esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Goal and Workflow  
DataToCare is a generic ICU treatment recommendation system that predicts for a target patient \(\overline{\hat{p}}\) a set of treatments R using the following workflow:  
1. Identify the aspects of the target patient's current state, \(\mathcal{S}[\overline{\hat{p}}]\), that are _abnormal_. We use a data-driven approach to determine abnormality: we sort the values for any test or measurement (e.g., temperature) of discharged patients. Those below a low percentile cutoff are considered low abnormal or and those above a high percentile cutoff are considered high abnormal.
2. Extract a set of 200 _relevant_ patients, \(\mathbb{P}(\mathcal{S}[\vec{p}])\), that are similar to the target patient \(\vec{p}\), where similarity is determined based on the abnormals in state \(\mathcal{S}[\vec{p}]\) (Section 3.2).
3. Train a set of \(|R|\) independent treatment classifiers, one for each treatment \(r\) in the set of treatments, R, administered to any patient in this set of relevant patients \(\mathbb{P}(\mathcal{S}[\vec{p}])\) (Sections 3.3 and 3.4). Each classifier applies to the state \(\{\mathcal{S}[p,t]\}\) of each patient \(p\) in \(\mathbb{P}(\mathcal{S}[\vec{p}])\) from the time of admission (\(t=0\)) to any later time (\(t>0\)). The classifier for treatment \(r\) is trained based on whether \(r\) was given within a time horizon of \(h\) hours after \(t\) or not.
4. Provide the list of predicted treatments for the target patient in state \(\mathcal{S}[\vec{p}]\) within time horizon \(h\). Because conditions change quickly in an ICU setting, we focus on predictions within the next two hours.  
**The Use of Abnormalities to Characterize Patients.** Patients are discharged from ICUs when they have been generally stabilized, even if they are not in perfect health. Our approach for determining normal vs. abnormal patient measurements discovers typical values for different measurements on discharge. Those measurements below most of those typical values are considered \"low abnormal\" (the lowest 10%), and those above most of those typical values are considered \"high abnormal\" (above the 90 th percentile, the highest 10%). All others are \"normal\".  
Experts and skilled decision makers in high-pressure situations such as the ICU often use recognition-primed decision-making to match the current situation to similar typical cases where certain actions were appropriate or successful [19]. By contrast, because _treatments often target current abnormalities_, we posit that the treatments received by patients with similar abnormalities are likely to be the most relevant. Our experiments in Section 5 show that this approach is particularly helpful for the rarest 40% of treatments.
# Two-Layer Security Algorithms to Prevent Attacks on Data in Cyberspace
## 4 Experiment
### Decryption Algorithm  
_Step 1:_ A _ciphertext (C)_ obtained in the encryption is used to decrypt the data, as shown in Equation (22) (Ciphertext).  
\[\%\downarrow Y+\textit{\}\&\textit{\}\&\textit{\}\&\textit{\}\&\textit{\}\& \textit{\}\&\textit{\}\&\textit{\}\&\textit{\}\&\textit{\}\&\textit{\}\&\textit{ \}\&\textit{\}\&\textit{\}\&\textit{\}\  
After converting the \"\(W_{[i]}\)\" values to _binary_, each _static key_ \"\(F_{[i]}\)\" value is converted to _8-bit binary_, as shown in Figure 14.  
_Step 4:_ The _binary text_ obtained from the _Vigenere cipher_ is _XORed_ with the _static key_.  
\"[x]\" used in encryption. The _XORed_ result \"\(P_{\oplus[i]}\)\" is shown in Figure 15.  
_Step 5:_ The _binary values_ \"\(P_{\oplus[i]}\)\" obtained from _XOR_ are converted to _decimals_, as shown in Figure 16.  
_Step 6:_ After obtaining the _decimal values_, each _value_ is converted to its _equivalent ASCII_ to obtain the _plaintext_, as shown in Figure 17.  
Figure 16: Decimal of each binary.  
Figure 14: Binary of each Static Key value.  
