esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Vision Transformer Models
### Vision Transformer Models
#### 3.1.1 Base Vision Transformer (ViT)
* Layer normalization (LN): LN is used to normalize the training data before each block, preventing the introduction of any new dependencies. This improves overall performance and training effectiveness. * Feed-forward network (FFN): The MHSA layers produce outputs that are processed by the FFN. It has a nonlinear activation function and two linear transformation layers. * Multi-layer perceptron: This layer uses the GELU activation function in a two-layer structure. To sum up, ViT utilizes the encoder part of the transformer framework. The encoder takes in a series of embedded image patches, along with positional information and a learnable class embedding added at the start of the sequence. The learnable class embeddingvalue is sent to a classification head linked to the encoder's output, which utilizes it to generate a classification outcome based on its condition.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Treatment** & **C** & **L1** & **L2** & **MSE 1** & \(p\)**-Value** \\ \hline
**Carcasses. n.** & 36 & 36 & 36 & & \\
**Live weight (LW)**, kg & 167.0 & 171.8 & 170.4 & 147.4 & 0.263 \\
**Carcass weight (CW)**, kg & 141.0 & 144.2 & 142.9 & 111.1 & 0.400 \\
**Carcass yield, \%LW** & 84.2 & 83.9 & 83.8 & 121.10 & 0.501 \\
**Backfat thickness, mm** & 30.3 & 31.4 & 28.4 & 38.31 & 0.134 \\
**Lean yield (F-o-M)**, \% & 52.03 & 51.45 & 52.78 & 8.30 & 0.150 \\
**Loin weight, kg** & 11.86 & 12.43 & 12.25 & 1.141 & 0.119 \\
**Thigh weight (TW)**, kg & 16.26 & 16.83 & 16.63 & 1.470 & 0.201 \\
**Loin yield, \%CW** & 17.11 & 17.38 & 17.21 & 1.485 & 0.702 \\
**Thigh yield, \%CW** & 23.43 & 23.54 & 23.38 & 0.934 & 0.833 \\
**Trimmed thigh weight, kg** & 13.56 & 13.97 & 13.82 & 0.944 & 0.266 \\
**Trimmed thigh weight loss, \%TW** & 16.60 & 16.98 & 16.92 & 1.743 & 0.499 \\ \hline \hline \end{tabular} \({}^{1}\) Mean square error.  
\end{table} 
Table 4: Slaughtering parameters and carcass quality of the three experimental groups (C = control, L1 = laverader nebulization once a day, L2 = laverader nebulization twice a day). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Treatment** & **C** & **L1** & **L2** & **SE 1** & \(p\)**-Value** \\ \hline
**Carcasses, n.** & 36 & 36 & & & \\
**Shoulder** & 1.09 * & 1.92 * & 1.53 & 0.159 & 0.051 \\
**Flank** & 4.56 & 4.46 & 2.92 & 0.451 & 0.585 \\
**Thigh** & 1.38 & 1.00 & 1.38 & 0.137 & 0.441 \\
**Legs** & 0.00 & 0.00 & 0.077 & 0.017 & 0.105 \\
**Total lesions on the body** & 7.03 & 7.38 & 5.92 & 0.527 & 0.950 \\ \hline \hline \end{tabular} \({}^{1}\) Standard error.  
\end{table} 
Table 3: Skin lesions (count per each body region and overall number) of the three experimental groups (C = control, L1 = laverader nebulization once a day, L2 = laverader nebulization twice a day) assessed on the carcasses. Means with an asterisk within the same row are tendentially different between each other (\(p<0.1\)).
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 3 Typical Flow Table Saturation Attack
### Case Studies--Adaptive Timeout
Moreover, some designs [15] adopt a feedback control to adjust the timeout value according to the flow table load. When the flow table occupation rises, the controller adjusts the timeout setting, in an effort to prevent potential flow table overflow. Suppose the timeout value inferred by the attacker is \(T_{m}\), and the attacker keeps sending attack flows with packet intervals as \(T_{fload}\) to keep the entries active, where \(T_{fload}<T_{m}\). In the attacking phase, ideally, every packet can hit the flow entry and refresh the idle timeout timer. However, if the flow table occupancy is increased, the timeout value of new entries  
Figure 4: Examples of probing timeouts using incremental search and binary search in the dynamic timeout scenario: (**a**) incremental search; (**b**) binary search. Figure 3: Basic timeout value decision process in adaptive timeout algorithm. is decreased from \(T_{m}\) to \(T^{{}^{\prime}}_{m}\), in which \(T^{{}^{\prime}}_{m}<T_{flood}\). When the attacker continues to send packets with the packet interval as \(T_{flood}\), the idle timeout timer cannot be refreshed by subsequent packets. Essentially, every time the attacker sends a packet in an attempt to hit the flow entry and keep it active, the entry has already expired, causing the packet to trigger a table-miss event. Consequently, security applications may detect the attack flows as abnormal based on the regular periodic packet-in messages.
