esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Phase-Field-Regularized Cohesive Zone Model
### Phase-Field-Regularized Cohesive Zone Model  
The geometric crack function \(\alpha(\phi)\) and the energetic degradation function \(\omega(\phi)\) are the two main functions that define the above phase field theory. According to the work of [PERSON] et al. [1; 25], it is thus possible to define an equivalent cohesive zone model explicitly due to the formula \(g(\phi)\) in the geometric crack function \(\alpha(\phi)\).  
\[\left\{\begin{array}{ll}\alpha(\phi)=\xi\phi+(1-\xi)\phi^{2}&\xi\in[0,2]\\ \omega(\phi)=\frac{1}{1+\xi(\phi)}&g(\phi)=\frac{a_{1}\phi(1+a_{2}\phi+a_{2}a _{3}\phi^{2})}{(1-\phi)^{p}}\end{array}\right. \tag{27}\]  
in which the exponent \(p\geq 2\), coefficients \(a_{1}>0\), \(a_{2}\), \(a_{3}\) can be determined by the failure strength \(f_{t}\) (uniaxial tensile strength) and the initial slope \(k_{0}\) and the ultimate crack opening \(w_{c}\) of the target softening law \(\sigma(w)\), respectively.  
\[k_{0}=-\frac{c_{0}}{4\pi}\frac{f_{t}^{2}}{G_{c}}\frac{\left[\xi(a_{2}+p+1)-1 \right]^{3/2}}{\xi^{2}} \tag{28}\]  
\[w_{c}=\frac{2\pi G_{c}}{c_{0}f_{t}}\sqrt{\xi P(1)}\underset{\phi^{*}\to 1}{lim} \left(1-\phi^{*}\right)^{1-p/2}\left\{\begin{array}{ll}0&p<2\\ \frac{2\pi G_{c}}{c_{0}f_{t}}\sqrt{\xi P(1)}&p=2\\ +\infty&p>2\end{array}\right. \tag{29}\]  
where \(P(1)=1+a_{2}+a_{2}a_{3}\). It can be found since the ultimate crack opening displacement is zero when \(p<2\). This is not in accordance with the features of quasi-brittle failure. Therefore, the exponential parameter in the energy degradation function \(\omega(\phi)\) is taken in the range of \(p\geq 2\). For the PF-CZM, \(\xi=2\) is taken; then, we have \(\alpha(\phi)=2\phi-\phi^{2}\) and \(c_{0}=\pi\).  
\[a_{1}=\frac{4}{\pi l_{0}}l_{\text{ch}},\ a_{2}=2\left(-2k_{0}\frac{G_{c}}{f_{t} ^{2}}\right)^{2/3}-\left(p+\frac{1}{2}\right) \tag{30}\]  
\[a_{3}=\left\{\begin{array}{ll}0&p>2\\ \frac{1}{a_{2}}\left[\frac{1}{8}\left(\frac{w_{c}f_{t}}{G_{c}}\right)^{2}-(1+a _{2})\right]&p=2\end{array}\right. \tag{31}\]  
for [PERSON]'s characteristic length \(l_{\text{ch}}:=E_{0}G_{c}/f_{t}^{2}\). The general softening law used in quasi-brittle failure models of concrete can be obtained by the parameterizing characteristic function (27). In particular, we give the equivalent parameters of [PERSON]'s [49] softening curve for the first time to the best knowledge of the authors:  
* Linear softening curve: \(p=2,\ a_{2}=-0.5,\ a_{3}=0\)* Exponential softening curve: \(p=2.5,\;a_{2}=2^{5/3}-3,\;a_{3}=0\)
* [PERSON] [49] softening curve: \(p=2,\;a_{2}=0.3114,\;a_{3}=0.9910\)
# On Analyzing Beamforming Implementation in O-RAN 5G
## 2 Related Work
### O-RAN Architecture  
RAN architecture has been defined by 3 GPP. Standards define, among others, RAN interfaces with user equipment (UE) and core network and include various RAN architecture options such as centralized RAN with ideal backhaul. The O-RAN Alliance followed the 3 GPP specifications with the suggestion of new workflows on some units. Figure 1 presents a holistic vision of the O-RAN architecture with the inclusion of key workflows for the functioning of beamforming. In the figure, all the names of the components include O-: to remark upon the openness of the solution. Such open architecture will provide an advantage to multiple vendors to develop concrete implementations of central units (CU), distributed units (DU), xApps and RAN Intelligent Controllers (RIC), etc. [5; 6; 7].  