Figure 13: Binary of each Vigenère index value.
###### Abstract  
Due to the highly demanding operating conditions of the Modular Multilevel Converter (MMC), as well as its inherently complex structure, electric field analysis of the MMC valve is crucial for the safe and stable operation of MMC-HVDC (MMC high voltage direct current) transmission systems. In this paper, a high-speed modeling method for an MMC valve based on data derivation is proposed. Firstly, the relationship between the MMC valve electric field calculation model parameters and the MMC-HVDC transmission system parameters was studied. Based on this, the data derivation system for the electric field calculation model parameters of all components of the MMC valve was established. A modeling parameters database based on empirical knowledge is also used in the calculation process of the data derivation system. The output model parameter matrix includes the geometric parameters, position parameters, and electrical parameters of the components. Based on the output matrix, the electric field calculation model of the converter valve can be quickly generated. Furthermore, to improve the accuracy of the calculations and reduce computation time, a discrete modeling method that combines mesh optimization was proposed. In the process of discretized electric field modeling, a mesh division influence factor based on the accuracy of electric field calculation is proposed. By adjusting the mesh division influence factor during the electric field calculation, the accuracy of the electric field calculation can be optimized rapidly. Finally, the effectiveness and practicality of the high-speed modeling method for the MMC valve are verified through comprehensive case studies conducted on the \(\pm\)320 kV onshore MMC-HVDC valve. It is demonstrated that the high-speed modeling method proposed in this paper can significantly reduce the modeling time of the converter valve and greatly improve the accuracy of electric field calculation.  
modular multilevel converter (MMC); data derivation; electric field calculation modeling parameters; discretization modeling 2025
# Locality-Sensitive Hashing for Information Retrieval System on Multiple GPGPU Devices
## 7 Conclusions  
The proposed search method in this paper can work efficiently for both CPUs and GPGPUs. However, the use of GPGPUs is entirely suitable for an online real-time information retrieval system. As the memory size of a single GPGPU device is limited, we recommend clustering the data for multiple GPGPU devices. We also proposed a parallel shuffling search for multiple parallel data  
Figure 13: Search time of S-PLSH on the big data set with multiple GPGPUs.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **S-PLSH** & **SES-LSH** & **PLSH** \\ \hline
**Dynamic Data set** & Yes & No & Buffered \\
**Data clusters/Node** & \(1\) & Multiple & \(1\) \\
**Network connections** & \(2\)n & \(2\)(n-\(1\)) & N/A \\
**GPGPU Support** & Yes & No & No \\
**Fully shuffle search** & Yes & No & N/A \\
**Distributed HashTable** & Yes & Yes & Yes \\
**Hierarchical Searching** & Yes & No & Yes \\
**Parallel Searching** & Yes & Yes & No \\
**Speed** & High & Normal & High \\ \hline \hline \end{tabular}
\end{table}
Table 3: The advantages and disadvantages of S-PLSH versus other recent multiple LSH cluster systems.  
clusters. The S-PLSH method reduces the duplicated searching process by sharing information among nodes and sequentially swapping parts of the search array. S-PLSH can be reconfigured to suit different data sets and computer systems.  
With the advantages of S-PLSH, the information retrieval systems using big data such as identify content that infringes copyright for audio, video, text or images can archive higher performance with the acceleration of GPGPU. On the other hand, the CUSTagedLSH helps to handle numerous queries at once by increasing the occupancy of GPGPUs.  
For future work, we will focus on optimizing the parallel shuffling search. We aim to reduce the network traffic between slaves, in order to increase the performance of parallel CUSTagedLSH on computers with a massive number of nodes.  
[PERSON] and [PERSON] conceived and designed the experiments. [PERSON] performed the experiments and collected the experimental data. [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
The research was supported by Japan Advanced Institute of Science and Technology (JAIST).  
The authors declare no conflict of interest.
