esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Antimicrobial Susceptibility Tests and Cefotaxime Resistance Transfer Assays  
Antimicrobial susceptibilities were determined by conducting the disk diffusion technique according to the European Committee on Antimicrobial Susceptibility Testing guidance (CA-SFM/EUCAST, 2016). A total of 22 antibiotics (Bio-Rad, Marnes-la-Couette, France) were used according to the CA-SFM guidelines (amoxicillin/ clavulanic acid, ceftazidime, cefotaxime, cefoxitin, cefepime, etrapenem, malidixic acid, ciprofloxacin, sulfatethodoxazole/trimethoprim, tetracycline, minocycline, gentamicin, tobramycin, netilmicin, amikacin, norfloxacin, tigecycline, colistin, nitrofurantoin, fosfomycin, chloramphenicol, and aztreonam). The minimum inhibitory concentration was determined for cefotaxime and ceftazidime using the agar diffusion method, following the Clinical and Labora  
Figure 1: Study design flow diagram (records were approved after repeated antibiograms and synergy tests. Results were compared to our inclusion criteria.).  
tory Standards Institute. ESBLs were distinguished using the test of synergy (TS) within amoxicillin/clavulanic acid and ceftazidine, cefotaxime, aztreonam, or cefepime (with and without doxacillin at 250 mg/L). The control strains used were ATCC700603 and ATCC25922 (EUCAST, 2015). Conjugation experiments utilizing _E. coli_ J53 (Rifampin resistant) as a receiver were performed to assess the transferability of C3G resistance. Our isolates were initially selected on Mueller Hinton (MH) agar (Oxoid Ltd., Basingstoke, UK) plates incorporating rifampicin within 400 mg/L. Resistance transfer experiments were implemented on brain-heart infusion broth (BHI) (Oxoid Ltd., Basingstoke, UK) for 18 h at 37 \({}^{\circ}\)C, and the obtained transconjugants were grown on MH agar plates after adding both cefotaxime (2 mg/L) and rifampicin (400 mg/L). The transconjugants' antimicrobial susceptibilities were tested using a previously mentioned method. The strain origins were recorded (Table 1). Frequencies were calculated using SPSS software version 26.
# Research on the Self-Drilling Anchor Pull-Out Test Model and the Stability of an Anchored Slope
## 3 Self-Drilling Anchor Bolt Anchorage Mechanism Simulation Study
### Model and Parameter Selection  
#### 3.2.1 Numerical Model Construction  
In FLAC3D software, the pile element nodes have six degrees of freedom, while the anchor bolt element only has axial degrees of freedom. Therefore, the pile element can accurately simulate the frictional interaction between the pile and the mesh elements and can be applied to simulate the complex mechanical behavior of anchor bolt pullout. Thus, in this study, the pile element is used to simulate the self-drilling anchor bolt uplift test.  
Figure 7: Schematic diagram of the mechanism of the pile unit.  
The size of the soil area for simulation is set to 3 \(\times\) 3 \(\times\) 8 m, and the Mohr-Coulomb constitutive model is used for simulation. It should be noted that, in reality, the soil is not a strict homogeneous body, and there may be local fractures and joints. However, to simplify the simulation, we assume that the soil is isotropic.  
We take the direction from the bottom to the top of the soil layer as the positive Y-axis direction, and the geometric center of the model as the coordinate origin. The soil is modeled using the radial-cylinder command, and the discretization is achieved by changing the segments and ratio parameters. The mesh density decreases outward, ensuring that the mesh around the anchor bolt research object is the densest, while the mesh in the edge soil can be relatively sparse due to its relatively weak influence. Finally, the reflect command is used to mirror the soil to complete the construction of the overall soil area model. In FLAC3D, the built-in pile element is used to simulate the self-drilling anchor bolt, and it is automatically bound to the adjacent soil elements. The anchor bolt parameters are based on the RS6-01 anchor bolt, with a length of 6 m, a diameter of 0.038 m, an anchor solid diameter of 0.1 m, an outer circle circumference of 0.3142 m, and a length of 5.75 m. The soil area of the model generates a total of 36,480 zone element mesh nodes and 37,800 zone mesh elements. The pile element is divided into 23 elements using the segments command to ensure independent nodes. For the establishment of the boundary conditions of the computational model, the fix velocity command is used to apply displacement and velocity constraints in the xyz directions at the bottom of the model soil and in the normal direction around the soil. The top surface of the soil is set as a free surface without constraints. The model and the arrangement of the model mesh division and boundary constraints are shown in Figure 8.
