esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Feature Importance Values
The best model of DS 3 in Figure 16b has a filter fraction of 100%, meaning no filtering occurred. It is identical to DS 1 in Figure 16a except for including the composition features. DS 1 has a filter fraction of 90%, meaning that nearly no feature reduction was performed. DS 9 in Figure 16e has the overall best model score and a much lower filtering fraction of 40%. It contains the same features as DS 1 and the mfcc signal features. For all three datasets, the resulting feature importance is very similar. Even more so, the results for the models of DS 1 and DS 9 are identical. The complexity reduction does not affect the resulting feature importance. DS 7 in Figure 16c seems to contradict this statement. However, aside from the many FFT features, five of the same features as before are listed in the top influences. As DS 7 performed significantly worse than the other three datasets, it can be assumed that even at 4% filter fraction, the multitude of remaining FFT features led to a high content of noise in its dataset, and the main information came from the same features as before.
## 4 Case Study Analysis and Results
### The Benefits in E-Procurement and Blockchain Applications  
Procurement is currently cross-managed in SAP through different functional modules, mainly MM--material management, SCM--supply chain management, FI--financial accounting [103,104]. At present, there is no specific procurement SAP module, apart from the so-called \"SAP Ariba\" [105]. This cloud-based solution allows suppliers and buyers to connect and do business on a single platform. Its application, however, is still somewhat limited to some companies that use SAP and volunteer in joining the network.  
A necessary shift for any purchasing transaction to a shared e-procurement platform is desirable for several reasons, ranging from reducing errors to ensuring timely operations. Therefore e-procurement brings many advantages:  
* Reduced procurement time and efficiency. Compared to traditional methods, the management of online purchases allows to speed up communication times between buyers and suppliers and improve order processing and thus efficiency.
* IT automation and productivity. The automation of procurement processes with the related alert system allows you to drastically reduce the preparation time of purchase orders and the management of requests by the various company departments concerned. This can substantially save time and increase productivity.
* Costs reduction. The cost reduction is significant in many respects: from paper savings to optimising the personnel involved in managing the practices to reduce inventories and warehouse waste.
* Security, reliability and safety. The e-procurement systems guarantee the transfer and retention of data in compliance with the stringent privacy regulations in force. Furthermore, the reliability of data transmissions (via VPN and encrypted) is ensured by a timely and system-wide security check.
* Flexibility and quality. Thanks to e-procurement platforms, the accessibility of information, the analysis and verification of offers, and the timely management and control of tenders are much more flexible and effective. Furthermore, to reduce the margin of errors and facilitate the use of the platform, there is also a dedicated service desk able to provide operational and functional support in real-time [106, 107, 108].
## 4 Discussion
### Future Research Opportunities  
Real-time detection and monitoring is rapidly advancing in fall protection technology, with recent studies focusing on models and systems to predict falls from height, but gaps remain in the integration of different technologies to monitor and prevent fall risks in real time.  
While fall prevention mechanisms have been used in the construction industry for decades, the continued reliance on traditional methods has slowed innovation, highlighting the need for future research to create more versatile and smart systems for proactive fall prevention [91].  
Computer vision is an emerging surveillance technology in the research. However, its ability to detect unsafe behaviour on construction sites is limited by technical challenges, in particular, the occlusion problem. To address these issues and improve the effectiveness of the model, the investigation of synchronised multi-camera systems is recommended [54].  
Radio-based monitoring technologies integrated into the BIM model have generally demonstrated reliable performance. However, further improvements are needed, particularly in the selection of the risk zone boundaries and the geometric accuracy of the BIM and the risk zones. Keeping the building model and its status up to date is critical to the overall effectiveness of the system [19].  
While BIM can bring significant safety benefits to construction, there are challenges in combining it with other technologies. Continued efforts are needed to advance knowledge and improve the use of BIM technologies to fully realise their potential benefits.
