esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Location has always been a primary concern for business startups to be successful. Therefore, much research has focused on the problem of identification of an ideal business site for a new business. The process of ideal business site selection is complex and depends on a number of criteria or factors. Since the ultimate goal of all businesses is to increase customer footprints and to thus increase sales, criteria including traffic accessibility, visibility, ease of access, vehicle parking, customers availability, etc. play important roles. In other words, we can say that optimal business site selection is a multi-criteria decision-making (MCDM) problem. MCDM is used to identify an optimal solution or decision out of many alternatives by utilizing a number of criteria. In mathematics, there exist a number of structured techniques for organizing and analyzing complex decisions, for instance, AHP, ANP, TOPSIS, etc. In this work, we present a hybrid of two such techniques to solve the MCDM problem for an optimal business site selection given a set of candidate sites. The proposed approach is based on the AHP (Analytic Hierarchy Process) and TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) approaches. The reason for using the proposed hybrid approach is multi-fold. The hybrid approach reduces the computational complexity and require less manual effort, thus improving the efficiency and accuracy of the proposed approach. Given a set of candidate locations for a new business, the proposed approach ranks the candidates. Thus, the candidate locations with higher ranks are identified as suitable or ideal. The approach comes up with the ranking of all of the candidate locations, thus giving business managers room to make calculated decisions. To show the effectiveness of the proposed approach, a detailed step-by-step case study is given to identify an ideal location in New York City for a new gas station. Furthermore, an experimental evaluation is also presented using a number of real New York City datasets.  
multi-criteria decision-making; AHP/TOPSIS hybrid approach; optimal site selection; GeoSpatial data; smart cities +
Footnote †: journal: Applied Sciences
## 3 Results
### Alpha-Phellandrene Properties as Pure Molecule
#### 3.3.2.  
Biopseticide and Repellent Activity  
Few are the studies trying to prove the potential of this single molecule as a biopesticide. For instance [PERSON] et al. [63] found promising results against a common crop pest, _Sitophilus granarius_ (Granary weevil), where 10.5 uL/mL of pure terpenoid induced the mortality of more than 90% of individuals after 24 h exposure. Similarly, in one of the earliest studies related to the toxicity of this compound, [PERSON] et al. [64] proved that alpha-phellandrene had a lethal concentration 50 (LC\({}_{50}\)) of 0.28 mg/cm\({}^{2}\) after 24 h contact exposure against adult females of _Blatella germanica_ (German cockroach), one of the most common indoor environmental pests. Furthermore, [PERSON] et al. [65] tested the effects of the single molecule, finding a LC\({}_{50}\) of 19.3 ppm against _Aedes aegypti_ and a LC\({}_{90}\) of 36.4 ppm against _Anopheles quadrimaculatus_.  
A study by [PERSON] et al. [66] confirmed the repellent activity of this monoterpene against _Bernisia tabaci_ (whitefly), which is responsible for severe losses in crop production and horticultural practices. Another study [67] focused on the repellency to _Stomoxys calcitrans_ using alpha-phellandrene alone or in synergy with _Calophyllum inophyllum_ nut oil fatty acids or their esters. [PERSON] et al. found that the protection time expressed in hours for alpha-phellandrene alone was about 0.5 h in a concentration of 0.25 mg/cm\({}^{2}\). The same concentration, together with 0.5 mg/cm\({}^{2}\) of oleic acid, repelled the female flies in 1.9 h, in 1.9 h with 0.5 mg/cm\({}^{2}\) of linoleic acid, in 1.6 h with the same amount of methyl oleate and in 1.6 h in synergy with 0.5 mg/cm\({}^{2}\) of methyl linoleate.
## 2 Materials and Methods
### Analysis Conditions
#### 2.1.1 Chemical Preparation  
The commercial chemical products of analytical grade were DMPO 5,5-dimethyl-1-pyrroline N-oxide (99%, Sigma Aldrich, St. Louis, MO, USA), dimethyl sulfoxide (99.9%, Sigma Aldrich), and sodium formate HCOONa (99%, [PERSON]). The other chemicals were NaOH (99%, reagent grade, [PERSON], Karlsruhe, Germany), hydrogen peroxide (35%, [PERSON]), copper sulfate CuSO\({}_{4}\bullet\)5H\({}_{2}\)O (98.0%, Merck, Darmstadt, Germany), and 1,10-phenanthroline (99.0%, Acros Organics, Geel, Belgium).  
