esa-sceva/satcom-chunk-collection huggingface dataset:
# An Update on Polyphosphate In Vivo Activities
## 8 PolyP Signalling
Biophysical methods such as small-angle X-ray and \({}^{31}\)P NMR have been established to analyse the structure of the polymer dependent on bound cations in dry form or solution, respectively. The analysis of polyP in clinical settings, its association with FXII activation states, and its role in diseases requires future research. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Detection Method** & **Description** & **Sensitivity** & **Reference** \\ \hline Light- and fluorescence microscopy & The use of specific dyes such as DAPI/Nile-Red for labelling polyP granules in cells enables qualitative detection + localisation of polyP & +/++ & [145] \\ \hline Flow cytometry & Localisation and quantification of polyP granules; fluorescent dyes such as DAPI + Nile-Red; enables sorting of cells based on polyP content & ++/+++ & [146; 147] \\ \hline Fluorescence in-situ hybridisation (FISH) & Identification of PAOs by specifically labelled probes for polyP formation or degradation genes enables the detection of DNA or RNA molecules in situ & ++ & [147; 148] \\ \hline Extraction procedures and polyphosphate quantification & Extraction of polyP from samples; measurement of phosphate concentration after hydrolysis of polyP; characterisation of different polyP fractions; quantification after enzymatic treatments & ++/+++ & [149] \\ \hline UREA polyacrylamide gel electrophoresis (UREA-PAGE) & Separation of polyP molecules based on their size; determination of the degree of polymerisation (DP); use of specific staining techniques & +/++ & [150] \\ \hline Electron microscopy (EM) & Visual detection of polyP granules within cells; use of contrast agents for labelling and visualisation of polyP granules & +/++ & [151; 152] \\ \hline X-Ray analysis (X-RAY) & Determination of the chemical composition of polyP granules; possible quantitative analysis; use of X-rays & ++/+++ & [153] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Methods for analysing polyphosphate.
# Numerical Simulation Analysis Method for Rockburst Control in Deeply Buried Caverns
## 1 Introduction
By advanced drilling, the stress concentration of the tunnel face can be effectively reduced which inhibits rockburst. Studying the impact of unloading on the increase in shear stress, [PERSON] et al. [14] and [PERSON] et al. [15] proposed that controlling blasting and enhancing support can effectively reduce the risk of fracture-type rockburst. Based on a summary of the formation conditions of rockburst, [PERSON] et al. [16] proposed adopting different prevention and control measures for various causes of rockburst. For light and medium rockburst we can use active control measures such as controlled blasting and borehole stress relief. For strong rockburst, we can use passive control measures such as anchorage reinforcement and support to mitigate the impact of the rockburst. In summary, the research on rockburst control primarily focuses on understanding the mechanisms behind rockburst. Additionally, various methods for preventing and controlling rockburst are proposed from an engineering perspective. However, there are few reports on how to evaluate the effectiveness of these rockburst control measures through theoretical analysis and quantitative calculation methods. Therefore, exploring the rationality of rockburst control measures theoretically is of great significance. This exploration will help in adopting reasonable measures to curb the impact of rockburst during underground cavern construction and ensure project safety, economy, and efficiency. In this paper, we utilize numerical methods to evaluate the effectiveness of rockburst control measures through dynamic simulations of construction and excavation. This study aims to enhance the understanding of rockburst control methods in underground rock engineering and provide technical support for the design and construction safety of rockburst prevention and control.
# Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots
## 4 Methodology and the Proposed Algorithms
### Problem Definition  
The problem of bus charging scheduling is defined similar to the job scheduling problems presented by [PERSON] et al. and [PERSON] et al. The bus \(b\) has an arrival time \(a_{b}\), departure time \(d_{b}\), charging start \(s_{b}\), charging length \(l_{b}\), and a height \(h_{b}\). The bus can charge in the interval \([a_{b},d_{b}]\) with the following condition.  
\[d_{b}-a_{b}\geq l_{b}>0. \tag{1}\]  
The difference \(d_{b}-a_{b}-l_{b}\) is defined as the shifting time \(\delta_{b}\). If the difference \(d_{b}-a_{b}\) equals to the length of the charging time \(l_{b}\), the shifting time is zero. In that case, there is only one possible charging interval for the bus. If on the other hand, \(\delta_{b}>0\) there are multiple possible charging intervals for the bus. Charging interval is defined as \([s_{b},s_{b}+l_{b}]\) so that  
\[[s_{b},s_{b}+l_{b}]\in[a_{b},d_{b}]. \tag{2}\]  
All possible charging intervals of bus \(b\) are defined with tuple \(P_{b}\) so that  
\[P_{b}=[[s_{b},s_{b}+l_{b}],\;s_{b}=a_{b}+\delta,\;for\;\delta=0,1,\ldots,\delta _{b}]. \tag{3}\]  
When calculating the intervals from the tuple \(P_{b}\) it is necessary to calculate the length \(l_{b}\) for each possible interval, since this length can change due to preconditioning.  
The total height \(H\) at the time instance \(t\) is the sum of all heights of buses charging at that time:  
\[H(t)=\sum_{b,l\in[s_{b},s_{b}+l_{b}]}h_{b}. \tag{4}\]  
Figure 5: Change in the length of the charging block (green) due to the preconditioning block (blue) for two different scenarios: (**a**) no overlapping with preconditioning; charging time is 1 h and 50 min and (**b**) overlapping with preconditioning for 15 min; charging time is 2 h and 2 min.  
The peak demand is the biggest height \(H_{max}\) occurring during the complete analyzed period [0,\(T\)]. The goal is to minimize the \(H_{max}\) by assigning one charging interval to each bus from its tuple of all possible charging intervals \(P_{b}\).  
This problem was proven to be NP-hard [25; 26].
This is particularly important in industries such as mining and materials handling, where mechanical failures can result in serious safety hazards. [PERSON] and [PERSON] [12] emphasized the importance of residual load-bearing capacity, highlighting the number of broken wires and metallic cross-sectionalloss due to abrasion or corrosion as key indicators of rope health. [PERSON] et al. [13] likewise described steel wire ropes as dynamic mechanical systems exposed to progressive damage. Their multiscale simulations confirmed that localized fatigue accumulates over time, eventually leading to full-system failure. [PERSON] et al. [14] compared PVF and non-PVF ropes in mining applications and found that wear was the leading factor in degradation, especially in multi-layer winding systems. PVF ropes demonstrated better performance in terms of bending fatigue and corrosion resistance, supporting their use in heavy-duty applications. In a long-term monitoring study, [PERSON] and [PERSON] [15] found that although the total breaking force of ropes decreased after each six-month service interval, values remained within safe limits--further emphasizing the importance of regular mechanical testing as part of preventive maintenance protocols. While previous studies have addressed rope degradation trends--including fatigue, corrosion, and mechanical wear--they do not classify ropes based on the internal uniformity of mechanical properties, particularly tensile strength. This research advances the field by introducing a novel wire-level classification system for steel ropes, grounded in real operational data from 112 ropes. Unlike conventional evaluations, which often overlook internal strength dispersion, our method applies statistical control charts and capability indices to assess wire strength variability. Most notably, the paper proposes a new classification approach based on an empirically validated \(\pm\)4% tolerance for wire strength class dispersion. This threshold represents an optimal balance between manufacturing feasibility and mechanical reliability and is supported by long-term monitoring of ropes used in demanding industrial environments. To our knowledge, no existing standard, including EN 12385 [16], prescribes such a narrow dispersion range for internal wire strength variability
## 1 Introduction
### High-Resolution Topographic Surveying and Photogrammetric Remote Sensing of Coastal Morphology  
In tidal environments, daily fluctuations in the water level mean that the water-worked seabed can be measured using topographic surveying, with the maximum coverage typically achieved during the lowest low tides. Previous work largely relied on RTK-GNSS, with a rover eventually mounted on a mobile platform, such as a quad, for monitoring the evolution of barred beaches and other coastal landforms [12; 19]. The DEMs generated have resolutions ranging from a few meters cross-shore to tens of meters alongshore, in link with the topographic complexity and bedform orientation, while survey coverage can reach up to several kilometers alongshore, allowing to account for the rhythmic nature of the coastal morphology (e.g., rip-channel systems, cusps and megacusps). Common to point-based surveying techniques, enhancing the spatial resolution is generally counter-balanced by increased acquisition time or reduced spatial coverage [20; 21; 22], which means that using RTK-GNSS for the fine-scale monitoring of coastal topography remains problematic.  
