esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment  
### Datasets and Evaluation Metrics  
This paper primarily employed two widely-used datasets in medical image nuclei segmentation tasks: the PanNuke [43] dataset and the Conic [44] dataset. The PanNuke dataset served as the primary training and evaluation dataset, comprising 7901 images of size 256 \(\times\) 256 pixels. This dataset encompasses 19 distinct tissue types and five cell categories, namely, tumor cells, non-tumor epithelial cells, inflammatory cells, connective tissue cells, and dead cells. Notably, the dataset exhibits significant class imbalance, particularly a severe underrepresentation of the dead cell category, which is prominently reflected in the statistics of both cell and tissue categories. The Conic dataset consists of H&E-stained colon tissue section images, containing 4981 images of size 256 \(\times\) 256 pixels. In this dataset, cells are finely categorized into six classes, including epithelial cells, lymphocytes, plasma cells, eosinophils, neutrophils, and connective tissue cells. To evaluate the network's segmentation accuracy under semi-supervised conditions, the data from the PanNuke and Conic datasets were partitioned, with the distribution presented in Table 1. A labeled training dataset was created through random sampling from the original training dataset, while the remaining portion formed the unlabeled dataset.  
We adopted Precision, Recall, F1-score, and Mean Intersection over Union (MIOU) as the evaluation metrics for the segmentation performance of the semi-supervised model.  
\[Recall=\frac{TP}{TP+FN} \tag{17}\]  
\[Precision=\frac{TP}{TP+FP} \tag{18}\]  
\[F1-\text{score}=\frac{2 Precision\times Recall}{Precision+Recall} \tag{19}\]  
where True Positive (TP) represents the count of correctly segmented target pixels, False Positive (FP) denotes the count of pixels from other classes incorrectly segmented as target pixels, and False Negative (FN) indicates the count of target pixels mistakenly segmented as non-target pixels. MIOU is a key metric for assessing the accuracy of semantic segmentation models. It is the ratio of the intersection to the union of the predicted and ground-truth values. It is expressed in Equation (20):  
\[MIOU=\frac{1}{k}\sum_{i=1}^{k}\frac{TP_{i}}{TP_{i}+FP_{i}+FN_{i}} \tag{20}\]  
where \(k\) represents the number of pixel categories, excluding the background class.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **The Proportion of Labeled** & **Labeled Training Set** & **Unlabeled Training Set** & **Validation Set** \\ \hline \multirow{4}{*}{PanNuke} & 1/4 & 1578 & 4734 & \multirow{4}{*}{1589} \\  & 1/8 & 789 & 5523 & \\  & 1/16 & 394 & 5918 & \\  & 1/32 & 197 & 6115 & \\ \hline \multirow{4}{*}{Conic} & 1/4 & 996 & 2988 & \multirow{4}{*}{997} \\  & 1/8 & 498 & 3486 & \\ \cline{1-1}  & 1/16 & 249 & 3735 & \\ \cline{1-1}  & 1/32 & 124 & 3860 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Division of the PanNuke and Conic datasets.
# Comparative Analysis of BERT and GPT for Classifying Crisis News with Sudan Conflict as an Example
## 2 Related Work
### Current Problems and Contributions  
In summary, despite significant advancements in text classification through models like BERT and GPT, several problems persist, particularly in handling MLC, low-resource datasets, and classification consistency. Traditional supervised classification approaches require a large amount of annotated training datasets, which are often unavailable for specific domains. While BERT improves classification accuracy through bidirectional context modeling, it demands extensive fine-tuning and computational resources. On the other hand, GPT's zero-shot and few-shot learning capabilities offer adaptability but may generate inconsistent outputs, making it less reliable for structured classification tasks.  
These problems become more pronounced in complex conflict-related articles. For example, MLC is required to classify articles into multiple categories (e.g., war crimes, humanitarian crises, and military operations), yet existing models struggle with overlapping categories and imbalanced datasets. The effectiveness of classification is further influenced by data constraints, ambiguous text, and entity overlaps, which complicate model training and evaluation. This research explored and optimized LLM-based classification techniques, focusing on prompt-based strategies with GPT and both standard and hyperparameter-tuned versions of BERT. By systematically comparing GPT's prompt-based methods against BERT's hyperparameter-tuned classification, the study evaluated approaches for improving classification performance.