## 2 Materials and Method  
### Model Experiments  
The test system for the axial-output method is shown in Figure 1, which mainly consists of two parts: the steel cylinder and the automatic load control system for axial confinement, and axial stress measurement. This method constrains the radial displacement so that the displacement occurs in the axial direction. The piston diameter is 0.2 mm smaller than the cylinder diameter, so that a small gap can be maintained between them and the piston can move freely along the axial direction. The main role of the piston is to seal the SCA slurry in the cylinder, so that the axial restraint can be applied. When the SCA slurry generates expansive pressures in the cylinder, the axial force can be transferred evenly to the load measurement device. The automatic load control system is composed of two parts: the servo-controlled loading machine and the automatic control software. The testing machine can achieve a maximum load of 3000 kN. The precision of the load cell is 0.5 kN. Taking the cylinder with a diameter of 113 mm as an example, the measurement precision can reach 0.01 MPa. The displacement was measured by an external small-deformation displacement sensor, with the maximum range of 5 mm and the precision of 0.0001 mm, which can fully satisfy the test requirements. The axial pressure was calculated by dividing the load cell record by the piston area. With the temperature measured by the gauge on the exterior cylinder wall, the circumferential strain was compensated. The axial pressure can then be obtained theoretically according to the cylinder stress theory in elastic mechanics.  
In order to obtain the time histories of radial and axial pressures for SCA with equal height-diameter ratio and zero axial displacement, cylinders with various diameters and heights and different SCA charges were considered, as listed in Table 1. The test results are shown in Figure 2.  
Figure 2 presents the time histories of radial and axial pressures corresponding to various cylinder diameters. The results indicate that: for any cylinder diameter, the radial and axial pressures increase first and then tend to be steady with time and increase with increasing cylinder diameter. Due to the boundary effect, the ratio between the radial and axial pressures increases with increasing cylinder diameter. Taking the data within 65 h as an example, the ratio gradually changes from 0.3 to 1.
# Structure and Optical Properties of Transparent Cobalt-Doped ZnO Thin Layers
## 2 Materials and Methods  
Solid solutions of Zn\({}_{1-x}\)Co\({}_{x}\)O for targets were obtained using the solid-phase reaction method, widely used in ceramic technology. High purity materials were used as input elements for the preparation of the charge. The concentration of Co was determined by the ratio of ZnO and CoO, and (ZnO)\({}_{1-x}\) and (CoO)\({}_{x}\) for the production of the target using the PLD method (described in detail in [31; 32]).  
The CoCO\({}_{3}\) powders were prepared by fine growth to a particle size of 100 nm and mixed with ZnO powder and a small amount of water in the planetary mill drums. The mixing time was determined by the degree of homogenization and was set to 16 h. The mixture was then annealed in air at 700 \({}^{\circ}\)C for 4 h. A press with a diameter of 15 mm and a thickness of 2.5 mm was formed under a pressure of 50 MPa on a hydraulic press without the use of plasticizers. Produced targets were annealed in a batch furnace at about 1000 \({}^{\circ}\)C for 3 h. In this way, a solid solution of Zn\({}_{1-x}\)Co\({}_{x}\)O with x = 0.20 was obtained  
Thin cobalt-doped zinc oxide layers (ZnO:Co) were deposited on Si (111), glass, Al\({}_{2}\)O\({}_{3}\), and SiO\({}_{2}\)/Si substrates in a vacuum using the pulsed laser deposition method (Detailed layer properties--see Supplementary Materials: Figures S1-S3, Table S1). The target of ZnO (cobalt doped) was prepared as described above. The substrate was prepared by sequential cleaning with ethanol. The layers were grown using the KGd(WO\({}_{4}\))\({}_{2}\):Nd\({}^{3+}\) laser, \(\lambda\) = 1067 nm, \(\Delta\lambda\) \(\sim\) 10 ns, pulse frequency 1 \(\div\) 2 Hz (N \(\sim\) 500 imp), with an energy density of 0.5 J/cm\({}^{2}\)--the typical deposition rate was approximately v \(\sim\) 0.5 nm/imp. The substrate temperature deposition was set to 30 \({}^{\circ}\)C [33; 34]. Some samples were post-annealed at different temperatures for 5 min in air with a NABERTHERM LI04 furnace.  