## 4 Non-Ideality and Sensitivity Analysis  
### Non-Ideal Gain and Sensitivity Analysis  
The non-ideal effects that influences the response of the VD-EXCCII are the frequency-dependent non-ideal current (\(\alpha_{P/N},\ \alpha^{\prime}_{P/N}\)), voltage (\(\beta_{P/N}\)) and transconductance transfer (\(\gamma\), \(\gamma^{\prime}\)) gains. These non-ideal gains result in a change in the current and voltage signals during transfer leading to undesired response. Taking into account the non-ideal gains, the V-I characteristics of the VD-EXCCII in (1-4) will be modified as follows: \(I_{W}=0\), \(V_{XP}=\beta_{P}V_{W},V_{XN}=\beta_{N}V_{W}\), \(I_{ZP+}=\alpha_{P}I_{XP}\), \(I_{ZP-}=\alpha^{\prime}_{P}I_{XP}\), \(I_{2N+}=\alpha_{N}I_{XN}\), \(I_{NN}=I_{W-}=\gamma^{\prime}_{N}I_{NN}\), \(I_{W}=I_{W\text{C}+}=\gamma g_{m1}(V_{P}-V_{N})\), \(I_{W\text{C}-}=-\gamma^{\prime}g_{m1}(V_{P}-V_{N})\), where \(\beta_{(P,\ N)}=1-\varepsilon_{v(P,N)}\), \(\alpha_{P}=1-\varepsilon_{ip}\), \(\alpha_{N}=1-\varepsilon_{in}\), \(\gamma=1-\varepsilon_{gn}\) and \(\gamma^{\prime}=1-\varepsilon^{\prime}_{gn}\). Here, \(\varepsilon_{v(P,N)}\) (\(\left|\varepsilon_{v(P,N)}\right|<<1\)) denote voltage tracking errors, \(\varepsilon_{ip}\), \(\varepsilon_{IN}\) (\(\left|\varepsilon_{iP}\right|\), \(\left|\varepsilon_{iN}\right|<<1\)) denote current tracking errors and \(\varepsilon_{gn}\), \(\varepsilon^{\prime}g_{m1}(\left|\varepsilon_{gn}\right|\), \(\left|\varepsilon^{\prime}g_{m1}\right|<<1\)) denote transconductance errors of the VD-EXCCII. The non-ideal analysis considering the effect of non-ideal current, voltage and transconductance transfer gains is carried out for VM, CM, TAM and TIM configurations to see its effect on the transfer function, \(f_{0}\) and \(Q\) of the proposed filters.
## 3 Cross-Layer Adaptive Feedback Scheduling
### Adaptive Feedback Scheduling Algorithm
#### 3.3.1 Proportional Coefficient  
As shown in Fig. 3, when the DMR is at a high level (relative to the desired level), changes in the sampling period will affect the DMR significantly. The DMR could then be brought back to the desired level by only a small change in the sampling period. Accordingly, the value of \(K\) should be set relatively small. Otherwise, when the DMR is at a low level, the effect of the change in sampling period on the DMR is less significant. To achieve the desired level of DMR more quickly, the value of \(K\) should be set larger.  
In this work, a simple yet illustrative algorithm given by (4) is used to adapt \(K\).  
\[K=\begin{cases}K_{0}\,/\,2&\rho(\text{k}){>}\rho_{r}+\Delta\rho^{+}\\ K_{0}&\rho_{r}\cdot\Delta\rho^{-}\leq\rho(\text{k})\leq\rho_{r}+\Delta\rho^{+ }\\ 2K_{0}&\rho(\text{k})\text{<}\rho_{r}\cdot\Delta\rho^{-}\end{cases} \tag{4}\]  
where \(K_{0}\) can be obtained from simulation experiments, \(\Delta\rho^{-}\) and \(\Delta\rho^{-}\) are user-specified parameters. Generally \(K_{0}\) inversely relates to the slope of the curve of DMR at the operation point, and consequently changes with the transmission rate \(r\) and the DMR setpoint \(\rho_{r}\).  