As shown in Figure 1, the internal structure consists of three main entities: the O-CU (Open-Central Unit), the O-DU (Open-Distributed Unit), and the O-RU (Open-Radio Unit). The O-CU and O-DU entities are connected by the F1 interface (mid-haul), and the O-DU is connected to O-RU by the front-haul interface. The configuration and split of these entities follow the 3 GPP standards, which have proposed eight possible splitting options. We will give a short description of the O-RAN components defined by 3 GPP standards with presenting the O-RAN specifications on the units and interfaces.
###### Abstract  
In this paper, we propose the Adaptive Power-Controlled Energy-Efficient Depth-Based Routing (APC-EEDBR) protocol. This protocol is designed to address the challenges posed by complex environments and limited resources in underwater-sensor networks. Employing a dual-weight adjustment mechanism and adaptive power control enables the protocol to achieve energy-efficient relay selection and enhance the link stability. The protocol adopts a cluster-free, hop-by-hop communication strategy and a cross-layer design to improve path stability and forwarding efficiency while mitigating hotspot issues in data aggregation areas. The simulation results demonstrate that the APC-EEDBR protocol effectively reduces energy consumption and communication overhead by approximately 16%, and significantly prolongs the network lifetime by about 39% compared with EEDBR.  
underwater wireless sensor networks (UWSNs); energy-efficient routing; adaptive power control; depth-based communication; load balancing; link stability; hop-by-hop forwarding; hotspot mitigation Article
# Independent System Calibration of Sentinel-1B  
[PERSON]  
1,*, [PERSON]  
1  
[PERSON]  
1  
[PERSON]  
1  
[PERSON]  
1  
1 German Aerospace Center (DLR), Microwaves and Radar Institute, Oberpfaffenhofen, D-82234 Wessling, Germany; [EMAIL_ADDRESS] (K.S.); [EMAIL_ADDRESS] (N.T.R.R.); [EMAIL_ADDRESS] (P.K.); [EMAIL_ADDRESS] (N.Y.-M.); [EMAIL_ADDRESS] (P.P.-I.); [EMAIL_ADDRESS] (Except for the wave mode, all modes are operated in dual polarization, realized by two separate receiving channels within the instrument. Beyond that, the IW and the EW modes use operationally the novel Terrain Observation by Progressive Scans (TOPS) mode in azimuth [2]. Furthermore, roll steering of the satellite is introduced in order to keep the pulse repetition frequency constant for a single swath around the orbit in different passes. However, the most challenging point with respect to calibrating such an advanced SAR system is to achieve an absolute radiometric accuracy of only 1 dB (3\(\sigma\)) in all operation modes, a novum for spaceborne SAR systems.  
In parallel to ESA's commissioning phase, the German Aerospace Center performed an independent system calibration of Sentinel-1B on behalf of ESA. This paper describes the different calibration activities performed by DLR in Section 2 and presents the results of the internal calibration in Section 3, the geometric calibration in Section 4, the antenna pointing determination in Section 5, the in-flight verification of the antenna model in Section 6, the radiometric calibration in Section 7 and the polarimetric characterization in Section 8. Beyond that, the initial results of the interferometric verification are presented in Section 9, whereby this topic will be discussed in detail in a separate article. Finally, a first radiometric cross-check between Sentinel-1B and Sentinel-1A is presented in Section 10.
