esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Due to their excellent heat resistance, superalloys are used predominantly in the manufacturing of engine parts and accessories for aircraft and aerospace equipment. The Monte Carlo simulation (MCNP-5) code was performed to estimate the mean track length of the incident photons inside six different alloys. Then, based on the simulated track length, other important \(\gamma\)-ray shielding parameters were calculated. In this study, the highest mass attenuation coefficient was obtained for alloys encoded MAR-302 and MAR-247 and varied in the range 0.035-72.94 and 0.035-71.98 cm\({}^{2}\)-g\({}^{-1}\), respectively. The lowest mass attenuation coefficient was found for alloys coded Inconel-718 and Nimocast-75 with a range of 0.033-59.25 and 0.32-59.30 cm\({}^{2}\)-g\({}^{-1}\), respectively. Use was made of a recently developed online program Phy-X/PD to calculate the effective atomic number, equivalent atomic number, and the buildup factors for the alloys of interest. The effective removal cross-section for the fast neutron was also calculated for the studied alloys: the highest value was found for the alloys coded with Inconel-718 (\(\sum_{\text{R}}=0.01945\) cm\({}^{2}\)-g\({}^{-1}\)) and Nimocast-75 (\(\sum_{\text{R}}=0.01940\) cm\({}^{2}\)-g\({}^{-1}\)), and the lowest value was obtained for alloy coded MAR-302 (\(\sum_{\text{R}}=0.01841\) cm\({}^{2}\)-g\({}^{-1}\)). Calculated data indicate that MAR-302 and MAR-247 are superior candidates for shielding of gamma-rays, while Inconel-718 and Nimocast-75 MAR-302 are suitable for the shielding of fast neutrons.  
MCNP; SRIM; superalloys; \(\gamma\)-ray and neutron; shielding parameters +
Footnote †: journal: Applied Sciences
## 1 Introduction  
### A Plethora of Ribonucleoprotein Complexes in Bacteria  
Ribonucleoprotein complexes are major players in gene expression regulation. The paradigm for ribonucleoprotein complexes is represented by ribosomes. Translation-competent ribosomes are formed by both stable and transient associations between various types of RNAs (rRNAS, tRNAS, mRNAs, tmRNA) and r-proteins (ribosomal proteins) (for review, see [1]). In addition, some r-proteins are involved in feedback regulations. Under specific conditions such as starvation, these proteins negatively regulate their own translation by binding to their encoding mRNA [2].  
Other ribonucleoprotein complexes involve the association of small RNAs (sRNAs) with specific proteins. The 6S RNA interacts with \(\sigma^{70}\)-containing RNA polymerase and regulates transcription at specific promoters [3]. The 4.5S RNA, a component of the Signal Recognition Particle (SRP), is essential for targeting signal peptide-bearing proteins to inner membrane (for review, see [4]). Another type relies on the association of RNA chaperones, such as Hfq, with regulatory sRNAs to facilitate interaction with their mRNA targets (for review, see [5]). Other RNA chaperones play important roles under specific conditions or in specific systems, such as the cold-shock CspA protein, which destabilizes mRNA structures and thereby facilitates translation at low temperature [6]. Ribonucleases (RNases) also associate transiently with various RNA species. For example, RNaseP and RNaseE are involved in mRNAs and sRNAs maturation, function, and decay (for review, see [7]).  
In this review, we focus on a third type of ribonucleoprotein complexes, which rely on the association of a regulatory protein directly with target mRNAs to modulate their translation. We will review the Csr (carbon storage regulator) network in which the CsrA regulatory protein plays a pivotal role. This network is particularly interesting as it involves sRNAs that regulates CsrA activity. We discuss in detail the various ribonucleoprotein complexes involving CsrA as well as the Csr network roles in bacterial physiology.
# Control-Flow Integrity: Attacks and Protections
## 5 Protection Techniques
### Opaque CFI (O-CFI)  
O-CFI comprises binary software randomization for CFI enforcement [47]. It protects legacy binaries without even accessing the source code. O-CFI adopts a novel combination of fine-grained and coarse-grained approach to deal with the control-flow challenges. It is able to obscure the graph of control-flow edges from adversaries who gains full access to view stack, heap, and other program information. An experiment demonstrates that O-CFI is able to provide significant protection against code-reuse attacks. CFI checks are done on Intel \(\times 86/\times 64\) memory protection extensions, MPX [69], which is hardware driven. It consists of a performance overhead of only 4.7%.  
