esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Framework for Advanced De-Identification of Protected Health Information
## 1 Introduction
Our research highlights the importance of sophisticated natural language processing (NLP) techniques in safeguarding health information and offers an effective approach for de-identifying EHR that comply with privacy laws. The primary aim of this research paper is to provide a strong framework for de-identifying sensitive data in EHRs by utilizing deep learning and machine learning techniques. This research employs various algorithms to guarantee the secure extraction of personal patient data while concurrently upholding the integrity of essential clinical information. The dataset utilized for this research is sourced from Harvard University,specifically designated for academic users, by the Data Use and Confidentiality Agreement established with Partners HealthCare System, Inc. This agreement confers access to de-identified patient discharge summaries for research purposes, with a particular focus on the enhancement of natural language processing (NLP) techniques within the healthcare domain. This research investigates the following pivotal inquiries:  
* How much have machine learning and deep learning algorithms contributed to the security of EHR? * Is the proposed algorithm capable of effectively removing sensitive patient data from a large dataset? * What is the significance of the Conditional Random Field (CRF) model in safeguarding patient information, particularly concerning the 23 critical categories of Protected Health Information (PHI) present in EHRs? * How effective is the Bidirectional Long Short-Term Memory (Bi-LSTM) deep learning algorithm at improving de-identification in medical records? The remainder of this paper is organized as follows: Section 2 encompasses a comprehensive review of related works in E-health records privacy. It critically discusses and analyzes various approaches and experimental findings from these works, while Section 3 outlines the research methodology, Section 4 presents the results and discussion, and Section 5 contains the conclusion.
## 3 Experiments and Analysis
### Wind Turbine Blade Surface Damage Dataset  
In this study, we collected 306 images of wind turbine blade in JPG format and 921 s of video data in MOV format from a wind farm using a UAV. After splitting the video data into image data, there are 4396 images in total. The input image size required by ED Net is 56 \(\times\) 56 pixels, while the image size captured by the UAV is 5472 \(\times\) 3684 pixels. The captured images are larger in pixels and can be appropriately cropped using OpenCV. When the captured images are not cropped to meet the requirements or have no major features, they can then be appropriately modified manually and cropped to a small 56 \(\times\) 56 image to meet the input requirements of the network. Then, we performed data augmentation on the dataset by adding random noise, image flipping, and clockwise rotation to the damaged images using OpenCV. After data augmentation, the entire dataset had 2648 images. Lastly, since the color of the image in this experiment has little impact on the identification of damage types, the dataset can be grayed to preserve the gradient, reduce the number of calculations, and accelerate the training speed. The pre-process is shown in Figure 8.  
Figure 7: The structure of DPCI_SE Block.  
It is categorized according to the type of damage, including normal, cracks, and surface shedding images, as shown in Figure 9. Among them, there are 600 normal pictures, 1008 pictures with cracks, and 1040 pictures with surface shedding.  
To mitigate overfitting as much as possible, the training and test sets were divided into 9:1 ratio before the experiment. Before training the network, the dataset was normalized to 56 \(\times\) 56 and batch normalization was performed to conform the image data to the standard normal distribution and accelerate the model's convergence rate.
## 4 Simulation Experiments: Security Analysis and Comparison
### Chosen-Plaintext Attack  
In a chosen-plaintext attack [50], the attacker can select a particular plaintext and observe the resulting ciphertext. The objective is to examine the obtained pairs of plaintext  
Figure 5: Comparison of the plaintext sensitivity of the proposed scheme with the state-of-the-art schemes [10; 11; 18; 38; 44].  
and ciphertext to gain information about the secret encryption key or to understand the encryption process, which can compromise the security of the system. In the proposed scheme, the secret encryption keys consist of elliptic curve parameters denoted as \(p,b\), and \(G\). It is important to note that for each plaintext, there is a variation in the values of \(p,b\), and \(G\). As a result, the attacker cannot deduce any information about the secret key based solely on the plaintext-ciphertext pairs. This characteristic demonstrates that the proposed scheme is secure against chosen-plaintext attacks.