In contrast, remote sensing techniques, such as UAV photogrammetry, have greatly improved, rendering very-high-resolution (e.g., submeter) topographic surveying at a large scale (e.g., beach scale) feasible. Using off-the-shelf drones and cameras, ground sampling distances (GSD) as small as a few centimeters can be achieved during typical beach surveys [23; 24; 25]. Photogrammetric processing relies on the SfM method, which is based on the scale-invariant feature transform (SIFT) algorithm [26] for matching corresponding points in images, and uses external information, mostly taking the form of GCPs measured independently, for scaling and optimizing the photogrammetric model. Yet, previous work showed that the resulting DEM quality (e.g., horizontal and vertical precisions) is largely variable (e.g., Refs. [23; 27; 28]) and depends on a wide range of parameters, among which survey design (e.g., flying height, image overlap, camera orientation, GCP number and distribution) and processing strategies (e.g., direct georeferencing, camera calibration and point-matching methods) play a critical role.  
Photogrammetry has been used previously in different domains of research to measure sedimentary bedforms of various dimensions, including particle clusters [29; 30], bed undulations, such as sand [31; 32; 33] and gravel bars [34; 35; 36], and sand dunes [37; 38; 39; 40; 41; 42], with measurements of small-scale bedforms generally performed in the laboratory. Hence, it is not yet known if fine-scale water-worked topographies can be efficiently and effectively measured using UAV photogrammetry in the field, such that there is currently a paucity of data for assessing coastal topographic roughness and shaping processes.
# Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks
## 5 Results and Analysis
### Comparison Test  
Two state-of-the-art methods were implemented and compared with the proposed analytical methodology: [PERSON] et al.'s method [16; 17] and [PERSON] et al.'s method [21]. These methods were validated and compared using scenario 1.  
The method proposed by [PERSON] et al. uses the synchronous machine model to consider the DG effect on the fault location. Therefore, it was compared with the method of this paper, using also the  
Figure 14: Performance curve for Scenario 3: (**a**) using the synchronous machine model, (**b**) using the Inverter-interfaced DER model in limiting current mode, and (**c**) using the IEDs information.  
INIDER electrical model. Because of the large number of results, they are presented in a bar graph using the average and maximum errors obtained for each fault type, as shown in Figure 15.  
The test results show the better accuracy of our method when compared with the method proposed by [PERSON] et al. [PERSON] et al.'s method presents errors of up to 11% and an average error of 2% for faults with fault resistances between 0 and 100 ohms. While for our method, the errors in estimating the fault distance are lower than 1.1%, and the average error is 0.6%. This difference in accuracy between both methods is caused mainly because of the capacitive effect, which is not considered in the formulation of [PERSON] et al.  
Similarly, the method proposed by [PERSON] et al. was compared with our method. For this comparative test, our method used information from the IED located at the DER, since the [PERSON] et al.'s method uses synchronized measurements at the DER terminal. Figure 16 presents the results obtained for [PERSON] et al.'s method and our method.  
Figure 16: Fault error comparison: [PERSON] et al.’s method [21] and proposed formulation using the information of IEDs.  
Figure 15: Fault error comparison: [PERSON] et al.’s method [16; 17] and the proposed formulation using the electrical model of the DER. DG: distributed generation.  