O-CFI involves static binary approach; therefore, it fails to protect code that is generated dynamically. It is incompatible with the Windows Component Object Model(COM). Moreover, MPX is much slower than software-based implementation and not protective against memory-based errors. O-CFI fails to commit a precise CFI. It is also unable to solve the challenges related to CFI implementation for COTS modern windows application [53].
###### Abstract  
Maritime transport plays a critical role in global logistics. Compared to road transport, the pace of research and development is much slower for maritime transport. It faces many major challenges, such as busy ports, long journeys, significant accidents, and greenhouse gas emissions. The problems have been exacerbated by recent regional conflicts and increasing international shipping demands. Maritime Autonomous Surface Ships (MASSs) are widely regarded as a promising solution to addressing maritime transport problems with improved safety and efficiency. With advanced sensing and path-planning technologies, MASSs can autonomously understand environments and navigate without human intervention. However, the complex traffic and water conditions and the corner cases are large barriers in the way of MASSs being practically deployed. In this paper, to address the above issues, we investigated the application of Large Language Models (LLMs), which have demonstrated strong generalization abilities. Given the substantial computational demands of LLMs, we propose a framework for LLM-assisted navigation in connected MASSs. In this framework, LLMs are deployed onshore or in remote clouds, to facilitate navigation and provide guidance services for MASSs. Additionally, certain large oceangoing vessels can deploy LLMs locally, to obtain real-time navigation recommendations. To the best of our knowledge, this is the first attempt to apply LLMs to assist with ship navigation. Specifically, MASSs transmit assistance requests to LLMs, which then process these requests and return assistance guidance. A crucial aspect, which has not been investigated in the literature, of this safety-critical LLM-assisted guidance system is the knowledge and safety performance of the LLMs, in regard to ship handling, navigation rules, and skills. To assess LLMs' knowledge of navigation rules and their qualifications for navigation assistance systems, we designed and conducted navigation theory tests for LLMs, which consisted of more than 1500 multiple-choice questions. These questions were similar to the official theory exams that are used to award the Officer Of the Watch (OOW) certificate based on the Standards of Training, Certification, and Watchkeeping (STCW) for Seafarners. A wide range of LLMs were tested, which included commercial ones from OpenAI and Baidu and an open-source one called ChatGLM, from Tsinghua. Our experimental results indicated that among all the tested LLMs, only GPT-4o passed the tests, with an accuracy of 86%. This suggests that, while the current LLMs possess significant potential in regard to navigation and guidance systems for connected MASSs, further improvements are needed.  
maritime autonomous surface ships; large language model; ship-handling theory test; mobile edge computing; mobile cloud computing 2024122381. [[https://doi.org/10.3390/math12152381](https://doi.org/10.3390/math12152381)]([https://doi.org/10.3390/math12152381](https://doi.org/10.3390/math12152381))
## 3 Implementation of the Methodology Using the NoLOAD Software
### The Automatic Differentiation Library, JAX  
As previously introduced in Section 2.3.1, AD is a technique that has been in existence for several decades [36]. Over the past ten years, a plethora of AD libraries has emerged, designed for applications involving machine learning and Artificial Intelligence. A selection of these libraries is categorized according to their implementation techniques [37; 38; 39; 40; 41; 42; 43; 44; 45], as illustrated in Figure 9:  
AD possesses the remarkable ability to derive control flows, encompassing constructs such as 'while' and 'for' loops, as well as 'if' conditional statements. This capability proves invaluable for a multitude of physical modeling applications, particularly in the realm of power electronics.  
In regard to the methodology proposed herein, the Python library, JAX [46], is employed, renowned for its Just-In-Time (JIT) compilation feature, which significantly accelerates code execution. This efficiency is particularly advantageous given the extensive simulations required in regard to optimization problems. Furthermore, JAX empowers programmers to define their own 'derivative' versions of Python algorithms when alternative methods for derivative computation are available. It facilitates the invocation of user-defined external functions, enabling the calculation of both their outputs and gradients. Notably, these functions may be implemented in languages other than Python, such as C, C++, or Java, a feature that has been validated within the NoLOAD framework.
