esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Beamforming Damage Imaging of Lamb Wave Based on CNN
###### Abstract  
Among damage imaging methods based on Lamb waves, the Minimum Variance Distortionless Response (MVDR) method adaptively calculates channel weights to suppress interference signals, improving imaging resolution and the signal-to-noise ratio (SNR). However, the MVDR method involves matrix inversion, which introduces a high computational burden to the implementation process and makes real-time damage detection challenging. We propose constructing a Convolutional-Neural-Network (CNN)-based network architecture based on the Delay-and-Sum (DAS) beamforming method. This architecture replaces the MVDR's adaptive weight calculation by establishing a nonlinear mapping from multi-channel data to weighting factors, enabling efficient high-resolution Lamb wave damage imaging with an enhanced SNR. To verify the effectiveness and imaging performance of the CNN-based method, damage in an aluminum plate is imaged using both simulation and experimental methods. The imaging results are compared and analyzed against those of the DAS and MVDR methods. The results show that the proposed CNN-based adaptive Lamb wave beamforming method, which combines the advantages of a high resolution and signal-to-noise ratio, as well as rapid imaging, can provide reference and support for real-time Lamb-wave-based Structural Health Monitoring (SHM).  
adaptive beamforming; CNN; damage imaging; deep learning; lamb waves Article
## 3 Materials and Methods
### Experimental Design
#### 3.5.2 Hardware and Software Platforms  
The hardware and software configurations employed for model training and evaluation were critical to ensuring efficient execution. The hardware platform consisted of a high-performance computing server equipped with four NVIDIA A100 GPUs, each with 40 GB of memory, enabling efficient training and inference of large-scale deep learning models. The server was powered by a 128-core AMD EPYC 7742 processor with a base clock speed of 2.25 GHz, supporting parallel computation tasks effectively. Additionally, the system featured 1 TB of RAM, accommodating the loading of large-scale datasets and the training of complex models. NVMe SSDs were employed as storage media, providing read and write speeds of up to 6.4 GB/s, facilitating rapid data loading and checkpoint saving during model training.  
The software platform was based on the Ubuntu 20.04 operating system and utilized the PyTorch deep learning framework (version 1.12.0) for model implementation. GPU parallelism was optimized using CUDA 11.6 and cuDNN 8.4. Data preprocessing and augmentation were performed using Python libraries such as Pandas and NumPy, with Albumentations employed for advanced image augmentation. Distributed training was implemented using PyTorch's Distributed Data Parallel (DDP) module, which significantly improved training speed while minimizing GPU communication overhead. All experimental code was written in Python 3.9, with results visualized in Jupyter Notebook (version 7.3.3).
## 7 Numerical Results and Discussion
### Performance under Dynamic Channel  
Finally, the behavior of the proposed receiver under dynamic channel conditions has been investigated. Our noncoherent receiver is robust to phase jitter as observed from Figure 12 and 13. The phase \(\theta\) of received chip sequence \(\left\{r_{m,k}\right\}\), \(1\leq k\leq K\), is modeled as a Wiener process according to \(\theta_{m+1}=\theta_{m}+\Delta_{m}\), where \(\Delta_{m}\) are zero-mean independent Gaussian random variables with known variance \(\sigma_{m}^{2}\) in each bit interval, and the initial phase \(\theta_{1}\) is considered to be uniform distribution. A jitter standard deviation up to four degrees does not degrade significantly the receiver performance. Another feature of the curves in Figures 12 and 13 is that they exhibit an irreducible error floor as SNR increases. Furthermore, the smaller the standard deviation is, the lower the error floor. The explanation is that the random phase increment \(\Delta_{m}\) generates a phasor \(e^{j\Delta_{m}}\) in the autocorrelator output \(A[m]\)  
Figure 11: Detection performance of proposed receiver. (**a**) BER performance of proposed receiver versus CFO; (**b**) PER performance of proposed receiver versus CFO. CFO: carrier frequency offset.  
Figure 10: BER and PER performances of [8] versus two particular CFOs. CFO: carrier frequency offset.  
which, in turn, produces decision errors even in the absence of noise. The smaller the standard deviation, the lesser the effect of the phasor \(e^{\beta\Delta n}\) on the autocorrelator output \(A[m]\).
