esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c} \hline \hline
**Thematic Area** & **Indicative EO Applications** \\ \hline Air quality/air pollution & Aerosol \\ Clouds & Precipitation \\ Atmospheric & Atmospheric dynamics/wind \\ Atmospheric events & Dust storms/dust intrusion \\ Climate change & Climate change \\ Atmospheric/climate models \\ \hline \multirow{6}{*}{Water} & Hydrological monitoring \\ Water quality/water pollution & Water resource management \\ Water & (Water) microbial risk assessment \\ Water leak detection & Water leak detection \\ \cline{1-1}  & Managed aquifer recharge \\ \cline{1-1}  & Hydrological-hydrogenical modeling \\ \cline{1-1}  & Water policies \\ \cline{1-1}  & Water diplomacy \\ \hline \multirow{6}{*}{Agriculture} & Precision agriculture \\ \cline{1-1}  & Irrigation scheduling \\ \cline{1-1}  & Agricultural policies \\ \cline{1-1}  & Soil health \\ \cline{1-1}  & Pest/disease control \\ \cline{1-1}  & Food security/food safety \\ \cline{1-1}  & Early warning systems \\ \cline{1-1}  & Damage assessment and mitigation strategies for extreme \\ \cline{1-1}  & weather events \\ \hline \multirow{6}{*}{Land} & Land cover/land use changes \\ \cline{1-1}  & Forest dynamics \\ \cline{1-1}  & Urban sprawl monitoring \\ \cline{1-1}  & Real estate \\ \cline{1-1}  & Heat island \\ \cline{1-1}  & Spatial planning \\ \cline{1-1}  & Urban and regional planning \\ \cline{1-1}  & Land management information systems \\ \cline{1-1}  & DEM generation \\ \cline{1-1}  & Photogrammetric applications \\ \hline \multirow{6}{*}{Disaster risk reduction} & Forest fire monitoring \\ \cline{1-1}  & Burnt area mapping \\ \cline{1-1}  & Systematic monitoring of gebazards \\ \cline{1-1}  & Soil erosion detection \\ \cline{1-1}  & Soil degradation/desertification \\ \cline{1-1}  & Flood monitoring \\ \cline{1-1}  & Epidemics/health \\ \cline{1-1}  & Impact assessment \\ \cline{1-1}  & Disaster management \\ \cline{1-1}  & Early warning systems \\ \cline{1-1}  & Decision support systems \\ \hline \multirow{6}{*}{Cultural heritage} & Risk assessment of cultural heritage regarding natural and \\ \cline{1-1}  & anthropogenic hazards \\ \cline{1-1}  & Protection of cultural heritage \\ \cline{1-1}  & Cultural heritage digitization (3D models) \\ \cline{1-1}  & Archaeo landscape assessment and modeling \\ \cline{1-1}  & Study of unexcavated areas \\ \cline{1-1}  & UAV photogrammetric applications \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Indicative EO Applications per thematic area. The analysis included the citation topics at the micro level, which were downloaded from Web of Science(tm). The citation topics at the micro level included 2437 topics which were algorithmically derived with the use of an algorithm developed by CWTS, Leiden [31]. Also, the analysis included the top 10 affiliations and the top 25 authors with the highest number of publications and their affiliations. Finally, data for the population, area, and gross domestic product (GDP) of each country within the EMMENA region were taken from the World Bank [7] and the International Monetary Fund [32] (Table 2).
## 4 Numerical Experiments and Results
### Example and Parameter Setting
#### 4.3.2 Sensitivity Analysis of Different Mixed Carbon Policy Scenarios  
First, in scenario S4, both the mixed carbon emission cost and total cost are suboptimal, and the vehicle management and use cost are the highest. The carbon emission is the lowest. This indicates that the emission reduction effect is the best in scenario S4, and the emission reduction actions have less impact on the economic benefits. Second, in scenario S2, the carbon emissions, total cost, total driving distance and total fuel consumption are the highest. This finding indicates that the distribution center expends many financial and  
Figure 6: Influence of different traffic congestion scenarios on the results of multi-compartment vehicle path planning. (**a**) Relevant costs under different congestion scenarios. (**b**) Driving distance and carbon emissions under different congestion scenarios. (**c**) Relevant cost growth rates under different congestion scenarios. (**d**) Related costs, fuel consumption and carbon emissions under different congestion scenarios.  