# A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior
## 2 Motivations  
The need for traffic modeling can be seen in its reliability in predicting the changes in traffic behavior and demonstrating the traffic status under different road network conditions. For example, modeling the characteristics of some traffic-related problems, such as traffic congestion, travel delays, and traffic accidents, will provide a description of the traffic status to help traffic management to control these traffic problems.  
Furthermore, when constructing a new urban plan, traffic management will employ the findings of the traffic modeling study to test and assess a set of transportation road systems offline. According to a number of research studies on the topic of road networks and transportation planning, urban areas that are poorly designed, such as Mumbai, New Delhi, and Mexico City, experience increased traffic congestion problems. As claimed in a statement by TomTom, a well-known GPS company, driving in cities that are poorly designed will require between 56% to 65% extra time for travel due to traffic congestion [8]. The use of traffic simulation models can aid in determining the level of improvement required in road networks designed to alleviate traffic congestion.  
Another significant motivation for traffic modeling studies is to improve road safety and reduce accident severity. As heavy traffic slows down on highways, drivers can begin cutting in front of others or engaging in distracting behaviors such as texting or making phone calls before the congestion eases. This type of behavior may result in accidents and raise the likelihood of traffic collisions. The Canadian Motor Vehicle Traffic Collision Statistics [9] show that traffic collisions led to more than 1900 premature deaths and 9000 serious injuries in Canada in 2018. Using traffic modeling to predict traffic collisions raises public awareness of the causes of these collisions in order to avoid them.  
Recognizing the importance of traffic modeling studies in assessing urban transportation systems has led to numerous research studies in traffic modeling. The following section of the survey begins with a brief comparison of the time intervals used in spatiotemporal traffic prediction and demonstrates the challenges in long-term and short-term traffic modeling prediction.
## 2 Materials and Analytical Methods
### Preparation of SCG Extracts  
In all of the verified variants of the extraction procedures, the preparation of SCG extracts was performed in accordance with the experimental extraction optimization design scheme. Briefly, the appropriate measured volume of solvent was added to one gram of dried sample of SCG blend in a Schott bottle. The extraction process was conducted at 60 \({}^{\circ}\)C for the desired time (Table 1). A laboratory shaker Elpin Plus type 357 (Lubawa, Poland) together with a water bath or laboratory Emmi-D60 ultrasonic heater bath (Salach, Germany) was used. After extraction, the sample was centrifuged (2500\(\times\)\(g\), 20 min, 4 \({}^{\circ}\)C). The collected supernatant was filtered through filter paper (Munktell, grade 389). The SCG extracts were kept at 2-8 \({}^{\circ}\)C in the dark until further analysis. The volume of the SCG extract obtained after each extraction variant was measured and used for calculations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Run**} & **Organic Solvent** & **Extraction** & \multirow{2}{*}{**SMR**} & **Ultrasound** & **Solvent** \\  & **Concentration** & **Time** & & **Assistance** & **Type** \\ \cline{2-6}  & **A** & **B** & **C** & **D** & **E** \\ \hline
1 & 60 (\(-\)) & 30 (\(-\)) & 40 (\(+\)) & Yes (\(-\)) & Ethanol (\(-\)) \\
2 & 60 (\(-\)) & 90 (\(+\)) & 10 (\(-\)) & No (\(+\)) & Methanol (\(+\)) \\
3 & 100 (\(+\)) & 30 (\(-\)) & 10 (\(-\)) & Yes (\(-\)) & Ethanol (\(-\)) \\