Besides the above equation, there are other advanced algorithms that could potentially be more efficient in adjusting \(K\), for example, the gain scheduling method from adaptive control theory. However, these complex algorithms also add burdens to online computations associated with the feedback scheduler, thus causing larger overheads.
# Multi-Center Healthcare Data Quality Measurement Model and Assessment Using OMOP CDM
## 4 Discussion  
This study differs from previous studies on data quality because it developed an index that can evaluate the quality of multiple institutions using a large cohort.  
Existing healthcare data quality studies suggest a conceptual model that can be applied to healthcare data through a literature review; however, few studies verify the proposed model using actual healthcare data [5; 20; 22; 23; 28; 30]. The verified literature has the limitation of coming from a small cohort; therefore, the present study expanded itself to utilize a large-scale, cohort-based multicenter study [6; 8; 9; 15; 16; 18; 21; 24; 27].  
In addition, an evaluation method was developed to compare the impact of errors on the healthcare quality results. The existing literature on data quality evaluation presents the net error rate and error distribution according to the quality dimension owing to the application of the data quality conceptual model. In this study, we propose a data quality evaluation method to review the causes of errors that affect healthcare data through multicenter quality comparisons according to the researcher's quality study design by expanding the results of the net error. In other words, the quality evaluation method refers to four evaluation criteria (NPR, WPR, NDPR, and WDPR) for easy access to expert reviews in evaluating healthcare data.  
Finally, when utilizing the opinions of experts, we can properly weight errors according to the degree of influence on the quality of medical institutions. Existing literature on data quality assessment emphasizes the importance of documentation and methods by which experts can review data quality results reports [8; 11]. Therefore, in this study, weights were assigned based on expert evaluations so that expert opinions and reviews can be reflected. Therefore, this study complements the existing literature by addressing the existing limitations and intuitively suggesting effects on the quality of medical institutions according to expert reviews.  
Our study has several limitations. Since the DQ4 HEALTH model proposed in this study confirms and verifies the overall quality of OMOP CDM, more detailed and specific quality verification rules should be expanded when conducting research on specific diseases and medications. For example, [PERSON] conducted a healthcare data study centered on the HIV care data-based routine data quality assessment (RDQA) model [27]. This shows the detailed data quality point of view by verifying the missing values. In addition, continuous research on data quality tools that can intuitively express diagrams and visualization functions should be expanded by applying the DQ4 HEALTH model. This was determined according to the multicenter automated quality evaluation function and quality evaluation results.  
Despite these limitations, this study analyzes the types of errors by presenting a new model that can be applied to the OMOP CDM after considering and integrating healthcare data quality studies and applying it to multiple institutions. This can be utilized in future studies.
## 5 Case-Study: Resource Fitting
### Experiment 1: Comparison of Native Performance of ML Backends
#### 5.1.3 FL Network Usage Pattern  
Although the previous remark effectively highlights the divergence in performance profiles between ML implementations, an important question arises regarding the dominance of recurrent or episodic events in these profiles. The network traffic patterns depicted in Figure 7a reveal that both ML backends exhibit recurring patterns, characterized by alternating periods of (a) relative inactivity, attributed to the initiation of local training on clients; and (b) high activity, attributed to the communication among clients and the server for the exchange of model updates and parameterization between rounds.  
Specifically, after the initial model dissemination, the communication among clients and the server unfolds in three distinct steps. In the first step, the client transmits the local training results (model weights) to the server. In the second step, the server aggregates the outcomes from all clients and computes performance metrics (i.e., entropy) to assess if additional rounds are required. Finally, in the third step, the server sends the updated weights back to the clients to initiate a new round of local training.  
As illustrated in the PyTorch plot of Figure 7b and in the zoom box of Figure 7b, the interval between the two peaks depends on the duration required for the server to aggregate and evaluate the results. A shorter duration results in the peaks appearing closer to each other. If the distance between peaks is less than twice the sampling interval, the synchronization step exhibits a \"rectangular\" shape. If the distance is less than one sampling interval, the peaks are calculated within the same interval, creating a \"triangle\" shape (as observed in TensorFlow-MNIST). In addition to the visualization artifacts, it is worth noting that _TensorFlow demonstrates steeper peaks and valleys compared to PyTorch, indicating larger communication messages and consequently higher demands for network bandwidth_.  