# Transfer Learning-Based Detection of Pile Defects in Low-Strain Pile Integrity Testing
## 1 Introduction
The primary contributions of this study are summarized as follows:  
* _Utilization of real-world data:_ Unlike previous studies that relied heavily on synthetic data, this study exclusively uses real reflectogram images obtained from actual construction sites, ensuring higher practical relevance and reliability. * _Addressing class imbalance:_ The dataset contains significantly more intact piles than defective ones, necessitating the use of oversampling techniques to enhance the representation of defective samples in the training phase. * _Application of transfer learning:_ Pretrained CNN models are used to extract meaningful features from the reflectograms, enabling accurate defect classification with a relatively small dataset. Table 1 presents a comparison of our proposed method with various machine learning approaches applied to defect detection in pile integrity tests. The dataset characteristics, defect types, and model performance metrics are provided to highlight the effectiveness of the proposed approach. Table 1 was enhanced by including computation time per inference to provide comprehensive insights into the models' efficiency. ConvNeXtLarge achieved an average inference time of 58 ms per sample, whereas lighter models such as MobileNetV2 performed inference in approximately 10 ms per sample. Our findings suggest that deep learning-based automated analysis offers a promising solution to the limitations of manual expert interpretation in LSPIT. The proposed approach contributes to the field by improving detection accuracy and reducing the time required for analysis, ultimately leading to improved quality assurance in pile foundation construction projects. The remainder of this paper is organized as follows: Section 2 provides an overview of the background and related work, summarizing various non-destructive testing methods used in pile integrity assessment and discussing recent advancements in deep learning-based approaches. Section 3 describes the dataset collection process, preprocessing steps, and the proposed transfer learning-based methodology for pile defect detection. In Section 4, experimental results are presented, including performance comparisons of different pretrained CNN models and an analysis of the model's classification capabilities. Finally, Section 5 concludes the paper with a discussion on the findings, implications for real-world applications, and potential directions for future research.
# Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping
## 2 Material and Methodology
### Data Acquisition
The viscosity and density of seawater depend on the salinity and temperature of the water, which may change based on the body of water, location, and period of the year. Likewise, the relative ship speed such as SOG is highly dependent on the ocean current [17]. Based on the studies of [PERSON] [17] and [PERSON] [18], ship heading and speed are influenced by the ocean current; these studies also suggested that the actual SOG is the vector summation of the current and heading direction where UV and VV are the speeds for the longitudinal axis (u-axis) and lateral axis (v-axis) directions of the earth, correspondingly. If the ocean current movement comes from the heading direction, the ship sailing will be against it; but if it is in the opposite direction, the ocean current will increase the SOG of the ship. Among all features, some features related to the static and dynamic information are single-valued and non-numeric features that have no effect on the results and are removed. The remaining features are listed in Table 2, which also includes the length, width, gross-tonnage, and deadweight of the ship which may have an effect on ship speed based on the information mentioned above. Next, because of the potential existence of missing and outlier data, the identification of anomalies and undesirable data points and pre-processing are needed in the following dataset acquisition stage. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Remark** & **No.** & **Features** & **Units** \\ \hline
1 & Max fraught & m \\ \hline
2 & Course over the ground (COG) & deg. \\ \hline
3 & True heading & deg. \\ \hline
4 & Total wave height & m \\ \hline
5 & Total wave direction & deg. \\ \hline
6 & Total wave period & sec \\ \hline
7 & Wind wave height & m \\ \hline
8 & Wind wave direction & deg. \\ \hline
9 & Wind wave period & sec \\ \hline
10 & Swell wave height & m \\ \hline
11 & Swell wave direction & deg. \\ \hline
12 & Swell wave period & sec \\ \hline
13 & Wind UV & m/sec \\ \hline
14 & Wind VV & m/sec \\ \hline
15 & Pressure at mean sea level (MSL) & hPa \\ \hline
16 & Pressure surface & hPa \\ \hline
17 & Ambient temperature & °C \\ \hline
18 & Sea surface salinity & Psu \\ \hline
19 & Sea surface temperature & °C \\ \hline
20 & Current UV & m/s \\ \hline
21 & Current VV & m/s \\ \hline
22 & Ship length & m \\ \hline
23 & Ship width & m \\ \hline
24 & Dead weight & tons \\ \hline
25 & Gross tonnage & tons \\ \hline Output & 1 & SOG & knots \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Chosen features.
