esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Moreover, to reduce the liquefaction hazards, in the stratigraphic environment of saturated sand, at the Yinping hydropower station, [PERSON] [32] used gravel piles to reinforce the liquefiable foundation, in the context of a large area and depth, but at a high cost. However, it should be noted that many of the above analyses are, essentially, based on a treatment scheme of gravel piles or stone columns. This scheme is referred to as isometric gravel piles, in this paper, and it is illustrated in Figure 1.  
Although isometric gravel piles can improve the bearing capacity and anti-liquefaction ability of soft foundation, their implementation may lead to higher construction costs. Therefore, implementing a combination of long-short gravel piles has been discussed. This scheme is referred to as interphase gravel piles in this paper, and it is shown in Figure 2.  
Figure 1: Isometric gravel piles. The load-bearing characteristics of the long-short piled raft foundation and the interaction mechanism between piles and soil were analyzed, and the results showed that anti-liquefaction and control of settlement could be achieved with such piles ([PERSON] et al. [33]). [PERSON] et al. [34] studied, via numerical simulations, the response of a rocking foundation that was founded in a soft clayey environment and reinforced by stone columns, and they found that the length of the short pile in long-short gravel piles had little effect on the load bearing capacity of soft soil foundation. Meanwhile, [PERSON] et al. [35] discussed the anti-liquefaction mechanism of long-short piles in the vibro-replacement stone column composite foundation and pointed out that there was no need for a large number of long gravel piles to reduce the excess pore water pressure in deep soil. According to the layout of the long-short gravel piles used in these studies, these long-short gravel piles are the interphase long-short gravel piles referred to in this paper. Although the pile interphase long-short gravel piles also have the effect of anti-liquefaction, because of the long total pile length and more pile materials, the project cost is higher. In summary, existing studies have mainly evaluated the performance of the gravel pile in improving the bearing capacity and anti-liquefaction capacity of foundation, but they have ignored the corresponding increases in engineering costs. In order to make gravel piles play a significant role in improving resistance to liquefaction, it is crucial to put forward a more effective, and cost-efficient, treatment method for anti-liquefaction in the deeply deposited saturated liquefiable soil.
## 2 Materials and Methods
### RNA Isolation and Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR)  
To isolate RNA, the cells were lysed using TRIzol reagent (1 mL) (Life Technologies, Rockville, MD, USA), following the manufacturer's protocol. After adding chloroform (0.5 mL) for 5 min, the supernatants were mixed with isopropanol (0.5 mL). cDNA was synthesized from RNA using a Transcript cDNA Synthesis Kit (Roche Molecular Systems, Inc., Pleasanton, CA, USA). The Universal Probe Library (UPL) method was used to quantify the expression of inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), and _GAPDH_ in the cells using a Light Cyler 96 system (Hoffmann La Roche, Basel, Switzerland). The PCR thermal cycling procedure was as follows: Initial denaturing at 95 \({}^{\circ}\)C for 600 s; 85 cycles at 95 \({}^{\circ}\)C for 10 s; and 60 \({}^{\circ}\)C for 30 s. Relative mRNA expression levels were quantified using the comparative 2\({}^{-\Delta\Delta}\)Cq method and normalized to _GAPDH_. The nucleotide sequences used in the qRT-PCR analysis were as follows: Inducible nitric oxide synthase (iNOS) (sense, 5'-CTTTGCCACGGACGAGC-3'; antisense, 5'-TCATTGTACTCTGAGGGCCTG-3'); cyclooxygenase-2 (COX-2) (sense, 5'-GATGCTCTCTCCGAGTGTG-3'; antisense, 5'-GGATTGGA ACAGCAAGGATT-3'); and _GAPDH_ (sense, 5'-AAGAGGATTGCCTGCCCTTAC-3'; antisense, 5'-CCATTTTGCTACGGAGCA-3') [19].
