esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Deep Learning Models, Including Transfer Learning for CAD Classification in Medical Imaging  
In this subsection, we introduce the process followed in this study on applying deep learning architectures, including transfer learning for benchmark CNN models in CAD diagnosis.  
In deep learning model development, the traditional pipeline is the neural network training from scratch, which depends highly on the size of the data provided. Transfer learning is an alternative, most preferred and used process in developing deep learning architectures [63]. This process offers the capability to sufficiently employ the existing knowledge of a pre-trained CNN through the use of ImageNet dataset so as to result in competent predictions.  
For an accurate classification process, an improved model training process is required, which derives from the incorporation of transfer learning during the training phase of the proposed CNN architectures. More specifically, the ImageNet [63; 64] dataset needs to be utilized for network pre-training, thus resulting in accurate classification of medical SPECT myocardial perfusion imaging scans into two categories, namely normal and abnormal (patient with ischemia or infarction). According to the relevant literature, the ImageNet dataset is employed by the popular CNN methods for model pre-training and includes 1.4 million images with 1000 classes. Based on this pre-training process, VGG16 and DenseNet models are trained to extract particular features from images through the assignment of constant weights on them. The number of the weight layers affects the depth of the model, along with the steps needed for feature extraction.  
The training dataset, representing 85% of the provided dataset of 224 SPECT MPI images, is loaded into the pre-trained models after undergoing a proper augmentation process. Hence, an improved CNN model is produced, which is inserted into the next testing phase. The remaining 15% of the provided dataset is accordingly incorporated into the evaluation process. The proposed transfer learning methodology of the state-of-the-art CNN models is graphically presented in Figure 4, regarding the examined dataset of 224 patients.  
Figure 4: The proposed methodology for SPECT MPI classification including RGB-CNN architectures.  
Following the process in which the benchmark CNN model is selected for the classification task, the exploration and identification of suitable, robust and efficient architectures of these CNN models come next for the specific problem solving, which concerns the identification of the correct category of CAD diagnosis. On this basis, the fine-tuning of the model parameters and the configuration of several other hyperparameters were successfully attained through a thorough investigation regarding the appropriate deep learning architecture. For comparison purposes, various common deep learning architectures such as Densenet, VGG16, Mobilientet and InceptionV3 were investigated.
## 6 System Development
### Microservices Interface
#### 6.2.6 API Gateway  
The API Gateway is the last building block of the system interface for the client, and it serves three main purposes in this setup:  
1. Provide routing to the designated microservice of a request;
2. Provide a security layer with the help of the Auth microservice;
3. Provide CORS protection.  
Cross-Origin Resource Sharing (CORS) is a security feature for web browsers. It permits or restricts web applications running at one origin (domain) to request resources from a different origin, mitigating potential security risks associated with cross-origin HTTP requests.  
The API Gateway integrates the security measures using the Spring Security package ([EMAIL_ADDRESS], VMware, Inc., Palo Alto, CA, USA, 2023) for adding the necessary JWT token checks upon a private route request. The Gateway relies on a Security Filter Bean to ensure that the appropriate JWT token metadata exists and that it is valid. If an invalid request is intercepted, the gateway will isolate the request and return the appropriate error code for the client to handle. If a request has a valid token, the API Gateway will validate the token with the help of the Auth microservice and extract the data contained in the JWT token.  
The JWT payload will be attached to the original request before routing it back to the appropriate microservice. In this case, basic information about the user (id, email, and nickname) was attached to the request with the help of HTTP Headers. This step will save further database reads at the microservice layer for retrieving user data.  
If the databases are segregated between multiple microservices, no relationship between data can be maintained. The main reason behind this drawback is the fact that the databases can be physically distributed across nodes and thus maintaining relationships between entities cannot be achieved. The constraints between entities are moved to the application layer and the integrity of data must be maintained with the help of application business logic. In most implementations, there is a constant need to interrogate databases or check resource permissions based on user credentials. Fetching the user resources at every action can potentially induce bottlenecks in terms of performance.  
