esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Estimation of HLC Detection Altitude
Presumably, this is due to either an error in the experimental determination of the HLC altitude or to the specific circumstances of its formation. It is convenient to consider the estimation error in terms of standard deviation. The values of this deviation can be seen in Table 2 (first column). The magnitude of the error is of the order of 1 km but with low variation, signaling the homogeneity of the data and the absence of specific outliers. For the neural network, we have chosen a model of an ordinary multilayer perceptron with one hidden layer of 15 neurons and an activation function in the form of a hyperbolic tangent. The output neuron has a linear activation function since we are trying to solve a regression problem. This architecture corresponds to 316 training parameters, which is smaller than the training dataset. Thus, it will be more difficult for the neural network to be overtrained. Figure 12 shows the scatter plots obtained with the neural network and data compression using PCA. A linear relationship can also be seen here. The values of the standard deviation are presented in Table 2 (second column). The values are of the same order of magnitude as the random forest method but exceed it. This is most likely due to insufficient data, making the training of a large network vulnerable to overtraining and a small network insufficient to reconstruct the dependency. Increasing the number of neurons in the hidden layer leads to a rapid overtraining of the network and an increase in the error, while decreasing it leads to an inability to reconstruct the dependency and also to an increase in the error. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RF (PCA)** & **NN (PCA)** & **RF (AE)** & **NN (AE)** \\ \hline Fold 1 & 1097.07 & 1232.56 & 1162.26 & 1320.61 \\ Fold 2 & 1048.35 & 1350.06 & 1065.12 & 1371.62 \\ Fold 3 & 1177.63 & 1433.94 & 1181.13 & 1383.15 \\ Fold 4 & 1095.24 & 1222.92 & 1150.78 & 1388.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The value of standard deviation in HLC altitude estimation (in meters) for random forest and neural network method with different data compression methods. Figure 12: Scatter plot of HLC center altitude determination using neural network and PCA compression. Figures 13 and 14 show scatter plots using autoencoder compression. The error rates are comparable to PCA compression. The use of compressed data also allows for the estimation of observed values, and both methods give comparable results. In all cases, there is a tendency to be able to estimate altitude using machine learning methods and meteorological observations. However, the magnitude of the error is too high to use these results for practical applications. This can be corrected either by adding additional specific data, such as taking into account the dynamics of profile changes over  
Figure 14: Scatter plot of HLC center altitude determination using neural network and AE compression. Figure 13: Scatter plot of HLC center altitude determination using the random forest and AE compression method.
# Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN
## 4 System Model  
The proposed System Model is categorized into network model and energy consumption model, which is elaborated below.
## 5 Thermoelastic Topography of GESA Coating: Topographic Mapping
It should be noted that in the previous analysis the effect of surface roughness on the SAW frequency content was neglected in the theoretical model describing the SAW propagation. From the image in Figure 13, an estimate for the roughness correlation length, represented by the average distance between two local _peaks_ in the topographic image, of \(R_{c}=200\)\(\upmu\)m was obtained. In [50; 51; 52] it was shown that surface roughness leads to a shift of the SAW frequency, mainly because of scattering, which introduces deviations from a straight travel path, and thus apparent slowing down of the wave. Provided that the SAW wavelength is much larger than the absolute roughness and roughness correlation length, a sufficient number of scatterers take part in the attenuation of the wave such that the frequency shift is inversely proportional to the square of the roughness scale. Inadvertently, this frequency shift alters the real SAW dispersion. However, in [53], Equation (13) shows that the attenuation tends to a constant value when the SAW wavelength moves towards the small wavelength limit (\(R_{s}\gg\lambda\)), as a result of an insufficient number of scatterers. The attenuation constant then becomes \(\alpha_{s}=CR_{\text{RMS}}^{2}/R_{c}^{3}\) with \(C\) a function of the surface material elasticity. For the GESA modified coating this constant can be calculated as \(C\approx 0.15\). Together with the roughness parameters extracted from Figure 13, an attenuation constant of \(\alpha_{s}\approx 0.026\) m\({}^{-1}\) is obtained, which corresponds to an acoustic mean free path of almost 9 m. In view of this, neglecting the scattering induced shift in the dispersion curve was a valid assumption for the wavelengths used in the current GILD setup.