## 4 Discussion
### Practical Benefits of the Research Method  
The methodology presented offers practical benefits by identifying the critical deficiencies in the construction scheduling process. By integrating expert interviews with Qualitative Comparative Analysis (QCA), this approach highlights the problem areas and specific combinations of deficiencies that can lead to increased risks in both schedules and budgets. These findings are useful for project managers and planners, who can utilize actionable information to develop targeted interventions. For example, recognizing that a \"Lack of planner experience\" (C\({}_{10}\)) often worsens issues related to \"Poor estimation of material quantities\" allows stakeholders to prioritize capacity-building initiatives or seek external consultation to improve the accuracy of quantity take-offs. Additionally, the proposed methodology aligns with the stages outlined in the PMBOK framework, providing a roadmap for integrating process improvements. Construction teams can utilize this structured guide to reassess their activities, from initial planning and task sequencing to detailed duration estimates. This approach safeguards that important factors are not overlooked, such as traffic management constraints and approval times. The definition of planning phases also enhances accountability among project participants, promoting more transparent communication and better resource allocation. Thus, the practical utility of this method extends beyond academic contexts; it serves as a framework for road infrastructure projects. It can be adapted to various contract types and regional contexts, improving project outcomes by addressing schedule and cost overruns. Finally, a practical analysis of Project #1 (Appendix B) shows causes C\({}_{3}\), C\({}_{4}\), C\({}_{6}\), C\({}_{7}\), C\({}_{8}\), C\({}_{10}\), C\({}_{13}\), C\({}_{16}\) and C\({}_{22}\). A detailed review of the documentation confirms that C\({}_{10}\) was the cause that generated the most variations in the schedule and costs. Design mistakes identified in the planning process led to new designs, which generated new activities and changes in the construction process. These, combined with delays in permit approvals and other shortcomings by the contractor, caused a time variation of 48.4% and cost variations of 29.1%, as shown in Appendix A.
## 1 Introduction
Finished products contain walls in different orientations, so it is necessary to conduct research for different shapes [1]. Another advantage is the use of nickel alloy as a workpiece material for thin-walled structures. In the available studies, we see a lack of leading works presenting research for this material group. The most studied material for thin-walled products is aluminum alloy. A few works related to the study of thin-walled structures in titanium alloy are observed [29]. Enormous interest has been observed in the Inconel 625 nickel alloy used as a material for numerous studies. [PERSON] et al. [30], in their paper, presented microstructural kinetics data required to investigate the feasibility of annealing heat treatment at lower temperatures for Additive Manufactured Inconel 625 Alloy at 700 \({}^{\circ}\)C. [PERSON] et al. [31] presented the effect of nano-oxides on the fracture properties of Inconel 625 nickel alloy. In the article by [PERSON] et al. [32], they compared the microstructure and mechanical properties of Ni-Cr-Mo alloys prepared from a single alloy using two new digital manufacturing techniques--selective laser melting (SLM) and soft metal milling (SMM) and one conventional--lost-wax casting (LWC). Developments in materials engineering are prompting research into the manufacture of new, strong materials. In the field of thin-walled structures for the aerospace industry, one can see, for example, the results of [PERSON] et al. [33], which presented fatigue performance for samples of porous structures made of beta titanium alloy obtained by laser powder bed fusion. The development of optical methods results in newer and newer measuring instruments appearing on the market, which make it possible to reduce measurement time and provide higher measurement accuracy [1]. The presented paper includes the results of dimensional deviations determined by the optical method using a 3D optical scanner, which gives value to the study. The 3D optical scanner is a relatively new measuring instrument, so conducting research using this machine is justified [21; 22]. In the aspect of dimensional deviation reduction, two approaches of adaptive milling strategy were used, which is a relatively new strategy and is therefore not yet widespread in scientific research.