Besides the CORS and authorization state checks, the API Gateway layer handles application routing. To define the routing strategy, the API Gateway will require two main pieces of information: the prefix of the incoming request and the destination address to which the service should be redirected.
## 4 Empirical Study and Result
### Analysis of Merging Behavior in Different Traffic States
#### 4.2.3 Speed Difference  
The descriptive statistics of speeds in Scenario 1 are shown in Table 2. The speed decreased from the merge lane to the outside lane in all four sites under light traffic states, while the trend is opposite under heavy traffic states. This may be because, under light traffic states, vehicles in the merge lane have an adequate gap in the target lane to merge at a relatively high speed. And since the outer two lanes already have a high percentage of trucks and a lower speed limit, the speed decreases when the number of merging vehicles increase. On the contrary, the merging vehicles have less influence on the outside lane, and merging to the middle lane is hard when the highway is congested. Thus, the average speed increased from the inside to the outside at Site C1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Lane** & **Count** & **Mean** & **std** & **min** & **25\%** & **Median** & **75\%** & **max** \\ \hline \multirow{3}{*}{A1} & Merge & 1808 & 81.47 & 8.28 & 35.72 & 77.02 & 81.56 & 86.79 & 111.40 \\  & Through & 1808 & 73.20  
The descriptive statistics in Table 2 reveal notable speed differences across the merge, through, and outer lanes under different traffic conditions. In light traffic scenarios, vehicles in the merge lane tend to travel at higher speeds, as they have more opportunities to find gaps in the target lane. The speeds in the outer lanes, which often carry a higher proportion of heavy vehicles, are generally lower. This difference allows merging vehicles to execute lane changes with relatively less disruption. However, in heavy traffic conditions, such as at Site C1, the outer lane speeds are significantly reduced, while the merge lane speeds remain relatively low. This creates a bottleneck effect where merging becomes more difficult due to the reduced speed differentials between the lanes. As a result, vehicles struggle to accelerate to match the speed of the adjacent lane, leading to more congested and hazardous merging conditions.
# Analyzing and Forecasting Vessel Traffic Through the Panama Canal: A Comparative Study
## 3 Results
### Model Comparison and Statistical Validation  
The model comparison criteria are presented in Table 2. The table summarizes the error metrics (RMSE, MAE, and MAPE) for each model, along with the Box-Ljung test statistic and its corresponding \(p\)-value.  
Figure 11: Forecast Using the NNAR (13,1,7)\({}_{12}\) Model.  
The comparative results of the models, in terms of error, show that the NNAR model achieved the lowest root mean square error (RMSE = 83.96) and a mean absolute percentage error (MAPE = 6.33) comparable to that of the SARIMA model (MAPE = 6.31). In contrast, the Holt-Winters model recorded the highest error metrics (RMSE = 106.17; MAE = 82.36; MAPE = 7.71), indicating a lower predictive capability.  
In this same line of analysis, two out of the three models passed the Box-Ljung test \(\left(\text{ARIMA}(1,0,1)(0,1,1)_{12},\chi 2=15.9,p=0.72\right)\) and (NNAR \(\chi 2=17.5,p=0.83\)), indicating that the residuals do not exhibit significant autocorrelation; therefore, these models provide a good fit to the historical series. Similarly, it was confirmed that the parameter estimates of the aforementioned models are statistically significant (\(p>0.05\)), suggesting that the models adequately captured the temporal structure of the data. By contrast, the Holt-Winters model produced a significant \(p\)-value (\(p=0.00\)), indicating the presence of autocorrelation in the residuals and, consequently, a poor model fit. Overall, the results indicate that the SARIMA and NNAR models offer better predictive performance and greater statistical adequacy compared to the Holt-Winters model.