###### Abstract
Thus, deep learning methods have been widely used in solving real-world problems with the advancement of artificial intelligence [22; 23; 24]. Recently, popular deep learning methods, such as the convolutional neural network (CNN) and long short-term memory (LSTM), have been applied to predict crop yield and outperformed traditional machine learning methods [13; 25; 26; 27]. For instance, [PERSON] et al. [27] predicted the county-level winter wheat yield in China using deep learning methods, including DNN (deep neural networks), 1D-CNN (1D convolutional neural networks), and LSTM, while [PERSON] et al. [13] found that the LSTM model outperformed the BPNN (back propagation neural network) and SVM (support vector machine) in predicting wheat yield. Deep learning methods have achieved high accuracy in predicting crop yield. However, as 'black-boxes', deep learning methods have the disadvantage of low interpretability. Therefore, how to improve interpretability is a crucial research question [28]. Existing studies using deep learning methods mainly focus on improving yield prediction performance without considering model interpretation. Improving interpretability is essential to ensure the model's reliability and to understand the environmental features relevant to crop yield. Recently, the attention mechanism has provided a channel for interpreting deep learning models by exploring the distribution of attention weights. Based on the attention mechanism, the transformer model was proposed in natural language processing [29], providing an approach to improve the interpretability of models. Precisely, the transformer models can explain the internal working of a model using self-attention weights. Compared with other traditional machine learning methods, transformer models have proved their state-of-the-art performance in natural language processing applications, such as generative language modeling [30; 31]. Moreover, the latest agricultural studies have used these transformer models in crop classification [32; 33; 34]. To our best knowledge, there are still no studies that use transformer models to predict crop yield based on remote sensing and climate data. In this study, we adopted a transformer model called Informer to improve the performance of district-level rice yield prediction and interpreted the internal mechanism of models from multiple perspectives. First, the Informer model was applied with multi-temporal inputs based on satellite and meteorological data from 2001 to 2016. Then, the Informer model was compared with LASSO, RF, XGBoost, and AtLSTM for performance evaluation. Finally, the potential driving factors for yield prediction were investigated by analyzing the feature importance and attention weights. This study seeks to address two questions:  
1. How does the Informer model perform for rice yield prediction compared with several traditional machine learning and deep learning models? 2. How can we interpret deep learning models for predicting crop yield based on attention mechanisms?
## 4 Discussion
Since the distance between the vessel upper and lower walls is greatest in the central cross-section, a reduction in elevation focusing level will result in a narrowing of the vessel inner diameter in the US image. It may be necessary to optimize the element array so that the main lobe width in the elevation direction becomes narrower in order to facilitate more accurate evaluation of the blood vessel diameter. Although it was demonstrated that an element array robust to positional and angular errors can be achieved by the optimization using the proposed method, there were limitations in both the optimization process and the evaluation. In the optimization process, the element directivity was calculated using only the center frequency. However, the actual US wave contains multiple frequencies, resulting in more complex directivity. Furthermore, in actual US elements, vibrations occur not only in the thickness mode but also in other modes, which makes the directivity more complex. Accordingly, when applying the element array-optimization method to actual US probes, it is necessary to use a directivity based on measurements of actual US elements. In addition, the far-field approximation was employed in the acoustic pressure field calculation. It should be noted that this method may not be applicable to probes designed for imaging shallow targets. The imaging performance evaluation was conducted solely in simulations. While robustness against white noise was evaluated in the blood vessel imaging, several factors, other than noise, differ between simulation and real conditions. These include sound speed inhomogeneity and reflections between the probe and the skin. Therefore, to apply the proposed method to actual probes, verification using real data will be necessary. The US imaging was conducted under the condition that the positional and angular errors of the element array relative to the imaging plane were known. However, in practical use of skin-attachable probes, these errors are not known. To ensure imaging of the correct cross-section, a method for identifying it from three-dimensional space will be required. In the simulations, the element array shapes were fixed. However, in actual flexible probes, the array shape changes to conform to the body surface. Therefore, to apply the optimized array to flexible probes, it is necessary to evaluate the focusing performance when the array is deformed.
## 3 Results and Discussion
### Confirmation of Flocculation Mechanism through Bonding Type Test  
The flocs underwent three chemical treatments, namely 5 M Urea, 0.5 M HCl, and 10 mM EDTA-Na\({}_{2}\). According to [38] (2015), EDTA-Na\({}_{2}\) and HCl have been seen to disrupt ionic connections, whereas urea has been found to disrupt hydrogen bonds. The observation results indicated that the flocs treated with urea had a significant impact. This phenomenon may be attributed to the interaction between urea and kaolin clay particles, disrupting hydrogen bonds between the particles and the biofoculant. Consequently, the flocs created experienced a collapse, resulting in the turbidity seen in the reaction system. The flocs treated with EDTA-Na\({}_{2}\) exhibited a minor disruption level, indicating weak ionic connections between the biofoculant and the kaolin particles. In addition, applying hydrochloric acid (HCl) to the flocs resulted in little or insignificant impact. This implies that hydrogen bonding is mostly the primary bonding mechanism between D2 flocculants and kaolin particles. Hydrogen bonding occurs when hydroxyl and amide functional groups are present [43]. The chemical structure of biofoculants contains functional groups that facilitate the bridging process by offering binding sites to attach contaminants or particles [44].  