# Presence of Cyanotoxins in a Mexican Subtropical Monometric Crater Lake
## 2 Materials and Methods
### Chemicals and Standards  
For cyanotoxin analysis, instrument solvents used for the preparation of mobile phases were of LC-MS grade (Fisher Optima, ThermoFisher, UK), and all chemicals were LC-MS reagent grade when possible. Sample preparation reagents were HPLC grade. Certified reference toxins (MC-RR, MC-LA, MC-LY, MC-LF, MC-LW, MC-YR, MC-WR, MC-HiI, MC-HtyR, MC-LR, Asp3-MC-LR and Nodularin) were all obtained from Enzo Life Sciences, Exeter, UK. A certified standard of [Dha\({}^{7}\)]-MC-LR and a pre-certified, freeze-dried matrix reference material of blue-green algae (RM-BGA, Lot 201301)containing a range of microcystis was obtained from the Institute of Biotoxin Metrology of the National Research Council Canada (NRCC). For preparation of a mixed stock solution, Certified Reference Material (CRM) ampoules were opened, and aliquots were accurately pipetted into a vial. A seven-level suite of working calibration standards was subsequently prepared through serial dilution of the mixed stock solution, using 50% methanol as the diluent, resulting in a calibration range between 0.33 ng/mL and 327 ng/mL. RM-BGA (280 mg) was extracted with 28.0 mL of 50% aqueous MeOH + 0.1% acetic acid prior to centrifugation (4500 rpm, 10 min), and the supernatant was collected prior to analysis.  
For PSTs analysis, sample preparation reagents were of HPLC grade. Certified reference toxins (STX, NeoSTX, GTX1, GTX2, GTX3, GTX4, dcGT2, dcGTX3 and dcSTX) were also obtained from the Institute of Biotoxin Metrology of the National Research Council Canada (NRCC). The standard solution of GTX2 and GTX3 contained dcGTX2 and dcGTX3 as minor components, but the exact contents for these toxins were not given.
## 5 Conclusions  
A first comparison between forward-scattered correlated power maps incoming from Galileo, BeiDou and GPS constellations is presented in this study, using ground-processed data collected between November 2015 and March 2019 by the UK TDS-1 mission. For similar surface conditions, results show that Galileo reflected signals generally achieve lower SNR than those from GPS, at least when using the current signal processing design and setup. According to theory, the wider bandwidth of Galileo reflections may correspond to improved surface resolution, but further work is needed for experimental verification. It is clear from this study that signal processing optimization is of critical importance for the design and development of future GNSS-R missions targeting the exploitation of multiple navigation systems. Demonstrated here for the first time is sensitivity of Galileo reflectance to geophysical parameters as detected by a spaceborne receiver, with both ocean wind speed and sea-ice presence shown to have similar relationships to reflected GPS signals, at least in shape. Unfortunately, due to the limited volume of available reflected BeiDou data at present, no conclusive analysis of the geophysical sensitivity of BeiDou signals can be carried out. A greater volume of data from a variety of surface conditions is required for detailed characterisation of the sensitivity of SNR to other sea surface conditions from both Galileo and BeiDou. This work demonstrates the promising nature of spaceborne GNSS-R systems for future wind speed retrieval and sea-ice detection using signals from a combination of different navigation systems, including GPS, Galileo, and BeiDou.  
The following are available online at [[http://www.mdpi.com/2072-4292/12/18/2927/s1](http://www.mdpi.com/2072-4292/12/18/2927/s1)]([http://www.mdpi.com/2072-4292/12/18/2927/s1](http://www.mdpi.com/2072-4292/12/18/2927/s1)). Figure S1: Galileo and GPS DDM Processing Examples, Figure S2: BeiDou DDM Processing Examples, Figure S3: Galileo SNR Wind Speed Relationship by AGSP, Figure S4: Power spectral density of the Galileo E1 OS signal, Figure S5: Frequency response of the TDS-1 front-end, Table S1: List of DDM processor settings.  
Methodology and investigations: M.L.H.; Software: [PERSON], [PERSON], [PERSON] ([PERSON]); Data Curation: [PERSON]. ([PERSON]), L.K.; writing--original draft preparation: M.L.H.; writing--review and editing: All; supervision: [PERSON], [PERSON], M.S., M.U.; project administration: [PERSON]. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was funded by ESA contract 4000123436/NL/FF/gp. G.F. was supported by NERC grant no. NE/N018095/1 (Ocean Regulation of Climate by Heat and Carbon Sequestration and Transports, ORCHESTRA).  