# A Modified TCP BBR to Enable High Fairness in High-Speed Wireless Networks
## 4 Evaluation of Experiments  
### Testbed Setup  
This section uses Network Simulator 3 (NS3), 5G, and WiFi networks to evaluate the performance of BBR-PG. The experimental topology is shown in Figure 3. NS3 is a network emulator that creates a virtual network running multiple hosts, links, and switches on a single machine. Based on the BBR implementation framework, a large number of simulation experiments have been carried out on NS3, where the condition variables include bottleneck bandwidth, RTT buffer size, etc. To eliminate uncontrollable network disturbances (e.g., background traffic, special token bucket policy procedures, etc.), the channel section was simulated using Netem/TBF and configured with different network environments. We set the default active queue management to the Drop-Tail policy and each packet size to 1 KB.  
The wireless network consists of four physical Linux hosts in the lab and two cloud servers (Node 1 in Beijing, China, and Node 2 in Silicon Valley, CA, USA). The servers have a latency of approximately 50 ms and 230 ms, respectively. For the 5G experiments, we used TCP CCAs implemented in the Linux kernel at the server, and the 5G mobile terminals were used as receivers to measure TCP and application performance over 5G networks.  
Moreover, to quantify the fairness at different buffer sizes, the Jain fairness index is introduced [30]. The Jain fairness index can be used to measure fairness in resource  
Figure 3: Experimental topology of the network.  
competition. The calculation method uses Equation (20), where \(n\) represents \(n\) sender and \(x_{i}\) is the throughput of the \(i\) link.  
\[J=\frac{(\sum_{i=1}^{n}x_{i})^{2}}{n\sum_{i=1}^{n}x_{i}^{2}} \tag{20}\]  
The Jain fairness index (hereafter referred to as the fairness index) can reflect the difference in throughput well, with values ranging from [0, 1]. The closer the fairness index is to 1, the better the fairness of bandwidth allocation is.
## 3 Experiments on Connections
### Connection Test Results
This observation highlights the importance of considering stress concentration in the bracing connection to achieve the necessary drift ratio for modular piers. In other words, initiating plastic deformation at the connection plate is crucial to prevent CFT column fractures. Figure 9: Strain history of SMP and component tests. Detailed local behaviors are represented by strain envelope curves in Figure 10. As shown in Figure 5b, PS1 denotes the strain gauge at the weld part, whereas PS2 and PS3 denote the strain gauges at the plastic hinge. The strain envelope curves for the weld, plastic hinge, and bracing members of BC1 are shown in Figure 10a. A fracture was observed at the weld part (PS3) at 950 \(\upmu\varepsilon\) and a load of 27.2 kN. At the same load, the plastic hinge (PS2) and bracing member (B1) exhibited strains of 1000 and 7500 \(\upmu\varepsilon\), respectively. Gauge PS3 exhibited an apparent fracture in the welded part. The strain envelope curves of BC2 are shown in Figure 10b, displaying a behavior similar to that of BC1, except for the bracing member (B1). Buckling was observed at a plastic hinge (PS2) at a measured strain of 920 \(\upmu\varepsilon\) and a load of 32.89 kN, indicating that buckling occurred before yielding the plastic hinge. The strain envelope curves for BC3 are shown in Figure 10c. During the test, both the bracing member (B1) and plastic hinge (PS2) reached yield strain (1500 \(\upmu\varepsilon\)), whereas the welded part (PS3) remained within the elastic range. The plastic hinge yielded earlier than the bracing member when the load was 28.13 kN. The strain envelope curves of BC4 demonstrated apparent plastic hinge behavior, as shown in Figure 10d. The yield strain of the connection part was reached at a load of 6.62 kN with no damage. The observed behavior of the connections suggests that the location of plastification in the connection can be designed by altering the details. In summary, although increasing the stiffness of the connecting plate can improve the energy absorption capacity, it can cause stress concentration in the CFT column, which  
Figure 10: Load–strain curve of connection parts. may cause the main member to fail first. Therefore, details that concentrate the stress in the connecting plate are required, and BC3, which shows the yield of the connection before buckling occurs, can be considered as the most suitable detail for protecting the main member, even though it has the lowest energy absorption capacity. Therefore, the connection detail of BC3 was selected for a full-scale modular pier specimen.