material resources, but the final economic benefits and emissions reduction effect are very low. It can be seen that scenario S2 is not conducive to energy conservation and emission reduction in distribution centers. In addition, it can be observed in scenarios S1, S2 and S3 that the cost of vehicle management and use is relatively small but that the total cost and total carbon emissions remain high. This result indicates that the reduction in vehicle management and use cost cannot reduce the final total cost and total carbon emissions. Finally, in scenario S5, the distribution center plans to travel more distance and pays more vehicle management and use cost, the total cost is the smallest, the effect of carbon emissions reduction is suboptimal and the carbon emission cost is relatively the lowest. This finding indicates that scenario S5 is suitable for the distribution center to consider both economic benefits and energy conservation and emission reduction (Figure 7).
# Highly Sensitive Non-Enzymatic Detection of Glucose at MWCNT-CuBTC Composite Electrode
## 2 Materials and Methods
### Electrochemical Experiments  
Cyclic voltammetry (CV), differential pulse voltammetry (DPV), linear sweep voltammetry (LSV), chronoamperometry (CA), and multiple pulse amperometry (MPA) measurements were carried out using a computer-controlled Autolab PGSTAT 302 potentiostat/galvanostat (EcoChemie, The Netherlands).  
The three-electrode system consisted of a MWCNT-CuBTC working electrode with a 0.196 cm\({}^{2}\) geometrical area, a platinum wire as a counter electrode, and a saturated calomel reference electrode (SCE). Before each voltammogram run, the MWCNT-CuBTC composite electrode was carefully polished with abrasive paper and then by a felt-polishing pad by using 0.3 \(\upmu\)m of alumina powder (Metrohm, Switzerland). The electrode was then sonicated for 5 min in pure water. All experiments were carried out with a typical cell of 50 mL at room temperature (25 \({}^{\circ}\)C). Before each detection experiment, ten repetitive cyclic voltammograms were run to achieve a stable response from the electrode.
# Energy-Aware Control of Error Correction Rate for Solar-Powered Wireless Sensor Networks
## 4 Experimental Results and Discussion  
We compared RS(36; 32) and RS(68; 32) and the recently-proposed two-level RS scheme [19] with EA-RS for a performance verification. RS(36; 32) uses only four symbols (32 bits) as parity bits for the error recovery of 32 data symbols (256 bits) in an RS block. This RS(36; 32) enjoys the advantage of the amount of energy used in the FEC being small, but the drawback is that the data recovery rate is also low. On the contrary, RS(68; 32) uses 34 symbols (272 bits) as parity bits for the error recovery of 32 data symbols (256 bits). Owing to the long parity bits, RS(68; 32) has the advantage of a high data recovery rate, but the amount of energy used is inevitably high. The two-level RS scheme is similar to EA-RS, in that the parity length is dynamically determined according to the energy state. However, unlike EA-RS, which determines parity lengths in further detail from minimum to maximum depending on the amount of energy, two-level RS selects one from only two schemes: RS(36; 32) and RS(68; 32).
###### Abstract
In fact, to our knowledge, there is no asymptotic analysis of BER and outage probability presented in the literature that can obtain novel insights into how channel model parameters affect the overall performance at high signal-to-noise (SNR) ratio. Therefore, by using the asymptotic behavior, a novel diversity order analysis for the different scattering scenarios is included. The presented results show that UOWC links under higher levels of scattering exhibit a lower average BER and outage probability than tap water scenarios. It follows, therefore, that particle-induced scattering can mitigate the random irradiance fluctuations and light blockages due to both small and large air bubbles. Monte Carlo simulations are included to verify the presented results. The remainder of this article is organized as follows. In Section 2, the system and channel models adopted in this work are described for small and large air bubbles from experimental measurements. In Section 3, we derive new closed-form analytic and asymptotic expressions for the average BER and outage probability when adopting the proposed channel models. Numerical results and discussions are presented and analyzed in Section 4. Finally, we summarize the main conclusions and some future lines of this work in Section 5.
