timing distance and (**c**) required lasing gain versus \(g_{0}\) for the single- and bound-soliton states respectively. Figure 6a,b are the theoretical single sideband RIN spectra, derived by taking the Fourier transform of the noise auto-correlation functions calculated by the linearized backpropagation method for both the single- and bound-soliton states respectively. The main difference of the noise spectra between the two states is the level of the flat spectral distribution from 1 kHz to the turning point near 100 kHz. The bound-soliton state has a lower noise level, which is in good agreement with the observed experimental tendency. Moreover, the flat bandwidth of the bound-soliton state is wider than that of the single-soliton state, i.e., about tens of kHz. This again agrees with the experiment. Some resonance peaks can also be resolved near the turning point for both states, which can be attributed to the coupling effects of different pulse parameters [4]. Since the relaxation oscillation effects of the Er-doped fiber lasers may also show up in this range of frequencies, one may not be able to resolve these resonance peaks experimentally. Figure 6c shows the integrated noise variance within the span from 1 kHz to 2.5 MHz versus different values of \(g_{0}\). The integrated RIN noise variance for the single- and bound-soliton states are \(7.56\times 10^{-7}\) and \(2.69\times 10^{-7}\) at \(g_{0}=2.5\) and \(g_{0}=5\) respectively, which corresponds to the energy fluctuations of 0.087% and 0.052% respectively. Both the single- and bound-soliton RIN variances decrease when the pulse energy gets larger. The bound-soliton states have smaller RIN noises compared to the single-soliton states of the same laser by following the similar lasing power scaling tendency as shown in Figure 6c.
# An Efficient Multi-Lane SpaceFibre Core for Spacecraft Data-Handling Networks
## 4 FPGA Implementation and Analysis
### Hardware Utilization  
As a promising hardware-based IP core, a low cost is a required property of the E-ML-SpFi core. Table 1 presents the hardware utilization of E-ML-SpFi core and the comparison of this work with other state-of-art designs in [22; 23]. As shown in Table 1, the design in [22] and the E-ML-SpFi core sustain a maximum data rate (MDR) of 6.25 Gbps per lane and each lane has configurable rates (CR). The design in [22], which is equipped with two VCs and four lanes, uses 6633.6 look-up tables (LUTs), accounting for 2% and 9287.04 flip-flops (FFs), accounting for 1.4%. The LUTs required by the design in [23], featuring two VCs and four lanes, are nearly three-times more than the design in [22], and the number of FFs increases to three times. The designs in [22; 23] have been implemented on the Xilinx Kintex UltraScale KU060, which contains 331,680 LUTs and 663,360 FFs. Xilinx KU060 is larger than Xilinx XC7Z100 in terms of LUTs and FFs. The E-ML-SpFi core, with four  
Figure 11: The ILA waveforms of hierarchical error recovery. (a) Far end; (b) near end.  
Figure 12: The ILA waveform of the modified word re-ordering block.  
Figure 13: The ILA waveform in the alignment subsystem.  
lanes and four VCs, is implemented on XC7Z100 FPGA, utilizing slice LUTs at 2.26% and FFs at 1.49%. We can conclude that the E-ML-SpFi core has similar hardware resources to the core described in [22], which supports only two VCs. Besides, the BRAMs used by the E-ML-SpFi core is reduced by 33%, compared with the design in [22], with four lanes and four VCs. As depicted in Table 1, the E-ML-SpFi core, equipped with four lanes and four VCs is also implemented on KU060, Kintex-7 XC7K325T and Virtex-6 XC6 VLX240T. The E-ML-SpFi core on KU060 and XC7K325T FPGAs consumes similar LUTs and FFs with the core on XC7Z100. The core on XC6 VLX240T occupies less LUTs and FFs compared with other FPGAs because the implementation of a Physical layer based on Virtex-6 is simpler than that of other boards. However, the simpler Physical layer leads to an MDR of 3.125 Gbps per lane. BRAMs used by the E-ML-SpFi core on different FPGAs are the same. Therefore, the E-ML-SpFi core is a more cost-efficient design, consuming fewer resources compared with other designs.