The reactants were introduced in a total volume of 300 \(\upmu\)L, in the following order: ultrapure water, alkali or acid, scavenger, spin-trap, activator, and H\({}_{2}\)O\({}_{2}\). Unless otherwise stated, the following concentrations were respected: 5.6 M DMSO, approximately 5 M sodium formate, 6.0 \(\times\) 10\({}^{-2}\) M DMPO, 1.5 \(\times\) 10\({}^{-5}\) M CuSO\({}_{4}\), 3.0 \(\times\) 10\({}^{-5}\) M Phen, 1.0 \(\times\) 10\({}^{-4}\) M H\({}_{2}\)O\({}_{2}\), and 6.0 \(\times\) 10\({}^{-2}\) M NaOH. For EPR experiments at 100 K, some trials were conducted with 10-fold Cu(II) and phenanthroline concentrations (1.5 \(\times\) 10\({}^{-4}\) M CuSO\({}_{4}\), 3.0 \(\times\) 10\({}^{-4}\) M Phen).  
Under near-neutral conditions (no addition of NaOH), the pH was between 7.5 and 9 (7.5-7.6 in the presence of H\({}_{2}\)O\({}_{2}\), around 8-8.6 in the absence of H\({}_{2}\)O\({}_{2}\), and pH 9 when adding sodium formate). Under strong alkaline conditions (0.02 M NaOH), the pH was of 12.5. The use of scavengers induced some pH variations. With formate, the pH was around 9 in the absence of NaOH and was equal to 12.5 with NaOH. With DMSO, the pH was around 9 alone, around 8 with Cu-Phen, 7.6 with Cu-Phen + H\({}_{2}\)O\({}_{2}\), and 14 with Cu-Phen + H\({}_{2}\)O\({}_{2}\) + NaOH.  
A \"Fenton control\" was performed for comparison with FeSO\({}_{4}\) replacing CuSO\({}_{4}\), at the same molar concentrations, and with the addition of H\({}_{2}\)SO\({}_{4}\) to reach a pH of 3.
# An Innovative Applied Control System of Helicopter Turboshaft Engines Based on Neuro-Fuzzy Networks
## 1 Introduction  
### The Relevance of the Research  
The development and advancement of helicopter turboshaft engine (TE) control systems play a crucial role in modern aviation technology [1]. Helicopters operate in highly dynamic and variable environments, where ensuring optimal engine performance across all flight conditions requires sophisticated control strategies [2]. Traditional control laws [3; 4; 5; 6], with fixed parameters, often fall short in addressing the complexity and variability seen during real-world operations, such as abrupt changes in altitude, air temperature, or engine load. There is a demand for adaptive systems that adjust engine parameters in real time, enhancing safety and efficiency.  
Adaptive control laws offer a promising solution by dynamically adjusting control parameters based on real-time data, ensuring optimal engine performance under various conditions. This approach improves fuel efficiency, prolongs engine life, and reduces failure risks [7]. Mathematical modeling is critical in developing such systems, providing afoundation for simulating engine behavior in diverse scenarios and allowing for fine-tuning before real-world application [8; 9].  
The latest innovations in artificial intelligence (AI) and machine learning bring new opportunities for enhancing adaptive control systems [10; 11]. AI-driven methods enable more accurate predictions of engine behavior, allowing for better anticipation and mitigation of potential issues. These innovations pave the way for safer and more efficient helicopter operations, advancing aviation technology.
## 3 Brittle Fracture Phase Field Model  
Let us consider an arbitrary solid \(\Omega\), with \(\partial\Omega\) the external boundary and \(\Gamma\) the internal discontinuity boundary, which represents an initial fracture. The displacement field in the solid is designated by \(\mathbf{u}\). A body force acting on the solid \(\mathbf{b}\) and a surface force \(\mathbf{f}\) on the boundary \(\partial\Omega_{\text{F}}\) are considered.  
According to [PERSON]'s theory, [PERSON] et al. (2016) proposed a variational approach for fracture problems. This indicates that the critical fracture energy density \(G_{c}\), also known as the critical energy release rate, is equivalent to the energy needed to generate a fracture surface per unit area. The total potential energy \(E_{tot}(\mathbf{u},\,\Gamma)\) is formulated as follows:  
\[E_{tot}(\mathbf{u},\,\Gamma)=\int\limits_{\Omega}\psi_{0}(\mathbf{\varepsilon})\ d \Omega+\int\limits_{\Gamma}G_{c}dS-\int\limits_{\Omega}\mathbf{b}.\mathbf{u}\ d\Omega- \int\limits_{\partial\Omega}\mathbf{f}.\mathbf{u}\ dS \tag{1}\]  
where, \(\mathbf{\varepsilon}=\mathbf{\varepsilon}(\mathbf{u})\) is the linear strain tensor and \(dS\) is the surface element.  