ERA-5 data were generated using Copernicus Climate Change Service Information [2019] neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus Information or Data it contains. OSISAF data were made available by EUMESTAT, copyright (c) 2020 EUMESTAT.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Histogram of Features in Testing Images  
Histograms of feature extracted from the test image objects are shown in Figure 11. All the features are positively skewed representing a positive relation with basmati grain size (head grain). The occurrence of eccentricity \(>\)0.9 is found for more than 16 rice objects in the test image. The aspect ratio is \(>\)2.4 for more than 17 objects. Similar results are also shown in the case of area, perimeter and major axis length. The histogram images as shown in Figure 11, also exemplify the head grain and broken grain percentage in the test images.  
Figure 11: Histogram of eccentricity, aspect ratio, equivalent diameter, area, perimeter and major axis length of test image objects.  
Figure 10: Classification performance of ANFIS, SVM and KNN for 10 test image samples. I1-I10 represents the test image IDs.
## 4 The COASYM-MAC Protocol
### System Model and Network Topology  
Let N nodes be randomly deployed over a certain area by \(G=(V,E)\), where \(V\) represents the set of all vertices (\(V=v_{1}\), \(v_{2}\)... \(v_{n}\)) and \(E\) is the set of edges which represents communication links between nodes. \(S\) represents the Base Station (BS). The BS is placed on one side of the sensor node. The wireless links are not symmetric, which is the main assumption of this paper. The summary of the used notation is listed in Table 3.
## 2 Proposed Calculation Method
### Calculation of the Horizontal Displacement in an Existing Tunnel
#### 2.3.1 Energy Conversion of Deformation Process of Existing Tunnel  
Relative corner movement and dislocation deformation occur between adjacent segments under the action of additional stress; they contribute to total deformation of the tunnel in the longitudinal direction. This causes the inter-ring tension and inter-ring shear generated between each of the adjacent tube sheet rings to resist deformation of the tunnel. According to analysis of the longitudinal deformation work and energy conversion of the tunnel, the additional stress caused by grouting is used to overcome resistance of the formation, the shear force between segment rings, and the tension between segment rings as follows:  
\[W_{\sigma}=W_{R}+W_{S}+W_{T} \tag{16}\]  
where \(W_{\sigma}\) is the total amount of work done by grouting additional stress, \(W_{R}\) represents the work done to overcome resistance of the formation, \(W_{S}\) indicates the work done to overcome the shear force between segment rings, and \(W_{T}\) denotes the work done to overcome the tension between segment rings.  
Integrating additional stress, resistance of the formation, inter-ring shear force, and inter-ring tension along the longitudinal direction of the tunnel yields the amount of the corresponding work. This paper does not elaborate on the specific derivation process, and the detailed integration process can be found in [16]. \(W_{\sigma}\), \(W_{R}\), \(W_{S}\), and \(W_{T}\) are expressed in Equations (17)-(20), respectively.  
\[W_{\sigma}=\int_{-ND_{t}}^{ND_{t}}w(y)\sigma(y)dy \tag{17}\]  
\[W_{R}=\int_{-ND_{t}}^{ND_{t}}\frac{1}{2}kD[w(y)]^{2}dy \tag{18}\]  
\[W_{S}=\sum_{m=-N}^{N-1}\frac{1}{2}k_{s}(1-j)^{2}[w((m+1)D_{t})-w(mD_{t})]^{2} \tag{19}\]  
\[W_{T}=\sum_{m=-N}^{N-1}\frac{k_{t}j^{2}D^{2}}{6D_{t}^{2}}[w((m+1)D_{t})-w(mD_{ t})]^{2} \tag{20}\]  
where \(N\) is the number of segment rings on one side of the tunnel affected by the additional stress at the center point, \(k_{s}\) represents the shear stiffness between the tunnel segment rings, \(k_{t}\) stands for the tensile stiffness between the tunnel segment rings, \(D\) is the tunnel diameter, \(D_{t}\) denotes the width of the segment ring, \(j\) indicates the proportional coefficient of the rigid body rotation effect of the segment ring, \(w(y)\) is the horizontal displacement of the tunnel, and \(m\) and \(m+1\) are the serial numbers of two adjacent segment rings.
# MLMD--A Malware-Detecting Antivirus Tool Based on the XGBoost Machine Learning Algorithm
## 2 Theoretical Background
### Malware Analysis  
The goal of malware analysis is to discover the functionality and structure of the particular malware and the attacker's intentions. The analysis methods can be divided into the following categories [6]:  
* Static analysis--analysis of a program without executing it. Basic static analysis looks for static information, such as strings, network addresses, called functions, and executable file header information. Advanced static analysis applies reverse engineering techniques using various special tools [7].
