esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hardware Design
### NB-IoT Module Design  
NB-IoT is built on a cellular network and can be directly deployed on the GSM network, UMTS network, or LTE network to reduce the deployment cost. This system utilizes the Fruit Cloud GA7 chip and STM32 for data transmission through the serial port, configures the NB-IoT module through AT commands, and directly connects to the IoT platform using the MQTT communication protocol to send the collected data within different subscription topics. NB-IoT technology can be utilized to link directly to the IoT platform through the existing base station to achieve a low-power wireless link in embedded devices. As the peripheral circuit of the IoT module has high signal transmission requirements, it is necessary to filter the ripple in the rectified output voltage as well as the electrostatic shielding, and it is necessary to complete the impedance matching of the antenna part to prevent the high-frequency circuit signals from reflecting back to the source point and to improve the energy efficiency. The peripheral circuit design of the NB-IoT module is shown in Figure 6. The following notations are used: MCU_RX and MCU_TX are used to connect to the serial port of the primary controller; SIM_VDD, SIM_DATA, SIM_CLK, and SIM_RST are used to connect to the SIM card; C7 is used to ensure chip voltage stability; C8 and C9 are filter capacitors; D1(Electro-Static discharge) prevents static electricity from affecting signals.
# The Use of Radio and Telemedicine by TMAS Centers in
## 4 Discussion
### Implications of Medical Assistance at Sea in Healthcare  
The implications of medical assistance at sea are numerous and far-reaching in the field of healthcare. The ability to provide medical care remotely, particularly in critical situations where time is essential has proven to be lifesaving in many instances [37; 41; 42; 43]. The advancement of technology has enabled healthcare professionals to virtually reach patients in remote areas, improving diagnosis, treatment, and outcomes. The use of telemedicine can also reduce healthcare costs by avoiding expensive and potentially risky emergency medical evacuations. Additionally, telemedicine can improve access to health care in underserved areas. Healthcare providers and policymakers will need to address questions of efficacy and ethics as remote medical services expand.
# DBCAN: DFormer-Based Cross-Attention Network for RGB Depth Semantic Segmentation
## 6 Conclusions  
In this paper, we present the DFormer-Based Cross-Attention Network (DBCAN), a novel architecture that builds upon the DFormer framework by introducing specific modifications aimed at enhancing RGB-D semantic segmentation. Our approach addresses key limitations in the DFormer architecture by improving the integration of spatial features during decoding and enhancing the alignment between RGB and depth modalities. To achieve this, we introduce three novel modules in the decoding stage: the Object-Region Generated Module (ORGM) to refine RGB feature representation, the Feature-Region Relation Module (FRRM) to align and weigh relevant RGB and spatial features, and the Spatial-Semantic Fusion Module (SSFM) to effectively fuse features from both modalities. These modifications enhance the performance of DBCAN, as demonstrated through extensive experiments. Our method achieves state-of-the-art results on key RGB-D datasets, validating the effectiveness of our approach. Additionally, ablation studies highlight the contributions of each module, providing further insight into how these modifications improve the overall architecture. By building on the strengths of DFormer while addressing its limitations, DBCAN proves to be a robust solution for RGB-D semantic segmentation tasks.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Data Availability Statement: The data presented in this research are available from the corresponding author upon request.
