esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) communication systems relying on hybrid beamforming structures are capable of achieving high spectral efficiency at a low hardware complexity and with low power consumption. Tensor-based joint beamforming with low-cost ultrasonic and RIS-assisted Dual-Hop Hybrid free space optical (FSO) mm Wave massive Multiple Input Multiple Output (MIMO) of vehicle-to-everything (V2X) is proposed. To address the occlusion problem for high-speed mobility of the vehicle, an RIS-assisted mixed FSO-MIMO V2X system is proposed. The low-cost ultrasonic array signal model is developed to solve the accurate direction-of-arrival (DOA) estimation. The ultrasonic-assisted RIS phase shift matrix based on subspace self-organizing iterations is designed to track the beam direction between RIS and vehicle. Specifically, the associated bandwidth-efficiency maximization problem is transformed into a series of subproblems, where the subarray of phase shifters and RIS elements is jointly optimized to maximize each subarray's rate. The vehicle motion state is transformed into a two-dimensional model for prior distribution to calculate the particle weights of the RIS phase. Multi-vehicle Tucker tensor decomposition is used to describe the high-dimensional beam space. We conceive a multi-vehicle joint optimization method for designing the hybrid beamforming matrix of the base station (BS) and the passive beamforming matrix of the RIS. A cascaded channel decomposition method based on Singular Value Decomposition (SVD) is used to obtain the combined matrix beamforming of BS and vehicle. Our simulation results demonstrate the superiority of the proposed method compared to its traditional counterparts.  
Keywords:V2X; FSO; joint beamforming; tensor decomposition; massive MIMO +
Footnote †: journal: _Photonics_
## 7 Related Work  
Due to its large-scale distributed nature, Federated learning (FL) presents unique challenges in creating FL orchestration systems including the minimization of communication cost, node heterogeneity, and unreliable network links [2; 9]. Evaluating FL system performance requires stress testing the infrastructure with representative workloads and analyzing a wide range of quantitative and qualitative performance metrics [44].  
Several FL frameworks exist in the ML community. LEAF offers FL training workloads and datasets [45], while FedML [46] and Flower [47] provide interoperability and extensibility across ML models. However, these frameworks often overlook the implications related to computing systems. Simulation-based studies like FedScale [17] examine the system-based performance of FL at scale by simulating realistic data partitioning approaches, network delays, and resource constraints. This approach, however, is only effective for theoretical estimations rather than benchmarking actual implementations. Oppositely, studies like EdgeTB [16] combine distributed learning techniques with virtualized testbed but do not explore the full range of implications related to FL and ML frameworks, such as resource fitting or parallel FL workflow execution. As a result, no evaluation framework currently examines resource fitting and co-location of FL workloads in cloud and edge environments while providing configurable workloads and datasets.  
Performance profiling on cloud-based environments with virtualized resources has been explored from various perspectives, such as resource allocation, workload co-location, and interference analysis in cloud data centers [48; 49; 50]. Existing efforts often extract traces from entire data centers without considering the performance of specific applications. PANIC focuses on extracting insights from complex applications and creating their resource profiles [51]. BenchPilot [44] automates the deployment and evaluation of big data streaming analytics frameworks on edge data centers, aiming for repeatability and reproducibility. Unfortunately, these frameworks do not provide specialized functionalities for FL workloads and primarily focus on general-purpose applications running on virtualized cloud or edge resources.  
Another aspect of research involves extracting performance insights from co-located FL workloads and analyzing how native cloud-based orchestrators place FL services in virtualized environments. [PERSON] et al. [52] analyze interference impact on hardware and application-level behaviors in co-located online and offline workloads on the cloud. Researchers also try to exploit this information to create more efficient workload schedulers. Frameworks like Perph [53] and Perphon [54] employ ML-based methods for performance prediction and resource inference, while CTCL scheduler [55] aims to improve resource utilization and minimize task evictions in containerized cloud computing environments. While these papers contribute to resource allocation, workload co-location, and interference analysis in cloud data centers, they lack a specific focus on FL pipelines.
