esa-sceva/satcom-chunk-collection huggingface dataset:
# Tolerance-Aided Interference Degradation for Optical OFDM in Power-Constrained Systems
## 3 Principles of Tolerance-Aided Interference Degradation Algorithm
### Received Signal Model and Effective Noise Models
\right.. \tag{8}\]  
Then, after the \(N\)-point fast Fourier transform (FFT) of the reconstructed signal \(r[n]\), the demodulated \(M\)-QAM symbol on the \(i\)th subcarrier can be modeled as  
\[\hat{X}_{i}=\frac{\alpha(c)\cdot\rho}{N}|H_{i}|X_{i}+n_{\mathrm{eff}},\;i=1,2, \cdots,N \tag{9}\]  
where \(\alpha(c)\) and \(|H_{i}|\) represent the clipping coefficient and the channel frequency magnitude of the \(i\)th subcarrier, respectively. \(H_{i}\) is the Fourier transform of \(h[m]\), which is estimated using the pilot signals. Based on [PERSON]'s theorem, the clipping coefficient is a function of the modulation index, which can be calculated from [17]:  
\[ \begin{split}\alpha(c)&=\frac{1}{\sigma_{\mathrm{x}}^ {3}\sqrt{2\pi}}\int_{-\infty}^{\infty}xy(x)\exp\left(-\frac{x^{2}}{2\sigma_{ \mathrm{x}}^{2}}\right)dx\\ &=1-\mathrm{erfc}\left(\frac{\sqrt{2}NP_{\mathrm{max}}}{c\sigma_ {d}}\right),\end{split}  \tag{10}\]  
where \(\mathrm{erfc}(\cdot)\) is the complementary error function, which is defined as \(\mathrm{erfc}(x)=2/\sqrt{\pi}\int_{x}^{\infty}\exp(-y^{2})\mathrm{d}y\). \(\psi(x,L)\) is a nonlinear function that represents the peak clipping distortion, represented as  
\[\psi(x)=\left\{ \begin{array}{ll}-2P_{\mathrm{max}},\;\;x<-2P_{\mathrm{max} }\\ x,\;\;\;\;-2P_{\mathrm{max}}<x<2P_{\mathrm{max}}\\ 2P_{\mathrm{max}},\;\;\;\;x>2P_{\mathrm{max}}\end{array} \right.. \tag{11}\]
# Monitoring System Enhancing the Potential of Urban Bekeeping
## 2 Methods
### Monitoring System for Urban Beekeping--Assumptions
#### 2.1.1 Metrological Assumptions
(2) Live video transmission may be used for multiple other purposes including company/institution image building, educational projects, or just for leisure and pleasure. These motives stimulate founding urban apiaries in many cases. (3) From a research and development perspective, inside-beehive observation is an attractive input for beekeeping-oriented applications settled in artificial intelligence (AI) [31]. Novel methods of pattern recognition in video data streams are constantly developed. Another challenge is the integration of video transmission and measurement data streams for the automatic labelling of the monitoring data. Currently, data labelling is mostly done manually, which makes it a prohibitive factor in AI systems developments, in general [31; 32; 33]. Hive weight is usually considered indicative of the activity of the honey bee colony. Truly, in the foraging season, the beehive weight is influenced by the collection of nectar, pollen, and the breeding activity of these insects [21]. Events like robbery or swarming also alter this parameter. In the winter season, the consumption of the honey stock by the colony would be expected to cause the beehive weight reduction. Weight is a comprehensive parameter [34]. However, beehive weight measurement may be confounded by multiple factors, which are not related to bee colony characteristics. For example, beehive maintenance operations like honey extraction, and adding/removing frames or modules change beehive weight. It is also altered by factors related to weather conditions. Beehives absorb water vapor from the moist air, during fog, rainfall, or wet snowfall and they desorb the moisture in the dry air. They also dry out in the sun. The wind can bias the beehive weight measurement. In urban conditions, vibrations are yet another important interference to be considered. Their sources are building installations, urban infrastructure, road traffic, and the wind. Vibrations and weather conditions particularly affect the weight measurement of hives mounted on the roofs. As this location is favoured in urban conditions, the confounding effect of these factors requires understanding. It is proposed to measure beehive weight with a high temporal resolution of 1 s. This corresponds to the temporal resolution applied for monitoring the physical and chemical parameters of air inside beehives and outside them.