# Extended SSH Model in Non-Hermitian Waveguides with Alternating Real and Imaginary Couplings
## 4 Topological Edge Modes
according to Im(\(\beta\)) = \(\delta\)/2. In contrast, the Im(\(\beta\)) of bulk modes displays a quite slow increase. As a result, the imaginary propagation constants of edge modes exceed that of bulk modes by choosing a proper \(\delta\). As \(\delta\) further increases, the propagation constants of bulk modes and edge modes tend to be equal. This can be understood from the coupled mode equation. According to the coupled mode equation, the eigenvalues of bulk modes are derived as  
\[\lambda_{\pm}=\pm\sqrt{-\frac{\delta^{2}}{4}+c_{1}^{2}-c_{2}^{2}+2 ic_{1}c_{2} \cos\varphi} \tag{5}\]  
Figure 5: The field distributions of two edge modes. (**a**) and (**c**) are the norm of electric field (IEI) for two topological edge modes. (**b**) and (**d**) are the component of electric field along the \(x\) direction (\(E_{x}\)). Figure 6: The supermodes for different detuned propagation constantans \(i\delta\). (**a**) The supermode for \(\delta\) = 0. (**b**) The supermode for \(\delta\) = 0.015. (**c**) Im(\(\beta\)) as a function of \(\delta\). The blue lines denote the maximum imaginary part of propagation constants for bulk mode. The red lines denote the topological edge mode. (**d**) and (**e**) are the field distributions (IEI) of two edge modes for \(\delta\) = 0.015. The eigenvalues of edge modes are \(\beta=\pm\,i\delta/2\). As \(\delta>>c_{1}\), \(c_{2}\), the eigenvalues of all bulk modes also tend to be \(\beta=\pm\,i\delta/2\). Therefore, the propagation constants of edge modes and bulk modes tend to be equal for extremely large \(\delta\). We also illustrate the field distributions of two edge modes for \(\delta=0.015\), as shown in Figure 6d,e. When chiral symmetry is broken, the fields of both edge modes are confined at one termination of the waveguides; one is at left and the other is right. Moreover, the fields of edge mode in Figure 6d are only confined at the amplified waveguides, while the fields of the other edge mode (Figure 6e) are mainly concentrated on attenuated waveguides. As a result, the two edge modes experience the strongest gain or loss. The proposed system may be experimentally realized using Fe-doped LiNbO\({}_{3}\) waveguides [43]. In this system, the optical gain can be accomplished through two-wave mixing. By selectively pumping the waveguides, alternating gain and loss then will be achieved. Moreover, imaginary coupling based on adiabatic elimination has also recently been realized in LiNbO3 waveguides [39]. The huge loss is achieved by incorporated Cr into the assistant waveguide. Therefore, we think the most probable realistic scenario may be realized in LiNbO\({}_{3}\) waveguides.
###### Abstract  
The transmission of the orbital angular momentum (OAM) beam has attracted a lot of attention in the field of free-space optical (FSO) communication. Usually, after transmitting in atmospheric turbulences, the helical phase-front of OAM beams will be severely distorted, and there will exist the intermode crosstalk. As a result, the performance of the communication system will degrade significantly. In this paper, we have investigated the influences of the level of the turbulence strength to the transmitting OAM beams by changing the refractive-index structural parameter of \(C_{n}^{2}\) and the number of turbulence random phase screens of \(N\) in simulation environment. Then, by adopting the Gerchberg-Saxton (GS) algorithm, which can be used to compute the pre-compensation phase and correct the distorted OAM beams, the retrieving performances of transmitting single and multiplexed OAM beams under different turbulence strengths were also investigated. The simulation results show that with increasing the atmospheric-turbulence strength levels determined by the parameters \(C_{n}^{2}\) and \(N\), the retrieving performances decrease dramatically. When the turbulence strength level is selected within an appropriate range, the OAM beams can be effectively retrieved by adopting GS algorithm and observing the power density spectrum. Notably, the retrieving performance for the transmission of a single OAM beam is better than that of the multiplexing OAM beam.  
Keywords:orbital angular momentum; free-space optical communication; atmospheric turbulence; atmospheric correction +
Footnote †: journal: Applied Sciences
## 4 Soil water and drought monitoring for early-warning applications
### Model Inputs  
The key input data to the water balance model are precipitation, potential evapotranspiration (PET), and soil water holding capacity, and crop coefficient. While the key crop coefficient values are obtained from the FAO publication ([PERSON], 1998), the other three main datasets are spatially distributed are described in brief below.
# A Survey of Sequential Pattern Based E-Commerce Recommendation Systems
## 2 Existing Sequential Pattern-Based E-Commerce Recommendation Systems
### Hybrid Model: HM RecSys16 [40]  
Summary of HM RecSys16 System [40]: A hybrid recommender system that combines the prefix span algorithm with traditional matrix factorization was proposed in [40]. SPM aims to find frequent sequential patterns in sequence databases, and is applied in this hybrid model to predict customer payment behavior, contributing to the accuracy of the model. The workflow of the system consists of three phases: Behavior Prediction, CF, and Recommendation.  
Purchasing Pattern Extraction  
The BPM (Behavior Pattern Model) utilizes the prefix-span algorithm to extract the most prevailing purchasing sequences from the warehouse in real time and match the sequences with customer behavior patterns for a customer browsing or adding an item to the cart. When the recommender system's behavior monitoring part detects the user's potential purchasing tendencies, the system fetches the user's historical behavior record from the sequence database and builds an item-user rating matrix like Table 21, in which each entry contains the historical behavior of the Ith user with respect to the Jth product.  
