esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Research on Spectrum Sensing Based on Deep Learning
## 2 Spectrum-Sensing Methods
### Single-Node Spectrum Sensing  
Single-node spectrum sensing, also referred to as local spectrum sensing (LSS), is a technique focused on sensing and analyzing the spectrum at an individual node. This node can be a wireless device, a sensor, or a specialized spectrum-sensing device.  
The underlying principle of single-node spectrum sensing involves sampling and analyzing the adjacent radio spectrum to extract spectrum usage information and features. These functions can be achieved using specialized software-defined radio (SDR) hardware that can receive and process wireless signals across different frequency bands. Applications of single-node spectrum sensing include:  
1. Spectrum monitoring and management: Single-node spectrum sensing monitors and manages spectrum utilization in specific frequency bands. It uses techniques to determine the utilization, interference, and possible availability of free spectrum in that band.
2. Spectrum sharing: Single-node spectrum sensing enables dynamic spectrum sharing. Nodes can monitor the available spectrum and share this information with other devices or networks to improve the efficient utilization of spectrum resources.
3. Interference detection and elimination: Single-node spectrum awareness detects potential sources of interference by monitoring the surrounding spectrum environment and taking appropriate measures to eliminate interference. This improves the performance and reliability of the communication system.
4. Traditional algorithms for single-node spectrum sensing mainly include energy detection algorithms, cyclostationary feature algorithms, and matched filter detection algorithms [25]. In the next section, these traditional methods are explained in detail, basic simulation experiments are performed, and finally, the advantages and disadvantages of each method are compared and summarized.
# Student Dropout Prediction for University with High Precision and Recall
## 4 SDP (Dropout Student Prediction System)  
This paper presents the SDP (Student Dropout Prediction) system for predicting students who are about to drop out of universities. The architecture of the SDP system is shown in Figure 3. We first preprocess the data using SMOTE and RandomOverSamplerSMOTEENN to treat the imbalanced data. Then, we combine the dropout prediction results of XGBoost and CatBoost to produce high precision and recall. The system utilizes both models depending on the university's status and needs. A high-precision model can identify candidates who require higher priority in consultation, and a recall model can encompass a wider range of potential dropouts. By using the two different models in a system, we were able to capture the advantages of the two models in a system. We first introduce the imbalanced data processor, then present the dropout predictor that combines the result of XGBoost and CatBoost methods.
# Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting +
## 2 Literature Review
### Video Prediction Methodology
The recent advances of deep learning models allowed for the reduction of assumptions on background processes for video prediction and learning spatiotemporal patterns directly from data. Mainly, image processing is associated with convolutional ANN (CNN), which simultaneously train multiple spatial kernels for feature extraction. Several consecutive layers of trained spatial kernels are supplemented by pooling layers for dimension reduction, rectifier layers for capturing non-linear dependencies, and a fully connected layers for output production. For video processing, CNN architectures were extended for handling temporal dimensions with recurrent ANN architectures. The long short-term memory (LSTM) network is one of recurrent architectures, which, in conjunction with CNN, is widely used for video prediction. The list of developed deep learning models for video prediction includes PredNet [17], MCNet [18], generative adversarial net-based (GAN) [19], dual motion GAN [20], PredRNN [21], among a dozen other architectures. The key problem for ANN architecture development remains the same--the identification of objects and the spatiotemporal patterns of their motion, and the separate prediction of a video background and moving objects. Classical spatial kernels and CNN architecture exploit the assumption of a fixed number of spatial neighbors for every pixel (usually defined by the grid structure). This assumption is too restrictive for spatiotemporal patterns in video streams--if a video frame contains several moving objects, then pixels that belong to each object are naturally more related to pixels of this object in previous frames than to pixels of other objects (even located within the spatial neighborhood). Thus, the structure of spatiotemporal relationships is better modeled by a graph than by a grid. The spatiotemporal graph contains vertices (individual pixels or regions on sequential video frames) and edges (relationships between vertices). The extension of CNN architecture that implements a convolution on a graph structure is named graph-based convolutional ANN (GCNN) and began emerging in 2019 [22]. Several GCNN architectures were proposed for video prediction: [PERSON] and [PERSON] [23] suggested an architecture with spatiotemporal graph convolution and the direction attention mechanism; [PERSON] et al. [24] developed a GCNN architecture with separate temporal and spatial routing; [PERSON] et al. [25] proposed a two-stream GCNN architecture for skeleton-based motion on video. Summarizing the historical outline of video prediction methodology, we concluded that the development of algorithms began from predefined spatial convolutions, utilized information from physical background processes, and have come to data-driven deep learning of complex spatial patterns in the form of spatiotemporal graphs.