# Building Survivable Software Systems by Automatically Adapting to Sensor Changes
## 2 Settings and Notations  
### Settings  
We study the general setting of sensor changes in the context of a compound sensor, i.e., a sensor consisting of multiple individual or component sensors. For example, a compound sensor can be a weather station containing several weather sensors measuring temperature, dew point, wind speed, etc. An instant of time at which some sensor(s) are replaced by new sensors is called a change point (We only address a single change point. Repeated invocation of our methods naturally handles multiple change points as well).  
In this paper, we consider two scenarios:  
* Individual Sensor Change: some but not all of the individual sensors in a compound sensor are replaced by another set of individual sensors. This corresponds to the cases where new individual sensors are plugged in manually or automatically when sensor failures or sensor upgrades occur.
* Compound Sensor Change: the entire compound sensor is replaced by a new compound sensor. This happens in practice for the reason that replacing the compound sensor is technically easier than replacing individual sensors in certain systems. This scenario is more challenging than individual sensor change, since no individual sensor from the compound sensor can be used to calibrate the new sensors.  
Automatic adaptation to sensor changes is challenging since there is no overlapping period between the new sensors and the replaced sensors. In individual sensor change, the remaining sensors are the key to link the information between the new sensors and the replaced sensors. We call the remaining sensors as reference sensors. Intuitively, if the reference sensors are correlated with both the new sensors and the replaced sensors, they can be helpful for reconstructing the replaced sensor values from the new sensor values. For compound sensor change, however, there are no reference sensors from the compound sensor because all sensors are replaced. In this scenario, adaptation to new sensors is very challenging or even impossible. To enable reasonable adaptation, therefore, we assume that we have access to some reference sensors outside the compound sensor. For example, in the context of weather stations, we can use sensors in other stations as reference sensors.  
Using the notion of reference sensors, the two scenarios can be viewed in a unified way:  
* Reference sensors always work properly.
* Replaced sensors are replaced by new sensors at the change point.  
Figure 2 visualizes this unified view and the corresponding notations (explained below).
# Enhancing Sustainable IoT Systems Through a Goal-Oriented Requirements Analysis Framework
## 4 Goal-Oriented Requirements Analysis Approach for IoT Software Systems  
To effectively address the complexities of the application domain, IoT systems require an appropriate modeling language and a structured analysis method that guide business analysts and process designers. In this context, a requirements analysis (RA) approach using the i* 2.0 modeling language within a model-driven framework is proposed. The components have been adapted to align with the specific terminology of IoT systems. To illustrate the approach, an application example is presented that focuses on the integration of IoT devices for air quality monitoring. By detailing this methodology, this research ensures transparency and reproducibility, facilitating its adoption in similar contexts.  
To further enhance the sustainability of IoT systems, the proposed framework integrates goal-oriented requirements analysis with MDD, providing a structured approach to linking user goals with the corresponding system actions required to achieve them. This is achieved by utilizing (i) task elements from the i* modeling language to analyze the impact of each device action and (ii) goals and sub-goals (quality attributes) to ensure a user-centric orientation in the requirements analysis. As a result, the system dynamically selects and executes device actions based on the current environmental state and the intended goals, enabling context-aware decision making.  
Designed for non-technical end users, the framework leverages the i* modeling language, which offers straightforward semantics and intuitive reasoning mechanisms, ensuring ease of adoption with a short learning curve. This approach provides a comprehensive overview of the IoT system while maintaining a strong user focus. End users can define and express their desired states in the smart environment through various interaction modes, including web-based applications, voice commands, or mobile applications, all of which are incorporated into the modeling process. Furthermore, the framework is inherently adaptable, allowing users to customize their goals through the user interface without requiring technical expertise or direct manipulation of the system logic.  