## 4 WSN Clustering
### K-Means Algorithm
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol** &
\begin{tabular}{c} **Probabilistic** \\ **CH Selection** \\ \end{tabular} & **Distributed** & **Pros** & **Cons** & **Ref.** \\ \hline \multirow{3}{*}{LEACH} & \multirow{3}{*}{Yes} & \multirow{3}{*}{Yes} & Load balancing to a certain & No multi-hop intranet & \multirow{3}{*}{[78]} \\  & & & level. Unnecessary collision & communication. & \\ \cline{3-5}  & & & avoidance. & Energy depletion and holes. & \\ \cline{3-5}  & & & Energy conservation. & Communication overhead. & \\ \hline \multirow{3}{*}{LEACH-C} & \multirow{3}{*}{Yes} & \multirow{3}{*}{No} & Whole networks’ global view. & \multirow{3}{*}{Communication overhead.} & \multirow{3}{*}{[15]} \\  & & & & Load balancing. & \\ \cline{3-5}  & & & Power conservation. & & Resources’ over utilization. & \\ \hline \multirow{3}{*}{CAF} & \multirow{3}{*}{yes} & \multirow{3}{*}{Yes} & Optimal routing. & \multirow{3}{*}{optimal CH selection.} & \multirow{3}{*}{[79]} \\  & & & Enhanced network lifetime. & & \\ \cline{3-5}  & & & Best performance in & & Communication overhead. \\ \cline{3-5}  & & & small-scale and & & Performance degradation in \\ \cline{3-5}  & & & static networks. & & large-scale networks. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cluster-based protocols’ comparison.
## 3 Case Studies
### Second Mahware
#### 3.2.2 Analysis of the Encrypted File  
One can easily see from Figure 7 that each block starts with a prefix (C1@, C3@, K@) and that some kind of encoding scheme is being used in the rest of each entry. Considering there are only digits and letters from A to F, it is reasonable to expect that every pair of symbols corresponds to a hexadecimal representation of an octet. The result obtained by decoding the first block of the message is illustrated in Figure 8, from which it is possible to note the absence of any printable characters. As the next step one can draw the histogram of the decoded information, but over more blocks from the original file,  
Figure 7: Encrypted file #2 sample.  
* strings -e l cftmon.exe
* strings -e l scvhost.exe  
The last command reveals an interesting string, the anagram \"ecalpneddih\", marked by the red rectangle in Figure 10. That could very well be the key for a Vigenere cipher, like in the previous case, and that hypothesis can be confirmed by being able to successfully decrypt the original information with it. Since the provided files contain intercalated messages originated from several compromised hosts, one needs a method to detect the current key position for each origin. Our solution to this problem is to align the key according to language statistics of the decrypted information, _i.e._, we should check whether the result is meaningful or not in the victim's mother language.