# Attention Mechanism Used in Monocular Depth Estimation: An Overview
## 4 Performance Comparison
### Comparison and Analysis  
KITTI [81] is an outdoor dataset that is widely used for MDE algorithm validation. The dataset contains a total of 93,000 RGB-D training images with corresponding ground truth. These images are collected from the city of Karlsruhe, the wild area and the highway, consisting of five categories: \"Road\", \"City\", \"Residential\", \"Campus\", and \"Person\". In this paper, we report and analyze the experimental results of the typical MDE algorithms on dataset KITTI. Although self-supervised MDE methods gained remarkable improvements recently, it is unfair to compare performance of self-supervised methods with that of supervised methods. Therefore, we report the comparison results of supervised methods and self-supervised methods separately, which are shown in Tables 2 and 3, respectively. The best results are in bold. Notably, not all of the methods mentioned above are included in Tables 2 and 3 since some methods do not publish experimental results on KITTI dataset. \begin{table}
\begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline \hline
**Models** & **Strategy** & **Methods** & \begin{tabular}{c} **Attention** \\ **Mechanism** \\ \end{tabular} & **Contributions** \\ \hline \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & [PERSON] et al. [19], 2022 & SSA & Fuse features of Transformer and CNN \\  & & MonoFormer [20], 2022 & SSA, CSA & Attention connection module \\  & & MonoViT [21], 2022 & SSA, CSA & Joint CNN and Transformer layer \\  & & [PERSON] et al. [80], 2022 & SSA, CSA & Fuse features of Transformer and CNN \\  & & Lite-Mono [18], 2022 & SSA, CSA & Lightweight framework with attention mechanism \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Compared to the CNN-based approaches [32; 33; 34; 35; 38; 39; 40], it can be found that Transformer-based methods perform better in MDE tasks in general [10; 16; 63; 66]. The reasons are summarized as follows: Firstly, MDE comes under the dense prediction problem, which requires predicting the depth information of all pixels in the image. However, CNN-based approaches apply enormous downsampling operations, resulting in substantial
###### Abstract  
Effective supply chain inventory management is crucial for large-scale manufacturing industries such as civil aircraft and automobile manufacturing to ensure efficient manufacturing. Generally, the main manufacturer makes the annual inventory management plan, and contacts with suppliers when some material is approaching critical inventory level according to the actual production schedule, which increases the difficulty of inventory management. In recent years, many researchers have focused on using reinforcement learning method to study inventory management problems. Current approaches were mainly designed for the supply chain with single-node multi-material or multi-node single-material mode, which are not suitable to the civil aircraft manufacturing supply chain with multi-node multi-material mode. To deal with this problem, we formulated the problem as a partially observable Markov decision process (POMDP) model and proposed a multi-agent reinforcement learning method for supply chain inventory management, in which the dual-policy and information transmission mechanism was designed to help the supply chain participant improve the global information utilization efficiency of the supply chain and the coordination efficiency with other participants. The experiment results show that our method has about 45% performance improvement on efficiency compared with current reinforcement learning-based methods.  
multi-agent reinforcement learning; supply chain; inventory management Article  
A Supply Chain Inventory Management Method for Civil Aircraft Manufacturing Based on Multi-Agent Reinforcement Learning
# Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)
## 3 IoHT Security Landscape
### Possible Points for Data Leakage
#### 3.2.1 Persistent Data  
Persistent data refers to the data stored on different healthcare system components, such as sensing and actuating devices, etc., and is prone to theft. Here, the features of the healthcare system where the data can be stolen are discussed.  
Healthcare devices such as sensors and actuators store the events logs (latest reading, configuration change, connection history, etc.) in the memory. As these devices usually have weak security configurations and store data in non-encrypted forms, an attacker can steal the data by exploiting these weaknesses.  
IoHT servers are the most attractive components of healthcare systems for attackers to steal health care data from as they contain the complete history of all the patients with their biodata.  