The proposed methodology for developing sustainable IoT systems integrates GORE and MDD through a three-stage transformation process. The first stage involves defining the i* RA model, capturing the objectives and dependencies of the system. In the second stage, model-to-model (M2M) transformation rules are applied using Query/View/Transformation (QVT) to convert the i* RA model into a UML class diagram, ensuring a structured representation of system components. Finally, in the third stage, a model-to-text (M2T) transformation is performed using Acceleo to generate Python source code from the UML class diagram, facilitating the implementation of the IoT system. The following sections provide a detailed explanation of this transformation process.
## 2 The Effects of PCC on Biological Samples
### In Vivo Experiments  
#### 2.1.1 Methods  
Animal procedures have been performed conforming to the directive 2010/63/EU of the European Parliament and approved by the Italian Ministry of Health and by Institutional Animal Care and Use Committee of Magna Graecia University of Catanzaro, Italy. Male Wistar Rats, weighing 300-350 g, were randomly divided into four experimental groups--control (CTRL) that received no treatment, Low intensity (LOW), Standard intensity (STD) and High intensity (HIGH)--according to the PCC applied power. Rats from each experimental group have been anesthetized by intra-peritoneal injection of Zoletil (zolazepam hydrochloride and diletamine hydrochloride; 20 mg/kg body weight) and Xylazine (10 mg/kg body weight). A slight cut was made at the femoral muscle level and the skin layer was removed. Right after, the PCC nozzle exit has been placed at 1 cm of distance from the muscle, and a single application at the relative intensity has been performed. The rats were euthanized by an overdose of Zoletil (100 mg/kg body weight) and Xylazine (10%). Biopsies of femoral muscle of each experimental group have been withdrawn within 10\({}^{\prime}\) and embedded in paraffin; 5 \(\upmu\)m cross sections were prepared. Hematologist/Rosin (H/E) stainings were performed following the standard protocol.
###### Abstract  
Lamb wave dispersion curves are useful for optimizing the inspection scanning distance that can be covered with good sensitivity in many current applications. However, one of the main problems concerning this calculation lies in selecting a numerical method that is computationally accurate and efficient. In this paper, Lamb waves dispersion curves are generated by the Scaled Boundary Finite Element Method, and by the Rayleigh-Lamb equation. For the semi-analytical case, waveguide cross-section discretization was performed using isoparametric elements and high-order spectral elements. The semi-analytical formulations lead to an eigenvalue problem that can be solved efficiently by calculating the couples of wavenumbers and frequencies that guarantee the wave mode propagation, the basis for generating the dispersion curves. These are compared with those obtained from the analytical solution for the symmetric and antisymmetric modes; in both cases, homogeneous plates of constant thickness are considered. The numerical results show good agreement when using a low number of isoparametric elements, or a single spectral element with shape functions of the order of six for computing the dispersion curves and wave structure. The calculation is given with low computational effort, and the relative variation with respect to the analytical reference values is less than 2%.  
dispersion curves; scaled boundary finite element method (SBFEM); guided waves ultrasonic testing (GWUT); high-order spectral elements; computational effort +
Footnote †: journal: Applied Sciences
## 3 Decentralized Treatment Systems
### Graywater Treatment
The flexibility of aerobic MBR configuration represents a significant advantage for implementing the system in remote areas, and its effectiveness was again recently confirmed by the treatment of kitchen GW by a pilot plant aerobic side-stream bioreactor integrated membrane process. A microbial consortium from the kitchen WW and NF membrane were combined. Removal values for COD, dissolved and suspended solids and turbidity reached or exceeded 95%, while the NF process removed the color. The permeate water was free of _E. coli_ bacteria and can be reused for gardening, floor cleaning, sanitation and other domestic applications. The NF membrane's performance showed high water recovery (maximum 82%) and low fouling rate even on long-time operations [133]. Nevertheless, some bacterial regrowth can occur in the reclaimed water, thereby impeding water reuse. The performance of a pilot-scale MBR and the identification of the dominant bacterial biofilm growing on the Pall ring media placed in the permeate pipeline was investigated by [PERSON] and colleagues. The MBR was continuously operated for 125 days with a constant hydraulic retention time of 2 days, under complete sludge retention, to generate permeate for non-potable reuse and agricultural irrigation without any disinfection treatment. The reduction in organic compounds, ammonium nitrogen, suspended solids, total coliforms and _E. coli_ was \(>\)90%. The following bacterial orders were predominantly observed in the biofilm: Sphingomonadales, Burkholderiales, _Platcomycetes_, Acidovorax, _Methyllibium_, _Blastomomas_ and Planctomyces. Knowledge of the predominant bacterial species in the biofilm could enable the selection of the appropriate disinfection method and prevent the biofouling of the RO membranes, facilitating further application of the MBR-RO processes for water reuse [134].