## 3 Matrix-Based Establishment of Mathematical Model
### Calculation of Occupied Space Penalty  
The principle regarding the occupied space penalty is that the component with the larger occupied space should be assembled preferentially. Account that components with a larger occupied space need more steps and motions when hoisting and are more prone to collision with installed components. Therefore, it is a safer and more efficient approach to assemble the larger components first. A violation of this principle incurs an occupied space penalty. These penalties are recorded in matrix \(S\), with its elements represented as \(s_{ij}\).  
If component \(i\) is assembled before component \(j\), \(s_{ij}\) will be calculated as Equation (7). The total space penalty (\(\sum P_{space}\)) of a certain sequence can be obtained by summing up the penalty values (\(P_{space}\)) of all the components in the sequence, as indicated in Equation (8).  
\[s_{ij}=\left\{\begin{array}{l}space_{j}/space_{i},\text{if }space_{j}> space_{i}\\ 0\hskip 56.905512 pt\text{if }space_{j}\leq space_{i}\end{array}\right. \tag{7}\]  
\[\sum P_{space}=\sum_{j=1}^{n}\left(P_{space}\right)_{j}=\sum_{j=1}^{n}\left( \sum_{i=1}^{j}s_{ij}\right) \tag{8}\]  
where \(space_{i}\) and \(space_{j}\) represent the occupied space of the \(i\)-th and \(j\)-th components, respectively. (\(P_{space}\)) represents the space penalty value of the \(j\)-th component in a sequence.  
The parameter \(n\) in Equation (8) represents the total number of the components.
# Design and Verification of Large-Scaled Flapping Wings for High Altitude Environment
## 2 Materials and Methods  
### Experimental Set-Up  
According to the same motion law, the inertial force of the flapping wing is intimately related to the planform and the distribution of the wing ribs. The deformation of the flapping wing is mainly caused by inertial force, and the variation of altitude has little effect on the inertial force except for the change of trim state. It can be approximately considered that the deformation at different altitudes is close, making it possible to optimize flapping wings suitable for high altitudes to low-altitude environments. Wind tunnel experimental methods were implemented to optimize the flapping wings at low altitudes.  
The complete experimental set-up for the measurement of aerodynamic forces/moments is shown in Figure 1. A six-axis sensor (ME K6D40) of measurement range \(\pm\)200 N and \(\pm\)10 N-m was placed at the wing root to measure the forces and moments. For the force variation measurement, the wings were connected to a spatial crank and rocker mechanism, which was controlled by a DC brushless motor. The whole system was mounted on a large range load cell to obtain the entire force and torque variation. A global filter was used to denoise the experimental data. The cut-off frequency of the filter was set to 20 Hz, which is higher than the maximum flapping frequency of the mechanism, and the data were sampled at 1000 Hz. A series of performance tests was conducted to optimize the flight speed, flapping frequency, and angle of attack of the flapping model. The test parameters are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Experimental Parameter** & **Value** \\ \hline Wind speed (m/s) & 4.5, 5.5, 6.5, 7.5 \\ Flapping frequency (Hz) & 2, 3, 4 \\ Flapping amplitude (\({}^{\circ}\)) & 45 \\ Angle of attack (\({}^{\circ}\)) & 0, 5, 10, 15, 20 \\ \hline \end{tabular}
\end{table}
Table 1: The ranges of the experimental parameters for the wind tunnel test.
## 1 Introduction  
Fin-tube heat exchangers are widely used [1], and a tube with a carved inner groove has been used to improve the tube's heat transfer performance [2]. For the assembly of fin-tube heat exchangers, a tube with a smaller diameter than the inside of the fin is first inserted inside the fin and then expanded to secure tight contact between the fin and tube using an expansion ball. However, in this process, the heat transfer efficiency decreases because of the change in the shape of the groove. Accordingly, many researchers have attempted to improve the expansion process.  