The results contrast those of [23] (2020), who discovered that the biofoculant generated by B. megaterium exhibited an ionic bonding nature in the bonding type test. This observation underscores the need for a more comprehensive comprehension of the behavioral dynamics of biofoculant-producing microorganisms to effectively leverage their economic potential, given their susceptibility to multifarious circumstances and situations. In summary, the underlying mechanism of action for biofoculant D2 involves a two-stage process. The first phase involves the neutralization of charges via the interaction with Ca\({}^{2+}\) ions, forming Ca\({}^{2+}\) and kaolin complexes. This interaction serves to stabilize and neutralize the overall charge. Subsequently, a bridging process ensues, whereby this mechanism gives rise to a three-dimensional configuration, wherein the functional groups present in the biofoculant, mostly polysaccharides, serve as the bridging component connecting the kaolin particles. The presence of these functional groups facilitates two forms of interaction, namely ionic and hydrogen bonding, resulting in the adsorption of biofiloculants onto the surface of kaolin clay particles [45].
## 5 Feasibility Study Through System Simulations
### NN Structure for Each Pattern-Matching Method and Model Training Details
#### 5.8.4 Notes on the Model Training Hyperparameters  
All of the models were trained with the RMSprop optimizer and an adaptive learning rate. A maximum number of 100 training epochs was selected for the three pattern-matching methods based on regression (1-SingFingIn-PosOut, 2-SingFingIn-AggPosOut, and 3-MultFingIn-PosOut), and 40 training epochs were selected for the pattern-matching method based on classification (4-MultFingIn-ClassOut). Observing the loss metric in the validation dataset for fixed tiles, FTV, we noticed that before reaching the selected maximum number of epochs, the loss metric either reached a minimum value or showed a trend with oscillations around a possible minimum value. We recall that the training is carried out \(N_{I}\) times with different initial random weights (as explained in Section 5.6.3).  
The batch sizes were determined through systematic experimentation when searching for an optimal NN structure in Mechanism 1 (Section 5.7.1). We recall that the models are trained with fixed tiles; however, in the end, they must generalize for moving tiles. A better generalization was observed, with smaller positioning errors, when adopting small batch sizes, particularly in the order of 32 data samples. The same observation has been reported in the literature, which discusses the degradation of the model's generalization when increasing the batch size [224]. Thus, the training of the models for all of the pattern-matching methods was carried out using batch sizes of 32 data samples.
## 2 Materials and Methods
### Evaluation Metrics  
The AUC-ROC metric was utilised to assess the accuracy of the proposed method. The evaluation of the model performance on the input images is based on classification measures, including true positives (_TPs_), true negatives (_TNs_), false positives (_FPs_), and false negatives (_FNs_). _TP_ represents correctly identified positive cases, _TN_ corresponds to correctly identified negative cases, _FP_ refers to negative cases incorrectly classified as positive, and _FN_ indicates positive cases incorrectly classified as negative. Based on these measures, a range of performance metrics may be derived. One of them is accuracy, which measures the overall correctness of the model, mathematically expressed as follows:  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN}, \tag{5}\]  
Although accuracy provides a general measure of performance, it may be inadequate for imbalanced data sets. To address this, precision and recall are often employed. Precision quantifies the proportion of true positive predictions among all cases predicted as positive, formally defined as follows:  
\[\text{Precision}=\frac{TP}{TP+FP}, \tag{6}\]  
Conversely, recall (or sensitivity) measures the proportion of actual positives that were correctly identified, represented as follows:  
\[\text{Recall}=\frac{TP}{TP+FN}, \tag{7}\]  
These two metrics are typically combined into the F1-score, which is the harmonic mean of precision and recall, offering a balanced evaluation of the model, mathematically expressed as follows:\[\text{F1-Score}=\frac{2\cdot(\text{Precision}\cdot\text{Recall})}{\text{Precision}+ \text{Recall}} \tag{8}\]  
Furthermore, the false negative rate (FNR) is employed in contexts where missing positive cases is critical, such as the actual medical context. FNR is defined as:  
\[\text{False Negative Rate}=\frac{FN}{TP+FN}, \tag{9}\]  
The experiments were conducted on a computing cluster equipped with two Nvidia T4 GPU units, each with 15 GB of memory (a total of 30 GB for GPU processing), supported with 29 GB of system RAM and a Ryzen 7 5700X 8-core processor at 3.40 GHz, along with 73.1 GB of disk storage.