## 2 System Model  
Consider a large-scale MU-MIMO DFRC wireless transmission time-division duplex (TDD) system model, where \(B\) transmitting antennas would support \(U\) single-antenna user equipment (UEs) within the same frequency resource. As depicted in Figure 1, each antenna has a pair of DACs. In this system, the BS utilizes the precoding technique along with CSI to map the symbol vector \(\mathbf{s}_{l}\in\mathbb{C}^{U\times 1}\) to the transmit signal \(\mathbf{X}\). Let \(\mathbf{S}=[\mathbf{s}_{1},...,\mathbf{s}_{L}]\in\mathbb{C}^{U\times L}\)represent the ideal symbol matrix, with \(L\) being the number of snapshots in a radar pulse. The communication channel from the BS to \(U\) users is denoted as \(\mathbf{H}\in\mathbb{C}^{U\times B}\), with elements \(h_{u,b}\) drawn from a complex normal distribution \(\mathcal{CN}(0,1)\)[30]. Here, we assume that the BS has perfect knowledge of CSI. (In practice, the assumption of perfect CSI is often unrealistic. Typically, the receiver acquires CSI through training processes and then transmits this information back to the transmitter using limited feedback mechanisms. Additionally, in TDD systems, channel reciprocity can be leveraged. This allows for channel training on the uplink, enabling the BS to estimate the channel characteristics. Consequently, the channel estimation for the downlink can be inferred from the uplink measurements [31]).  
Subsequently, by applying nonlinear precoding techniques alongside DACs, the transmit signal matrix \(\mathbf{X}\) undergoes quantization in both its real and imaginary components, expressed as  
\[\mathbf{X}=[\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x}_{L}]=\mathcal{T}( \mathcal{P}(\mathbf{S},\mathbf{H}))\in\mathbb{C}^{\mathcal{B}\times L} \tag{1}\]  
where \(\mathcal{P}(\cdot)\) denotes the precoding operator, which may be either linear or nonlinear, responsible for mapping the symbol matrix \(\mathbf{S}\) to an unquantized transmit matrix [32]. The operator \(\mathcal{T}(\cdot)\) is utilized to quantize each element's real and imaginary parts of the precoded matrix. The mapping \(\mathcal{P}(\mathbf{S},\mathbf{H})\) uses the known channel \(\mathbf{H}\) to properly shape the transmit signal so that the precoded data \(\mathbf{X}\) effectively suppress multi-user interference. Furthermore, each precoded entry is represented as \(x_{b,l}\in\mathcal{X}\), where \(\mathcal{X}\) denotes the set of quantized complex values. We initialize \(\mathbf{X}\) by setting it to a random complex matrix or a zero matrix and then projecting onto the discrete set \(\mathcal{X}\). This initialization ensures a feasible starting point. The iterative process then refines \(\mathbf{X}\) to achieve the desired performance.
# Industrial Image Anomaly Detection via Self-Supervised Learning with Feature Enhancement Assistance
## 1 Introduction
Furthermore, to obtain multi-scale features, we have designed a Multi-Scale Feature Aggregation (MSFA) module to aggregate features extracted from different layers of the networks, thereby minimizing feature redundancy and allowing for tighter feature space aggregation. After obtaining the multi-scale features, we learn the latent space representation of the features through a simple encoder and finally obtain the classification results through a classifier. Through this carefully crafted solution, we have not only effectively addressed the challenge of scarce samples in industrial production environments but also successfully mitigated the domain bias issues commonly encountered when directly applying pre-trained models for anomaly detection and image classification, thereby achieving more precise and reliable model performance. Our main contributions are outlined as follows:  
1. We have designed a new unsupervised anomaly detection framework, FEPDM, which reduces bias between the source and target domains while significantly decreasing the number of model training parameters by conducting training at the feature level. 2. We have introduced a novel anomaly simulation strategy for self-supervised learning of the model. This strategy generates anomaly blocks with diverse textures and structures, closely resembling real-world anomalies. 3. We have elaborated a multi-scale feature aggregation module capable of integrating multi-resolution, multi-semantic features from different network hierarchies. This module effectively reduces the dimensionality of these features to the target dimensions through an efficient dimensionality reduction strategy. 4. Extensive experiments on today's most challenging real-world datasets, such as MVTec and BTAD, demonstrate that our approach exhibits excellent performance and generalization. The subsequent sections of this article are structured as follows. In Section 2, the existing literature on unsupervised anomaly detection and self-supervised learning is comprehensively reviewed. Section 3 presents a detailed introduction of the method proposed in this paper. Section 4 comprehensively outlines the experimental setup, presents the results, conducts thorough analyses, and includes ablation studies to validate the proposed method. Lastly, Section 5 summarizes the content of this article and discusses potential future research directions.