# A Trusted Routing Scheme Using Blockchain and Reinforcement Learning for Wireless Sensor Networks
## 4 Experimental Analysis and Evaluation
### Efficiency
#### 4.3.3 Token Transaction Throughput  
The token transaction throughput shows the blockchain system's ability to handle concurrent token transactions. We tested the throughput of transactions processed by our PoA blockchain system and the PoW blockchain system under the token transaction request rate ranges from 0 to 5000 times/s. The experimental results are shown in Figure 10.  
As we can see, the token transaction throughput increases steadily as the rate of concurrent requests increases, and the curve gradually flattens out as the throughput reaches its peak. Finally, the throughput of our blockchain system based on PoA consensus mechanism is stable at 3300 times per second, while that of the traditional blockchain system based on PoW consensus mechanism is only about 1900 times per second. From the experimental results, we can see that a PoA-based scheme has more efficient transaction processing capacity in the face of high request concurrency. It is appropriate and correct to take PoA algorithm as the consensus mechanism algorithm of the blockchain system, and this PoA blockchain-based routing scheduling scheme can effectively cope with the situation of large concurrent requests in the routing environment.
# A Model and Quantitative Framework for Evaluating Iterative Steganography
## 2 A Model for Iterative Steganography
### Encoding Function  
We assume that in each iteration, the entire message, \(m\), is encoded. However, it is also possible to consider scenarios where the message, \(m\), is divided into disjoint parts, with each part treated as a separated message subjected to independent encoding.  
First, we define the _encoding function_, \(f\)', which encodes the message, \(m\), in the \(i\)-th iteration of the cover (refer to Equation (11)).  
\[f^{\prime}:\mathcal{I}\times\mathcal{M}\rightarrow\mathcal{I} \tag{11}\]  
\[\bigwedge\limits_{i\in\{1...Z\}}s_{i}:=ft(c_{i},m)\]  
where \(m\)--the message to be encoded in the \(i\)-th iteration.  
The _iterative encoding function_, \(f\), is defined as the function that encodes the message, \(m\), in the cover (refer to Equation (12)).  
\[f:\mathcal{C}\times\mathcal{M}\rightarrow\mathcal{S} \tag{12}\]\[\bigwedge_{i\in\{1...Z\}}s^{i}:=f\left(c^{i},m\right)\]  
where  
\(c^{i}\)--the cover composed of \(i\) iterations;  
\(s^{i}\)--the steganogram created by the function \(f\) through encoding the message \(m\) into the cover \(c^{i}\).  
An iterative steganogram can be regarded as a vector formed by sequentially appending the values of \(f^{\prime}\) calculated for consecutive iterations (refer to Equation (13)).  
\[\bigwedge_{i\in\{1...Z\}}f\left(c^{i},m\right)=\begin{cases}[c_{1}],&i=1\\ f\left(c^{i-1},m\right)\bigoplus c_{i},&i\
otin I\\ f\left(c^{i-1},m\right)\bigoplus f^{\prime}(c_{i},m),&i\in I\end{cases} \tag{13}\]
# HyperIO: A Hypervisor-Based Framework for Secure IO
## 4 Related Work
### Hypervisor  
T-PIM [29] uses a full hypervisor to run two virtual machines. One virtual machine is a general workstation, whereas the second provides a secure input. T-PIM automatically switches between the two virtual machines when a sensitive input is required. Compared to T-PIM, HyperIO, based on a thin hypervisor, has a much smaller TCB and better performance.  
Qubes OS [30] uses a full hypervisor (Xen [31]) to run multiple virtual machines. The displays of all the virtual machines are combined such that the system behaves as expected from the user's perspective. The frame of each window indicates the virtual machine which hosts the window's application, while Qubes OS improves overall security, a vulnerability in the kernel can disclose the compromised virtual machine's sensitive information. Furthermore, similarly to T-PIM, Qubes OS has a large TCB and non-negligible performance overhead.  