# An Introduction of a Modular Framework for Securing 5G Networks and Beyond
## 3 Proposed Network Service Security (NSS) Framework
### Network Service Security Architecture  
To address the security threats in 5G-enabled D2D communication and to provide the secure delivery of network services, the proposed security framework assumes that network access security has been achieved. The main concern is the secure access of services by the UE from the SP. The UE should be able to obtain authorisation to access and share the data with another UE, hence achieving service security. The verification of the authenticity, integrity and provenance of the named data object against the producer must be performed before the UE is granted access. Another concern is whether the right data are being published and can be restricted even during out of coverage. The UE also should be able to share data without involving the HN even during coverage, which addresses one of the D2D security problems.  
After a successful authentication procedure with the network, the UE can request access to services of SP via HN, and the SP verifies the UE and grants access to UE. Security is implemented by various security mechanisms, which should be interoperable with each other. Before addressing security at the different levels, these levels must be defined and the security model function of each level has to be specified.  
To address the security issues of accessing the network and services, a unified modular architecture is required, as shown in Figure 2, to support the proposed security framework, and the generic architecture consists of the following security entities which are modified according to various security models:  
* UE: The end-user's device that is trying to access the services;* HN AAA servers consist of security anchor function (SEAF) as SN or authenticator, Authentication Server Function (AUSF) for authentication from the HN if the service is in a home-controlled environment. From a network perspective, the Authentication Credential Repository and Processing Function (ARPF) maintains keys and other security contexts that are utilised for primary authentication [6];
* External AAA servers for service authorisation and secondary authentication in local and external DNs authenticate and authorise the UE to access the service and permission delegation to share with other UE;
* SS: A server storing the content/services that the UE is trying to access which could be controlled by either the HN as an internal SP or controlled by an external SP.
## 2 Related Work
Considering this, our proposal for managing interference in a 3-tier cellular HetNet demonstrates the main difference with existing ones. \begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Ref-Year** & **Main Contribution** & **Method** & **Limitation** \\ \hline
[29], 2024 & Proposed multi-agent DRL-based joint RA and transmit power scheme to maximize energy efficiency. & Deep Q-network model with a graph attention network (GAT). & Unlicensed and optimally assigned licensed spectrum are used. \\
[30], 2023 & Proposed a multi-agent RL for resource and power allocation to maximize throughput with satisfied QoS. & Multi-agent DRL framework. & Limited to macro and D2D \\
[31], 2023 & Proposed an energy-efficient DRL-based RA scheme. & Co-operative distributed RA algorithm. & Limited to co-channel interference. \\
[15], 2022 & RL-based resource allocation scheme for multi-tier cellular HetNets. & RL-based approach & Not suitable for large network; high computational time. \\
[26], 2022 & Resource allocation in multi-channel cellular systems using DL framework. & Deep neural network (DNN) approach & Depends on CSI availability \\
[21], 2022 & Resource allocation using Deep neural network (DNN) in underlay cellular networks & DNN and heuristic approaches & QoS constraints are violated. \\
[27], 2021 & Joint resource allocation and power control to maximize system performance. & DRL-based ML algorithm & Limited scenarios considered. \\
[22], 2021 & Double Deep Q-Network (DDQN)-based resource allocation sum rate maximization and energy efficiency. & DDQN approach & Focus on one-time slot only. \\
[23], 2021 & Joint user association and resource allocation investigation for D2D-enabled ultra-dense cellular networks. & Double-dueling-deep network & Q- network considered as an agent. \\
[24], 2021 & DRL-based spectrum access scheme for D2D communication in underlay networks. & DDQN approach & Limited scenarios for resource re-usage. \\ \hline
**Our work:** & The proposed DRL method provides improved system performance over heterogeneous cellular networks by optimizing resource allocation and adjusting transmit power dynamically for all users on the network. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of related work on ML-based resource allocation.