The crystal quality of the obtained layers was controlled by X-ray diffraction (XRD) with CuK\({}_{\alpha}\) radiation (Bruker D8 Advance, Bruker AXS, Karlsruhe, Germany). The optical transmission spectra of the layers were obtained using CARY 5000 spectrophotometer.
## 4 Results
### Relative Field Capacity  
For control soil without amendments, the _RFC_ was lower at CAS than at GIN (_RFC_ = 0.43 and 0.53, respectively; \(\Delta=-19.1\%\)). In both cases, the relative field capacity was lower than the minimum optimal value (_RFC_ = 0.6), and both soils were water-limited. Therefore, an effective treatment was expected to induce an increase of the _RFC_.  
Adding the amendment determined an increase of the _RFC_ in all considered cases (Figure 6) but the _RFC_ vs. the _ar_ relationship was stronger with the BIO (\(R^{2}\) = 0.79-0.91) than with the COM and the VER (\(R^{2}\) = 0.33-0.59) (Table 1).  
Therefore, the slopes of the \(\theta_{100}\) and \(\theta_{s}\) vs. the _ar_ relationships (Table 2) that were similar enough as not to affect the _AC_ vs. the _ar_ relationship were not so similar to also impede detection of the increasing _RFC_ vs. the _ar_ relationship that was observed for both soils with all the amendments.  
A minimum amendment rate was defined for both soils and the three amendments. At CAS, the _ar\({}_{min}\)_ was equal to 146 t/ha of COM, 108 t/ha of VER, and 54 t/ha of BIO. At GIN, the _ar\({}_{min}\)_ was equal to 87 t/ha of COM, 127 t/ha of VER, and 52 t/ha of BIO. Therefore, reaching the lower limit of the optimal _RFC_ range required a relatively small amount of BIO and higher amounts of COM and VER. More COM (by 1.7 times), less VER (by 1.2 times), and a similar amount of BIO (values differing by 1.03 times) were necessary at CAS as compared with GIN.  
A maximum amendment rate was definable for the BIO in both soils, the VER at CAS, and the COM at GIN. In particular, the maximum rate of the BIO was equal to 120 t/ha at CAS and 114 t/ha at GIN. The maximum rate of the VER at CAS was 167 t/ha, and that of the COM at GIN was equal to 234 t/ha (Table 1).  
Therefore, both the minimum and the maximum rate of BIO were comparable between the two soils, even if the _RFC_ values in the absence of treatment were different. Lower amendment rates were required with the BIO than with the other two amendments. Only the BIO allowed us to define both a minimum and a maximum rate for both soils.
# Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models
###### Abstract  
Dental caries is one of the most prevalent and chronic diseases worldwide. Dental X-ray radiography is considered a standard tool and a valuable resource for radiologists to identify dental diseases and problems that are hard to recognize by visual inspection alone. However, the available dental panoramic image datasets are extremely limited and only include a small number of images. U-Net is one of the deep learning networks that are showing promising performance in medical image segmentation. In this work, different U-Net models are applied to dental panoramic X-ray images to detect caries lesions. The Detection, Numbering, and Segmentation Panoramic Images (DNS) dataset, which includes 1500 panoramic X-ray images obtained from Ivisonlab, is used in this experiment. The major objective of this work is to extend the DNS Panoramic Images dataset by detecting the cavities in the panoramic image and generating the binary ground truth of this image to use as the ground truth for the evaluation of models. These ground truths are revised by experts to ensure their robustness and correctness. Firstly, we expand the Panoramic Images (DNS) dataset by detecting the cavities in the panoramic images and generating the images' binary ground truth. Secondly, we apply U-Net, U-Net++ and U-Net3+ to the expanded DNS dataset to learn the hierarchical features and to enhance the cavity boundary. The results show that U-Net3+ outperforms the other versions of U-Net with 95% in testing accuracy.  