## 3 Network Architecture and Topology
### Execution Subsystem  
The Execution subsystem performs the necessary actions which foster changes in the traffic flow according to the objective of the ITS application. It is mainly composed of devices providing visual and acoustic stimuli to drivers, though others aimed at vehicle automation would also pertain to this subsystem. Different equipment may be used. Traffic lights or Variable-Message Signs installed along the roads are attractive options which provide strict control and adaptability to different situations, respectively. They offer the advantage of being widely adopted road infrastructures, suitable for reuse in ITS applications, which help reducing deployment costs. The use of panels attached to the sensing nodes is another possible solution; nevertheless power supply restrictions of unwired nodes limit their application to small panels or informative leds. Finally, the employment of in-vehicle systems offers, on the one hand, the possibility of presenting customized information for every vehicle and, on the other hand, the chance to use acoustic signals and messages that diminish distractions while driving. In addition, the information from the road systems can be integrated into In-Vehicle Infotainment (IVI) systems, for example, for its fusion with digital maps or other information services (transport timetables, weather forecasts, _etc._). Currently, the considerable and increasing number of smartphones, navigators or tablets paves the way for the adoption of in-vehicle systems. However, there is a growing interest of vehicle manufacturers to incorporate IVI systems in their products as an added value. In this respect, new automobile models from important companies are equipped with systems such as BMW's iDrive [43], Audi's MMI [44], Ford's SYNC [45] or GENIVI Apollo from the GENIVI alliance [46], which aim at facilitating the development of IVI applications.
### Literature Review
Apart from this, if the works referenced in these reviews are analyzed, it can be deduced that the common practice in them is to propose an EMS that is compared with the optimum EMS (normally, the DP or any of its variants). The results show how close to the optimum they are. It would be interesting to compare the proposed EMS with RB strategies that are implemented in real HEVs as a way of determining the room for improvement of commercial HEVs. However, comparison with RB strategies is less common in this kind of work. Another problem of the works that propose novel EMSs is that each study considers a different vehicle model and the units taken for comparing different performance parameters (e.g., fuel consumption) are different. It would be necessary to express the results in dimensionless form to make possible the quantitative comparison of all the works. In summary, it is clear that although the development of novel EMSs that seek the optimum performance of HEVs is booming, in the real world, the HEVs continue to rely on well-known RB strategies. Therefore, there are some questions that arise from this fact:  
1. How far are the proposed EMSs with respect to the RB EMSs that are implemented in the HEV industry? 2. Is it possible to quantify how these strategies are moving closer to the optimum? 3. Would it be possible to identify the technologies that obtain the best results? Taking everything into account, the contribution of this work is to elaborate a quantitative comparison of works that developed EMSs. Furthermore, the focus of this work is to compare the proposed EMSs not only with the optimum but with RB strategies that are used in the HEV industry. Since several studies do not provide results of their models against RB strategies, it is proposed as another contribution of this work to complete their results using the tool FASTSim developed by the National Renewable Energy Laboratory (NREL) [12] to reproduce the vehicle models proposed and obtain their results when they operate using a RB strategy. FASTSim was chosen since it matches well with the fuel economy test of the United States Environmental Protection Agency (EPA) for light-duty vehicles. The key components and vehicle outputs have been validated by comparing the model outputs to test data for many different vehicles to provide confidence in the results. FASTSim's efficiency estimates match most EPA test data within 5%, and almost all within 10%, for many different vehicles and powertrains (its database includes hundreds of existing vehicles which were validated with these ranges). The RB strategy that the NREL tool uses for its simulations is explained in Appendix A.
## 1 Introduction
### Definitions
#### 1.2.4 Cloud Manufacturing  
Due to the success of CC with the business model of pay-as-you-go services, a new concept directly emerged, Cloud Manufacturing (CM). CM is a paradigm that will revolutionize the manufacturing industry with highly collaborative and innovative manufacturing-service-oriented production. The most important feature of CM is the easiness and convenience of sharing a variety of different types of distributed manufacturing resources. This vision is known as Manufacturing as a Service (MaaS) [13].  
According to [PERSON] et al. [17], a CM architecture is composed of ten layers (Figure 4) in which seven functional layers of the architecture are facilitated by the three layers of knowledge, cloud security, and network.  
Resource layer: Composed of manufacturing resources and abilities that can be distributed across several different providers.  
Perception layer: Using IoT, it senses the manufacturing resources and provides a connection to the CM platform enabling communication and interactions between a cloud platform and the physical devices.  
Virtualization layer: Encapsulates into Manufacturing Cloud Services (MCS) the manufacturing resources and abilities, by virtualizing these items, enabling access to service-oriented technologies and CC technologies.  