# Activity Prediction Based on Deep Learning Techniques
## 3 Design of Network Models
### LSTM Network
#### 3.2.2 LSTM Structure  
Due to structural drawbacks, RNN is not appropriate for predicting data with long-term dependence. Meanwhile, the LSTM, an advanced RNN model, has memory blocks in the hidden layer with several gates to handle data with long-term dependence, as shown in Figure 7. LSTMs deal with the long-term dependence issue by selectively controlling inputs and outputs with the gates. Since its introduction in 1995 [26], it has been advanced to include forget gates and peepholes, in addition to existing input and output gates. The peephole plays a role in notifying the states of the memory block to the three gates (yellow edges in Figure 7). It is very useful when a special action needs to be taken depending on a certain condition while processing time-series data.  
Figure 6: A sample structure of an RNN. There are three input nodes, four hidden layer nodes, and one output node. The nodes are interconnected with each other.  
Figure 7: An LSTM memory block. This figure shows the jth memory block in a hidden layer. \(\mathbf{x}^{(t)}\) and \(\mathbf{h}^{(t-1)}\) represents input vectors at present state and previous hidden layer state, respectively. \(\tau_{g}\), \(\tau_{h}\), \(\tau_{f}\) are the activation functions at the input, output, and the input and output gates, respectively. d, p, and q indicate the number of connections to the input nodes, hidden nodes, and output nodes, respectively. \(\mathbf{w}^{g}\), \(\mathbf{w}^{j}\), and \(\mathbf{w}^{o}\) are the weights for the recurrent edges connected to the inputs, input gates, and output gates, respectively. The symbols/and * refer to the linear activation function and multiplication respectively. In this figure, the biases are omitted for the simplicity of the diagram.
## 3 Proposed Algorithm
### Complexity Analysis  
Computational complexity is a critical metric for signal detectors in large antenna systems. Thus, we compute and analyze the computational complexity of the proposed SAOR and CGA-SAOR algorithms in this subsection. Since the hardware complexity is dominated by the multipliers, the complexity is analyzed in terms of the required number of complex-valued multiplications. The total computations predominantly arise from the preprocessing and iteration steps. Typically, the preprocessing includes the computation of the regularized Gram matrix \(\mathbf{A}\) and the matched filter vector \(\mathbf{b}\). Both conventional MMSE and iterative detectors, including the proposed SAOR and CGA-SAOR algorithms, as well as AOR- and SOR-based methods, involve this preprocessing step. Therefore, we compute the complexity of the iterative detectors required in later steps. The first part of the calculations comes from the initialization. The complexity of the diagonal-based initialization \(\mathbf{s}^{(\mathbf{0})}=\mathbf{D}^{-1}\mathbf{\hat{y}}\) involves the multiplication of the inverse of the diagonal matrix \(\mathbf{D}^{-1}\) and the \(K\times 1\) vector \(\mathbf{\hat{y}}\), as well as the calculation of \(\mathbf{D}^{-1}\). Hence, the number of complex-valued multiplications needed to obtain the initial solution is \(2K\). The second
# Security Vulnerabilities in LPWANs--An Attack Vector Analysis for the IoT Ecosystem
## 2 Systematic Review
* **Q4--**What is the progress of research papers using the range of technologies (LoRa, Sigfox, LPWAN, and NB-IoT) and the set of security-related topics? Figure 5: Systematic review results by category (“Security”, “Tech” and “Smart”). **Answer**: The results obtained date form 2010 and, in this year, 3 research papers were counted, increasing to 119 in 2019. **Q5--**What is the progress of research papers using the range of technologies (LoRa, Sigfox, LPWAN, and NB-IoT) and the set of security-related topics regarding the \"smart\" application's context (such as _smart campus_, _smart environment_, and _smart monitoring_)? **Answer**: The general term _smart_ was the one that ranked higher with a total of 86 studies, starting in 2014 with 1 study and reaching 35 by 2019. In second appears _smart monitoring_ with 25 papers, starting in 2017 with 5 studies and achieving 8 in 2018. In third place appears _smart environment_ with 16 papers, with 4 studies in 2017 and rising to 5 in 2018 and 2019 respectively. Lastly, _smart campus_ presented only 2 papers, with 1 in 2017 and another in 2019.