## 1 Introduction
However, Wi-Fi positioning usually requires the positioning method of fingerprint comparison, and the fingerprint database needs to be established in advance, the workload is large, and the positioning accuracy is about 3-5 M. Optical navigation is also a new navigation method rising in recent years, but optical navigation can only provide relative position and is greatly affected by light [18; 19]. Pseudolites can be used in indoor and outdoor navigation and positioning and have good navigation and positioning accuracy, but the positioning performance in indoor and outdoor switching zones and indoor narrow and long zones is poor [20; 21; 22]. A single navigation source cannot solve the problem of navigation and positioning in indoor and outdoor complex and changeable environments. People have successively studied the navigation and positioning technology of multisource fusion [23; 24; 25]. However, in complex and changeable environments, the indiscriminate fusion of multisource sensor information will reduce the navigation and positioning accuracy of the carrier and affect the stability of navigation and positioning. Therefore, it is necessary to study the multisource elastic fusion navigation and positioning method. At present, the research mainly focuses on the integration of satellite navigation, Wi-Fi, Bluetooth, 5G, inertial navigation, visual sensors and other navigation sources [7]. The amount of research on multisource elastic fusion navigation and positioning is relatively little, and how to make reliable use of elastic navigation resources is still unknown. This paper presents an indoor and outdoor multisource elastic fusion positioning algorithm based on particle filters. The algorithm flexibly selects the currently available sensor data and weights according to environmental elasticity to realize continuous and stable navigation and positioning in indoor complex environments. The innovative contribution of this paper is mainly reflected in the following aspects:  
1. The relationship between inertial sensor data and Wi-Fi, Bluetooth and pseudolites is deduced, and the multisource sensor integrated positioning model is constructed;  
2. An elastic navigation and positioning method based on particle filters is proposed, which can adaptively select the current multisource sensor data and sensor particle weight according to the environmental change, form the optimal elastic fusion positioning model and solve the problem of seamless navigation and positioning in indoor and outdoor complex environments;  
3. The indoor and outdoor complex environments are built, and a large number of experiments are carried out to verify the navigation and positioning performance of the proposed algorithm in complex and changeable environments.
## 4 Results and Discussion
### Cost Analysis of the HRES  
_PV module:_ The installation cost of the PV module was assumed to be 230 JPY/W for the residential rooftop, and the annual operation and maintenance cost was estimated at 5% of the installation cost with a lifetime of 20 years [43, 44].  
_Electrolyzer:_ The alkaline electrolyzer was selected in this study with a total lifetime of 10 years. The installation cost of the alkaline water-electrolyte was estimated at 170 JPY/W [45], together with an annual operation and maintenance cost of 3 JPY/W [45].  
_Fuel cell:_ The installation cost for the fuel cell was estimated at 400 JPY/W with a total lifetime of 10 years [45]. The operation and maintenance cost of the PEM fuel cell was considered at 1% of the installation cost.  
_Hydrogen tank:_ The installation cost of a hydrogen tank was estimated at 150,000 JPY/ kgH2 with a total lifetime of 20 years, together with operation and maintenance costs of 9000 JPY/year.  
_SCWG reactor:_ The unit cost of the SCWG reactor is estimated on the basis of the amount of the biomass feedstock, which can be processed in the reactor. In this research, it was approximated around 42,000 JPY/(kg\({}_{\text{feedstock}}\)/day) [9]. The hydrogen production cost of the SCWG was estimated at 610 JPY/kg\({}_{\text{H2}}\)[46]. This value is much higher than the hydrogen production cost of conventional methods, such as natural gas reforming, which is about 140 JPY/kg\({}_{\text{H2}}\)[47].
# Influence of Different Fibers on Performance of Bitumen Binders and Thin-Overlay Bitumen Mixtures
## 2 Materials and Methods
### Methods
#### 2.2.4 The Force-Ductility Method  
In line with NB/SH/T 0184-2010 [46], the test for the force-ductility method (FDT) of bitumen materials was conducted. The force-ductility behavior of fiber-reinforced bitumen was documented, allowing for a comparison with the standard curve associated with SBS bitumen. To assess how fibers affect bitumen, the areas of viscosity toughness, denoted as \(S\), and yield strain energy, indicated as \(E\), were computed. The relevant formulas for these calculations are provided in Equations (2) and (3):  
\[S=\int_{D}^{0}Fdx \tag{2}\]  
where \(S\) is the area of toughness (N\(\cdot\)mm), \(D\) is the ductility (mm), and \(F\) is the force (N);  
\[E=F_{\text{max}}\times D_{\text{max}} \tag{3}\]  
where \(E\) is the yield strain energy (J), \(F_{\text{max}}\) is the peak force (N), and \(D_{\text{max}}\) is the elongation (mm) corresponding to the peak force.  