4 & 100 (\(+\)) & 90 (\(+\)) & 10 (\(-\)) & No (\(+\)) & Ethanol (\(-\)) \\
5 & 60 (\(-\)) & 30 (\(-\)) & 10 (\(-\)) & Yes (\(-\)) & Methanol (\(+\)) \\
6 & 80 (0) & 60 (0) & 25 (0) & Yes (\(-\)) & Ethanol (\(-\)) \\
7 & 100 (\(+\)) & 30 (\(-\)) & 40 (\(+\)) & Yes (\(-\)) & Methanol (\(+\)) \\
8 & 100 (\(+\)) & 90 (\(+\)) & 10 (\(-\)) & Yes (\(-\)) & Methanol (\(+\)) \\
9 & 100 (\(+\)) & 90 (\(+\)) & 40 (\(+\)) & Yes (\(-\)) & Ethanol (\(-\)) \\
10 & 60 (\(-\)) & 30 (\(-\)) & 40 (\(+\)) & No (\(+\)) & Methanol (\(+\)) \\
11 & 100 (\(+\)) & 30 (\(-\)) & 40 (\(+\)) & No (\(+\)) & Ethanol (\(-\)) \\
12 & 60 (\(-\)) & 90 (\(+\)) & 10 (\(-\)) & Yes (\(-\)) & Ethanol (\(-\)) \\
13 & 80 (0) & 60 (0) & 25 (0) & Yes (\(-\)) & Methanol (\(+\)) \\
14 & 60 (\(-\)) & 90 (\(+\)) & 40 (\(+\)) & No (\(+\)) & Ethanol (\(-\)) \\
15 & 80 (0) & 60 (0) & 25 (0) & No (\(+\)) & Methanol (\(+\)) \\
16 & 100 (\(+\)) & 90 (\(+\)) & 40 (\(+\)) & No (\(+\)) & Methanol (\(+\)) \\
17 & 60 (\(-\)) & 30 (\(-\)) & 10 (\(-\)) & No (\(+\)) & Ethanol (\(-\)) \\
18 & 80 (0) & 60 (0) & 25 (0) & No (\(+\)) & Ethanol (\(-\)) \\
19 & 60 (\(-\)) & 90 (\(+\)) & 40 (\(+\)) & Yes (\(-\)) & Methanol (\(+\)) \\
20 & 100 (\(+\)) & 30 (\(-\)) & 10 (\(-\)) & No (\(+\)) & Methanol (\(+\)) \\ \hline \end{tabular}
\end{table}
Table 1: Experimental design used in the first step of the extraction optimization. The process variables are presented as real and (coded) values.
## 1 Introduction  
### Preface  
Keshan disease is an unidentified endemic cardiomyopathy [1]. It was discovered in Keshan County, Heilongjiang Province, in 1935, and was therefore named Keshan disease. By the end of 2015, Keshan disease was distributed in 16 provinces [2], municipalities, and autonomous regions in China, involving 328 counties, 2953 townships, and a population of about 60 million [3]. Geographically, it formed a broad band extending from the northeast to the southwest in China [4].  
Keshan disease has a concentrated high-incidence season in both northern and southern disease areas. It is more common in winter in the northeast region and in summer in the southwest region. Nowadays, it occurs sporadically throughout the year. There was a high-incidence period from 1955 to 1978. After 1978, the incidence rate showed a downward trend. In 1959, the incidence rate of acute and subacute Keshan disease in the disease area reached 60.18/100,000, and 40.42/100,000 in 1970, with a fatality rate of more than 40% [1]. During the Keshan disease surveillance from 2000 to 2004, a total of 2096 cases of potential and chronic Keshan disease were detected nationwide in five years, including 113 new cases of potential Keshan disease and 29 cases of chronic Keshan disease, with an average annual incidence rate of 3.1% [5].  
The main pathological changes in this disease are myocardial parenchymal degeneration, necrosis, and fibrosis, resulting in the failure of cardiac systolic and diastolic function.  
It is an independent endemic cardiomyopathyopathy and a serious local public health problem in China [6; 7].  
Due to its regional high incidence, studying the environmental mechanism of the onset of the disease to prevent its occurrence has always been a hot topic in scientific research.  
This research conducts a comparison of the environmental selenium in the high-incidence areas of Keshan disease in Chinese loess and black soil, analyzes the differences between the two kinds of environmental selenium, and proposes that Keshan disease can occur in both high-selenium and low-selenium environments; in the loess area where selenium is generally deficient, it puts forward in-depth planting suggestions to maximize the selenium content of crops to ensure the selenium intake of the population; and this research provides a basis for the prediction and prevention of Keshan disease.