SGXIO [11] uses a hypervisor to provide a secure input and output for SGX enclaves. The hypervisor includes drivers for devices whose input or output need to be protected. Furthermore, the hypervisor emulates two virtual devices for each such device: secure and non-secure. The operating system can access the non-secure device, whereas an SGX enclave can only access the secure device. The multiplexing between the two devices can be temporal, i.e., at different times, the actual device is owned exclusively by the operating system or the enclave. It can also be spatial, i.e., the devices' existing resources are divided between the operating system and the enclave. SGXIO provides a generic solution for the trusted IO path problem, and its ideas can be used to implement HyperIO as an enclave. However, the device emulation harms both the simplicity and performance of systems based on SGXIO. The design of HyperIO is much simpler, thus allowing it to have a small TCB and achieve a minor performance penalty.  
[PERSON] et al. [32] described a generic method to build a trusted IO path between a device and a user application, using a thin hypervisor as a trusted codebase and isolation provider for the sensitive IO. Specifically, the authors discussed the applicability of their design to USB devices. Using these ideas, HyperIO can be extended to support not only PS/2 keyboards but also USB keyboards.  
In our prior work, HyperPass [33], we described a hypervisor-based method that allows the user to enter their password securely. Unlike HyperIO, HyperPass is not a generic framework. HyperPass can be considered an application of HyperIO, similar to FireSafe and ClipCrypt. However, some of the ideas of HyperPass are implemented in HyperIO.
# Joint Hybrid Beamforming Design for Millimeter Wave Amplify-and-Forward Relay Communication Systems
## 1 Introduction
To make it tractable [16], reformulated the problem by using the penalty dual decomposition technique. Currently, there are still few researchers focusing on HBF design for mmWave relay communication systems on the basis of channel estimation errors. Against this background, the above problem of HBF design for the mmWave relay communication system is addressed in this paper. Instead of considering only relay nodes, all nodes are jointly optimized in this paper. Additionally, we take both perfect and imperfect CSI into account. It is worth noting that our proposed algorithms can be applied to both HD and FD mode. For clarity, the main contributions of our work are summarized as follows:  
* We first consider the HD relay system under the assumption of perfect CSI. Different from conventional MSE minimization problems, where the power constraints introduce additional complexity, we model the improved MSE (IMSE) as the optimization objective by designing a power scaling factor. * To deal with the complicated non-convex problem, a manifold optimization (MO) based alternating optimization algorithm is proposed, which decomposes the problem into three sub-problems. Simulation results demonstrate that our proposed method is superior to its counterparts by approaching the full digital solution. * In terms of the FD relay communication system in the practical scenario, we develop a robust HBF algorithm. To mitigate SI introduced by the FD model, we further develop a null-space-projection (NP)-based SI cancellation (SIC) method, which has no limit on the number of RF chains in contrast with traditional methods. Simulation shows that the proposed approach can achieve sufficient suppression of SI, providing significant beamforming gain. The remaining sections are organized as follows. Section 2 presents the system model and the mmWave channel model. Section 3 introduces the HBF design for the HD AF relay communication system. The SIC-NP and HBF-MO-FD-R algorithms for the FD AF relay communication system are proposed in Section 4. Section 5 provides our simulation results. Finally, Section 6 draws conclusions. _Notations_: In this paper, vectors and matrixes are denoted by bold lowercase letters and uppercase letters, respectively. \(||\cdot||\) and \(||\cdot||_{F}\) stand for the Euclidean norm of a vector and the Frobenius norm of a matrix. \(\text{tr}(\cdot)\) and \((\cdot)^{H}\) represent the trace and conjugate transposition operations, respectively. \(|\cdot|\) represents the magnitude of a complex number. \(\text{E}\{\cdot\}\) denotes the expectation operator. \(\text{d}(\cdot)\) and \(\
abla(\cdot)\) denote the differential and the gradient of a function. \(\mathbb{R}\) and \(\mathbb{C}^{m\times n}\) denote the set of all real matrixes and all complex \(m\times n\) matrixes. \(\mathbf{I}_{m}\) denotes the \(m\times m\) identity matrix. Some important symbols and their meanings are listed in Table 1.