## 2 Finite Element Model
### Validation of the Method  
The Coupled Eulerian-Lagrangian (CEL) methodology was applied in this study to investigate the large deformation behavior of an undrained soil constitutive model. This CEL methodology was conducted through comparative evaluation of pipe penetration behavior with RTSS method predictions and centrifuge model test results [32; 33]. With a pipe diameter (\(D_{\text{p}}\)) of 0.8 m, the soil's undrained shear strength (\(S_{\text{u}}\)) followed the relationship \(S_{\text{u}}=2.3+3.6z\) kPa, where \(z\) represented depth. The effective unit weight of the soil was 6.5 kN/m\({}^{3}\), and the pipe-soil interface exhibited a friction coefficient of 0.3. The penetration velocity of the pipe was set at 1/20 th of its diameter (40 mm/s). This CEL model was assessed in this study, as shown in Figure 5. The vertical resistance, \(V\) (N), was normalized by \(D_{\text{p}}S_{\text{u}}\), and the pipe displacement, \(W\) (m), was normalized by \(D_{\text{p}}\), while \(D_{\text{p}}\) was the diameter of the pipe and \(S_{\text{u}}\) was the undrained shear strength of the soil. The results simulated by the CEL method closely aligned with the predictions of the RTSS method and the centrifuge model test. Therefore, the CEL method can be effectively applied to simulate the penetration process of the penetrator under large soil deformation in this study.  
Figure 4: The mean pressure of the soil before penetration. \(\sigma\) represents mean pressure.
# Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey
## 8 Challenges and Future Directions
### Generative AI and Adversarial Attacks in Blockchain Anomaly Detection
#### 8.2.1 Potential Approaches Related to GenAI  
GenAI powered anomaly detection (GADE) approaches hold significant promise in enhancing anomaly detection within blockchain networks. GADE methods can complement the existing processes outlined in recent research.  
GADE could deal with the challenge of associating addresses with users in public blockchain networks. Unsupervised learning, particularly data clustering, has proven to be a reliable strategy in aggregating addresses linked to a user. By employing GADE-based procedures, cluster analysis could achieve higher accuracy and scalability, rendering the identification of malicious user activities more effective. Moreover, the dynamic interplay between supervised and unsupervised techniques, as explored in recent research, could be further optimized using GenAI approaches. By harnessing the strengths of both paradigms, ensemble strategies powered by GADE could enhance the overall model's performance in detecting complex anomalies within blockchain networks. For example, integrating GADE and unsupervised algorithms could improve their accuracy in detecting anomalies, especially in scenarios where the data are complex. Thus, cyber defenders can improve their intelligence by identifying emerging threats and extracting relevant data. In a nutshell, the GenAI tools can be used in analyzing network traffic data, system output, and large volumes of log files. This could assist defenders to automate and speed up their incident response process. GenAI can also be used in generating secure code and writing secure code. However, attackers can also misuse GenAI to create malicious code, phishing attacks, and social engineering attacks [193].  
While the existing approaches provide valuable insights to anomaly detection, incorporation of GenAI approaches could substantially enhance the accuracy, scalability, and efficiency of anomaly detection algorithms.
# Research on Microstructure and Mechanical Properties of Laser Welding of BR1500 HS Joints
## 3 Results and Discussion
### Microhardness
#### 3.3.2 Effect of Laser Power on Microhardness  
When the welding speed is 75 cm/min, the microhardness of the joint before and after quenching under different laser powers is shown in Figure 10. It can be seen from Figure 8a that as the laser power increases, the widths of FZ and HAZ first increase and then decrease, reaching the maximum value when the power is 3000 W, which can also be seen in Figure 11. It can also be seen from Figure 9a that when the welding speed is constant, as the power increases, the change in the width of the softened area and the minimum hardness is different. When the welding speed is constant and the laser power increases, the power absorbed by the molten pool does not necessarily increase, but the energy of the plasma on the upper and back of the workpiece increases. This part of the energy will transmit to the workpiece through conduction and radiation, so the width of the softened area increases [29; 30]. The thermal cycle of each temperature point is mainly affected by the welding speed. The welding speed remains unchanged, and the change in the thermal cycle of each temperature point is not obvious, so the minimum hardness does not change significantly.  