Physicians/Response Team devices are mostly mobile devices that physicians or response teams use to monitor and process healthcare data. These devices are susceptible to memory leakage attacks. Personal computers, mobile phones, tablets, or any specific devices to monitor healthcare data fall into this category. Due to direct communication with IoHT data servers, these devices are highly vulnerable to different security attacks. Figure 6 is presenting attacks an attacker can execute on devices operating at different layers. Effect of one compromised device can propagate at different layers because these devices are communicating with each other.
# Determination of Factors Influencing Road Transport Demand: Evidence from Croatia
## 3 The Impact on Road Transport Demand in Croatia
### Economy Factors
#### 3.2.1 Gross Domestic Product (GDP) in the Republic of Croatia  
Gross domestic product (GDP) is a macroeconomic indicator that reflects the value of all finished goods and services produced within a country during a specific period, and it is presumed to influence transportation demand trends. In addition to GDP, GDP per capita, as well as the annual growth rates of GDP and GDP per capita, were analyzed.  
According to data from the Croatian Bureau of Statistics [31], during the analyzed period from 2003 to 2016, the average GDP amounted to 64,247,078, while GDP per capita averaged 69937. When examining the real GDP growth rates (at market prices), an average annual increase of 1.11% was observed. A significant drop in the GDP growth rate occurred in 2008, continuing into 2009, followed by growth until 2012, when another notable decline of \(-\)2.24% was recorded (Figure 25). The trend of GDP per capita mirrors the trend of total GDP.  
Figure 25: Trend analysis of GDP in the Republic of Croatia (source: [31], authors’ processing).  
Graphical analysis of GDP growth rates and AADT (Average Annual Daily Traffic) growth rates reveals a similar trend during the analyzed period (Figure 25). An analysis of GDP by counties (NUTS 3) reveals that four counties--Zagreb, Split, Rijeka, and Osijek--account for 53% of the country's GDP, representing 19% of all counties.
# Non-Local Effects in Brillouin Optical Time-Domain Analysis Sensors
## 4 Conclusions  
Non-local effects are amongst the main constraints on the performance of BOTDA sensors since their onset entails limits to the SNR of the detected probe wave. Conventional pump and probe BOTDA sensors have their probe power severely contained by these effects. Dual-probe setups significantly alleviate the issue, enabling the use of higher probe power. However, they are still prone to second-order non-local effects that distort the pulse spectrum and constrain the probe power to well below the theoretical limit set by the Brillouin threshold of the fiber. This constraint is even more severe when pump pulse coding techniques are deployed. Furthermore, the dual-probe BOTDA sensor performance is still seriously compromised when pump pulses with limited ER are deployed because the pump depletion is increased.  
The method for probe optical frequency modulation or dithering provides a comprehensive solution to most of these issues. Its use leads to BOTDA setups that can deploy probe power above the Brillouin threshold of the fiber. Moreover, it can provide side benefits such as amplification of the pump pulses, reduction of ER-related impairments and enhancement of SNR in pulse coding BOTDA. All this with very little complexity added to the conventional dual-probe BOTDA setup.  
The authors wish to acknowledge the financial support of the Spanish Ministerio de Economia y Competitividad through the projects TEC2013-47264-C2-2-R and TEC2016- 76021-C2-1-R, FEDER funds and the Universidad Publica de Navarra.  
[PERSON] and [PERSON] wrote the paper. All the authors reviewed and critiqued the manuscript.  
The authors declare no conflict of interest.