## 2 Materials and Methods
### Methodology
#### 2.4.1 Land Cover Dataset at HRB from LCMM Method  
LCMM is a comprehensive land cover mapping method using multiple classifiers and multisource remotely sensed imagery. Multisource remotely sensed data have advantages in spatial resolution (VHSR images from Google Earth), temporal resolution (monthly HJ-1/CCD images), and spectrum (Landsat/TM). In the meantime, multiple classifiers including time series analysis, SVM, thresholding, object-based method, and decision trees were all employed for different classification purposes. All the classifiers and data were successfully integrated by LCMM, and a land cover dataset at HRB with a high accuracy of over 90% was made in a simple and efficient way, which has been largely downloaded (463 downloads on 12 January 2020) from the datacenter website and widely used for different applications and scientific research, such as vegetation parameter retrieval [36], eco-hydrological modeling [37], land process modeling [38] and so on. However, only land cover maps in 2011-2015 were made because of data availability. In addition, this land cover dataset is monthly, so it needs to be aggregated to a yearly map based on the classification system in Table 3 before using and the mapping rules for different classes can be found in Table 3. In addition, the accuracy can be improved further after 2015, because the Sentinel-2/MSI data from ESA and GF1/6-WFV data from China can compose a higher frequency of time series images with better quality.
## 3 Materials and Methods
### Step 4: MOOM-OEP Formulation
#### 3.5.3 Constraints  
The optimization is subject to the following:  
* Operational Constraints [32]: \[\sum_{k=1}^{p}Y_{ijk} =1\quad\forall i,j\] (9) \[M\text{U}_{ij} \leq\text{Capacity}_{ij}\quad\forall i,j\] (10) \[TP_{ij} \geq\text{Demand}_{ij}\quad\forall i,j\] (11)
* Environmental constraints: \[EC_{ij} \leq\text{Threshold}_{\text{Energy},ij}\quad\forall i,j\] (12) \[CE_{ij} \leq\text{Threshold}_{\text{Carbon},ij}\quad\forall i,j\] (13) \[WG_{ij} \leq\text{Threshold}_{\text{Waste},ij}\quad\forall i,j\] (14)
* Cycle Time Constraints: \[CT_{ijk}\geq t_{\text{process},ijk}\quad\forall i,j,k\] (15)
* Resource Allocation Constraints: \[\sum_{k=1}^{p}Z_{ijk}\leq\text{ResourceLimit}_{ij}\quad\forall i,j\] (16)
* Activity Sequencing Constraints: \[t_{\text{start},ijk+1}\geq t_{\text{end},ijk}+t_{\text{transition},ij}\quad \forall i,j,k\] (17)
* Cloud Capacity Constraints: \[\sum_{i=1}^{n}\sum_{j=1}^{m}C_{ij}\leq\text{CloudCapacity}_{\text{total}}\] (18)  
where \(C_{ij}=f_{\text{compute}}(X_{ijk},Y_{ijk})\). The effective management and optimization of such cloud capacity to ensure efficient resource utilization is an important consideration, often tackled with data-driven approaches and machine learning [27].  
* Binary Decision Constraints: \[Y_{ijk}\in\{0,1\}\quad\forall i,j,k\] (19)
# Many-Objective Optimization for Anomaly Detection on Multi-Layer Complex Interaction Networks
## 3 Method
### Centrality-Based Many-Objective Optimization Approach  
For many-objective optimization of the network centrality, we define one objective function for each layer; per layer, our method is applied to minimize the eigenvector centrality of that layer, while overall, all layers are obviously taken into account by our proposed many-objective optimization approach. Using this method, for a multiplex network \(G\) with layers \(G_{1},...,G_{m}\), \(m\) is thus defined as the number of objective functions.  
Obviously, each node in each layer of the network can have a different centrality--either high or low and dominated or non-dominated. The node centrality is said to be non-dominated if there is no other point better than or equal to the centralities in different layers and better in at least one criterion or in one layer. For computing the non-dominated subset from a finite set of \(n\) solutions, the algorithm by [PERSON], [PERSON], and [PERSON] is the fastest known approach [63]. It accomplishes this task with a time complexity in \(O(n\log n)\) for \(m=2,3\) and \(O\big{(}n(\log n)^{m-2}\big{)}\) for \(m>3\). For our proposed approach, we compute the Pareto Front using many-objective optimization (by minimizing eigenvector centrality) to find the set of non-dominated solutions, i.e., the set of nodes with very low importance in the multi-layer network. The set of nodes in the resulting Pareto Front, i.e., the set of non-dominated solutions, is then the basis to select suspected anomalous nodes.