Matrix Factorization-based Collaborative Filtering  
The CF method is used to find a set of customers whose purchased and rated items overlap the user's purchased and rated items. The algorithm generates recommendations based on a few customers who are most similar to the user and generates preference tendencies of the users based on their historical purchasing record. The basic matrix factorization model is used, which factorizes the user-item matrix into two matrices, one representing features of the products and another representing the preferences of users. Multiplying the two matrices provides predictions about the user's preferences for all products.  
\[r_{ui}=q_{i}^{T}*p_{u}\]  
The \(r_{ui}\) represents the rating of item i by user u; the latent factor model is then used to learn the factor vectors \(p_{u}\) and \(q_{i}\) by minimizing the regularized squared error on the set of known ratings.  
\[\sum_{(u,i)\in\mathbb{K}}(r_{ui}-q_{i}^{T}.p_{u})^{2}+\lambda(\|q_{i}\|^{2}+\| p_{u}\|^{2})\]  
Recommendation Phase: the payment behavior patterns extracted from the behavior prediction phase and the preferences collected using the CF method are combined to select target items as suggestions. In the first step, the customer's real-time behavior sequences are generated and stored in a database called the candidate database. The candidate database is scanned at regular intervals and the sequence containing payment patterns is sent to the recommender system as a potential purchasing sequence. Second, for the potential buyers, the preference information from CF phase which represents the preference degree towards each product is generated. Because the sequential mining phase generates both the payment sequence and the category of the target item, the category-matched items in the preference vector are recommended.
## Appendix A  
A source-to-destination direct signaling chain is illustrated in Figure 11, where \(X\) denotes binary i.i.d information sequences of length \(\mathcal{L}_{x}\), \(Y\) represents the information sequences with \(\mathcal{L}_{y}\)-bit produced by the encoder of the source. \(Z\) corresponds to the symbol sequences with \(\mathcal{L}_{z}\)-bit transmitted to the decoder across a wireless channel. A joint encoder \(\mathcal{E}_{n}\), functioning as an association of source and channel encoders, maps each sequence \(X\) to a codeword of length \(\mathcal{L}_{z}\), expressed as \(Z=\mathcal{E}_{n}(X)\). For simplicity, modulation is omitted in Figure 11. The transmission from the source S to UR\({}_{1}\) and UR\({}_{2}\) and that from UR\({}_{1}\) and UR\({}_{2}\) to the destination are orthogonal due to spatial/temporal separation.  
As per the source-channel separation theorem [41] Theorem 3.7, the overall joint source-channel coding rate is given by the following:  
\[\hat{K}=\frac{\mathcal{L}_{y}/\mathcal{L}_{z}}{\mathcal{L}_{y}/\mathcal{L}_{ x}}=\frac{\mathcal{L}_{x}}{\mathcal{L}_{z}}, \tag{11}\]  
where \(\mathcal{L}_{y}/\mathcal{L}_{x}\) denotes the source coding rate, and \(\mathcal{L}_{y}/\mathcal{L}_{z}\) represents the spectrum efficiency, which encompasses both the channel coding rate and the modulation order.
## 4 SAR Payload Unit Design
### Radio Frequency Unit  
As displayed in Figure 13a, the implemented RFU had a mass of 1.5 kg, DC power consumption of 30 W, and comprised transmit, receive, frequency synthesis, control, and power circuits. Military-grade components compatible with the space-grade parts were selected for each circuit. The overall functional block diagram of the RFU is illustrated in Figure 13b. The transmission signal was amplified by a factor of four in the transmission circuit and up-converted from the S-band (2.4125 GHz) to the X-band (9.65 GHz) for band stretching of the Tx waveform, and the reception signal was converted to the L-band (1.5 GHz) using the mixer in the reception circuit. The frequency synthesis circuit generated an L-band (1.2 GHz analog-to-digital converter (ADC) clock), S-band (2 GHz digital-to-analog converter (DAC) clock), and X-band (LO signal) signals using a space-grade ultrastable 10 MHz oven-controlled crystal oscillator (OCXO) (Axtal AXIOM75 SL). Additionally, the control circuit controlled the transmission/reception status and signal gain using a microcontroller unit (MCU) (ATMEL ATmega128L-8A).  
Figure 13: **(a)** Exploded view and **(b)** architectural block diagram of the S-STEP RFU.  
In particular, the five circuits in the transceiver were integrated into a single module to reduce the weight to suitable limits for a miniature SAR satellite. An expanded view of the integrated module of the S-STEP RFU is presented in Figure 13a. A PCB with integrated transmit and receive functions and a PCB with frequency synthesis and control functions were fastened to the upper and lower surfaces of the double-sided machined housing, respectively. Moreover, bulkheads were used for the internal electromagnetic shielding.  