* Dynamic analysis--analysis of a program during execution. It is performed in a secure virtual environment, in which the activities of the executed program are observed; these are then used to deduce the purpose of the program [8]. In a secure environment, registry changes, network activity, function calls, disk file modifications, and so on are observed.  
Both types of analysis have their advantages and disadvantages. Static analysis is more secure, as malicious code is not directly executed, and it can be used to detect any malicious intent. However, the disadvantage of static analysis lies in its being ineffective against advanced obfuscation techniques aiming to prevent source code analysis [7]. Dynamic analysis allows one to reveal the functionality of a program without the need (eventually) for tedious code analysis. However, to its disadvantage, a secure environment is necessary for its execution [8], as running a malicious sample on a real system could be a risk;moreover, more advanced malware can often detect virtual environments or wait for either a time or a specific event and thus refrain from malicious activity.
## 3 Matrix-Based Establishment of Mathematical Model
### Calculation of Weight Penalty  
The weight penalty principle states that the component with the larger weight should be assembled preferentially. If this principle is violated, a weight penalty will be given. All the weight penalties are stored in matrix \(W\), whose matrix element is \(w_{ij}\).  
If component \(i\) is assembled before component \(j\), \(w_{ij}\) will be calculated as Equation (5) [7; 25; 26]. As indicated in Equation (6), the total weight penalty (\(\sum P_{weight}\)) for a specific  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \(\mathbf{p_{j}}\) & \\ \hline  & 1 & 2 & 3 \\ \(p_{i}\) & 1 & 0, 1, 1 & 0, 1, 1 & 0, 1, 1, 1 \\  & 2 & 1, 0, 1, 1 & 1, 0, 1, 1 & 0, 1, 1, 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The moving wedge table of components in Figure 1.  
Figure 2: The interference matrices of the example in Figure 1.  
sequence can be calculated by aggregating the penalty values (\(P_{weight}\)) corresponding to all the components in the sequence.  
\[w_{ij}=\left\{\begin{array}{l}weight_{j}/weight_{i},\text{if }weight_{j}> weight_{i}\\ 0\hskip 56.905512 pt\text{if }weight_{j}\leq weight_{i}\end{array}\right. \tag{5}\]  
\[\sum P_{weight}=\sum_{j=1}^{n}\left(P_{weight}\right)_{j}=\sum_{j=1}^{n}\left( \sum_{i=1}^{j}w_{ij}\right) \tag{6}\]  
where \(weight_{i}\) and \(weight_{j}\) represent the weight of the \(i\)th and \(j\)th components, respectively. (\(P_{weight}\)) represents the weight penalty value of the \(j\)th component in a sequence.  
The parameter \(n\) in Equation (6) is the total of the components.
# Rateless Coded Uplink Transmission Design for Multi-User C-RAN
## 1 Introduction  
The Internet of Things (IoT) is the trend of the next generation of communication networks. To achieve the IoT, information collection needs to be completed by massive sensors [1]. The International Data Corporation predicts that by 2020, the number of internet-connected devices in the IoT will grow to 50 billion [2]. The large amount of data generated by sensors and devices will create huge network traffic, which brings great challenges to the traditional wireless access networks. On the other hand, it has been noted in [3; 4; 5; 6] that cloud radio access network (C-RAN) is a promising wireless network architecture which can adapt to the rapidly growing IoT traffic and improve the Quality of Service (QoS).  
C-RAN is characterized by the separation of the remote radio head (RRH) and the baseband unit (BBU) of each access point in the network. In C-RAN, each RRH is located closer to the user, while each BBU is backward centralized into a virtual BBU pool. The BBU pool and RRHs are connected by high-speed fronthaul links. Since joint signal processing for multiple RRHs can be realized at the BBU pool, C-RAN can achieve a considerably higher spectrum efficiency compared with the traditional cellular networks, and C-RAN can also improve network resource utilization, reduce interference, and reduce energy consumption and overall hardware costs [7; 8].