Previous studies have examined the influence of the expansion speed and expansion ratio (the ratio of the outer tube diameters before and after expansion) on the tube create generation, expansion force, and heat transfer efficiency. [PERSON] et al. investigated the optimum expansion ratio to minimize the occurrence of tube crease [3]. [PERSON] et al. analyzed the expanded tube thickness and the expansion force according to the expansion ratio [4]. Numerical simulations were used to study the effect of the expansion ratio on the gap between the tube and the fin and on the thermal conductance after expansion [5]. Although the speed of the expansion ball did not have a large effect on the expansion force [6], the geometry tolerances produced when manufacturing the tube can compromise the heat transfer performance [7]. The friction between the expansion ball and the tube is the most significant factor for increasing the expansion force, which was accurately predicted numerically when the applied friction coefficient was below 0.3 [8]. In a numerical study, [PERSON] et al. observed that non-uniform height of the tube's inner grooves provided better contact pressure between the tube and fin after expansion [9]. Previous studies also investigated the influence of the slope angle on the expansion force [10] and on the formation of tube creases [11; 12]. The effect of the expansion ball curvature on the expansion force and tube's inner groove deformation has also been studied [13; 14]. Elliptical, hemispherical, and conical expansion ball shapes influence the expansion force during expansion and the tube thickness after expansion [15], and the size and operating interface temperatures affect the heat transfer efficiency [16].  
Despite the considerable amount of research, there is still a great demand in the industry for an increase in the heat exchanger efficiency that can reduce production and maintenance costs and improve the performance. Therefore, this study focused on increasing the heat exchanger performance by minimizing both the deformation of the tube's inner grooves and the expansion force in the tube expansion process whose minimizations were considered as the expansion performance in this study. To achieve this goal, we first determined the variables that had a significant impact on the expansion performance and then analyzed their role in the expansion performance through experiments and numerical analyses. A new method has also been proposed to determine the severity of adhesion of tube's inner grooves using the expansion force rate.
###### Abstract  
Electric field enhancement mediated through sharp tips in scattering-type scanning near-field optical microscopy (s-SNOM) enables optical material analysis down to the 10-nm length scale and even below. Nevertheless, the out-of-plane electric field component is primarily considered here due to the lightning rod effect of the elongated s-SNOM tip being orders of magnitude stronger than any in-plane field component. Nonetheless, the fundamental understanding of resonantly excited near-field coupled systems clearly allows us to take profit from all vectorial components, especially from the in-plane ones. In this paper, we theoretically and experimentally explore how the linear polarization control of both near-field illumination and detection can constructively be implemented to (non-)resonantly couple to selected sample permittivity tensor components, e.g., explicitly to the in-plane directions as well. When applying the point-dipole model, we show that resonantly excited samples respond with a strong near-field signal to all linear polarization angles. We then experimentally investigate the polarization-dependent responses for both non-resonant (Au) and phonon-resonant (3C-SiC) sample excitations at a 10.6 um and 10.7 um incident wavelength using a tabletop CO\({}_{2}\) laser. Varying the illumination polarization angle thus allows one to quantitatively compare the scattered near-field signatures for the two wavelengths. Finally, we compare our experimental data to simulation results and thus gain a fundamental understanding of the polarization's influence on the near-field interaction. As a result, the near-field components parallel and perpendicular to the sample surface can be easily disentangled and quantified through their polarization signatures, connecting them directly to the sample's local permittivity.  
nanoptics; materials science; nanotechnology; nanoellipsometry; s-polarization; p-polarization; s-SNOM; resonant; non-resonant Article
# Analyzing Benford's Law's Powerful Applications in Image Forensics
## 1 Introduction
Such methods to detect fraudulent data can also be applied when using convolutional neural networks, for example, in combination with orbital-field matrix and Magpie descriptors to predict the formation energy of 4030 crystal material [17], or combined with inception blocks and CapsuleNet to study the surface accessibility of transmembrane protein residues [18]. Crystal recognition is another field where it is crucial to determine the reliability of digital images before attempting to classify them using the Mask R-CNN model [19]. One can also verify whether grayscale images (images that are used to measure crystal size distributions) have been altered or not, [20]. Another concept used in many branches is represented by cross-sectional images. When studying how the transformation of the image of a grained structure in a cross-sectional plane reflects structure deformation [21], it is also important to prove the authenticity of the images. Quantitative image analysis could also benefit from methods that can prove the security of digital images, for example, when studying the evolution of mosaicity during seeded Bridgman processing of technical Ni-based single crystal superalloys [22], or when  
Figure 1: The Newcomb–Benford Law. using X-ray diffraction imaging to study crack geometry and associated strain field around Berkovich and Vickers indents on silicon [23]. In the following sections, the way we have chosen to approach JPEG compression in relationship with the NBL is discussed in detail.