## 3 Results and Discussion
### Optimizing Synthesis Conditions
#### 3.1.4 Effect of Reaction Temperature  
Figure 6 shows the effect of the reaction temperature on the intrinsic viscosity of the hybrid flocculants. In Figure 6, it can be observed that the intrinsic viscosity of the hybrid flocculant increases with an increase in the reaction temperature. The intrinsic viscosity reached a maximum at 60 \({}^{\circ}\)C and then slightly decreased, which was consistent with the general laws of free-radical polymerization. This was because, with the increase in the reaction temperature, the molecular motion speed accelerated, the collision probability increased, the polymerization speed accelerated, the polymerization speed of the monomers rapidly increased, and the relative molecular weight of the product also increased. However, when the temperature rose to a certain point, the initiator initiation speed accelerated, and the free radicals of the system reached their highest concentration in a short time. The probability of a collision between free radicals also increased rapidly, resulting in an increase in the speed of chain transfers and chain termination reactions; therefore, the characteristic viscosity of the product decreased, and the relative molecular mass also decreased.
# Application Ranges of EPB Shield TBM in Weathered Granite Soil: A Laboratory Scale Study
###### Abstract
[10] performed a preliminary study on choosing a soil conditioning agent using  
Figure 1: Choice of soil conditioning agent in earth pressure balance (EPB) shield tunnel boring machine (TBM) depending on particle-size gradation curves in coarse ground soil (Reprinted with permission from ref. [1]). two types of weathered granite soils that were within the previously proposed application range of EPB shield TBM shown in Figure 1 proposed by [1]; the authors found that soils can be well-conditioned with only foam, that is, without polymers or fines, if the water content of the excavated soil is at optimal values. This research extended that preliminary research by [10] through the following comprehensive studies:  
1. More comprehensive ranges were studied of weathered granite soils having different particle-size gradation curves and percentages of fine particles (less than #200 sieve size) ranging from 0% (extremely cohesionless soil) to 17.1%. 2. Upper and lower bounds of water content, as well as optimum contents dependent upon fine particle-size contents, were tested. If the water content of the excavated soil was more than the upper bound value, the slump value might be larger than 20 cm, which is too fluidic; then, extracting the excavated soil through a screw conveyor may not feasible. If the water content is less than the lower bound value, then it is not feasible to achieve the slump value of 10 cm without adding water. 3. Finally, application ranges of EPM shield TBM and choice of conditioning agents were determined for weathered granite soils; the ranges depend on the soil's water content. The ranges proposed in this study may differ from those proposed by [1], and one of the primary reasons for this difference might be the particle-crushing characteristics of weathered granite soil.
# On Channel Estimation in LTE-Based Downlink Narrowband Internet of Things Systems
## 4 NB-IoT Downlink Channel Estimation
### Summary of the Proposed Channel Estimation  
The entire channel estimation process has been discussed adequately in the different sections above. The main goal of this work was to propose a low complexity and low power-hungry channel estimation technique which will be capable of keeping the demand of balancing between the performance and complexity of NB-IoT systems. In this work, we derive the proposed channel estimation scheme based on the optimum LMMSE channel estimator. We take the benefit of the high accuracy of LMMSE, and at the same time, we resolve the high complexity issue of the LMMSE by the application of SVD, banded, and overlap banded method while the performance is preserved at an acceptable level. To sum up, the detailed flow diagram of the proposed overlap banded SVD-based LMMSE channel estimation method is depicted in Figure 7.  
Figure 6: Splitting of the channel autocorrelation matrix \(\Phi_{\mathbf{\overline{\Psi}}\mathbf{\overline{\Psi}}}\) by applying the overlapping method.