The typical force-ductility curve of SBS bitumen is shown in Figure 1. The curve is divided into four stages. In the OA-elastic stage, the force rises rapidly to \(F_{\text{max}}\), and the bitumen mainly shows its viscosity. In the AB-yield stage, the force fluctuates slightly. In the BC-stress relaxation stage, the internal damage accumulates and the tensile force decreases sharply. In the DC-creep stage, the polymer in the modified bitumen gradually changes from a disordered state to an orderly arrangement, and the bitumen strength increases slightly in this process.
## 2 Method
### Sensitivity Analysis  
Sensitivity analysis is applied to assess the relationship between inputs and outputs and to compare models with alternative energy supply systems for space heating and DHW. Determining how each input contributes to the output is performed by carrying-out multivariate linear regression analysis employing estimates known as Standardized (Rank) Regression Coefficients (S(R)RC). It is based on a standardized input matrix and output vector, demonstrated in Equation (3) for each of the inputs (\(x_{j,i}\)) and the output (\(y_{j,i}\)).  
\[std\_x_{j,i}=\frac{x_{j,i}-\mu_{x}}{\sigma_{x}},\ std\_y_{j,i}=\frac{y_{j,i}- \mu_{y}}{\sigma_{y}} \tag{3}\]  
The standardized coefficients are then used for the regression model, which applies the least-squares method to calculate a line fitting Equation (4) for the five inputs (\(x_{1}\)-\(x_{5}\)) and output data (\(y_{1}\)).  
\[std\_y=b_{1}std\_x_{1}+b_{2}std\_x_{2}+\ldots+b_{n}std\_x_{n}\ldots+\varepsilon \tag{4}\]  
where \(b_{i}\) denotes the regression coefficients responding to each \(x_{i}\) parameter and \(\varepsilon\) is a constant.  
The obtained regression coefficients (\(bi\)) are used to determine the importance of the respective input, as its value quantifies its sensitivity. The larger the coefficient, the more sensitive it is. The sensitivity index (\(SI\)) is estimated as the relative share of each regression coefficient to the absolute sum of all coefficients, as illustrated by Equation (5).  
\[SI_{x(i)}=\frac{b_{i}}{\sum\lvert b_{i}\rvert} \tag{5}\]  
Given that the applied regression model is linear, the standard coefficient of determination (R\({}^{2}\)) is assessed to verify or disprove the linearity of the baseline model. The R\({}^{2}\) value ranges from zero to one and represents the difference between estimated and actual y-values. R\({}^{2}\) value close to one indicates a good correlation of the studied sample, thereby a linear LCC calculation model. On the other hand, if the R\({}^{2}\) value is closer to zero, the linear regression is not suitable for predicting the output.
## 6 Simulation Results and Discussion
### Mean Acquisition Time Using Square Spiral Scan  
Figure 10(a),b depict the performance of the square spiral scan method, which combines the properties of both spiral and raster scanning. The square spiral scan begins from the region of high probability (the center) and moves towards the region of low probability (the edges) of the scan area. The mean acquisition time, \(E[T_{\mathrm{ss}}]\), is approximately \([0.16,\,0.47,\,1.64]\) seconds for \(\sigma=[10,20,40]\) \(\upmu\)rad as the size of the uncertainty area increases. The square spiral extends outward in discrete squares, efficiently covering the entire uncertainty area. As expected, \(E[T_{\mathrm{ss}}]\) is smaller when the satellite position error is small and increases as the satellite is farther from the center, as depicted in Figure 10(b).
## 2 Piezoelectric-Driven FTS System
### FTS System Description  
The key requirements for FTS system design are to achieve a large stroke, high precision, and high stiffness to maintain high resistance and sensitivity to external cutting forces [8]. In view of the application requirements, we proposed a piezoelectric-driven FTS system based on a three-dimensional bridge amplification mechanism [26], as shown in Figure 1b. From a structural point of view, it adopts a three-dimensional bridge amplification structure, which is composed of a single-arm bridge type and a double-arm bridge type connected in series. The two-stage amplification mechanism is combined by a threaded connection. The piezoelectric ceramic is installed between the two input ends of the main amplification mechanism. Its position is fixed and pre-tightened by bolts, and has input and output guide constraints, thereby improving the stiffness and accuracy of the system.