Heat dissipation and material selection are crucial for withstanding the space environment. Thus, a thermal pad was applied between the housing and bottom to dissipate heat to the bottom, and a PCB base satisfying the outgassing standard was selected. Additionally, the inner surface of the housing was treated with chromate and the outer surface was treated with black anodizing to prevent corrosion.
# Weakening Detection of Composite Structure Adhesive Layer Based on Nonlinear Guided Waves
## 2 Theoretical Background
### Impact of Poisson's Ratio of the Adhesive Layer on Dispersion Characteristics  
Figure 5 presents the phase velocity dispersion curves of adhesive composite structures at different Poisson's ratios ranging from 0.33 to 0.39. Similarly, the phase velocity dispersion curves for various modes are shown in Figure 6. The phase velocities of the A0, S0, A1, A2, and S2 modes are almost unaffected by changes in the Poisson's ratio. The phase velocity of the S1 mode is significantly influenced by Poisson's ratio from the initial frequency to 0.8 MHz, but the effect gradually diminishes. Within the frequency range from 0.8 MHz to 1.05 MHz and above 1.4 MHz, the phase velocity is essentially unaffected  
Figure 4: Aluminum–acrylic resin–polystyrene dispersion curves of phase velocities for different modes with varying second-order elastic modulus of the adhesive layer: (**a**) A0 mode; (**b**) S0 mode; (**c**) A1 mode; (**d**) S1 mode; (**e**) A2 mode; (**f**) S2 mode.  
by changes in the Poisson's ratio. However, the phase velocity is even less affected by changes in the Poisson's ratio from 1.05 MHz to 1.4 MHz. Therefore, appropriate modes and frequency ranges are selected to avoid those significantly influenced by changes in the Poisson's ratio.  
Figure 5: Aluminum–acrylic resin–polystyrene phase velocity dispersion curves under variation in the Poisson’s ratio of the adhesive layer.  
Figure 6: Aluminum–acrylic resin–polystyrene dispersion curves of phase velocities for different modes with varying Poisson’s ratio in the adhesive layer: (**a**) A0 mode; (**b**) S0 mode; (**c**) A1 mode; (**d**) S1 mode; (**e**) A2 mode; (**f**) S2 mode.  
In summary, the phase velocity dispersion curves of composite structures are influenced by both the elastic modulus and Poisson's ratio. The effect of these changes differs among modes and frequencies. Therefore, avoiding modes and frequencies significantly affected by the elastic modulus and Poisson's ratio is advisable for selecting an appropriate mode. As illustrated in Figures 4 and 6, the chosen mode is the A1 mode at a fundamental frequency of 0.5 MHz and the S1 mode at its second-order harmonic frequency of 1.0 MHz. This mode operates within a frequency range largely unaffected by second-order elastic modulus and Poisson's ratio. Furthermore, the phase velocities of the A1 mode at 0.5 MHz and the S1 mode at 1.0 MHz are nearly equal. The second harmonic mode is the S1 mode, which also satisfies the symmetry under the single harmonic excitation. This represents an excitation mode that meets the phase-matching condition [16], providing a foundation for subsequent simulations and experiments.
# Low-Computational Extended Orthogonal Matched Filter Structure for Multiuser Detection
## 3 Proposed EOMF Structure  
### Approach to an EOMF with Multiuser Detection  
From Equation (16), \(N^{2}\) greatly affects the computational complexity of the EOMF, in other words, the sum of the correlation value calculations of each MF. In the case in which the total number of desired users is \(M_{d}\), when multiuser detection is performed by using \(M_{d}\) ordinary EOMFs, \(N^{2}\) is multiplied by \(M_{d}\), and the computational complexity increases significantly. In addition, the circuit scale also  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Computation** & **Computational Complexity** \\ \hline (a) \(y_{\text{beam}}=Re\big{[}\mathbf{U}^{H}\mathbf{X}\big{]}\) & \(K\) \\ (b) \(y_{\text{null}}=Re\big{[}\mathbf{V}^{H}\mathbf{X  
increases since the number of MFs is \(M_{d}\) times. Hence, it is necessary to detect multiple users without increasing the number of MFs.  