Core Cloud Service Layer: Sometimes referred to as core middleware, it provides two categories of service: the MCS and the Cloud Manufacturing core services (CMfg). MCS is the result of the encapsulation of manufacturing resources and abilities that can be invoked by end-users. CMfg are the main services provided to manage, access, and invoke MCS for three category users (provider, operator, and consumer).  
Application layer: Contains applications according to specific demands such as cooperative management systems, cloud-manufacturing-based ERP, etc.  
Portal layer: Provides human-machine interaction for user access.  
Enterprise cooperation application layer: Includes commerce, business, and manufacturing cooperation.  
Figure 4: CM architecture, based on [17].  
Knowledge layer: Provides the knowledge needed for the seven functional layers described above.  
Cloud security layer: Provides mechanisms and strategies specifically for CM system security.  
Network: Also referred to as the communication layer, it provides communication support for users, operations, resources, services, etc., in CM systems.  
CM can only be fully deployed with the contributions of other emergent technologies such as IoT, CPS, CC, Semantic Web Technology, Service-oriented technology, Virtualization, High-performance computing, System Management Technologies, Big Data analytics, Cybersecurity Mesh, Zero Trust, and others related to security.  
CM can be one of the new solutions for the challenges that manufacturing environments face since it provides tools for resource sharing and the use of globally distributed, scalable, sustainable, and service-oriented manufacturing systems.
## 3 System Implementation and Verification
### Temperature Prediction Using the CNN and LSTM Models
In other words, the proposed CNN-LSTM model has shorter training and prediction times than the LSTM model. However, the LSTM, as an extended type of recurrent neural network (RNN), inherently achieves higher accuracy. This is why the performance of the LSTM model is superior to that of the CNN-LSTM model, despite the latter's efficiency in training and prediction.
## 5 Data Simulation
### LRI1B Simulation
#### 5.4.3 Laser Frequency Error  
The master (SAT1) laser frequency can be divided into three parts, as shown in the following equation [31; 32]:  
\[\begin{split} f_{1}&=\tilde{f}_{1}+\delta f_{1}\\ &\approx\tilde{f}_{1}+\left(\frac{\Phi(\mathbf{r}_{1})-\Phi_{0}}{c^{2 }}-\frac{1}{2}\frac{v^{2}}{c^{2}}\right)\tilde{f}_{1}+\delta f_{\text{cav}}, \end{split} \tag{45}\]  
where \(\tilde{f}_{1}\approx 281.6\,\text{THz}\) and the last two terms represent the frequency error due to the relativistic effects and the cavity instability, respectively; \(\Phi(\mathbf{r}_{1})\) denotes the Newtonian gravitational potential at the position of SAT1 carrying a negative sign, and \(\Phi_{0}\) at the geoid including the centripetal potential caused by the Earth's rotation. \(\Phi(\mathbf{r}_{1})\) can be calculated using the gravitational force models during the forward procedure, while the absolute value of the constant \(\Phi_{0}/c^{2}\) equals the scale difference \(L_{\text{G}}\) between the terrestrial time (TT) and the geocentric coordinate time (TCG):  
\[-\frac{\Phi_{0}}{c^{2}}=L_{\text{G}}=6.969290134\times 10^{-10}. \tag{46}\]  
The term \(\delta f_{\text{cav}}(t)\) was generated according to Equation (1). The range error induced by the laser frequency variability can be computed as  
\[\delta\rho_{\text{LF}}=\frac{\delta f_{1}}{\tilde{f}_{1}}\rho_{\text{inst}}. \tag{47}\]  
The ASD of \(\delta\rho_{\text{LF}}\) is shown in red in both Figures 4 and 6.
## 2 Results and Discussion
### Supervised Analyses of Cinnamon VOCs Dataset by SPME/GC-MS
#### 2.3.1 OPLS-DA of Authenticated Cinnamon Drugs  
Supervised MVA analysis for the five authenticated cinnamon drugs showed relatively strong OPLS-DA score plot model generating validation parameters of R\({}^{2}\) = 92.9%, Q\({}^{2}\) = 69.7%, and _p_-value = 0.0042, Supplementary Figure S3A. The model also succeeded in segregating investigated samples into four main groups confirming the previous finding, including the segregation of CV and CVM from other samples. Furthermore, CA and CI were unique species appearing as extremes at the right along PC1 and down along PC2 of the plot, respectively, Supplementary Figure S3A. Asides, (_E_)-cinnamaldehyde and _O_-methoxy cinnamaldehyde (P77) were more associated with CV and CVM, while coumarin and unknown aromatic hydrocarbon (P72) were correlated with CA in the OPLS-DA loading plot, Supplementary Figure S3B.