# Parameter Estimation and Error Calibration for Multi-Channel Beam-Steering SAR Systems
## 4 Experiments and Discussion
### Performance Analysis of Estimation Methods
The figures for proposed SS method at SNR = 0 dB have not obvious correlation with \(Fu\), which indicates SS method is not sensitive to \(Fu\), because SS method is based on the shift between two images after pre-processing, which will partly neutralize the error due to increasing \(Fu\). Therefore, the SS method is in most cases more accurate a bit. The performance of the phase estimation method (ME method) against SNR and \(Fu\) and its comparison with the AWLS method described in [27] is shown in Figure 14. It can be seen that the proposed ME method performs better than AWLS against SNR and \(Fu\). Generally, the precision of both estimates become better as \(Fu\) increases. This is because the azimuth ambiguous energies outside the main bandwidth are folded into the effective bandwidth. The azimuth ambiguity signal rate dominates when the PRF is less than \(PRF_{uni}\). The sharp rise in the estimation error from AWLS as  
Figure 12: The performance of DRR error estimation method versus \(SNR\) (**a**) and \(Fu\) (**b**). _Fu_ changes from 1.15 to 1.2 is an artefact caused by the covariance matrix becoming singular when coincident sampling [31; 32; 33] occurs, i.e., the sampling of the first channel for one pulse coincides with the sampling of the last channel for the previous pulse, but the proposed method would not be affected in case of coincident sampling.
## 2 Design and Optimization of Proposed Unit Cell  
The transmitarray is a type of electromagnetic surface antenna that is either a two-dimensional periodic or quasi-periodic artificial electromagnetic structure. A reconfigurable electromagnetic surface was designed by introducing tunable electric devices on the electromagnetic surface unit cell. The EM waves can be controlled in real time through tunable electronic devices for beam agility/scan purposes. In this section, we design a one-bit electronically reconfigurable controlled electromagnetic surface unit and array; the core issue is how to make ordinary transmitarray unit cells have \(0^{\circ}/180^{\circ}\) numerically controlledphase shift capability. Therefore, an excellent one-bit phase-shift structure should meet the following characteristics: ideal 180\({}^{\circ}\) phase difference in the two-phase states, simple structure, and compact integration with the transmitter and receiver structure. Based on the above considerations, a commonly used design method for the transmitarray unit cell is the phase-shifting structure of the transmitter. In this type of unit cell design, the reconfigurable device is usually integrated into the design of the receiving patch antenna or the transmitting patch antenna to change the quantization state. The phase of the EM wave is changed through the electronic device that controls/changes the path of the resonant patch antenna.
## 3 Proof-of-Concept Methodology
### Field Trial  
In February and March 2021, we conducted a field trial utilizing the standard method outlined above. The field trial included 15 hives located at the USDA-CMAVE. Each hive consisted of two boxes without queen excluders, with 13 hives consisting of a deep box with a medium super and the remaining two hives consisting of two deep boxes. A minimum of nine frames was in each of the 30 boxes. COVID-19 regulations at the site where we conducted the field trial restricted the time we could spend in the apiary. Thus, we were not able to remove all SHBs from each hive as outlined in step 1.2 of the field trial standard method. Instead, we standardized the number of SHBs in each hive to ten seen during an inspection of ten frames. We saw more than ten SHBs in two of the 15 hives. We used the bounce method in the top box of these two hives to lower SHB populations below our 10-SHB threshold. We inoculated the hives with 100 SHBs from a lab colony maintained at the USDA-CMAVE.  