Figure 7: Network profiles profile for different framework/datasets/models. (**a**) Outbound Network Traffic for the CIFAR10 dataset. (**b**) Outbound Network Traffic for the MNIST dataset. The ratio between the sampling interval and the local processing can affect the visualization.
# Modeling of Historical Marine Casualty on S-100 Electronic Navigational Charts
## 5 Discussion and Limitation
### Visualization and Scalability in High-Density Environments  
The portrayal testing highlighted critical issues of symbol congestion and readability when rendering large datasets on ENCs. As demonstrated in the case study, displaying several hundred incidents simultaneously--particularly in coastal waters or busy port approaches--can lead to excessive visual clutter. Such saturation diminishes the interpretability of the chart and can potentially obscure safety-critical information.  
Although spatial filtering based on route proximity significantly improved clarity, further enhancements are needed to manage visual overload. Possible strategies include dynamic clustering of symbols, generalization into density-based heatmaps, and selective rendering based on temporal or severity thresholds. These techniques must be carefully designed to balance information richness with visual clarity and computational performance, especially in resource-constrained ECDIS environments.
# Quantum Approximation for Wireless Scheduling
###### Abstract  
This paper proposes an application algorithm based on a quantum approximate optimization algorithm (QAOA) for wireless scheduling problems. QAOA is one of the promising hybrid quantum-classical algorithms to solve combinatorial optimization problems and it provides great approximate solutions to non-deterministic polynomial-time (NP) hard problems. QAOA maps the given problem into Hilbert space, and then it generates the Hamiltonian for the given objective and constraint. Then, QAOA finds proper parameters from the classical optimization loop in order to optimize the expectation value of the generated Hamiltonian. Based on the parameters, the optimal solution to the given problem can be obtained from the optimum of the expectation value of the Hamiltonian. Inspired by QAOA, a quantum approximate optimization for scheduling (QAOS) algorithm is proposed. The proposed QAOS designs the Hamiltonian of the wireless scheduling problem which is formulated by the maximum weight independent set (MWIS). The designed Hamiltonian is converted into a unitary operator and implemented as a quantum gate operation. After that, the iterative QAOS sequence solves the wireless scheduling problem. The novelty of QAOS is verified with simulation results implemented via Cirq and TensorFlow-Quantum.  
quantum approximate optimization algorithm (QAOA); maximum weight independent set (MWIS); NP-hard; wireless scheduling; quantum application
## 6 Implementation and Discussion of Results
### Sentiment Analysis Phase  
In the sentiment analysis phase, various classification algorithms coupled with different preprocessing models are tested to determine the most accurate algorithm. The results, as shown in Table 1, underscore the superiority of the combination of TFIDF and BERT, which yielded a remarkable accuracy of 96.8%. Extensive analytics, including classification techniques and model overfitting identification, were performed. Visualization, especially using artificial neural networks (ANN) with BERT and TFIDF, played a crucial role in comprehending the training-prediction dynamics. The ANN model exhibited exceptional performance, boasting an accuracy rate of 97%, as depicted in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Accuracy** & **Remarks** \\ \hline TFIDF + ANN & 85\% & Base Model \\ BERT + ANN & 96.2\% & 11.2\% improvement over the base model \\ TFIDF + BERT + ANN & 96.8\% & 0.6\% improvement over BERT + ANN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Findings on gold data using sentiment analysis.  
Figure 7: The architecture of VMD plus LSTM.  
Table 1 and Figure 8 show that BERT with TFIDF and ANN provided better accuracy than the other combinations. The BERT model is a robust predictor without bias, given these predicted outputs and determined values. BERT provided performance based on sensitivity to the length of sentences, number of words, and opposite statements.  
The robustness of the BERT model, free from biases, is evidenced by its performance, which was influenced by various factors such as sentence length, word count, and contradictory statements.
## 6 Algorithms and Frameworks in Federated Learning
### Frameworks and Tools
#### 6.7.1 TensorFlow Federated (TFF)  
TensorFlow Federated arises from the lineage of the TensorFlow framework. As a specialized extension, TFF brings the strengths of TensorFlow into the domain of FL. The framework gives a comprehensive ecosystem, which means that developers have access to a vast library of functions and tools specifically crafted for federated scenarios. One of the standout features of TFF is its local simulation environment. This environment is invaluable for developers, providing them with a sandbox to iteratively refine and test their federated models without the problems of full deployment.  