The results obtained show the better accuracy of our method when compared with the method proposed by [PERSON] et al. This better accuracy is observed in the maximum errors obtained for each fault type. Nonetheless, for some fault types (A-g, B-g, C-g, C-A, and ABC) the average errors are very low and are comparable to those obtained with the method of this paper.  
Figure 17 compares two state-of-the-art methods with the one proposed in this paper; the last one considering IED information, or the DER electrical model. This comparison is made for one case of the scenarios evaluated in the comparison tests. The figure allows to understand why the [PERSON] method presents a low performance: it depends on the fault resistance and the location of the fault. The figure also shows that the performance of the method proposed in this paper, using the DER electrical model, is comparable with the performance of [PERSON] et al.'s method. This is relevant because it shows the robustness of our method, even when the IED information is not available.
# SENTINEL: A Secure and Efficient Authentication Framework for Unmanned Aerial Vehicles
## 5 Security Protocols in SENTINEL  
In this section, we present the security protocols between drones and ground stations showing to generate a _flight session key_ for drone authentication, and how the ground station authenticates the drones with the flight session key.
## 2 Materials and Methods  
### Paper Collection  
IEEE Xplore and SCOPUS were selected as the reference database in order to have a broad coverage that includes both scientific and technological fields. During a preliminary exploration, we found two main topics containing information of interest for our purposes. The first was concerned specifically with anticipatory postural adjustments to turning. The other analyzed human kinematics during turning and sometimes included information about pre-turn maneuvers that can be interesting to analyze anticipatory adjustments. Therefore, the final search was based on queries from the following groups of keywords: (GAIT and ANTICIPATION and TURNING); ((GAZE or HEAD or TRUNK or HIP) and TURNING and KINEMATICS). Additionally, we defined different synonyms for the search, such as WALK in place of GAIT, or STEERING or CHANGE OF DIRECTION in place of turning.  
The inclusion criterion for the screening of papers was as follows:  
* The experiments included a change in direction during normal walking of healthy adult subjects. Sustained circle-walking was not considered.
* The results included quantitative values about the time/spatial anticipation from kinematics (positions, velocities or accelerations) of variables of upper body segments.
* The studies were published in English.
* The studies were published in a journal.  
An initial search was conducted in January 2016 (SCOPUS, IEEE Xplore), which included 2015. 397 raw references. Title and abstract were independently reviewed by the three authors and decision about their relevance for the study was taken after consensus. 24 papers were selected. Seven of them were removed after a preliminary read.  
During the subsequent execution of the study, the authors individually screened (title, abstract, preliminary read) new papers found using varied sources (SCOPUS, Web of Science, IEEE Xplore and Google Scholar). These new papers were considered at different literature updates (September 2017, February 2018). After a detailed analysis, one paper was incorporated into the study.  
Selected papers were distributed among the authors. Each author collected the relevant data from the assigned articles, with the final supervision of the other two.  
From the finally considered works, nine of them did not provide precise quantitative results and were not used for quantitative analysis. Relevant findings from these works were qualitatively considered in the discussion when appropriate.  
Figure 2 summarizes the process using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram.
## 4 Discussion  
Based on the use of the CR-MRTM method proposed in this study, surface and interbed multiples can be effectively imaged in a VSP observation system without the need for the signal separation of seismic data. Our method improves the effective illumination range of VSP imaging, thereby aiding subsequent tasks such as oil and gas reservoir exploration.  
Similar to other RTM methods, the approach proposed in this study also requires solving the two-way wave equation to compute the forward- and back-propagating wavefields. Therefore, to ensure the final imaging quality, the proposed method requires an accurate velocity model. Future research could focus on the construction of accurate velocity models, such as integrating physics-informed neural networks with full-waveform inversion, which would improve the accuracy of full-waveform inversion results [33].  