## 1 Introduction
If a sensor network could be installed to monitor damage and degradation of the bonded joint an \"as required\" inspection program could be used. The most straightforward method of implementing such a structural health (SHM) network onto an aircraft is during the design stage [13]. It has been suggested that by implementing structural health monitoring during design, weight savings of up to 15% could be achieved at component level [14]. A long-established technique for detecting damage in structures is by using acousto-ultrasonic induced Lamb waves. The principle involves exciting a piezoelectric transducer mounted to the structure's surface which induces a Lamb wave that is then detected by another transducer mounted at a different location on the structure. If damage occurs within the field between the two sensors, the signal propagation is altered resulting in a quantifiable difference in the signal received. This technique can be extended to networks of multiple sensors to improve detection capability. To enable complex adhesive bond defects such as kissing bonds (where bond fails but still remains in close contact with the bond face hence appearing to still be bonded. These are notoriously difficult to detect [15]) to be detected using acousto-ultrasonic techniques, sensor networks need a high probability of detection. An understanding of Lamb wave interaction with stiffeners and their defects will enable better sensor network design whilst also driving towards minimizing the weight penalty the sensors add to the overall structure. Following a review of the use of Lamb waves in SHM and their study using laser vibrometry, this paper presents the results of an experimental investigation of the interaction of Lamb waves with adhesively bonded stiffeners (with and without disbonds) using 3D scanning laser vibrometry. Results demonstrating Lamb wave interaction are shown, together with post-processing of the vibrometry data with a windowed root-mean-square technique. The results are further compared with the results of ultrasonic inspection of the disbond and its associated geometry is shown to correlate very closely with disturbances of the Lamb waves as they interact with the stiffener and the damage, demonstrating the power of Lamb wave-based SHM systems to detect the presence of adhesive failure.
## 2 Materials and Methods
### Statistical Analysis  
The data were analyzed using SPSS version 22.0 (SPSS Inc., Chicago, IL, USA). The data are reported as mean and standard deviation. Data normality was verified using the Shapiro-Wilk test. The assumptions of sphericity were assessed by Mauchly's test. Whenever an assumption was violated, a Greenhouse-Geisser correction was applied on the degree of freedom if the epsilon (\(\varepsilon\)) value was <0.75 and a Huynh-Feldt correction was applied if \(\varepsilon\) was >0.75. A one-way analysis of variance for repeated measures was used to compare the variables related to different exercise forms. When a difference was found, a Bonferroni post hoc test was applied. Partial eta squared (\(\eta_{p}\)\({}^{2}\)) was used to evaluate effect sizes. The alpha level was set at 0.05 for all the tests.
# Deformation Analysis of Large Diameter Monopiles of Offshore Wind Turbines under Scour
## 2 Deformation Analysis Method  
### p-y Curve  
In the analysis of lateral loaded pile foundations, the soil is modeled as a system of uncoupled springs distributed along the depth, and the pile response can be obtained from the differential equation:  
\[EI\frac{dy^{4}}{dx^{4}}+p(x)=0 \tag{1}\]  
where \(EI\): pile bending stiffness, \(y\): pile displacement, \(p\): soil resistance, and \(X\): length along the pile.  
The key to solving the differential equation is the soil resistance function \(p(x)\), which includes linear and nonlinear assumptions. The linear method assumes that the soil resistance \(p\) is proportional to the pile displacement \(y\) at any depth, which means the stiffness of soil spring could be represented by a constant. The other solution assumes that the soil resistance function \(p(x)\) is non-linear, and the stiffness of soil spring could be described by p-y curves. Through field test or lab element test [13], the p-y curve at any depth under the mudline (Figure 3) could be constructed.  
[PERSON] [14] and [PERSON] et al. [15] proposed p-y curves for soft clay and stiff clay, respectively, and [PERSON] et al. [16] gave the expression of p-y curves in sand. American Petroleum Institute's recommendations [17] are widely used in practical engineering. [PERSON] et al. [18] proposed a method applicable to both soft and stiff clay. Table 1 lists several p-y curve models, and [PERSON] et al. [19] made a review on the p-y curve for monotonic and cyclic and discussed problems in existing studies.  
Figure 3: P–y curve at different depths and calculation diagram.