## 3 Proposed Work
### Algorithm for adaptive fuzzy c-means clustering based on CCF.  
The system nodes cluster head is selected among the nodes with the highest significant \"SINR\", \"congestion level\", and \"survivability\". Utilizing the \"adaptive fuzzy c-means (AFCM)\" \"clustering algorithm\", the sensor nodes are organized into groups. Support kernel matrices are constrained in this situation by purposefully using the CCF factor in clustering [24; 25; 26]. There are numerous initial cluster centers in this algorithm. The AFCM algorithm uses fuzzy memberships to distribute the information data for each class.  
\[\widetilde{j}_{en}=\sum_{l=1}^{L}\sum_{m=1}^{M}(v_{ij})^{n}\frac{\|\hat{S}_{l}- q_{m}{}^{2}\|}{CCF_{l}} \tag{7}\]  
\(\widetilde{S}_{l}\) indicates the support value, \(q_{m}\) indicates the \(m^{th}\) Cluster center and \(n\) indicate the constant esteem in condition (4), Where \(CCF\) shows that the cluster head is selecting a cluster \(l\) factor, mentioned in condition (5), \"the membership function\" expresses the possibility of a pixel's belongingness into a target cluster [27; 28; 29]. The conditions (8) and cluster centers update the \"membership functions\" and \"cluster centers\" (9).  
\[\overline{v}_{lm}=\frac{1}{\sum_{k=1}^{q}\biggl{(}\frac{\|\widetilde{S}_{l}- q_{m}/\overline{\sigma}_{l}\|}{\|\widetilde{S}_{l}-q_{k}/\overline{\sigma}_{l}\|} \biggr{)}^{\frac{2}{n-1}}} \tag{8}\]  
The condition is used to process the cluster's centroid (9),  
\[z^{{}^{\prime}}_{m}=\frac{\sum_{l=1}^{L}\overline{v}_{lm}^{n}.\widetilde{S}_{ l}}{\sum_{l=1}^{L}\overline{v}_{lm}^{n}} \tag{9}\]  
This calculation is repeated until the coefficients' difference between two cycles is approximately at the predetermined limit.  
\[\max_{lm}\|\overline{V}_{lm}^{(k)}-\overline{V}_{lm}^{(k+1)}\|<\psi \tag{10}\]  
Condition (8) \(\psi\) is a number between 0 and 1. Computation is carried on until efficient clustering is achieved. In Algorithm 1, this AFCM clustering is indicated [30].  
After the nodes have been clustered, it is assumed that the clustered nodes forward the packets. The \"AQLG operation\" is performed on each packet before the retransmission or rebroadcasting [9].
## 3 Measurement Setup and Results  
### 67 GHz S-Parameter Measurement Set-Up  
A 67 GHz S-parameter measurement setup was implemented to verify the new GaN HEMT small-signal model considering the source via effect proposed in Section 2. Figure 9 shows the block diagram of a 67 GHz S-parameter measurement setup. Two-port RF signals come from a 67 GHz vector network analyzer, and through on-wafer probing, the RF signals are input and output to a GaN HEMT device under test with dc biases through a dc source generator and two bias tees. This setup is connected to a PC through general purpose interface bus (GPIB) cables and automatically measures S-parameters according to biases and frequencies through IC-CAP software. In particular, since GaN HEMT devices have high operating voltage and high output power, it is necessary to carefully check the maximum input voltage and RF power allowed by the equipment and accessories.  
Figure 8: Block diagram of the proposed GaN small-signal modeling procedure.  
Figure 7: Top view of full EM simulation of a GaN HEMT using ADS momentum.
# Contrasting Dual Transformer Architectures for Multi-Modal Remote Sensing Image Retrieval
## 2 Materials and Methods
### Model Optimization  
We use global average pooling to the representation matrices \(Z_{tL}\) and \(Z_{vL}\) (while ignoring the \(W_{class}\) and \(x_{class}\) tokens) acquired from the text and image encoders, respectively, followed by L2-normalization to get the features \(f_{t}\) and \(f_{v}\). In addition, if the resulting feature representations have different dimensions one can use an additional linear projection layer to bring them to the same dimension. In Algorithm 1, we provide the PyTorch-style pseudo code of the proposed cross-modal retrieval approach with its default parameters.  