## 3 Data Collection to Cloud: Automation and Real-Time Processing
### Data Transmission: Edge Computing and Fog Computing
#### 3.2.1 Real-Time Data Transmission Protocols and Technologies  
Real-time data transmission is critical for automated irrigation management systems as it enables the timely delivery of sensor data to the cloud for processing and decision-making. The message queuing telemetry transport (MQTT) protocol has emerged as a popular choice for real-time data transmission in IoT networks due to its lightweight nature, publish-subscribe architecture, and suitability for low-bandwidth networks [77]. Client-server IoT networks, such as those based on MQTT, play a crucial role in the real-time data transmission for automated irrigation management systems [78]. This architecture enables efficient data collection, processing, and dissemination, facilitating the integration of various components within the automated irrigation management pipeline. Other application layer protocols, such as XMPP [79], CoAP [80], SOAP [81], and HTTP [82], have also been explored for real-time data transmission in IoT networks, each with its strengths and weaknesses depending on the specific application and scenario [83].
## 2 Materials and Methods
### Data Extraction  
The primary outcomes are to systematically review the diagnostic performance of ADC values in differentiating MIBC from NMIBC and high-grade disease from low-grade disease. We extracted data on study design, study period, the number of patients or tumors, demographics of patients and tumors including pathological T (pT) stage and histological grade, surgical modalities utilized to obtain tumor specimens for pathological diagnosis, magnet strength, and b values used to calculate ADC values for all eligible studies. We further extracted data on the associations of ADC values with pT stage and histological grade. Sensitivity, specificity, and area under the curve (AUC) using the optimal cutoff ADC values were also extracted. Median (range) sensitivity, specificity, and AUC among eligible studies were obtained by calculating median values of those reported in each study. Because TURBT specimens were used for pathological diagnosis in most patients included in this review and the presence or absence of muscle invasion (pT2) is critical for decision making regarding therapeutic strategies, the pT stage was dichotomized as NMIBC (\(\leq\)pT1) and MIBC (\(\geq\)pT2) in this study. The representative cases of NMIBC and MIBC are shown in Figures 1 and 2, respectively.
# Review of Dew Point Evaporative Cooling Technology for Air Conditioning Applications
## 4 Dew Point Evaporative Coolers Research
### Optimization the DPIEC
They found optimal average air velocity in dry channels (below 1.0 m/s), the range of channel length (1-1.75 m), the range of channel gap (3-5 mm) and the working to intake air (0.3-0.4). [PERSON] presented a few pioneering studies in the dew point indirect evaporative cooler optimization strategy. They developed a different statistical model using a group method of data handling-type (GMDH) neural network. For example, they studied the design of dew point evaporative coolers at various climatic conditions [36]. The key performance indicators were considered to be life-cycle costs, annual water consumption, and the annual average of the COP values. The analysis shows that in very hot and dry climate, the counter-regenerative configuration performs the best. On the other hand, in other climates, the cross-flow configuration was the better alternative. Another interesting study includes an hourly optimization strategy to enhance dew-point indirect evaporative coolers. [PERSON] et al. [37] modeled the entire system working the whole year, and using multi-objective optimization they optimized the system for each hour of its operation. They found the best system for four different climatic conditions. The key performance indicators were operating cost, the coefficient of performance, and water consumption, while the velocity and working to air ratio are the decision variables. The authors presented an extensive annual analysis of the operation of these systems, and their optimization method can be applied to any evaporative dew point exchanger. Some authors presented a novel optimization approach in the field of dew point evaporative cooling. [PERSON] et al. [42] developed digital twins using a feed-forward neural network (FFNN) and multi objective evolutionary optimization (MOEO) using a genetic algorithm (GA) for a counter-flow dew point cooler with a novel guidelines irregular dew point cooler (GIDPC). They indicated optimum conditions of the system for different climates and established system annual electricity consumption and COP, and obtained improvements of the system. [PERSON] et al. [43] also presented an explainable and interpretable deep neural network (DNN) model for a GIDPC. They implemented different algorithms to optimize the cooler in terms of cooling efficiency maximalization with simultaneous minimization of construction costs. On the base of projected hourly future weather data, the system performance was simulated, including the increase of power consumption from the year 2020 to 2050 caused by global warming. Due to the optimization carried out, the energy savings were reported.