## 3 Experiment  
### Experimental Setup  
In this paper, an ultra-wideband microwave breast cancer detection system was proposed for testing the proposed algorithm. As shown in Figure 6, the system consisted of six small ultra-wideband Vivaldi antennas arranged in an antenna array, a Keysight P5023B multi-port vector network analyzer (VNA) with a frequency range of 9 KHz-14 GHz, a laptop computer, and a circular plastic mount for securing the antennas. The VNA and the ultra-wideband antenna were connected via six coaxial cables to transmit and receive signals. The breast phantom was exposed to an ultra-wideband microwave signal covering the frequency band from 2.5 to 8 GHz, striking a balance between penetration depth and image resolution [31]. In this study, the number of sampling points was set to 1001, which increased the calculation workload but also expanded the scanning distance of the VNA [28]. The VNA had an output power of 10 dBm. Ultra-wideband microwave signals were sequentially transmitted from one port of the VNA through the antenna to the experimental model, while the remaining ports collected the scattered signals. The received signals were stored in a laptop, and the images were then reconstructed using the proposed MICF-DMAS algorithm.  
The system utilized a three-dimensional section of the breast, facing the center of the breast, for imaging purposes. To achieve this, a 3-D antenna array was employed in the Z-direction to scan the breast slices [25]. A circular support made of an acrylic plate with a diameter of 50 cm was used to hold six small ultra-wideband Vivaldi antennas [32] forming the antenna array. The antennas were spaced at 60\({}^{\circ}\) intervals, ensuring mutual coupling between them was less than \(-\)20 dB. The center of the antenna array aligned with the center of the breast model, and each antenna was positioned 5 mm away from the outer surface of the breast model. Using such an arrangement allowed avoiding the use of high-intensity microwaves [25]. Each antenna, measuring 45 mm \(\times\) 40 mm \(\times\) 1.2 mm (Figure 7), was printed on a Fr4 substrate and had a bandwidth of 2.5 GHz-10 GHz. Signal  
Figure 6: Microwave imaging system.  
transmission was carried out sequentially, with one antenna transmitting at a time while the rest received the signal. This process was repeated until each of the six antennas had transmitted a signal.
# Diagnosis of Intracranial Tumors via the Selective CNN Data Modeling Technique
## Accuracies
### Evaluation of Selected Architecture  
Architecture 13 was chosen as the optimal architecture, as the performance was good in terms of _MVA_, _LVCEL_, _TT_, and _OFV_. A confusion matrix was used to calculate the recall, precision, and F1 score for better evaluation and assessment as follows:  
\[Precision=\frac{True\ Positive}{True\ Positive+False\ Positive} \tag{6}\]  
\[Recall=\frac{True\ Positive}{True\ Positive+False\ Negative} \tag{7}\]  
\[F1\ Score=\frac{(2\times Precision\times Recall)}{(Precision+Recall)} \tag{8}\]  
Figure 8 shows the confusion matrix of the optimal convolutional neural network architecture, where 51 non-augmented images were considered for metric evaluation. As data augmentation was used for enlarging the original dataset, so we cannot use augmented images for the evaluation of our architectures. Therefore, we considered only 51 images for better testing of our optimal architecture. We also evaluated architectures based on 371 augmented images where results gained were quite similar but for fair results, non-augmented images were considered.  
Figure 8: The confusion matrix for architecture 13. We can observe that true positive = 28, true negative = 19, false positive = 3, and false negative = 1.  