```
Mini-batch size: default b=120, # Regularization parameters Imabda1=lambda2=0.5 # Initial value Temperature parameter: Taux=0.07 # model_f_NLP and model_f_ViT: NLP and Vision transformer encoders # Optimizer: SGD(lr=0.1, momentum=0.9, nesterov=True) # Criterion=nn_CrossEntropyloss0, Number of epochs: 100 # Load mini-batch of image-text pairs of size b from the training set for X, t in loader: # Feed the image-text pair into the model logits_image=model_f_ViTOX) logits_text=model_f_NLP() # L2 Normalization logits_image=logits_image/logits_image.norm(dim=-1,keepdim=True) logits_text=logits_text/ logits_text.norm(dim=-1,keepdim=True) # Similarity matrix Sim_Mat= logits_image @ logits_Text.t0/Taux # Set class labels: 0,1,2,...,b-1 labels = torch_Tensor(np.arange(b)).long0 # image-to-text and text-to-image classification losses (Equation (8)). loss=lmabda1*Criterion(Sim_Mat,labels)+lmabda2* Criterion(torch.transpose(Sim_Mat,0,1), labels) # Backward loss loss loss.backward0 # Clip the gradients for numerical stability torch_nn.utils_clip_grad_norm_(model.parameters(0,0.1) # Optimization step optimizer.step0 optimizer.zero_grad0
```  
**Algorithm 1**Cross-modal text-image matching PyTorch-style pseudocode
# Study on Water Inrush Characteristics of Hard Rock Tunnel Crossing Heterogeneous Faults
###### Abstract  
Fault water inflow is one of the most severe disasters that can occur during the construction of hard and brittle rock tunnels. These tunnels traverse brittle fault breccia zones comprising two key components: a damage zone dominated by low-strain fractures and an internally nested high-strain zone known as the fault core. Structural heterogeneity influences the mechanical and hydraulic properties within fault breccia zones, thereby affecting the evolving characteristics of water inflow in hard rock faulting. Based on the hydraulic characteristics within hard rock fault zones, this paper presents a generalized dual-porosity fluid-solid coupling water inflow model. The model is utilized to investigate the spatiotemporal evolution patterns of water pressure, inflow velocity, and water volume during tunneling through heterogeneous fault zones in hard rock. Research findings indicate that when tunnels pass through the damage zones, water inrush velocity is high, yet the water volume is low, and both decrease rapidly over time. Conversely, within the core regions of faults, water inflow velocity is low, yet the water volume is high, and both remain relatively stable over time. Simulation results closely align with the water inflow data from China's largest cross-section tunnel, the Tiantai Mountain Tunnel, thus validating the accuracy of the evolutionary model proposed in this paper. These findings offer a new perspective for devising effective prevention strategies for water inflow from heterogeneous faults.  
hard rock tunnel; water-rich fault; heterogeneous faults; generalized dual porosity model; stress-seepage coupling
## 5 Performance Evaluation
### Simulation Environment  
The simulation environment of the Quality-of-Experience (QoE) and Profit-aware Resource Allocation framework in CRAN was designed using the CloudSimSDN [11] simulation toolkit. The simulated CRAN environment consists of one BBU pool with five BBUs. The computation speeds of the BBUs vary from 20 to 50 MHz. Through the simulation environment, it has also been imaged that 10 RRHs are geographically distributed to interact with end users directly and that they are supported by the BBUs of the BBU pool. The arrival pattern of the computational resource requests from the RRHs to the virtualized BBU pool is Poisson distributed, and the size of each request ranges from 20 to 600 KB. Heterogeneous attributes (\(d_{r}\), \(q_{r}\) and \(n_{r}\)) associated with each request may have random values. Here, a random waypoint mobility model [24] is envisaged in which users with mobile wireless devices move independently to a randomly chosen destination with a random speed. The received signal strength or the RSSI values of the user devices vary due to their mobility pattern. The simulation is run for 500 s. Each data point in the graph corresponds to the mathematical average of the results from 50 simulation runs. In order to emulate a real CRAN system environment and for a fair comparison, application types, attributes, and system parameters are carefully selected for a realistic simulation scenario [25; 26; 27]. Table 3 shows the system parametric values used in our simulation.