The advantages of TFF are numerous. Its seamless integration with TensorFlow means that developers familiar with TensorFlow can easily transition into the federated realm with minimal difficulty. Additionally, due to the widespread adoption of TensorFlow in the machine learning community, TFF benefits from extensive documentation, community-driven content, and numerous tutorials. However, every tool has its challenges. The richness of TFF can also pose challenges, especially for newcomers. The vastness of the TensorFlow ecosystem can sometimes lead to a steep learning curve. Nonetheless, the rewards upon overcoming this learning curve are substantial.
## 3 Protocols Verifying the Authenticity of Digital Files in Distributed Networks
### Description of the Protocols with CRP Mechanisms
#### 3.1.1 Enrollment Cycle  
The protocol has two steps: the initial set up or enrollment performed secretly by the client or designate, and the verification of the authenticity cycle performed openly in the distributed network. Algorithm 4 for enrolling file \(F\) is shown as follows:  
Figure 3: Probability to have the length of the subset at x when K is 256-bit long. The average length of the subsets is centered at 128 with the bulk of the distribution in the 100 to 160 range.  
During the enrollment of File F, a randomly selected seed L generates a key pair (Sk; Pk) with an asymmetrical algorithm such as Dilithium. The file F is encrypted with Sk to generate the ciphertext C. The randomly selected nonce \(\omega\) is concatenated with C to form a d-bit long file C* that is at the center of the CRP mechanism. A randomly picked seed S is converted into a set of N, D-bit long, challenges generating a set of N, P-bit long responses. The responses are concatenated to generate the ephemeral key K, which encrypts the public key Pk and forms ciphertext M*. After completion of the enrollment cycle, the responses, C*, and keys K, Sk, and Pk are erased. The output is C and stream {S, \(\omega\), M*}.
## 2 Related Works  
### Convolutional Network for Network Traffic Prediction  
STDenseNet [14] is a model designed to predict spatiotemporal patterns by leveraging DenseNet [22]. Through the convolution layers in DenseNet, the model captures local spatial features and makes it possible to capture wider spatial features as the model goes deeper. To capture temporal characteristics well with CNN, STDenseNet applies the DenseNet structures separately to two key components: closeness and period. Closeness captures short-term fluctuations in data volume, and period identifies periodic patterns in data fluctuations based on daily trends. It allows the model to make predictions based on the current data trends and preserve past fluctuation patterns simultaneously. The predictions from these components are then combined using parametric fusion, where each output is assigned a learnable weight before final aggregation.  
HSTNet [15] extends STDenseNet by incorporating various mechanisms to enhance feature extraction ability. First, it uses external factors such as holiday information as additional inputs, thereby improving its ability to model complex temporal variations. Furthermore, it introduces deformable convolution within the dense blocks, enabling the model to adjust its receptive field dynamically. So, it enables the capture of more complex spatial dependencies effectively. Moreover, HSTNet introduces an attention branch, which modulates the outputs of both components by applying higher weights to pixels with higher traffic density from input data. It tries improving STDenseNet to capture spatiotemporal features well by applying deformable convolution as an attention mechanism and proves that the attention mechanism itself is helpful for accurate prediction.  
However, the attention branch in HSTNet obtains a spatial map only from inputs and directly uses it for prediction. This design restricts to reflecting spatio-temporal features from its hidden layers. And deformable convolution, which can be considered as the attention since it calculates convolution only with offset applied pixels, may lead to suboptimal performance. Because its offsets are updated via gradient descent, it is hard to reach pixels with extremely high offsets, potentially overlooking important information even if those pixels have more correlation than their neighbor pixels. However, there are not enough studies that examine the position and type of attention. So, in this paper, we explore the optimal position and search for the optimal attention type for network traffic prediction.  
Recent studies no longer rely on CNN solely and instead focus on hybrid architectures that combine CNNs with other neural network architectures. For example, Ref. [24] combines transformer structure and residual convolution networks, Ref. [21] combines 3d convolution layers, and convlstm. We adopted the CNN structure due to its fast inference speed and aim to improve performance by incorporating attention modules.