artificial intelligence; dental caries; deep learning; dental panoramic X-ray images; image segmentation; machine learning; medical image; nested U-Net models; U-Net3+
## 1 Introduction
ReMAHA-CatBoost leverages the relief-F algorithm to obtain feature importance and MAHAKIL to avoid generating data with a centralized distribution, effectively alleviating the imbalance issue in traffic accident data;
* To address the concern of Mahalanobis distance computation in MAHAKIL, which does not consider feature importance, we employ the relief-F algorithm to obtain a diagonal matrix representing feature importance and perform weighted Mahalanobis distance calculations;
* Additionally, to tackle the problem of generating overly divergent data and handling the overall large sample size in classical MAHAKIL algorithms, we utilize mini batch K-means to cluster the samples before data generation;
* Furthermore, we leverage the advantage of CatBoost in reducing gradient bias, enhancing the model's generalization capabilities;* We evaluate ReMAHA-CatBoost and other three sampling algorithms combined with CatBoost, as well as ReMAHA combined with four other prediction models on the US-Accidents dataset. The results demonstrate that ReMAHA-CatBoost outperforms other models in imbalanced traffic accident data, highlighting its generalization and effectiveness in the domain of traffic accidents. The structure of this paper is outlined as follows: in Section 2, we delve into pertinent research concerning traffic accident prediction and approaches addressing data imbalance. Section 3 expounds upon the utilized dataset and delineates the architecture design of ReMAHA-CatBoost. The subsequent section, Section 4, presents a detailed analysis of the experimental results. Section 5 engages in a discussion of the research findings presented herein. Finally, Section 6 summarizes the broad body of work and real-world contributions of this study.
# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 1 Introduction  
Ensuring services and cloud infrastructure operation in the face of unexpected events is challenging. Modern solutions, such as those based on microservices architectures, which use a simplified application structure and separate them into essential and mostly reusable functions, help to improve application performance, scalability, and fault tolerance.  
Technologies such as software containers support service packaging, deployment, and orchestration. These technologies comply with resiliency requirements such as high availability, scalability on demand, load balancing, and traffic limitation. In addition, computing and network infrastructures must be allocated efficiently and dynamically, and must adapt to changing conditions [1]. Network virtualization technologies, distributed storage systems, and resource allocation solutions in heterogeneous environments can help to ensure this.  
Resilience at both the network and application levels is typically achieved by applying redundancy mechanisms via hardware and software. Consequently, infrastructure has gradually evolved to include cloud-based, hybrid, and distributed approaches. Advances in cloud computing, containers, virtualization, DevOps, replication, distributed databases, and network technologies have involved moving toward the greater use of software to manage workloads and traffic intelligently, and to provide better support for resilient services [2].  
In this work, we identify the challenges we must consider to provide resilience to services and infrastructure in the cloud. We describe the technologies used to guarantee the resilience of applications based on mechanisms that aim to ensure that the services and the computational and network infrastructure are available. This paper makes the following contributions:  
* A revision of the communication technologies and computational infrastructures that provide fault-tolerance mechanisms needed to support resilient cloud services.
* A description of the challenges that need to be addressed to design resilient cloud services over computational infrastructures.  
* The design and implementation of a resilient platform approach based on a microservices architecture and the mechanisms to provide resilience to infrastructure and services.
* An evaluation of the viability of efficiently leveraging our platform to deploy data analytics applications.  
The remainder of this paper is organized as follows. In Section 2 we provide background information on computational and communications mechanisms to improve the resilience of cloud systems. Section 3 describes the resilience challenges faced by cloud infrastructure. In Section 4 we present our approach to a software platform to facilitate the construction of resilient applications. Section 5 describes the mechanisms we designed to ensure resilience in the proposed platform. Section 6 validates the platform through a test scenario. Section 7 discusses related work, and Section 8 concludes the paper.