We divided the 15 hives into the following three treatments of five hives/treatment: solvent control--100% acetone; experimental compound--300 ug/g actamiprid; and positive control--5 ug/g fipronil. We selected the lowest concentrations of each compound that exhibited 100% mortality in laboratory bioassays. We selected these high concentrations to validate our method and reduce the chances that SHBs would develop resistance to the compounds. We prepared all pesticide solutions at the UF HBREL.
## 2 Study Area and Materials
### Hydrological-Topographic Factors
#### 2.2.1 Surface Factors  
The characteristics of surfaces that are vulnerable to flooding are typically lowlands, gentle slopes, and concave terrains, and can be estimated through spatial analysis. In this study, a digital topographic map drawn to a scale of 1:5000 (2018) provided by the NGII [27] was used to calculate the topographic factors of the study area. The digital topographic map was converted into a 30 \(\times\) 30 m digital elevation model (DEM) through spatial analysis because the contour lines and elevation points were composed in a vector form. Raster calculations were performed with this DEM (elevation) to calculate five surface factors including slope, profile curvature, plan curvature, topographic wetness index, and stream power index (Figure 3).  
Figure 2: Inundation traces that occurred in 2001 in Seoul, the study area.  
Elevation is the most representative factor explaining the characteristics of a surface that is prone to flooding; more lowlands means the area is more vulnerable to flooding. Since the flow velocity is slow in areas with gentle slopes, the runoff from rainfall accumulates and causes a flood. Curvature can be calculated as the second derivative of the surface, and can be classified into profile curvature and plan curvature, respectively,  
Figure 3: Topographic factors: (**a**) Elevation; (**b**) Slope; (**c**) Profile curvature; (**d**) Plan curvature; (**e**) Topographic wetness index (TWI); (**f**) Stream power index (SPI).  
depending on whether it is calculated in a direction parallel to or perpendicular to the slope. Profile curvature is the curvature in the downward direction of the slope, and flooding is likely to occur in a concave terrain (positive). Plan curvature is the curvature in the horizontal direction of the slope, and runoff is likely to accumulate in a valley (negative). The topographic wetness index (TWI) was derived from the study of [PERSON] and [PERSON] [30] and can be calculated as shown in Equation (1). The TWI means that the gentler the slope (\(\theta\)) of the target grid and the larger the basin area (\(a\)) of the upstream region, the higher the potential wetness index of the region. The stream power index (SPI), which was proposed by [PERSON] et al. [31], represents the degree of sediment movement and erosion from surface runoff, and is calculated as shown in Equation (2).  
\[TWI=\ln(a/\tan\theta) \tag{1}\]  
\[SPI=\ln(a\times\tan\theta) \tag{2}\]
# Efficient Small-Object Detection in Underwater Images Using the Enhanced YOLOv8 Network
## 3 Approach  
In this paper, we first replace the YOLOv8 Darknet-53 backbone with FasterNet-T0 [11] to reduce model parameters and flops, speed up model training, and achieve model lightweighting. Secondly, we add a prediction head for small objects, generating low-level, high-resolution feature maps that are more sensitive to small-object detection. Thirdly, we introduce Coordinate Attention [13] to help the network find the regions of interest in the images. Finally, we replace tShe convolutions in the Neck with Deformable ConvNets v2 [12], and replace the 3 \(\times\) 3 convolutions in the Bottleneck of the C2f structure with Deformable ConvNets v2. This deformable convolution can automatically augment the offsets of feature respective fields, leading to more accurate feature extraction and improved detection accuracy. The improved YOLOv8 overall structure is shown in Figure 1, and the details of the improvement modules are described in the following sections.
# Development of Intelligent Fuzzy Controller for a Two-Axis Solar Tracking System
## 2 Hardware Framework of Sum Track System  
### Solar Cell  
Solar cells can be divided into two types: Bulk Type and Thin Film Type. Solar cells are made of different materials and have different values of efficiency. Table 1 shows the efficiencies of different materials used in solar cells. Based on cost and generation efficiency, we choose monocrystalline silicon to perform the experiments [13; 14; 15].