Moreover, current RTM studies predominantly employ artificial sources, such as Ricker wavelets, as signal sources, which help mitigate the contamination of seismic data using noise. However, utilizing wavelets with greater randomness as seismic signals can further enhance the simulation of seismic wave propagation in the subsurface medium. [PERSON] and [PERSON] proposed a method that uses circular harmonic wavelet transforms to randomly generate artificial accelerations, which more accurately reflect the non-stationarity of seismic motions [34]. Therefore, the use of random seismic wavelets in reverse-time migration imaging methods represents a promising area for future research.  
In addition, the quality of seismic data also significantly affects the final imaging quality, particularly when the elastic wave equation is used to simulate the wavefield propagation process. This is also one of the key factors influencing the RTM imaging quality in practical applications. Although the proposed method can effectively image multiples in seismic data, research on signal separation and noise removal remains necessary. Furthermore, in the case of elastic waves, the wavefield consists of a significant number of converted shear waves that also contain a large amount of information regarding the subsurface structure. In existing research, converted waves are typically treated as noise and removed. Therefore, exploring how to effectively utilize converted waves for imaging is a promising research direction.
## 3 V2X System Architecture When Integrating Blockchain Technology and MEC
### Integration of Mobile Edge Computing (MEC) into the V2X System  
Limited resources, a dynamic road user environment and long distances between cloud servers make it difficult to support computationally intensive services and cause problems such as additional latency, jitter and high power consumption [31]. To solve these problems, the European Telecommunications Standards Institute, in 2014, proposed the use of mobile peripheral computing [3]. Cloud-based models require the transmission and storage of V2X system object data in specialized data centers, which increases the likelihood of information leakage or loss and long latency, which can lead to accidents [24]. In addition to this, the devices require additional services, such as computing and updating the user rating and network segment selection, which is a very time-consuming process; further, it becomes necessary to resort to mobile edge computing technology. Since we are dealing with emergency event messages, the timeliness of the dissemination of messages is of paramount importance.  
It should be noted that, when the RSU load is high, MEC can perform load balancing to other RSUs or to its own facilities. We consider each unloading task as a service to the edge infrastructure. Moreover, as the number of network participants and events increases, the energy consumed for computation and data transmission increases.  
The loading pattern of RSUs and MEC depends on the corresponding traffic flow in that region. It is assumed that multiple RSUs share common edge server resources. To select segments and reduce service delays for excessive loads, it is necessary to expand the computational capacity of MEC. It is important to provide a balanced unloading scheme for different RSUs and to provide decision-making capacity for messages that require high reliability and system responsiveness.  
It is necessary to consider that attackers could intervene to manipulate the results of the computation, which could have serious consequences in the operation of the requested algorithm, resulting in the need to allocate additional resources for a particular service request. In the proposed model, MEC using blockchain technology based on smart contracts between service providers and mobile peripheral computing devices stores data related to each service request, such as the amount of resources allocated, hash of computation results and associated object identifiers and timestamps. This chain provides transparency in resource allocation and automates each process through smart contract mechanisms to reduce human intervention. MEC servers provide services and send the results of calculations to the appropriate RSUs and the cloud.
# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 4 Double-Scale Channel State Prediction
### Large-Scale Channel State Prediction
#### 4.1.2 Calculation of Stored Series Length  
Since historical channel state series increases with time and will consume large storage space and increase the prediction complexity, we propose to take advantage of the fluctuation features. Considering the certain changing cycle in a long-time period, only a few cycles of historical channel state series need to be stored to reduce the amount of storage and accelerate the prediction speed.  
To calculate the spectrum of historical channel state series, the Fourier transform is used. Then, the frequency \(f_{max}\) with maximum amplitude is selected. The time \(T_{p}\) corresponding to \(f_{max}\) represents the characteristic time of channel fluctuation. The historical channel state series with a length of \(T_{p}\) can reflect the characteristics of channel fluctuation. Thus, the historical channel state series with a length of \(\beta T_{p}\) is stored for channel prediction, \(\beta>1\).