The elastic energy density \(\psi_{0}(\mathbf{\varepsilon})\) in the isotropic linear elasticity is provided by:  
\[\psi_{0}=\frac{1}{2}\lambda tr^{2}(\mathbf{\varepsilon})+\mu tr(\mathbf{\varepsilon} ^{2}) \tag{2}\]  
where \(\mu\) and \(\lambda\) are Lame constants.  
Furthermore, according to the variational approach ([PERSON] et al., 2016), the crack initiates, propagates and branches when the minimum potential is reached and the irreversible condition is fulfilled. The irreversible condition means that a material cannot regain its initial state after a crack formation.
## 2 Results and Discussion
### Selectivity Pattern of the E-nose  
Figure 3 shows the selectivity pattern of all polymer layers at 50 ppm concentration of each analyte. The sensor array is alternatively exposed to different analytes with the same concentration and normalized according to layer height. The astonishing observation is the capability of the sensor array to distinguish isomeric compounds as \(\alpha\)-pinene and \(\beta\)-pinene. Similarly all other layers showed maximum selectivity towards their corresponding templates. Thus the designed sensor array proved to be sensitive and selective for online monitoring of terpen emanated from fresh and dried herbs. In addition, a sensing ability of sensor for analytes with similar structures is beneficial in regard of analyzing complex mixtures. Such problems are best addressed by multivariate analysis and the collected data should be analyzed through modern data analysis tools which are important part of today's electronic bio-mimetic instruments.
## 2 Establishment of the Calculation Model for FSPR Steady State Temperature Field
### Analytical Solution for Freezing Temperature Field Model in Object Plane of FSPR  
Substituting Equation group (4) into Equation (16), we get:  
\[T=\frac{\gamma_{Z}}{\varphi_{Z}+\eta_{Z}}\cdot(T_{f}-2T_{0})+2T_{0} \tag{17}\]  
where:  
\[\gamma_{Z}=\frac{1}{2}\ln\biggl{[}\left(\frac{r}{R_{2}}\right)^{n}+\left( \frac{R_{2}}{r}\right)^{n}-2\cos n\theta\biggr{]}+\frac{1}{2}\ln\biggl{[} \left(\frac{r}{R_{2}}\right)^{n}+\left(\frac{R_{2}}{r}\right)^{n}-2\cos n( \theta+\beta)\biggr{]}-\frac{2n\ln\frac{R_{2}}{R_{1}}\ln\frac{R_{3}}{R_{2}}}{ \ln\frac{R_{3}}{R_{1}}}+\frac{n\ln\frac{R_{2}^{2}}{R_{1}R_{3}}}{\ln\frac{R_{ 3}}{R_{1}}}\cdot\ln\frac{r}{R_{2}}\]  
\[\varphi_{Z}=\ln\frac{nR_{0}}{R_{2}}-\frac{n\ln\frac{R_{2}}{R_{1}}\ln\frac{R_{ 3}}{R_{1}}}{\ln\frac{R_{3}}{R_{1}}}\]  
\[\eta_{Z}=\frac{1}{2}\ln\biggl{(}e^{\frac{nR_{0}}{R_{2}}}+e^{-\frac{nR_{0}}{R_ {2}}}-2\cos n\beta\biggr{)}-\frac{n\ln\frac{R_{2}}{R_{1}}\ln\frac{R_{3}}{R_{ 1}}}{\ln\frac{R_{3}}{R_{1}}}+\frac{n\ln\frac{R_{2}^{2}}{R_{1}R_{3}}}{2\ln\frac {R_{3}}{R_{1}}}\cdot\frac{R_{0}}{R_{2}}\]  
Equation (17) is the analytical solution to steady-state temperature field of FSPR when considering different soil freezing points as shown in Figure 3.
## 3 Results
### The Impact of Vibration Frequency on the Performance of the Seeder Drill  
As depicted in Figure 7, compared to the impact of amplitude on the performance of the seed metering device, the vibration frequency had a more significant effect. Analyzing the experimental results of the relationship between the device's vibration frequency and discharge rate, it can be seen that the seed discharge rate of the device remained stable between 7.6 and 8.2 g/min when the vibration frequency was between 0 and 10 Hz, with minimal impact from the vibration. However, as the vibration frequency increased from 10 to 24 Hz, the seed discharge rate of the device decreased rapidly and exhibited a linear relationship with the frequency change, with the linear equation given by y = 0.45476x + 12.09349 and a coefficient of determination R\({}^{2}\) = 0.92376.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Plan** & **Vibration Amplitude/mm** & **Vibration Frequency/Hz** \\ \hline A & 0, 1, 2, 3 & 6 \\ B & 3 & 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental parameters of the test bench for the sowing uniform test of the double-row hole-wheel-type seed metering device under vibration.  