## 4 Antimicrobial Agents
### Compounds Targeting Metabolism  
Studies into bacterial metabolism show that certain metabolites are necessary for biofilm formation and stability [121]. Recent reports showed small-molecule metabolites and correlated metabolism were essential for biofilm development and dispersal. [PERSON] et al. found that in the early phases of biofilm development, tricarboxylic acid (TCA) cycle activity was increased, iron metabolism and the transport was reorganized, a metabolic shift had occurred from fatty acid biosynthesis to fatty acid degradation, and a switch from acetate to acetoin fermentation took place in _B. subtilis_[122]. In another study by [PERSON] et al., they first evaluated the difference in metabolism between the biofilm and planktonic populations of UTI89 (uropathogenic _E. coli_, UPEC strain) by using mass spectrometry-based targeted and untargeted metabolomic methods, together with cytological imaging, that enabled in identifying the targeted metabolites and related metabolic pathways involved in biofilm development [123]. Interestingly, they could also find distinct changes in both metabolism and phenotypic morphology in two patterns. Moreover, they recognized and categorized 38 differential metabolites and three of the associated metabolic pathways, namely carbohydrate metabolism, amino acid metabolism, and glycerolipid metabolism, were changed typically during biofilm production [123]. In a different study, tea tree oil showed antimicrobial and antibiofilm activity against _S. aureus_ (Table 2) and also changed its metabolism by dramatically affecting the expression of genes associated with the pyrimidine metabolism pathway, purine metabolism pathway, glycine, serine, and threonine metabolism pathway, and amino acid biosynthesis pathway [52]. Some research also showed that treatment with exogenous amino acids for example L-arginine was able to control the biofilm formation by repressing the genes that are essential in the formation of _S. mutans_ biofilm EPS [124]. Hence, from the recent knowledge about biofilm metabolism, key biofilm metabolites and the chief metabolic pathways could be sorted out. Metabolic engineering into these pathways could be the next essential approach in tackling biofilms.
# Modeling of Swab and Surge Pressures: A Survey
## 1 Introduction  
Swab and surge is a well-known problem for drilling and well construction operations. Researchers have been investigating this problem since the 19 th century. Swab and surge refer to pressure fluctuations due to lowering or withdrawing the pipe from the hole. See Figure 1.  
Swab and surge pressure fluctuations are either positive or negative. They are positive when lowering down the pipe and negative when withdrawing the pipe. The intensity of these pressure fluctuations depends on the speed of the lowering down (tripping in) or withdrawing the pipe out (tripping out). If the tripping speed is very high, the corresponding pressure fluctuation is high, and in some cases, this can be higher than the fracture pressure of the formation. This will cause fracturing the formation, partial or in some cases full losses, and in a worst-case scenario well collapse can occur. If the tripping speed is too low, this leads to a slow tripping operation, which is considered non-productive time (NPT), increasing the overall well budget.  
The reverse of positive pressure fluctuation is also true. If the pipe is withdrawn very fast from the hole, this will lead to negative pressure in the wellbore and well inflow and can cause kick. In a worst-case scenario well incident or blowout can happen.  
Researchers over the years have looked into swab and surge, utilizing mathematical equations and physics to understand the phenomena. As a result, some empirical, mathematical, and physics-based models are available for swab and surge simulations. This paper looks into some literature on the swab and surge in wellbore pressures. The literature study presented in Section 2 presents a thorough analysis of five prominent works. Themodels given in these works were developed when pressure measurements were unavailable during the tripping in or tripping out operations. However, measuring the downhole pressure and temperature is possible with the emergence of high-speed telemetry. Hence, it is now possible to build newer models for the processes using data-driven techniques on top of mathematical equations and physics with these new technologies; Section 3 presents a new proposal.
## 1 Introduction  
The application of the holmium YAG (Ho:YAG) laser in minimally invasive urological procedures has been a milestone in the treatment of kidney stones. Ho:YAG is a solid-state, pulsed laser, enabling a high absorption of laser energy via water. This results in efficient lithotripsy through both the photo-acoustic and photo-thermal decomposition of stones [1]. Despite the immense technological progress and the introduction of new generations of lasers into daily urological practice, the Ho:YAG laser continues to play a leading role in flexible ureteroscopy [2]. The Ho:YAG laser exhibits thermomechanical activity, and the primary concern associated with its use revolves around potential thermal injuries to the soft tissues within the urinary tract, which may occur due to the overheating of irrigation fluid, resulting from the direct absorption of infrared-laser energy [3]. Earlier studies demonstrated that the denaturation of urinary tract proteins takes place at 43 \({}^{\circ}\)C [4], and coagulative tissue necrosis occurs at a temperature of 54 \({}^{\circ}\)C in porcine kidneys [5]. One of the kidney-dependent determinants is the anatomical capacity of the pelvicalyceal system. Taking into account the modified Takazawa renal pelvicalyceal anatomical classification subtype, the size of the renal pelvis is individually a highly variable parameter. The interquartile range of the cross-sectional area of the renal pelvis in a single pelvis (Type A) spans a wide range, from 41 to 111 mm. Furthermore, in the case of a Type B pelvis, the cross-sectional area of lower bifurcated branches in a divided pelvis constitutes a significantly smaller space (on average, 26 mm) [6]. The volume of fluid in which laser activation takes place is a crucial factor in determining the degree of temperature elevation [7]. From the standpoint of laser thermal interactions, the reduced capacity of the system presents a risk factor for hyperthermia, especially considering the use of high-energy devices in therapy. Among the modifiable factors, one should mention the laser power applied, the duration of its work, the irrigation flow rate, and the baseline temperature of the irrigation fluid.  