Figure 7: Training curve of validation loss for each CNN architecture per epoch.  
From Equations (6)-(8), we calculated \(Precision=0.90322\), \(Recall=0.9655\), and \(F1-Score=0.93331\). Our model performed outstandingly, as all the metrics were above 90%. Therefore, we can claim that our architecture performed excellently in diagnosing brain tumors. Figure 9 shows the AUC-ROC where the area under the curve was found to be 0.737, which was good.
## 2 Theory and Method
### Assumptions  
In the previous studies, there are some assumptions for simplifying the problem for simulation. And assumptions have been proposed as follows [32; 37]:  
1. The influence of anisotropy on the microstructure on the rock salt dissolution process is neglected, such as stratigraphic dipping, salt crystal direction, bedding, etc.
2. Insoluble is 100% precipitated, and pure salt rock is considered with no insoluble sediments.
3. There is no heat exchange, and the system remains isothermal.  
In the simplified assumptions, each additional assumption encourages the deviation from reality. Considering the accuracy and efficiency of the calculations, it is essential to assume that salt rock is anisotropic. The coupled model takes account the effects of fluid flow, primarily investigating the impact of flow on leaching process. Including insoluble materials would make the simulation almost infeasible due to the significantly increased complexity. However, it is practical to incorporate the influence of temperature in addition to fluid flow, which can enhance the accuracy of the simulation.  
Figure 3: A diagram of thermal convection and thermal conduction during solution mining. (The yellow arrows represent thermal conduction, and the blue arrows represent thermal convection.)  
In this study, a 3D multi-physics coupled flow model is established to describe the process of multi-step horizontal salt cavern construction. The assumptions were corrected as follows.  
1. The salt rock is isotropic with no cracks and fractures, ignoring the influence of anisotropy during the dissolution process.
2. Insolubility is fully precipitated, and we ignored its influence on fluid flow and brine convection. The salt rock is pure with no insoluble sediments.
# Ammonia Gas Sensors: Comparison of Solid-State and Optical Methods
## 1 Introduction  
Ammonia is a highly toxic chemical substance, common in biological processes, and applied in technical installation processes (cooling systems, chemical industry, and motor vehicles). The American Conference of Industrial Hygienists has set a limit to ammonia concentration in air of 25 ppm for long-term exposure (8 h) and 35 ppm for short-term ones (15 min) [1]. In medicine, the concentration of ammonia in the breath between 2500 and 5000 ppb is directly related to organ dysfunction and diabetes [2]. Therefore, the design of novel techniques and sensors which allow accurate and fast in-situ detection of trace ammonia concentration is highly desirable. Such sensors should satisfy the specific requirements: high sensitivity, enhanced selectivity, short response time, reversibility, high reliability, low energy consumption, low cost, safety, broad range of measurement at various operation temperatures, etc.  
The issue of ammonia detection by gas sensors has attracted many researchers. A recent review paper about ammonia sensing focused on chemical mechanisms of gas sensing by solid-state or electrochemical sensors, with limited details about optical methods [3]. In our studies, we focus on optical methods, developed in our research teams. Moreover, another way of solid-state sensors modulation by UV irradiation was proposed and discussed. This method was advanced in the research teams preparing this review. These problems have not been considered in such a way in other papers about gas sensing.  
Very advanced approaches like gas chromatography-mass spectrometry (GC-MS) and selective ion flow tube-mass spectrometry (SIFT-MS) are accurate for NH\({}_{3}\) measurement, but their use iscomplicated and require qualified staff, laborious samples preparation procedures, and time consuming measurements, as well as not-compact and expensive instruments which are difficult to maintain. Many applications require faster and easier tools. Therefore, solid and optical detection methods are rapidly developing (Figure 1). In this paper, much attention was paid on the optical sensors.  
We would like to underline that we preselected the considered ammonia gas sensors and there are other promising technologies which can be effectively used for ammonia sensing because of low production costs or measurement methods. Good examples are biosensors utilizing bacteria cultures or nanotechnology [4; 5; 6; 7; 8].