###### Abstract
Recently, metasurfaces based on Dammann grating have been proposed for realizing versatile beam functions in different diffraction orders [25; 26; 27; 28] or switchable functions empowered by tunable materials [29; 30]. Combining the phase of the Dammann grating and the special beams to generate special beam arrays using metasurfaces has been demonstrated, such as the Airy beam array [31; 32], Bessel beam array [33; 34] and vortex beam array [25]. Moreover, a Dammann grating metasurface has been designed for generating spot arrays within a large field of view in the far field [35]. However, the laser source in these designs needs to be collimated by a traditional lens before being directed to the Dammann metasurface, which increases the bulkiness of the entire system for practical use. Additionally, the scale of the spot array generated by a traditional Dammann grating is limited [36], and scaling up is challenging without additional optical elements, such as cascading another Dammann grating [37]. In this study, we proposed a metasurface-based all-in-one collimating splitter with high uniformity and a large field of view by synthesizing the phase of the Dammann grating and the lens. By optimizing the phase profile using an inverse design method, we realize a 3 \(\times\) 3 Dammann grating, achieving a large field of view (FOV) of 62\({}^{\circ}\)\(\times\) 62\({}^{\circ}\) and a uniformity of 1.29%. Through the joint effect of the space-spectrum Fourier transformation of the lens and the diffraction effect of the Dammann grating, we demonstrate that a singlet metasurface chip can greatly increase the number of spot arrays in the far field by N \(\times\) N times on the N \(\times\) N fiber array platform. This design can be easily transplanted to the vertical-cavity surface-emitting laser (VCSEL) platform to derive a more compact spot array generator for depth-sensing applications on smart devices.
## 2 Related Work and Contributions  
This work is part of an ongoing project. In [11] it was proposed to use SVD for the design and construction of SCMA codebooks; later it was proposed in [21; 22] to use neural networks in the receiver to improve performance in the detection and decoding of SCMA codewords, even under adverse SNR conditions. In this work, the scope of SVD-SCMA is expanded, showing that it is possible to increase spectral efficiency. The performance of the method proposed in this work was compared with recent proposals that use spatial modulation and antenna arrays to increase spectral efficiency.  
SVD-SCMA produces codebooks with an optimal distance between the points of the codeword, which improves bit error rate (BER). In the works described in [5; 6; 7; 8; 9; 10; 11], four codewords (\(M~{}=~{}4\)), are encoded into two bits. The authors in [14; 15; 16] proposed to use SM-SCMA to increase SE. In SM-SCMA, the information of each user is divided in two parts; one part is used to select the active transmitting antenna and the other part is directly assigned to an SCMA codeword according to each user's codebook. Detection and decoding at the receiver estimates both spatial and modulated symbols by performing an exhaustive search or by using one of the low-complexity decoding algorithms. These systems can transmit three bits at a time: a 2-bit spatial symbol and 1-bit modulated symbol, using more than one antenna. In [16], the authors proposed Rotational Generalized Spatial Modulation (RGSM-SCMA), achieving an SE and BER similar to SM-SCMA using a lower number of antennas by activating more than one antenna at a time. In this work, we propose three-bit encoding using a combination of Singular Value Decomposition SCMA (SVD-SCMA) [11] with an 8-APSK constellation. The SVD-SCMA performance is compared to the phase rotation method [5; 6; 7], SM-SCMA and RGSM-SCMA [16]. For the detection and decoding of SM-SCMA and RGSM-SCMA, MPA was used while for SVD-SCMA a receptor based on neural networks was implemented. The results of this work show that it is possible to increase the spectral efficiency of the uplink with a lower BER using only one transmitting antenna and one receiving antenna.  