###### Abstract  
Spherical antenna array (SAA) exhibits the ability to receive electromagnetic (EM) waves with the same signal strength, regardless of the direction-of-arrival (DoA), angle-of-arrival, and polarization. Hence, estimating the DoA of EM signals that impinge on SAA in the presence of mutual coupling requires research consideration. In this paper, a subspace pseudointensity vectors technique is proposed for DoA estimation using SAA with unknown mutual coupling. DoA estimation using an intensity vector technique is appealing due to its computational efficiency, particularly for SAAs. Two intensity vector-based techniques that operate with spherical harmonic decomposition (SHD) of an EM wave obtained from SAA are presented. The first technique employed pseudointensity vectors (PV) and operates quite well under EM conditions when one source is in operation each time, while the second technique employed subspace pseudointensity vectors (SPV) and operates under EM conditions when multi-sources and multiple reflection cause more challenging problems. The degree of correctness in the estimation of the DoA via the PVs and SPVs is measured using baseline methods in the literature via simulations, adding noise to stationary, single-source and multi-source methods. In addition, incorporating mutual coupling effects, data from experiments, which are the generally acceptable ground truth when examining any procedure, are further used to illustrate the robustness and efficiency of the proposed techniques. The results are sufficiently inspiring for the practical deployment of the proposed techniques.  
direction-of-arrival; multi-source; SAA; pseudointensity vector; subspace pseudointensity vector; measured data; mutual coupling +
Footnote †: journal: Applied Sciences
# An Improved Contact Force Model of Polyhedral Elements for the Discrete Element Method
###### Abstract  
The discrete element method (DEM) serves as a robust tool for simulating the mechanical behavior of granular materials. The accuracy of the DEM simulation is determined by the calculation of contact forces between elements. Compared to spherical elements, the contact modes of polyhedral elements are more complex. In this work, the contact force between polyhedral elements under different contact modes was investigated by experiment. Based on the experimental results, the normal stiffness coefficient in the Cundall's contact model was modified. The improved contact force model was then applied in the DEM simulation and validated by means of comparison with the results of packing experiments. The research results demonstrate that the improved contact force model can be effectively applied to the simulation of different contact modes between polyhedral elements. The results of the packing experiment highly coincide with the results of the DEM simulation, which confirms the accuracy and reliability of the improved contact force model.  
granular materials; discrete element method; contact mode; contact force
## 4 Discussion  
The study set out to achieve three objectives: to estimate phenological parameters from MODIS VI time series, to compare the utility of MAIAC-processed data against standard product data and to assess the predictability of seasonal transitions and vegetation productivity from MODIS PRI. Numerous studies have employed similar methods linking NDVI time series with flux tower measurements [9; 44]; however, there is currently very limited research published on the use of satellite-derived PRI for estimating phenology.
## 3 System Model
### Quantum Layer
#### 3.1.2 Adversary Model  
In end-to-end key distribution, secret keys are converted from ciphertext to plaintext at relay nodes, making these points attractive targets for eavesdroppers. Each untrusted relay node has a security probability indicating the likelihood that it has not been compromised. Attackers can only access key information from successfully compromised relays.  
The security of a single-path key distribution depends on all uncompromised relay nodes on the path. In contrast, multipath key distribution security relies on at least one path remaining secure.  
Implementing an MTD strategy, which involves dynamically changing network parameters to confuse potential attackers, can further enhance security. This approach complicates the attack process and requires adversaries to adapt to changing network configurations. Therefore, in our proposed model, illustrated in Figure 3, the attacker faces the challenge of deciphering multidimensional parameters, such as ensuring the security of at least one path and the generated key pairs at the correct time, to successfully decrypt the transmitted key. Moreover, the attack process in our model is further complicated by the adaptive penalty-based approach. This means that even if the attacker succeeds in compromising a particular node, this node will face penalties in subsequent transmissions, requiring the attacker to adapt and change the targeted relay to achieve further success.
# A Multi-Agent Deep Reinforcement Learning System for Government Interoperability
## 4 System Design
### Communication Protocols  
The Foundation for Intelligent Physical Agents (FIPAs) agent communication language (ACL) is a standardized protocol designed to facilitate structured communication between agents in a multi-agent system. By ensuring seamless information exchange, FIPA-ACL enhances coordination and interoperability, allowing agents to collaborate efficiently in complex administrative environments.  
In addition to structured communication, a shared ontology plays a crucial role in defining concepts and relationships relevant to municipal administration. This ontology ensures semantic interoperability by standardizing key elements such as financial reports, permits, and municipal resolutions. By establishing a common understanding across different governmental units, the shared ontology improves data consistency and enhances the effectiveness of decision-making processes [26].  
Furthermore, behavior models govern the actions of agents, enabling structured behaviors for data verification, task allocation, and deep reinforcement learning (DRL)-based adaptation. These models help agents follow predefined rules while also learning from their environment, improving their ability to manage tasks dynamically. By integrating structured communication, shared ontology, and adaptive behavior models, the system ensures efficient and intelligent municipal administration.