## 5 Conclusions  
The development of a new coating on Ti-alloy composed of chitosan, polyethylene oxide and bioglass and made by Electrospinning was tested with osteoblast-like cells (SaOS-2) and Gram-positive _S. epidermidis_ to investigate osteoconductive activity and bacteriostatic behavior. The data obtained through pre- and post-treatment analyses revealed differences between the different substrates, which led to the following conclusions:  
* By adding bioglass to the polymeric solution, the viscosity increased significantly and this involved changes in the fibers' morphological and physiochemical properties (diameter of the fibers and wettability);
* Both coatings showed a more effective action against bacteria over time compared to uncoated Ti-alloy, even if in the case of coating of nanofibers with bioglass there was an initial adhesion and proliferation due to the presence of glass, partly inhibiting the antibacterial effect of chitosan;
* Cells adhered to all substrates, but the coating incorporated with bioglass provided a better performance regarding the formation of mineralized tissue because some areas related to mineralized organic matrix (osteocalcin and osteopontin) were homogeneously distributed on the entire surface.  
In conclusion, applying a composite coating of chitosan fibers, polyethylene oxide and bioglass on Ti-alloy substrates results in the combined action of chitosan and bioglass bringing significant improvements to the surface properties of the substrates, inducing stronger and durable antibacterial resistance and simultaneously implementing the formation of mineralized tissue.  
Conceptualization: F.B. and H.N.D.; methodology: H.N.D., P.P.V.; software: M.Z., P.P.V.; validation: G.P., E.O., and T.A.; formal analysis: F.B., M.Z.; investigation: F.B., H.N.D., P.P.V.; resources: O.M., N.T., W.S.; data curation: F.B., H.N.D.; writing--original draft preparation: [PERSON]; writing--review and editing: [PERSON], E.M.; visualization: [PERSON]; supervision: [PERSON], [PERSON], [PERSON]; project administration: [PERSON], E.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# The Effect of Green Areas on Urban Microclimate: A University Campus Model Case
###### Abstract  
Urbanization and the reduction of green spaces have significantly contributed to problems such as rising temperatures and declining air quality in urban areas. This study examines the impact of different types of green areas--broadleaved trees, coniferous trees, shrubs, and vines--on urban temperature regulation at the Recep Tayyip Erdogan University Zihni Derin Campus. Surface temperature, humidity, ambient temperature, and wind speed measurements were collected using an infrared thermometer over a one-year period under various climatic conditions (August, October, January, and April) and at different times of the day (09:00 AM, 01:00 PM, and 05:00 PM). To quantitatively assess the cooling effect of each type of green area, a Response Surface Methodology (RSM) was applied, and a predictive formula was developed to estimate the cooling impact of various green areas under different environmental conditions. These formulated models enable the estimation of the temperature reduction provided by these four plant types based on different input parameters, achieving an accuracy of approximately 92% or higher without requiring direct measurements. The findings of this study provide a robust methodological framework and a practical tool for optimizing green space designs, mitigating urban heat island effects, and enhancing urban living comfort under various climatic conditions.  
1  
Academic Editor: [PERSON]  
6 February 2025  
Revised: 1 April 2025  
Accepted: 4 April 2025  
Published: 15 April 2025  
**Keywords:** broadleaved trees; coniferous trees; green areas; RSM; urban temperature
## 4 Experimental Results
### Analysis of Results
#### 4.4.2 Experiment 2: Calculation of Indicators' Weights for Traffic Trend Prediction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Guideline Layer \(U\)** &
\begin{tabular}{c} **Guideline Layer** \\ **Weights** \\ \end{tabular} & **Indicator Layer** & **EM Weights** \\ \hline \multirow{4}{*}{Road Factors \(U_{1}\)} & \multirow{4}{*}{0.524} & \(U_{11}\) & 0.053 \\  & & \(U_{12}\) & 0.080 \\  & & \(U_{13}\) & 0.223 \\  & & \(U_{14}\) & 0.168 \\ \hline \multirow{4}{*}{Natural Factors \(U_{2}\)} & \multirow{4}{*}{0.183} & \(U_{21}\) & 0.060 \\  & & \(U_{22}\) & 0.051 \\  & & \(U_{23}\) & 0.072 \\ \hline \multirow{4}{*}{Human Factors \(U_{3}\)} & \multirow{4}{*}{0.293} & \(U_{31}\) & 0.073 \\  & & \(U_{32}\) & 0.083 \\ \cline{1-1}  & & \(U_{33}\) & 0.137 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The weighting of road traffic trend indicators for the EM method. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Guideline Layer \(U\)** & \ \begin{tabular}{c} **Guideline Layer** \\ **Weights** \\ \end{tabular}  & **Indicator Layer** & \begin{tabular}{c} **GT-AHP-EM** \\ **Weights** \\ \end{tabular}  \\ \hline \multirow{4}{*}{Road Factors \(U_{1}\)} & \multirow{4}{*}{0.2225} & \(U_{11}\) & 0.0202 \\  & & \(U_{12}\) & 0.0317 \\  & & \(U_{13}\) & 0.0865 \\  & & \(U_{14}\) & 0.0841 \\ \hline \multirow{4}{*}{Natural Factors \(U_{2}\)} & \multirow{4}{*}{0.2472} & \(U_{21}\) & 0.0689 \\  & & \(U_{22}\) &  
disposal to reduce losses. Visibility (\(U_{23}\)) is the second factor that affects the road after urban road emergencies. The higher the visibility after an emergency event, the more rapid the traffic management emergency disposal will be. If visibility is low, the probability of secondary traffic events increases and more unknown situations are likely to occur. Urban road monitoring and early warning should be strengthened and protected on roads with low visibility. The results of AHP, EM and GT-AHP-EM methods are shown in Figure 7, which are highly subjective and depend on the subjective intention and experience of decision makers. The weights of the indicators determined by the decision makers relying on the empirical judgment of the traffic state and the weights of the indicators obtained by using objective data analysis will be different, respectively. The road factor is static, and the decision makers prefer to choose to combine their own summarized experience to judge the traffic status, so the six indicators of road length \(U_{11}\), one-way road width \(U_{12}\), number of traffic lights \(U_{13}\), number of intersections \(U_{14}\), number of roadway law enforcement officers \(U_{31}\) and number of road sections under construction \(U_{33}\) in Figure 7 are calculated by the AHP method and EM method. The results vary greatly. The indicators of natural factors have been studied a lot, and there are specific operation rules summarized, so the results of the weights calculated by the AHP method and the EM method are similar in the four indicators of rainfall intensity \(U_{21}\), temperature \(U_{22}\), visibility \(U_{23}\) and traffic flow \(U_{32}\).
###### Abstract  
A straightforward chemical polymerization process was used to create the polyaniline/LiClO4/CuO nanoparticle (PLC) nanocomposite, which was then exposed to varying doses of electron beam (EB) radiation and studied. The FESEM, XRD, FTIR, DSC, TG/DTA, and electrochemical measurements with higher EB doses showed clear changes. The FTIR spectra of the PLC nanocomposite showed variations in the C-N and carbonyl groups at 1341 cm\({}^{-1}\) and 1621 cm\({}^{-1}\), respectively. After a 120 kGy EB dose, the shape changed from a smooth, uneven surface to a well-connected, nanofiber-like structure, creating pathways for electricity to flow through the polymer matrix. The EB irradiation improved the thermal stability by decreasing the melting temperature, and the XRD and DSC studies reveal that the decrease in crystallinity is attributed to the dominant chain scission mechanism. The enhanced absorption and red shift in the wavelength (from 374 nm to 400 nm) observed in the UV-Visible spectroscopy were caused by electrons transitioning from a lower to a higher energy state, with a progressive drop in the band gaps (\(E_{\mathrm{g}}\)) from 2.15 to 1.77 eV following irradiation. The dielectric parameters increased with the temperature and electron beam doses because of the dissociation of the ion aggregates and the emergence of defects and/or disorders in the polymer band gaps. This was triggered by chain scission, discontinuity, and bond breaking in the molecular chains at elevated levels of radiation energy, leading to an augmented charge carrier density and, subsequently, enhanced conductivity. The cyclic voltammetry study revealed an enhanced electrochemical stability at a high scan rate of about 600 mV/s for the PLC nanocomposite with the increase in the EB doses. The _I-V_ characteristics measured at room temperature exhibited nonohmic behavior with an expanded current range, and the electrical conductivity was estimated, using the _I-V_ curve, to be around 1.05 \(\times\) 10\({}^{-4}\) S/cm post 20 kGy EB irradiation.  