## 3 Proposed Scheme: MC-TIRP  
This section proposes a mission-critical trust-based intelligent routing protocol (MC-TIRP). The proposed scheme consists of several components for trusted route discovery, trust evaluation, and trust update and maintenance. This section also introduces the process for deriving trust values from MC-WSNs as the basis of the discovery and maintenance of a trustworthy and reliable path using Q-learning. As a first step, the proposed algorithm performs the process of discovering multiple paths, excluding malicious nodes based on the trust evaluation value. After finding a trust-based multi-path, Q-learning is performed by considering trustworthiness, QoS, and energy factors to transmit mission-critical data in a distributed manner. In addition, learning results reflect historical records, so it can effectively counteract malicious nodes that perform on-off attacks at specific times. The algorithm operates in a distributed transmission method that sacrifices data of relatively low importance to ensure the reliability of data transmission with high mission importance in consideration of the PBAS (precedence based assured service) concept. Finally, the trust route maintenance and update phase manages multiple routes and processes data. This step ensures distributed data transmission while periodically managing and maintaining the calculated trust value for multipath. It also enables flexible response in case of network problems. We define the notation of the proposed scheme in Table 1.
# Numerical Analysis on Self-Propulsion of a Waterjet-Propelled Ship with Different Propulsion Models
## 3 Results and Discussions
### Computations of Self-Propulsion
#### 3.2.2 External Flow Field  
Figure 11 shows the wave patterns of the waterjet-propelled ship with the MRF model and body-force model. It can be seen that the wave patterns with the two different models are almost the same, except for a little difference near the nozzle outlet.  
Table 4 lists the hull resistance of the bare hull, MRF model and body-force model. It reveals that the self-propelled hull resistances are larger than the bare hull resistances, and that the relative errors of self-propelled hull resistance with the MRF model and body-force model are less than 0.7%.  
Figure 10: Convergence history of the net force \(F_{net}\) of waterjet-propelled ship with the body-force model.  
Figure 12 shows the jet stream with the MRF model and body-force model under \(Fr=0.542\) (design speed). Ideally, the counteracting torque caused by the guide vanes should be as large as the impeller torque with an opposite direction to cancel out the swirl in the flow induced by the impeller. However, at this design speed, the torques acting on the impeller and guide vanes are \(-13.11\) N\(\cdot\)m and \(11.06\) N\(\cdot\)m, respectively. As a result, the jet surface with the MRF model is not smooth in Figure 12. In contrast, the body-force  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(Fr\) & **BH \(R_{H}\) (N)** & **MRF \(R_{HM}\) (N)** & **BFM \(R_{HB}\) (N)** & **Relative Error \(E\) (\(\frac{R_{H}-R_{HM}}{R_{HM}}\))** \\ \hline
0.300 & 98.48 & 107.94 & 107.26 & \(-0.63\%\) \\ \hline
0.400 & 172.15 & 183.13 & 183.03 & \(-0.05\%\) \\ \hline
0.500 & 269.00 & 278.94 & 277.66 & \(-0.46\%\) \\ \hline
0.542 & 309.52 & 316.72 & 316.52 & \(-0.06\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hull resistance of the bare hull (BH), MRF model and body-force model (BFM).  
Figure 11: Wave patterns of the waterjet-propelled ship with the MRF model and body-force model (BFM). (**a**) \(Fr=0.3\); (**b**) \(Fr=0.4\); (**c**) \(Fr=0.5\); (**d**) \(Fr=0.542\).  
model predicts a smooth jet surface, which is attributed to a swirl-free jet flow of the body-force model.
## 2 Materials and Methods  
### Reference Levels for Magnetic and Electric Fields  
In many countries, specific regulations and standards govern exposure to EMFs. The effects of electric and magnetic fields on human health have been a significant research focus for many years, as potential health implications from exposure are a concern in industry, medicine, and daily life. Numerous countries have established regulations and standards for exposure to low-frequency magnetic and high-frequency EMFs. The ICNIRP plays a crucial role in evaluating the effects of electric and magnetic fields on human health and establishing limit values. These ICNIRP limit values, grounded in scientific research, aim to minimize potential risks associated with daily electric and magnetic field exposure. They serve as general guidelines for managing and controlling exposure levels to these fields, applicable to occupational and general public settings.  