First, the spreading sequence of the desired user is necessary to detect multiple users. If the desired user is \(m_{d}\) (\(m_{d}\) = 1, 2, \(\ldots\), \(M_{d}\)), then an MF with the same sequence as the spreading sequence \(\mathbf{c}_{m_{d}}\) is required. On the other hand, sequences of MFs other than MF\({}_{m_{d}}\) that have the spreading sequence \(\mathbf{c}_{m_{d}}\) must be orthogonal to \(\mathbf{c}_{m_{d}}\) in the case of multiuser detection. Thus, it is assumed that MF\({}_{m_{d}}\) has a spreading sequence of desired users \(m_{d}\), and MF\({}_{m^{\prime}}\) (\(m^{\prime}=M_{d}+1,\ldots,N\)) has a sequence that is orthogonal to the spreading sequence of the desired users, as generated by the Gram-Schmidt orthonormalization. Next, it is considered that \(M_{d}\) LCs are required to generate the interference replicas. Finally, with regard to beamforming and null steering in the adaptive array antenna, it is considered to be necessary to individually perform \(M_{d}\) desired users. Hence, the weight vector \(\mathbf{U}^{m_{d}}=\left[\mathbf{u}_{1}^{m_{d}}\), \(\mathbf{u}_{2}^{m_{d}}\), \(\ldots\), \(\mathbf{U}_{K}^{m_{d}}\right]^{\mathsf{T}}\) for beamforming for the desired user \(m_{d}\) is obtained by \(M_{d}\) NLMS algorithms. In addition, null steering is performed to accelerate the convergence of the error in the LC. This step is performed simultaneously for the desired user \(m_{d}\), and the output is input to MF\({}_{m^{\prime}}\). Although multiple desired signals are suppressed, the performance is expected to improve even if it is inferior to conventional EOMF suppression.
## 5 Experiments and Results
### Fault Detection and Classification
#### 5.2.4 Implementation of Transfer Learning  
In the context of our study on anomaly detection within PFBs, we strategically adopted a transfer learning paradigm, leveraging the rich, pre-existing knowledge encapsulated within models initially trained on the ImageNet dataset [66]. The ImageNet database, renowned for its comprehensive size and variety, serves as a pivotal resource in the development and evaluation of state-of-the-art image-classification technologies. Utilizing these pre-trained models as a starting point, we effectively endowed our models with a preliminary understanding of diverse image characteristics. This pre-acquired insight proves invaluable in navigating the intricate and nuanced features present in thermal images of WTBs. The infusion of transfer learning into our methodology markedly accelerated the training process. This acceleration stems from the initial broad exposure to the ImageNet dataset, equipping the models with a fundamental grasp of a wide array of image attributes. Consequently, tailoring these models to our specialized requirement of identifying anomalies in PFBs became more time-efficient than initiating model training from a blank slate. This expedited training period underscores the practical advantages of transfer learning, particularly in scenarios involving elaborate and voluminous datasets, as encountered in our investigation.
## 2 Principle
### Generation of PSK Signal  
As for the generation of PSK signal, the amplitude of \(s_{p}(t)\) is set to 0 V and 4 V, corresponding to bit '0' and bit '1'. After optical-to-electrical conversion, the PSK signal with eight wavebands can be generated at the output of the BPD. The electrical spectrum is displayed in Figure 4, which show that the eight wavebands covering from 10 GHz to 80 GHz. The spectrum distribution accord well with Equation (7). Furthermore, no obvious spurs can be observed in spectrum diagram benefitting from the balance detection.  
Two phase coding signals with frequency of 10 GHz and 80 GHz have been filtered to analyze and display in Figure 5; the waveforms are displayed in Figure 5a,d. Apparent phase difference between the coded waveform can be observed in the diagrams of waveform. In simulation, the coding signal cos[\(\beta_{ASP}(t)\)] is \"11110100100011010111\" and the binary signal matches well with the recovered phase information; every phase difference is corresponding to a phase jump of \(\pi\) rad, which are presented in Figure 5b,e. Figure 5c,f illustrate the autocorrelation of generated signals, and the key parameters of autocorrelation like pulse compression ratio (PCR), peak-to-sidelobe ratios (PSR), and full width at half-maximum (FWHM) has also been displayed. The PSR and FWHM of the 10 GHz signal is 8.1067 dB and 0.3896 ns, respectively, and the 80 GHz signal's PSR and FWHM is 7.9732 dB and 0.4043 ns, respectively. After calculation, PCRs of two signals are 20.5312 and 19.7873, respectively, and the theoretical PCR of phase coding signal is 20 (duration time \(\times\) bit rate). The results are very close to theoretical value, which shows good pulse compression performance.  
Figure 4: The spectrum of PSK signal at eight wavebands.  
Figure 5: The waveforms and recovered phase information of signals, and respective autocorrelation ((**a**–**c**), 10 GHz; (**d**–**f**), 80 GHz).