Figure 6: Sowing performance based on the relationship between vibration amplitude and (**a**) seeding quality and (**b**) the seeding uniformity variation coefficient.  
Regarding the seed metering uniformity coefficient, the variation coefficient fluctuated between 35.81% and 44.58% when the vibration frequency was between 0 and 10 Hz, indicating stable and favorable uniformity. When the vibration frequency was between 10 and 24 Hz, the variation coefficient of seed metering uniformity increased rapidly and showed a certain linear relationship with frequency, with the equation given by y = 1.93577x + 24.1756 and a coefficient of determination \(R^{2}\) = 0.87973. Given that field operations of the seed drill require the variation coefficient of seed metering uniformity to be \(\leq\)45%, vibration frequencies greater than 10 Hz significantly impact the performance of the seed metering device.
## 3 Implementation of the Proposed BIM-GIS Utility Infrastructure Framework
### Integrated BIM-GIS Modeling  
The UAV-collected information is available in point clouds depicting the surface information in a high resolution with great details. The depth, diameter, material, and type of underground pipes were collected from the GPR survey and converted into a spreadsheet with all the information in fields. The steps involved in the development of the integrated BIM-GIS model are illustrated in Figure 13. The case study site model can be represented using a surface representation that provides all the details, including ridges and valleys. The triangulated irregular network (ITN) surface was used to model the topography information. A BIM tool, civil 3D, was utilized to convert the point cloud model into a surface model. The modeling of utility pipe networks included the existing pipe modeling and the newly designed utility system. The processed information from the GPR survey and information collected from the local authorities were used for the underground utility models. The utility pipes were modeled in civil 3D using the available information. A 3D BIM model was generated, appending the utility system's semantic information of utility pipes. The benefit of appending extra information to the pipe section is that it can be utilized during the later stages for operation and maintenance stages. The added information was stored in the IFC schema. On the basis of the integration process of IFC and CityGML explained in the previous section, we integrated the 3D BIM model of the utility network into ArcGIS, utilizing the utility network GIS extension. The different types of utilities were stored in different layers. The models of utility network are presented  
Figure 11: Georeferenced 3D point cloud model showing different views with ground control points (GCPs).  
Figure 12: GPR electromagnetic scan with interpretation after data processing.  
in Figure 14a,b. The different colors characterize the utility types with different semantic properties that facilitate the stakeholders at different stages.  
Figure 13: The process of developing surface and subsurface utility infrastructure model.
###### Abstract  
Early evaluation of patients who require special care and who have high death-expectancy in COVID-19, and the effective determination of relevant biomarkers on large sample-groups are important to reduce mortality. This study aimed to reveal the routine blood-value predictors of COVID-19 mortality and to determine the lethal-risk levels of these predictors during the disease process. The dataset of the study consists of 38 routine blood-values of 2597 patients who died (\(n=233\)) and those who recovered (\(n=2364\)) from COVID-19 in August-December, 2021. In this study, the histogram-based gradient-boosting (HGB) model was the most successful machine-learning classifier in detecting living and deceased COVID-19 patients (with squared F1 metrics F12 = 1). The most efficient binary combinations with procalcitonin were obtained with D-dimer, ESR, D-Bil and ferritin. The HGB model operated with these feature pairs correctly detected almost all of the patients who survived and those who died (precision > 0.98, recall > 0.98, F12 > 0.98). Furthermore, in the HGB model operated with a single feature, the most efficient features were procalcitonin (F12 = 0.96) and ferritin (F12 = 0.91). In addition, according to the two-threshold approach, ferritin values between 376.2 ug/L and 396.0 ug/L (F12 = 0.91) and procalcitonin values between 0.2 ug/L and 5.2 ug/L (F12 = 0.95) were found to be fatal risk levels for COVID-19. Considering all the results, we suggest that many features combined with these features, especially procalcitonin and ferritin, operated with the HGB model, can be used to achieve very successful results in the classification of those who live, and those who die from COVID-19. Moreover, we strongly recommend that clinicians consider the critical levels we have found for procalcitonin and ferritin properties, to reduce the lethality of the COVID-19 disease.  