# Combining Optimization and Simulation for Designing a Robust Short-Sea Feeder Network
###### Abstract  
Here we study a short-sea feeder network design problem based on mother and daughter vessels. The main feature of the studied system is performing transshipment of cargo between mother and daughter vessels at appropriate locations at sea. This operation requires synchronization between both types of vessels as they have to meet at the same location at the same time. This paper studies the problem of designing a synchronized feeder network, explicitly accounting for the effect of uncertain travel times caused by harsh weather conditions. We propose an optimization-simulation framework to find robust solutions for the transportation system. The optimization model finds optimal routes that are then evaluated by a discrete-even simulation model to measure their robustness under uncertain weather conditions. This process of optimization simulation is repeated until a satisfactory condition is reached. To find even better solutions, we include different performance-improving strategies by adding robustness during route generation or exploiting flexibility in sailing speed to recover from delays. We apply the solution method to a case based on realistic data from a Norwegian shipping company. The results show that the method finds near-optimal solutions that offer robustness against schedule perturbations due to harsh weather. They also highlight the importance of considering uncertainty when designing a short-sea feeder network with transshipment at sea.  
m _artifime transportation; liner network design; synchronization; weather uncertainty; optimization simulation_
## 3 Background and Theory
### CAR Montage Filter  
The final preprocessing method applied to our EEG data is the common average reference (CAR) montage [25]. This technique in EEG signal processing is designed to minimize noise shared across all EEG channels. By re-referencing each channel to the collective average of all channels, CAR enhances the clarity of brain electrical activity relative to this average, thereby improving the signal-to-noise ratio and the overall quality of the data. Let X denote the matrix of EEG signals, where rows represent time samples and columns represent channels. The CAR operation is mathematically expressed in Equation (5):  
\[CAR=X-\frac{1}{N}{\sum}_{i=1}^{N}X_{i} \tag{5}\]  
where the following apply:  
* \(CAR\) is the EEG data matrix after the filter has been applied;
* \(X\) represents the original EEG data matrix;
* \(N\) denotes the total number of EEG channels;
* \(Xi\) corresponds to the signal from the \(i\)-th channel.
## 2 Case Scenarios
### Multi-Doctor Visits with the Same Patient  
Case 2: [PERSON] experienced persistent back pain for two weeks and initially thought it was a sprain. After applying a plaster without relief, he visited [PERSON] at a local hospital. [PERSON]'s final diagnosis was membranous nephropathy, but [PERSON] felt the diagnostic tests prescribed were excessive, indicating overtreatment.  
[PERSON] then visited another local hospital, where [PERSON] also diagnosed membranous nephropathy and suggested a treatment plan involving a biological agent (rituximabab). However, [PERSON] found [PERSON]'s treatment plan too expensive, costing between CNY 40,000 and CNY 60,000, mostly out-of-pocket since it was not covered by health insurance. Given that his only symptom was back pain, he opted not to proceed with the treatment at that hospital. After these two consultations, [PERSON] sought a third opinion at a top-tier hospital in a significant city. Doctor [PERSON] also diagnosed him with membranous nephropathy and proposed a treatment plan, combining steroids and immunosuppressants (methyl prednisolone plus cyclophosphamide), lasting one year at a cost of around CNY 10,000.  
The patient visited three different hospitals and consulted with different doctors. Despite similar consultation processes, issues arose, such as excessive diagnostic testing by doctors and inconsistent treatment plans. These issues undermined the patient's trust in the healthcare system and could also become the root cause of doctor-patient disputes. The main challenges in resolving medical disputes include complex processes, low-resolution efficiency, and the influence of subjective factors. Effectively addressing these real-world problems, optimizing medical processes, and enhancing doctor-patient consultations' transparency are key issues the current healthcare system needs to solve.  
We categorize these issues into two types of cognitive differences encountered during the consultation process: the discrepancy caused by the difference between the patient's perception of the doctor and the doctor's self-perception (as part of Figure 1a),and the discrepancy between the patient's expected perception of their symptoms by the doctor and the doctor's actual perception of the patient's condition (as part of Figure 1b).