This work is organized as follows: In the next section the method to design the SVD-SCMA codebooks is described; later, the receiver used in this work is addressed, which is based on neural networks, as well as the training stage of the neural network. Next, the results of this work are shown. First, the results of the training stage of the neural network are described and later on the BER performance compared with SM-SCMA and RGSM-SCMA. Finally, the conclusions are described, and the final considerations of this work are discussed, as well as possible extensions of it.
## 4 Discussions
### Limitations and Future Works
#### 4.5.2 Limitations of Current Approaches  
While blockchain storage models offer robust security and decentralization, they face several key limitations:  
Scalability Constraints: On-chain storage requires network-wide validation, which limits transaction throughput and increases latency. Layer-2 solutions and sharding techniques have been proposed to mitigate this, but their implementation remains technically challenging [17; 18; 22].  
Security Risks in Off-Chain Storage: Off-chain storage methods depend on external infrastructures (e.g., IPFS, cloud storage), which may introduce risks related to data integrity and availability [12; 15; 109].  
Hybrid Implementation Complexity: Although hybrid models aim to balance scalability, security, and cost, their integration requires sophisticated cryptographic mechanisms and interoperability between on-chain and off-chain components, making large-scale adoption challenging [36; 47; 97].
## 4 Case Study--The Failure of the Gypsum Tailings Dam in East Texas in 1966 (FGT66)  
### Numerical Setup  
The present numerical model was applied to simulating the failure of the gypsum tailings dam in East Texas in 1966 (FGT66). The reservoir was a rectangular shape and reached a height of 11 m when the failure took place. The slide was triggered by seepage at the toe of the embankment. An estimated 80,000-130,000 m\({}^{3}\) of gypsum was released in this flow failure. The released material traveled about 300 m before it came to a stoppage, with an average velocity of 2.5-5.0 m/s [60]. In this paper, the numerical setup was composed based on the geometry reported by [PERSON] [60], shown in Figure 5. The size of the tailings reservoir was \(280\times 110\times 11\) m, and the breach was 120 m long. The center cross-section of the breach was located at y = 220 m. The computational domain (510 in length, 400 in width, and 12 m in height) was discretized into a uniform mesh with a grid size dx = 2.0, dy = 2.0, dz = 1.0 m. The number of the grid was 612,000. The bottom boundary (at z = 0 m) was a no-slip boundary condition. The downstream (x = 400 m) and lateral boundaries (y = 0 and y = 400 m)  
Figure 4: The free-surface profiles of Bingham flow on an inclined plane. (**a**) Snapshots (**unit: s**); (**b**) validation at t = 14 s. The experimental data and theoretical results are obtained from [PERSON] [12]. The numerical results are simulated by the Bingham model.  
Figure 3: Numerical setup of Bingham flow on an inclined plane based on the experiment of [PERSON] [12].  
were free-slip walls. The downstream and lateral boundaries would not affect the simulation results because the domain was set to be much larger than the predicted tailing pattern. The gypsum tailings material was expected as a Bingham material. Based on the parameters reported by [PERSON] [60], [PERSON] [61], and [PERSON] [20], the yield stress of the tailings was \(\tau_{y}=10^{3}\) Pa, the viscosity of the liquefied zone was \(\mu_{B}=50\) Pa s, and the density was \(\rho=1400\) kg m\({}^{-3}\). The viscosity of the plug zone was suggested to be infinite (e.g., \(\mu_{A}=10^{10}\) Pa s) by [PERSON] [34], [PERSON] [45], and [PERSON] [28].
# Adaptive Software Defined Equalization Techniques for Indoor Visible Light Communication
## 6 Measurements
### Amplifier and Bias Tee  
The amplifier constructed by Chinese engineers was used during the measurements. It operated at 1 to 500 MHz and amplified the signal by 1.6 W.  