COVID-19; mortality-risk biomarkers; immunological tests; ferritin; procalcitonin; histogram-based gradient-boosting; classifier machine learning; artificial intelligence +
Footnote †: journal: Medical Informatics, Faculty of Medicine, Erzincan Binali Yildirrm University, Erzincan 24000, Turkey 1
## 3 Materials and Methods
### Feature Extraction  
We used windowing technique to deconstruct temporal features into spatial features to analyze different frequencies. A Hann window was used for smoothening of edges in FFT calculations. In this work, since our focus was on understanding the performance of windowing for the anomaly detection task, we used sliding windows with and without overlap. We used log-scaled mel spectrograms as input features to our SNN model (referred to as SpikeSireNet henceforth in this article). The window length and hop length were varied in the order of \(2^{x}\) to obtain optimal design choices for better performance. For the mel transformation, we imposed a lower frequency limit of 50 Hz to cover the noise signals so that the network could easily differentiate between noise characteristics. The upper limit was set according to respective window size. For example, when w = 512 signals were extracted, the frequency was only within 0-513 Hz. We considered a total of 64 mel channels to constrain the feature range in most of our experiments, and we also varied the mel channels in a range between 32 to 128 channels. Furthermore, the features were converted to dB scale and a min-max normalization was applied to each time slice.
# Current Knowledge on the Fungal Degradation Abilities
## 3 Mineralization or Crystallization Development  
Calcium carbonate solubilization by the action of fungal acids can often occur coupled with the recrystallization of minerals in the substrate [2; 3; 5; 6; 49; 50; 51]. Such mineralization singularities can lead to the development of various biodeterioration phenomena [52; 53]. They occur from the reactions of secreted acids (especially oxalic acid) with stone cations [32] and often result in the formation of carbonates and/or calcium magnesium oxalates [5; 6]. Characterization of fungal crystallization abilities in cultural heritage scenarios has been helpful to study the biodeteriorative contribution of isolates retrieved from air, limestone monuments, stone stela, wall and mural paintings [35; 41; 42; 43; 53; 54; 55; 56]. Fungal mineralization ability screening is usually conducted using B4 (with calcium acetate) or modified B4 (with calcium carbonate) media and adapted formulations [57]. Moreover, CaCO\({}_{3}\) modified Malt agar, Nutrient agar (NA) with CaCl\({}_{2}\) and the above mentioned CMEA and CR2A media have also been found useful for such purposes [35; 42; 43; 58; 59]. Positive mineralization development is usually evaluated by the microscopical visualization of neo-formed minerals around or in fungal hyphae after a period of incubation. Moreover, further characterization of these crystals can also be achieved by applying analytical methodologies such as X-ray powder diffraction (XRD) and/or energy dispersive X-ray spectroscopy (EDS) in conjunction with scanning electron microscopy (SEM) methodologies. So far, circa sixty species have been found to display mineralization abilities in plate essays and, as similarly found for fungal calcium carbonate dissolution, multiple _Aspergillus_ and _Penicillium_ species have also denoted this biodeteriorative profile (Table 2). Such findings can be correlated with their long-known abilities to secrete oxalic acid, among various other acids [41]. Nonetheless, a relevant number of species from genera _Alternaria_, _Cladosporium_, _Colletotrichum_, _Pestalotiopsis_ and _Trichoderma_ putatively displaying these biodeteriorative abilities can also be verified. Typical minerals detected include calcium carbonate in the form of calcite and vaterite-calcite, weddellite, whewellite, hydroxyapatite, hydrocerussite, pyromorphite, phosphate and other still unidentified calcium oxalates and minerals. The detection of species from these genera might indicate a putative threat to materials highly susceptible to fungal acidosis and biomineralization, such is the case of limestone monuments and murals [5; 6; 60].
# Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching
## 3 Methods
### Detection Based on MNet
#### 3.3.3 Detection Module
Assuming that the true label corresponding to the \(i\)-th patch is \(y_{i}\in\{0,1\}\), the corresponding loss of the training set containing a total of \(N\) patch samples is shown in Equation (17). \[L_{conf}=-\frac{1}{N}\sum_{i=1}^{N}y_{i}log(\hat{y}_{i})+(1-y_{i})log(1-\hat{ y}_{i}) \tag{17}\]  
Equation (18) is the final ball detection loss. \[L_{det}=L_{k}+\lambda_{size}L_{size}+\lambda_{conf}L_{conf} \tag{18}\]  
where \(\lambda_{size}\) and \(\lambda_{conf}\) are the balance factors. \(\lambda_{size}\) will always be set to 0.1 and \(\lambda_{conf}\) set to 1. In the prediction stage, the entire image is first cropped into multiple overlapping patches. After each patch passes the detection model, a prediction set of classification, center point, and size will be obtained. Find the corresponding center point coordinates and size based on the index corresponding to the patch with the highest classification prediction probability, and then add the center point coordinates to the offset during tiling to obtain the ball detection frame relative to the entire image.