PLC nanocomposite; electron beam irradiation; structural properties; dielectric parameters; cyclic voltammetry +
Footnote †: journal: Applied sciences  
Article
## 1 Introduction
This streamlines workflows by eliminating physical plaster models, saving space, and preventing loss or damage. Even anatomically challenging areas can be rescanned as needed to ensure optimal quality of the digital upper jaw models [17]. As previous measurement studies have largely been based on the analysis of digitized plaster models, which come with the previously mentioned limitations, new digital measurement methods are essential for analyzing maxillary growth and measuring the jaw segments of patients with unilateral cleft lip and palate to complete the digital workflow. With the advantages of intraoral 3D scanning (IOS) mentioned above, digital measurement methods improve the accuracy and reproducibility of assessments, which is crucial for identifying anatomical variations associated with unilateral cleft conditions. These advanced techniques allow for detailed maxillary analysis, enabling clinicians to capture subtle changes that might be overlooked by traditional methods. Through digital model analysis for these patients, specialized software patches allow for adjustments and continuous expansion of analyses. Collected measurement data remains permanently available, enabling the creation of comprehensive digital databases and can be easily shared between specialized centers. This digital workflow facilitates better communication and coordinationamong multidisciplinary teams who are necessarily involved in the therapy and long-term care of this special patient group. Scientific research concerning growth-related changes of the maxillae in patients with unilateral cleft lip and palate vary in the number of measurement points and their terminology [10, 18, 19, 20, 21]. While existing studies measure the cleft width as the distance between the alveolar ridge crests of the larger and smaller segments, the actual point of closest approach between the segments is not evaluated. Furthermore, previous studies have not measured the subsegment width, which would allow for a more detailed assessment of growth processes in different sagittal regions of the alveolar ridges. Moreover, most previous measurement studies lack a precise guide for implementing a measurement method into the digital workflow. The aim of this study was to introduce a new digital measurement method for digital models of patients with unilateral cleft lip and palate with the analysis software OnyxCeph3 TM (Image Instruments(r) GmbH, Chemnitz, Germany) and its integration into the interdisciplinary Frankfurt approach. The integration of OnyxCeph3 TM computer-aided measurement is expected to improve the accuracy and efficiency of evaluating digital models for patients with unilateral cleft lip and palate, facilitating more informed decisions and personalized treatment strategies, ultimately enhancing patient outcomes.
## 4 Prediction of Reinforcement Tensile Force  
### Calculation Method of Reinforcement Tensile Force  
As previously analyzed, the total earth load acting on the facing column is carried by the toe and reinforcement layers together. It can be assumed that the connection load in each reinforcement layer is equal to a portion of the active earth pressures acting on the facing within the contributory area of this layer, which can be expressed as the following:  
\[T_{\text{con,i}}=K_{i}S(\gamma z+q) \tag{6}\]  
Figure 16: Influence of backfill friction angle \(\varphi\) on the ratios of toe and reinforcement loads.  
where \(T_{\text{con,i}}\) = the connection load in a reinforcement layer; \(K_{\text{t}}\) = the lateral earth pressure coefficient, which is lower than the active earth pressure coefficient \(K_{\text{a}}\) in Equation (5); \(S\) = the vertical spacing between adjacent reinforcement layers; \(\gamma\) = the unit weight of backfill; \(z\) = the depth of a reinforcement layer below the wall top; and \(q\) = the surcharge pressure. According to the influence of toe restraint on the fraction of the total earth load carried by the reinforcement layers, \(K_{\text{t}}\) can be considered a function of \(K_{\text{a}}\) and the toe interface friction angle \(\varphi_{\text{t}}\). Furthermore, the wall height \(H\), facing batter \(\omega\), reinforcement stiffness \(J\), reinforcement spacing \(S\), and backfill friction angle \(\varphi\) should be considered in the calculation of \(K_{\text{t}}\) as a result of their influence on the reinforcement load ratio. In addition, Figure 17 shows that the toe restraint condition affects \(T_{\text{con,i}}\) differently at different depths; thus, the depth z should also be regarded as a factor related to the value of \(K_{\text{t}}\). Therefore, \(K_{\text{t}}\) is a function of \(K_{\text{a}}\), \(\varphi_{\text{t}}\), \(H\), \(\omega\), \(J\), \(S\), \(\varphi\) and \(z\). The backfill cohesion \(c\) is not included in the function of \(K_{\text{t}}\) because the scope of the proposed method is limited to GRS walls constructed with cohesionless soil. It is noted that c should be considered in the prediction of connection loads in reinforced cohesive soil walls because of its significant effect on the lateral earth pressure.