The objective of this study was to assess the effect of laser settings and ureteral access sheath size on peak maximum temperature and the rate of temperature increase in the system during holmium laser lithotripsy. Utilizing the 3D model allowed for the execution of retrograde intrarenal surgery (RIRS) procedures under nearly identical conditions, employing chemically synthesized stones of comparable size and density. The investigation aimed to analyze the variations in real-time intrapelvic temperature (IPT) generated during RIRS with two different sizes of ureteral access sheaths (UASs) (10/12 Fr vs. 12/14 Fr), utilizing distinct energy settings produced by the holmium: YAG laser.
# Machine Learning Detection of Cloud Services Abuse as C&C Infrastructure
## 2 Background
### Command and Control Communication Channels
\begin{table}
\begin{tabular}{c c} \hline \hline
**Field** & **Description** \\ \hline \multirow{4}{*}{COFF Header} & \(\bullet\) & Machine: Type of machine that the object file is intended to run on. \\  & \(\bullet\) & NumberOfSections: Number of sections in the object file. \\  & \(\bullet\) & TimeDateStamp: Time and date that the object file was created. \\ \hline \multirow{4}{*}{\begin{tabular}{c} Optional \\ header \\ \end{tabular} } & \(\bullet\) & Linker version: Version of the linker that created the object file. \\  & \(\bullet\) & Code and data sizes: Sizes of the code and initialized and uninitialized data in the object file. \\  & \(\bullet\) & Entry point address: Address of the entry point for the object file. \\  & \(\bullet\) & ImageBase: Address of the executable in memory. \\  & \(\bullet\) & CheckSum: Value used to validate the integrity of the image. \\  & \(\bullet\) & DllCharacteristics: DLL characteristics of the executable. \\  & \(\bullet\) & Import Table: List of DLLs and functions imported by the executable that can provide information about the functionality of the executable and indicate potential malicious behavior. \\  & \(\bullet\) & Export Table: List of functions exported by the executable that can provide information about the functionality of the executable and indicate potential malicious behavior. \\  & \(\bullet\) & Resource Directory: Resources used by the executable, such as icons, cursors, and bitmaps, that can provide information about the appearance and behavior of the executable and indicate potential malicious behavior. \\  & \(\bullet\) & Relocation Table: Information used by the linker to adjust addresses in the code when the executable is loaded into memory that can provide information about how the executable is organized and indicate potential malicious behavior. \\  & \(\bullet\) & Debug Information: Information used by debuggers to help debug the executable that can provide information about the internal structure of the executable and indicate potential malicious behavior. \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Section Table \\ \end{tabular} } & \(\bullet\) & Name: Name of the section. \\  & \(\bullet\) & VirtualSize: Size of the section in memory. \\  & \(\bullet\) & VirtualAddress: Address of the section in memory. \\  & \(\bullet\) & SizeOfRawData: Size of the section in the object file. \\  & \(\bullet\) & PointerToRawData: Location of the section in the object file. \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Derived \\ Features \\ \end{tabular} } & \(\bullet\) & presence\_of\_CIS\_domains: If any of the CLS domains appear in sections of the PE file, the value is 1; otherwise, it is 0. \\  & \(\bullet\) & potential\_C\&C\_api\_calls: If any of the potential C\&C API calls appear as an import function in the PE file, the value is 1; otherwise, it is 0. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PE file fields and derived features for malware classification.