Figure 8: Microhardness of joint at different welding speeds: (**a**) before quenching; (**b**) after quenching.  
Figure 9: Macro morphology of the joint under different welding speeds: (**a**) 65 cm/min; (**b**) 75 cm/min; (**c**) 100 cm/min; (**d**) 125 cm/min; (**e**) 150 cm/min.  
Figure 9b shows the microhardness of the joint after quenching under different laser powers. The microhardness of the whole joint is between 400 HV and 500 HV. Furthermore, the distribution of the microhardness is uniform. According to the microhardness analysis, it can be concluded that the process parameters, including welding speed and laser power, only impact the microhardness of the joint before quenching and have little influence on the microhardness of the joint after quenching. The quenching process significantly increases the hardness of BM and IRZ, while for FZ, CGZ, and FGZ, it is only a process of homogenizing the microstructure and microhardness.
## 5 Data Imbalance
### Data Imbalance Handling Techniques Future Research Directions
#### 5.5.2 Meta-Learning and GAN Integration  
Combining meta-learning with GANs could create more adaptive and generalizable models. Meta-learning helps models quickly adapt to new attack patterns [124], while GANs generate realistic minority class samples, addressing data imbalance and improving model robustness [125; 126].
## 6 Discussion
### Business Applications  
Numerous use cases have been identified that underscore the importance of the proposed framework utilizing autoencoders. In addition to detecting damage in ship motors, identifying issues in the hull and diesel generators is also critical. The hull is prone to exhibit faulty behavior multiple times during a ship's operational life, primarily due to extreme loading, challenging operating conditions, and aging, which often leads to lengthy maintenance processes. These factors necessitate regular maintenance interventions by regulatory authorities. Furthermore, ships frequently encounter harsh marine environments throughout their lifespan, which accelerates corrosion and biofouling--two prevalent types of long-term damage that can result in catastrophic incidents, environmental pollution, and significant economic repercussions.
# High-Throughput Bit-Pattern Matching under Heavy Interference on FPGA
## 5 Pattern Matching under Interference and Simulations
### Error-Free and Error-Present Simulations  
For the error-free scenario, no misdetections were reported for all 200,000 patterns, even though the weights of the comparators are not equal to the number of bits per symbol for each level.  
For the error-present input, the Gilbert-Eliot Model was applied along the following lines. For a BER of \(10^{-1}\), we assumed that \(P_{E}=0.1\). We substituted \(h=0.5\), as is common practice [10], and we ended up with \(r=4\times P\). Since \(1-P\) and \(1-r\) were larger than \(P\) and \(r\), the value of \(r\) could not be higher than 0.5. We picked a lower value for the maximum value of \(r\), which is 0.4. This gives us a range of 0.1 and lower for \(P\). We generate 100 values for \(P\) between the range of 0.1 and 0.01, which is the usual range given for the \(P\) literature [10; 11; 12]. Additionally, the same process was followed for BER = 5 \(\times\)\(10^{-2}\). This gives us the values of \(r=9\times P\) with \(P\in[0.044,0.010]\). The error model was then applied to the previously error-free input stream for each value of \(P\) separately (100 simulations) for  
Figure 6: Gilbert–Eliot channel model.  
both BER values (200 simulations of 200,000 patterns). The average rate of missed patterns was \(2\times 10^{-3}\) for BER = \(10^{-1}\) and \(10^{-4}\) for BER = \(5\times 10^{-2}\). The biggest losses were reported when the value of P approached 0.1, while the losses were the lowest when the value of P was closer to 0.01. However, the percentage could be improved by increasing the Hamming distance between the memory entries.
# Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies
## 3 Laboratory Tests and Numerical Approach Method  
### Core Flow Experimental Method  
The core flow experimental method is based on [PERSON]'s law, which is used to find the permeability of different core samples. The experiment is conducted in two stages to estimate the magnitude of the formation damage after the core is replaced [33; 34]. This is more suitable for studying single-factor formation damage but difficult to reflect the formation damage pattern on a larger scale. Moreover, because the laboratory-based core flow experimental conditions are idealized, the cores used for evaluation remain in their original state. Therefore, the dynamic changes in formation properties cannot be taken into account which could make the experimental results significantly different from the real damage that occurs in the downhole formation [33]. [PERSON]'s law can be represented by:  
\[K=\frac{Q\cdot\mu\cdot L}{A\cdot\Delta P}\times 10^{2} \tag{4}\]  
where \(K\) is the permeability, \(10^{-3}\)\(\upmu\)m\({}^{2}\); \(Q\) is the flow rate, cm\({}^{3}\)/s; \(L\) is the sample length, cm; \(A\) is the cross-sectional area of the sample, cm\({}^{2}\); and \(\Delta P\) is the differential pressure, MPa.
## 3 Materials and Methods
### Feature Selection Using the LightGBM Algorithm  
Our feature vectors for proteins are sparse high-dimensional vectors, which may lead to a negative impact on the training of subsequent CNN models. To improve the performance of subsequent classification models, it is common to apply feature selection to the initial feature vectors ahead of the training the classification model. Therefore, we carried out feature selection by estimating feature importance based on the LightGBM algorithm [8]. Specifically, we firstly trained a LightGBM model with initial feature vectors on the training dataset and then calculated the feature importance as the average importance over all decision trees of the trained LightGBM model, where the importance of a feature for a decision tree is measured by the sum of the decrease in gini impurity over the internal nodes that choose the variable (feature) to partition the associated region into two subregions. The higher the score of the feature, the higher the importance of the feature. Then, all features are sorted by their gain scores. Finally, the optimal feature subset is  
Figure 2: Feature representation based on di- and tripeptide motifs.  
selected according to the sorted features. As a result, the importance of a feature \(\ell\) can be calculated as follows:  
\[\mathcal{I}_{\ell}=\frac{1}{M}\sum_{m=1}^{M}\mathcal{I}_{\ell}(T_{m}) \tag{1}\]  
\[\mathcal{I}_{\ell}(T)=\sum_{t=1}^{J}\triangle\mathcal{G}_{t}I(v(t)=\ell) \tag{2}\]  
where, \(\mathcal{I}_{\ell}(T)\) represents the importance of the feature \(\ell\) for the tree \(T\), \(\triangle\mathcal{G}_{t}\) represents the decrease in gini impurity in the internal node \(t\), \(I()\) is the indicator function, \(v(t)\) represents the chosen variable (feature) for the node \(t\), \(M\) is the number of trees in the LightGBM model and \(J\) is the number of internal nodes in the decision tree \(T\).  
After feature selection, we use Formula (3) to normalize each feature to a real number between 0 and 1, where \(x\) is the original value of each feature and \(max\) and \(min\) are the maximum and minimum values of each feature in the training dataset. In this way, we eliminated differences in the magnitude for all features and ensured that the convolutional neural network can correctly capture the potential relationship between features.  
\[x^{*}=\frac{x-min}{max-min} \tag{3}\]
## 2 Configuration and Design Scheme of the Feeding Structure  
### Evaluation of SP-Feed Performance  
The configuration of sequential rotation 2 \(\times\) 2 CP antenna array with SR phase feed is shown in Figure 1. The CP antenna element is a single-fed truncated corner square patch, which is sequentially rotated and fed by the proper excitation through the SP feed. In practice, the inter-element distance is the key factor that can alter the broadside gain, sidelobe level, and cross-polarization discrimination (XPD). The C-to-C spacing of the elements is d = 0.535\(\lambda_{0}\) = 1.16\(\lambda_{g}\), where \(\lambda_{0}\) and \(\lambda_{g}\) are the free space wavelengths and guided wavelength, respectively. The appropriate size of the SR phase feed is \(2r\) should be evaluated at 0.164\(\lambda_{0}\) = 0.355\(\lambda_{g}\) insofar the size of the resonant CP antenna is about 0.185\(\lambda_{0}\) = 0.4\(\lambda_{g}\).
## 2 Models for Various Types of Pumped Storage
### Lower-Level Scheduling Model
#### 3.2.2 Lower-Level Constraints  
The constraints of the lower-level model include wind power output limitations, load demand constraints, thermal power generation, restrictions on the minimum start and stop times for thermal units, thermal power ramping constraints, and system power balance.  