# Screening of Some Romanian Raw Honeys and Their Probiotic Potential Evaluation
## 2 Materials and Methods
### Physico-Chemical Characterization of Raw Honey Samples
#### 2.2.4 Quality Analysis of Raw Honey Samples
Sample preparation was based on the purification and extraction of tetracycline residues from honey using affinity columns with metallic chelates [35]. The chromatographic system (Shimadzu VP Series, Japan) consists of: binary pump, degasser, system controller, column oven, auto-injector, and detector set at 360 nm. The separation was performed on a Nucleosil 100 RP-18, 5 \(\upmu\)m column, 250 \(\times\) 4.6 mm ID, equipped with guard column. The mobile phase is 10 mM oxalic acid, acetonitrile, and methanol (16/3/2). The flow rate was 1 mL/min, injection volume: 50 \(\upmu\)L and column temperature: 35 \({}^{\circ}\)C. For sample preparation, an Alltech vacuum Manifold with polypropylene mini-columns of 3 mL with 10 mL reservoir, containing a frit at the bottom (Supelco), was used. The extraction solution is sodium succinate buffer 0.1 M, eluent McIlvine-EDTA-NaCl buffer, and chelating Sepharose fast flow resin in 20% methanol suspension [35]. From a stock solution of 0.1 mg/mL oxytetracycline and tetracycline, different dilutions were made (5-75 \(\upmu\)g/L) and were injected separately in duplicate. Following the chromatograms registrations, calibration curves were constructed, to be used in unknown concentration determination from the honey samples. The oxytetracycline or tetracycline residue content of the sample was calculated by comparing the signal area corresponding to the sample with that of the standard solution, taking into consideration the dilution. There is a direct proportionality between the concentration and the signal area given by those. Calculation was made following the equation:  
\[\text{Residue concentration }(\upmu\text{g/kg})=(\text{V}_{\text{ elution}}/\text{m}_{\text{sample}})\times\text{C}_{\text{c}},\]  
where:  
\[\text{V}_{\text{elution}}\text{--}\text{elution volume from sample preparation }(3\text{ mL}),\]  
\[\text{m}_{\text{sample}}\text{--}\text{honey sample mass }(\text{g}),\]  
\[\text{C}_{\text{c}}\text{--}\text{concentration from the calibration curve}.\]  
Every batch of analysis contained one pure standard mixture run and fortified samples to exclude uncertainty from inconclusive results. The results are expressed in \(\upmu\)g/kg honey (ppm).
## 2 Materials and Methods  
### Location and Facilities  
This trial was carried out in the Experimental Centre of the National Technological Centre of the Auxiliary Industry of Agriculture, Tecnova Foundation, located at the Majada Ortigas Site (36\({}^{\circ}\)53\({}^{\prime}\) N; 2\({}^{\circ}\)22\({}^{\prime}\) W, 184 m elevation above sea level), in the Municipal District of Viator, in the province of Almeria, in the southeast of Spain. The trial was carried out during an autumn-winter crop cycle in which the climatic conditions were the usual ones in the southeast of Spain, with values of the air temperature close to 18 \({}^{\circ}\)C from the beginning of the trial until the month of November and almost with the absence of cloudy days [25].  
The execution of this trial was carried out in a multi-span greenhouse of 1.344 m\({}^{2}\) in total area. The structure of this greenhouse was made with galvanized steel pipes and wires, with lateral and central enclosures made with flexible plastic covers, with a height of 6.5 m in the ridge, 4.5 m in the gutter, and 4 m in the grid. The lateral and upper windows were automatically controlled with a climatic control system.  
The soil of this greenhouse was an imported soil with three different layers: a heavy imported soil of 20 cm depth placed over the original soil of the Experimental Centre, covered with a layer of manure of 3 cm depth, and covered with an upper sand layer of 10 cm depth used as mulch.  
The irrigation and fertilization of the crop were carried out using a drip irrigation system, with paired rows of dripper lines located at a distance of 1.2 m between pairs of lines and 0.5 m between two dropper holder branches that are part of the same pair of lines, and with the emitters within the same dropper holder branch located every 50 cm. The installation of the drip irrigation system had self-compensating drippers with a unit flow of 3 L per hour. The fertigation system used in this experiment was controlled automatically with an irrigation unit provided with a programmer and six tanks of concentrated nutrient solution.  
Fogging water was supplied with a particle size of 8 um and water supply pressure of 0.2-3.0 bar between 10 a.m. and 4 p.m. when the VPD exceeded 1.2 kPa, so the VPD values were controlled not to exceed 1.2 kPa during that period. In addition to VPD regulation through the fogging water supply, other cultivation conditions such as the composition of the nutrient solution and the root zone environment in all treatments were carried out in the same manner. In addition, the VPD was artificially lowered through a consistent fogging water supply to the experimental environment without considering the recommended VPD values for each crop growth stage mentioned above.