# Power Prediction of Bifacial Si PV Module with Different Reflection Conditions on Rooftop
## 2 Modeling and Method
### Irradiance Prediction of the Rear Side  
When the solar module is installed, the value of the total irradiance (\(I_{Total}\)) reaching the module can be divided into the front irradiance (\(I_{Front}\)) and the rear irradiance (\(I_{Rear}\)). The amount of irradiance entering the front side of the module can be easily measured by installing an irradiance detector. On the other hand, it is difficult to measure the irradiance on the rear side because of scattered and direct irradiance. Therefore, the irradiance entering the rear of the module is divided into a direct reflection component and an indirect reflection component.  
The amount of total irradiance (\(I_{Rear}\)) on the module by the rear environment is expressed as the sum of direct reflection and indirect reflection. Therefore, the total amount of irradiance on the rear side of the module is assumed to be as given by Equation (4).  
\[I_{Rear}=I_{Rear,dir}~{}+~{}I_{Rear,diff} \tag{4}\]  
To express the direct irradiance and scattered irradiance, we use the concept of view factor described in Section 2.1. Therefore, Equation (3) can be expressed by Equations (5) and (6).  
\[I_{Rear}=\alpha DNI\frac{1-cos(180^{\circ}-\beta)}{2}+\alpha(GHI-DNI)(\frac{1 -cos(180^{\circ}-\beta)}{2}-F_{m}) \tag{5}\]  
\[I_{Rear}=\alpha DNI\frac{1+cos\beta}{2}+\alpha(GHI-DNI)(\frac{1+cos\beta}{2}- F_{m}) \tag{6}\]  
\(\frac{1+cos\beta}{2}\) is a value showing the area where the sunlight is reflected and scattered by the rear environment on the rear part of the module due to the view factor when the ground and module are installed at an angle of \(\beta\). \(\alpha\) is the coefficient of albedo due to the rear environment. \(F_{m}\) is defined by the View Factor affecting to the module from the ground based on the process of obtaining \(F_{ms}\). In Equation (6), the values of \(\alpha\), \(\frac{1+cos\beta}{2}\), and \(F_{m}\) are considered as constants. The total irradiation (\(GHI\)  
Figure 3: The components of the reflected radiation to the rear side.  
and the amount of _DNI_ depend on the location, time, and environment of the place where the solar module is installed.
# Ultra-Low-Loss Silicon Waveguides for Heterogeneously Integrated Silicon/III-V Photonics
###### Abstract  
Integrated ultra-low-loss waveguides are highly desired for integrated photonics to enable applications that require long delay lines, high-\(Q\) resonators, narrow filters, etc. Here, we present an ultra-low-loss silicon waveguide on 500 nm thick Silicon-On-Insulator (SOI) platform. Meter-scale delay lines, million-\(Q\) resonators and tens of picometer bandwidth grating filters are experimentally demonstrated. We design a low-loss low-reflection taper to seamlessly integrate the ultra-low-loss waveguide with standard heterogeneous Si/III-V integrated photonics platform to allow realization of high-performance photonic devices such as ultra-low-noise lasers and optical gyroscopes.  
ultra-low-loss waveguide; silicon photonics; heterogeneous integration; narrow linewidth lasers; high \(Q\) resonators +
Footnote †: journal: Applied Physics Letters
## 5 Discussion and Conclusions
### 5.1. Scarity of Datasets
The unique characteristics of the ocean environment present significant challenges in data acquisition, which is often costly and time-consuming. Consequently, numerous studies depend on limited experimental data for modeling, hindering the ability of models to effectively adapt to the complex and variable nature of the ocean environment [106]. To enhance the accuracy and reliability of noise prediction, it is imperative to establish a diversified hydrodynamic noise dataset. The development of a diverse hydrodynamic noise dataset has emerged as a pivotal area of research. A comprehensive dataset encompassing diverse flow conditions and noise source types could enhance the precision of noise prediction models and provide substantial data support for subsequent algorithm optimization and practical applications. The issue of data representation continues to impede the implementation of intelligent algorithms within the domain of hydrodynamic noise research. However, with the continuous advancements in data acquisition technology and the maturation of data enhancement technology, the potential of intelligent algorithms for noise prediction and control will be more fully realized in the future. The establishment of more diverse noise datasets, in conjunction with advanced algorithmic models, will empower researchers to more accurately simulate and predict the behavior of fluid noise, thereby providing a more reliable theoretical foundation for practical noise control and optimization.