(1) Constraints on wind power output.  
The predicted wind power is taken as the theoretical maximum output of wind power [24]. Therefore, the actual wind power output must satisfy the following constraints.  
\[0\leq P_{\text{Wind},t}\leq P_{\text{Wind},t}^{*} \tag{28}\]  
(2) Constraints on load demand.  
The actual load demand is not allowable to exceed the predicted load demand at any time.  
\[0\leq P_{\text{Load},t}\leq P_{\text{Load},t}^{*} \tag{29}\]  
(3) Constraints on thermal power generation.  
Thermal power unit output must be within its minimum and maximum operational limits at any time.  
\[P_{\text{Coalmin},m}U_{t,m}\leq P_{\text{Coal},t,m}\leq P_{\text{Coalmax},m} U_{t,m} \tag{30}\]  
where \(P_{\text{Coalmin},m}\) and \(P_{\text{Coalmax},m}\) represent the minimum and maximum output of the \(m\)-th thermal power unit, respectively; \(U_{t,m}\) is a Boolean value, which can only take 0 or 1,indicating the operational state of the \(m\)-th thermal power unit at time \(t\), where 1 represents an operation and 0 represents shutdown.  
(4) Constraints on the minimum start and stop times for thermal units.  
Thermal power must adhere to minimum on-off time constraints [2], encompassing both minimum startup and shutdown times. The formulas are as follows:  
\[U_{t,m}-U_{t-1,m}\leq U_{\tau,m},\tau\in[t+1,\min(t+T_{\text{on},m}-1,T)] \tag{31}\]  
\[U_{t-1,m}-U_{t,m}\leq 1-U_{\tau,m},\tau\in[t+1,\min(t+T_{\text{off},m}-1,T)] \tag{32}\]  
where \(\tau\) represents the time interval; \(T_{\text{on},m}\) and \(T_{\text{off},m}\) represent the minimum startup time and minimum shutdown time for the \(m\)-th thermal power unit, respectively.  
(5) Constraints on thermal power ramping.  
\[P_{\text{Coal},t,m}-P_{\text{Coal},t-1,m}\leq R_{m}^{\text{U}} \tag{33}\]  
\[P_{\text{Coal},t-1,m}-P_{\text{Coal},t,m}\leq R_{m}^{\text{D}} \tag{34}\]  
where \(R_{m}^{\text{U}}\) and \(R_{m}^{\text{D}}\) represent the upward and downward ramping capabilities of the \(m\)-th thermal power unit, respectively.  
(6) Constraints on system power balance.  
The total power generation of the power system must equal the actual load demand at any time.  
\[P_{\text{Wind},t}+\sum\limits_{n=1}^{N}P_{\text{Gen},t,n}+\sum\limits_{m=1}^{M }P_{\text{Coal},t,m}-\sum\limits_{n=1}^{N}P_{\text{Pump},t,n}=P_{\text{Load},t} \tag{35}\]
# An Accurate Forest Fire Recognition Method Based on Improved BPNN and IoT
## 3 Results
### Internet of Things Monitoring System Test and Analysis  
By monitoring environmental forest data, the hidden dangers that may lead to a forest fire can be perceived early, providing an advanced warning to minimize the occurrence of disasters. When a forest fire is about to occur, the micrometeorological information of the forest environment changes. By setting a threshold in the online forest fire monitoring system, an early warning of a forest fire can be realized to prevent it. When a forest fire occurs, the environment forest information around the fire also initially changes. When the monitored forest environment parameters exceed a certain threshold, the online forest fire monitoring and warning system initiates an alarm. The UAV is applied to collect the forest fire image data over the forest and transmit the monitored image signal to the BPNNFire algorithm and DCNN model deployment platform in real time. From May to August 2021, WSNs for online forest fire monitoring were deployed in Guangdong Shaoguan Longshan and Guangdong Longyandong Forest Farmsto verify the function of the forest fire monitoring/early warning system and conduct real-time online monitoring of environmental indicators, such as air temperature, humidity, soil moisture, and illumination. The online forest fire monitoring/early warning system processed the monitored forest fire image online to detect a fire promptly.