Figure 1 presents frequency-dependent ICNIRP reference levels for B [28] and electric fields [29; 30] regarding occupational and general public radiation exposure. For occupational exposure to B, the reference levels are 200,000 \(\upmu\)T for 1 Hz frequencies, 1000 \(\upmu\)T for 50 Hz, 300 \(\upmu\)T for 1 kHz, and 100 \(\upmu\)T for 400 kHz. Correspondingly, for the general public, these values are set at 40,000 \(\upmu\)T, 200 \(\upmu\)T, 80 \(\upmu\)T, and 27 \(\upmu\)T, respectively [28]. For the frequency range between 80 MHz and 6 GHz, which is the focus of our study, the limit values range from 61 V/m to 137 V/m for occupational exposure and from 28 V/m to 61 V/m for general public exposure. Concerning base stations operating at specific frequencies, the limit values for occupational exposure to electric field strength are as follows: 84.385 V/m for 800 MHz, 90 V/m for 900 MHz, 127.2 V/m for 1800 MHz, 137 V/m for 2100 MHz, and 2600 MHz. For the general public, ICNIRP has determined limit levels of 38.8 V/m, 41.2 V/m, 58.3 V/m, 61 V/m, and 61 V/m, respectively, for base stations operating at 800 MHz, 900 MHz, 1800 MHz, 2100 MHz, and 2600 MHz [29; 30].
# Modulation of Inflammatory Cytokine Production in Human Monocytes by cGMP and IRAK3
## 4 Materials and Methods  
### Cell Culture and Treatments  
Human monocyte (THP-1) cells (American Type Culture Collection ATCC TIB-202) and THP-1-BLUE (InvivoGen, San Diego, CA, USA) are non-adherent suspension cells cultured in Roswell Park Memorial Institute (RPMI) 1640 medium (Gibco, Thermo Fisher Scientific, Melbourne, Australia) with 10% (\(v/v\)) Fetal Bovine Serum (FBS; Gibco) in 5% CO\({}_{2}\) at 37 \({}^{\circ}\)C in a tissue culture incubator. All cells were split for 3 passages once taken from frozen stock before testing for mycoplasma using the PCR Mycoplasma Detection Kit (TOKU-E, Bellingham, WA, USA) and confirmed to be negative for mycoplasma before use. Cells were grown to 70-80% confluency before they were passaged by removing approximately 80% volume of cell medium solution in the flask and back-filled with the same volume of fresh medium and were only used up to a maximum passage number of 30. For THP-1-BLUE cells, 10 ug/mL Blasicidin S hydrochloride (Merck, Melbourne, Australia) was added every alternative passage to maintain selection pressure for NF\(\kappa\)B-inducible secreted embryonic alkaline phosphatase (SEAP) reporter genes. For SEAP assay and ELISA experiments, cells were treated with 8-bromoquanosine 3\({}^{\prime}\),5\({}^{\prime}\)-cyclic monophosphate sodium salt (8-Br-cGMP; Merck), or 1H-[1,2,4]oxadiazol[4,3-a]quinoxalin-1-one (ODQ; Merck), or diethylamine NONDate sodium salt hydrate (DEA NONOate; Merck), and/or lipopolysaccharideaccharide (LPS, _Escherichia coli_ 055:85; Merck) at the indicated concentrations for 18-24 h; the negative control cells were mock-treated with phosphate-buffered saline (PBS). The stock solutions of LPS, 8-Br-cGMP and DEA NONOate were made up in PBS, and ODQ was resuspended in ethanol. These stock solutions were then diluted in PBS to make working solutions.
## 4 Discussion
### Limitations  
This is a retrospective study that was conducted in the Oman adult population based on CTA images. The findings of this study apply to the origin, V2 and V3 segments of the VA. Only one investigator identified all the variations of the VA. To ensure accuracy and quality control, standard measurement protocols were applied, and the investigator was blinded to the scans. Further, the investigator was trained on the techniques related to the measurements taken in this study. Future studies with a large sample need not only report the prevalence of VAs in the Middle Eastern population, but also their clinical implications and outcomes.