## 2 Materials and Methods
### Signal Collection
The sEMG signal was recorded using the Ultime System electromyograph (Noraxon, Scottsdale, AZ, USA). Before the measurement was started, electrode-skin impedance levels were checked using a built-in function of the MR software version 3.18.98 (Noraxon, Scottsdale, AZ, USA) to ensure proper electrode adhesion. The sEMG signal was recorded during the tested exercise during a four-beat walk and a two-beat trot. The transitions between gaits were assessed visually based on the characteristic movement patterns. The tested exercise was performed in a straight line and lasted 10 s. The signal was sampled at a frequency of 2000 Hz. The tested exercise was repeated six times. During each measurement, each horse took at least eight strides. Data were stored using MR software version 3.18.98 (Noraxon, Scottsdale, AZ, USA). The measurement free of artifacts and loss of electrode contact was selected for further analysis. Figure 1: The proximal aspect of the forelimb of the examined horse. (**A**). Skin surface preparation, with the position of the selected extensor muscles indicated by dashed lines. (**B**) Placement of surface electrodes in relation to the selected extensor muscles, represented by solid lines. (**C**) Placement of measuring system, consisting of two surface electrodes, a connecting cable, and one sEMG sensor, marked by dotted line. The m. extensor carpi radialis is marked with blue lines, the m. extensor digitorum communis with green lines, the m. extensor digitorum lateralis with yellow lines, and the m. extensor carpi ulnaris with orange lines.
# A New Under-Sampling Method to Face Class Overlap and Imbalance
## 5 Results
### Evaluation of the Impact on the Imbalance Ratio
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline  & **Original** & **RUS** & **CNN** & **NCL** & **TL** & **ENN** & **OSS** & **EUS** & **EE** & **BC** & **RUSBOOST** & **SBC** & **ClusterOSS** & **DBMIST-US** \\ \hline yeast-0-5-6-7.9\_vs.4 & 9.4 & 1.0 & 1.7 & 8.0 & 9.1 & 8.2 & 7.3 & 1.0 & 1.0 & 1.0 & 1.6 & 7.5 & 0.4 & 1.0 \\ glass-0-1.6\_vs.2 & 19.3 & 1.0 & 2.2 & 8.7 & 10.1 & 8.4 & 9.6 & 1.0 & 1.0 & 1.0 & 1.0 & 8.9 & 4.2 & 1.1 \\ glass2 & 11.6 & 1.0 & 2.0 & 10.1 & 11.4 & 9.8 & 10.5 & 1.0 & 1.0 & 1.0 & 1.5 & 9.9 & 1.8 & 1.1 \\ shuttle-0-vs-c4 7 & 13.9 & 1.0 & 0.1 & 13.8 & 13.9 & 13.8 & 0.3 & 1.0 & 1.0 & 1.0 & 1.5 & 13.8 & 0.4 & 1.2 \\ yeast-1.5\_vs.7 & 14.3 & 1.0 & 2.8 & 12.1 & 14.0 & 12.5 & 12.1 & 1.0 & 1.0 & 1.0 & 1.5 & 11.4 & 2.0 & 1.0 \\ glass4 & 15.5 & 1.0 & 1.0 & 14.5 & 15.5 & 14.9 & 4.2 & 1.0 & 1.0 & 1.0 & 1.5 & 15.6 & 5.0 & 1.1 \\ ecoli & 15.8 & 1.0 & 1.0 & 15.2 & 15.8 & 15.4 & 10.3 & 1.0 & 1.0 & 1.0 & 1.5 & 14.9 & 0.9 & 1.1 \\ page-blocks-1.3\_vs.4 & 15.9 & 1.0 & 1.0 & 15.3 & 15.8 & 15.2 & 4.5 & 1.0 & 1.0 & 1.0 & 1.5 & 14.7 & 3.0 & 1.0 \\ glass-0-1.6\_vs.5 & 19.4 & 1.0 & 1.2 & 18.3 & 19.3 & 18.7 & 5.9 & 1.0 & 1.0 & 1.0 & 1.4 & 18.7 & 2.0 & 1.1 \\ yeast-1.4-5\_vs.7 & 22.1 & 1.0 & 4.1 & 19.6 & 2.1 & 18.8 & 19.8 & 21.8 & 1.0 & 1.0 & 1.0 & 1.5 & 16.9 & 2.3 & 1.0 \\ glass5 & 22.8 & 1.0 & 1.2 & 21.6 & 22.7 & 22.0 & 7.6 & 1.0 & 1.0 & 1.0 & 1.4 & 22.0 & 3.8 & 1.1 \\ yeast-2.5\_vs.8 & 23.1 & 1.0 & 2.4 & 21.9 & 22.9 & 22.0 & 21.2 & 1.0 & 1.0 & 1.0 & 1.5 & 21.0 & 0.8 & 1.0 \\ flare-F & 23.8 & 1.0 & 3.1 & 22.0 & 23.7 & 21.8 & 23.7 & 1.0 & 1.0 & 1.0 & 1.5 & 23.2 & 0.1 & 1.1 \\ yeast84 & 28.1 & 1.0 & 2.5 & 26.4 & 27.8 & 26.6 & 24.7 & 1.0 & 1.0 & 1.0 & 1.5 & 22.1 & 0.3 & 1.1 \\ yeast-1.2-8.9\_vs.7 & 30.6 & 1.0 & 4.3 & 28.3 & 30.2 & 28.4 & 30.1 & 1.0 & 1.0 & 1.0 & 1.5 & 29.6 & 0.4 & 1.0 \\ yeast5 & 32.7 & 1.0 & 1.1 & 32.0 & 32.6 & 32.0 & 25.0 & 1.0 & 1.0 & 1.0 & 1.5 & 32.0 & 0.3 & 1.1 \\ ecoli-0-1.3\_vs.2 & 39.1 & 1.0 & 2.1 & 38.1 & 38.9 & 38.0 & 17.3 & 1.0 & 1.0 & 1.0 & 1.4 & 37.6 & 2.2 & 1.3 \\ abalone-1.7\_vs.7-8.9 & 39.3 & 1.0 & 2.5 & 37.5 & 39.1 & 38.2 & 37.3 & 1.1 & 1.0 & 1.0 & 1.5 & 31.0 & 0.3 & 1.1 \\ yeast6 & 41.4 & 1.0 & 2.9 & 40.1 & 41.1 & 39.9 & 35.5 & 1.0 & 1.0 & 1.0 & 1.0 & 1.5 & 40.2 & 0.4 & 1.1 \\ poker-8.9 Another way of evaluating the impact of each under-sampling algorithm on the imbalance ratio is looking at the percentage of average reduction of the imbalance ratio given in the last row of Table 4. Results reveal that the top-4 methods were RUS, EUS, EE, and BC with an average reduction of 91.55%, which correspond to the algorithms that produced perfectly-balanced data sets (imbalance ratio equal to 1). However, DBMIST-US with an average reduction of 91.29% was clearly very close to the top-4 methods. On the other hand, the average reductions achieved with NCL, TL, ENN, OSS, and SBC were extremely low, which suggests that these algorithms are not the most appropriate for under-sampling.
## 1 Introduction
For each mixed pixel under the unmixing process, the composition of the inner parts is known, but the composition of adjacent pixels is unknown. The distribution and extent of adjacent pixels cannot be estimated based on the inner parts and abundance of central pixels. In other words, the solution to the underdetermined problem in the linear spectral unmixing process might lie in the approximation in the adjacent land region instead of the estimation of the local region. The proposed elastic-window-based method in this paper adjusts its window size dynamically based on the land covers of mixed pixels, ensuring the unmixing linear system is not underdetermined. The rest of the paper is organized as follows. In Section 2, the theoretical basis of inner part inversion using a linear spectral mixing model is reviewed, and the imperfection in approximating the adjacent land region is revealed. In Section 3, an iterative procedure to approximate the local land surface is discussed, and an elastic-window-based algorithm is introduced. In Section 4, a case study is conducted to validate the proposed method based on the GlobeLand30 dataset. Finally, the conclusion and future research are presented.