The Mini-Circuits ZX85-12G-S+ bias tee was used to modulate Philips light source. It offered a low insertion loss of 0.6 dB, wideband operation (0.2 to 12,000 MHz), a high current capability of 400 mA, and a rugged antibody construction. The bias tee itself was a diplexer, which used the low-frequency port to set the bias, and the high-frequency port passed the RF signal, but blocked the biasing. The combined port used both bias and RF to modulate the light source itself.
## 3 Results
### Expression Analysis of Terpene-Related Genes in Different Organs of L. pubescens  
Candidate terpene biosynthetic genes identified in the _L. pubescens_ transcriptome were confirmed through NCBI homology BLAST searches, showing a high identity with other orthologous genes and proteins from a broad range of plant species. These genes were designated _LpDXS_, _LpMCT_, _LpMCS_, _LpHDR_, _LpIDI_, _LpAACT_, _LpHMGS_, _LpHMGR_, _LpMVK_, _LpPMK_, _LpMVD_, _LpGPPS_, _LpSQS_, and _LpGGPPS_ (Table S3). On the basis of the information presented in Table S3, we designed specific primers and used these for real-time PCR (Table S4) to characterize terpene synthesis in _L. pubescens_, with _LpActin_ being used as a reference gene for normalization. The analysis revealed that the expression levels of _LpDXS_, _LpMCT_, _LpMCS_, _LpHMGR_, and _LpPMK_ were higher in stems than in roots and leaves, whereas those of _LpHDR_, _LpAACT_, _LpHMGS_, _LpMVK_, _LpSQS_, and _LpGGPPS_ were higher in the stems and leaves, and _LpGPPS_ and _LpIDI_ levels were higher in the leaves (Figure 4).  
Figure 4: Expression of terpene biosynthetic genes in different organs of _Lavandula pubescens_.
## 3 CPS Resilience Quantitative Assessment Framework
### Cyber Layer: Security Architecture and Modeling  
#### 3.3.1 Diversity-Redundancy Security Architecture  
Redundancy is a widely adopted strategy in CPSs to improve fault tolerance and enhance system dependability. Traditional architectures typically employ homogeneous redundancy, relying on identical components, such as controllers or software instances, to provide backup functionality during failures.  
However, against increasingly sophisticated and stealthy cyber threats, a single exploit targeting a shared weakness can simultaneously compromise all homogeneous redundant units, undermining the intended fault tolerance and severely weakening system resilience.  
To address the limitations of homogeneous redundancy in the face of advanced cyber threats, we propose a diversity-redundancy security architecture that incorporates voting and recovery mechanisms. This design targets critical components such as industrial controllers, information transmission units, and supervisory control elements, which often serve as attack surfaces in CPS environments.  
As shown in Figure 6, the architecture integrates four core functional modules: diverse redundancy, voting awareness, fault isolation and failover, and dynamic recovery. These modules work together to improve the system's ability to detect, contain, and recover from cyber-induced faults or intrusions in a timely and autonomous manner. The key features of the proposed architecture are summarized as follows:  
* Diverse Redundancy: Multiple redundant units are deployed using heterogeneous implementations (e.g., different hardware platforms, operating systems, or control logic), minimizing the risk of simultaneous compromise due to shared vulnerabilities.
* Voting Awareness: A runtime majority voting mechanism monitors the output consistency of redundant units. Discrepancies are used as indicators of possible compromise or malfunction, serving as an early warning mechanism.
* Fault Isolation and Failover: Upon detection of abnormal behavior, the compromised node is automatically isolated. Control responsibilities are seamlessly handed over to a healthy redundant unit, ensuring uninterrupted operation.
* Dynamic Recovery: The isolated node enters a secure recovery mode, where it is re-initialized using trusted baseline images or reconfigured through remote attestation and software refresh, before being reintegrated into the redundant pool.  
This architecture not only enhances fault tolerance but also embeds adaptability and self-healing capabilities--hallmarks of cyber resilience. It is particularly suited for high-assurance CPS environments where sustained functionality under adversarial conditions is critical.