esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Buildings are one of the largest energy consumers in the world, and have great energy saving potential. Thermal systems and lighting systems take most of the energy in a building. Comparing with the optimization solutions developed for a thermal system, the research of improving the lighting system is insufficient. This study aims to improve the lighting environment and reduce the energy by optimizing the building design, which has the largest potential for cutting energy economically compared with the other stages in the life cycle of a building. Although many approaches have been developed for building design optimization, there is still one big problem obstructing their successful practices, in that the designers who take the responsibility of making building designs are not experts in building physics, thus they are not capable of calculating the most appropriate parameters and operating the professional software to optimize their designs. Therefore, this study proposes a user-friendly method for designers to improve building designs. Firstly, Building Information Modeling (BIM) and particle swarm optimization algorithm are applied to build an intelligent optimal design search system. The optimized design from this system can largely use daylighting for internal illumination and save energy. Secondly, different types of lighting control systems are compared and the one which can save maximal energy is added to the selected optimal design. A case study demonstrates that optimized designs generated by the proposed design method can save large amounts of life cycle energy and costs, and is effective and efficient.  
building design; building performance simulation; energy conservation; building information modeling +
Footnote †: journal: Applied Sciences
## 5 Experiments
### Low-Level Experiments  
Our low-level experimentation was like our high-level experimentation. For our detailed experimentation, we only compared the GoogleNet and ResNet algorithms. We again opted to pretrain the models to take advantage of transfer learning techniques. We also chose to continue using cross-entropy loss and stochastic gradient descent both to compare the algorithms equally and as a matter of convention. We continued to use the same image augmentation techniques for this round of experimentation as well.  
We used the entire dataset for these experiments. In all, we had 114,000 training images, 24,400 validation images and 24,400 testing images. In each of the training, validation, and testing sets, there were eight times as many samples in the no damage category as any other category, so class weighting was applied for this experimentation as outlined in our methods section.  
We also changed some of the other parameters accordingly. We raised the number of epochs to 100 and raised the batch size to 64. The former was to perform more robust training across all the samples, the latter to help expedite training so all training could be performed in the 12 h window allotted for running experiments on the server we used. We also chose to set the decaying learning rate to start at 0.01 and be divided by 10 a total of four times over the course of each experiment.  
After completing our low-level experimentation, we placed the model we developed from the GoogleNet algorithm in our pipeline. We tested the complete pipeline using over 200 full satellite images of 1024 \(\times\) 1024 pixels each. The pipeline was able to detect and semantically label the buildings appropriately and produce the desired damage magnitude images as outlined in our methods section. An example is shown in Figure 2.
# Intelligent Fusion Structure for Wi-Fi/BLE/QR/MEMS Sensor-Based Indoor Localization
## 3 Mems Sensor-Based Localization, Optimization, and Evaluation Framework
### Observations and Trajectory Optimization
The attitude-estimation performance using inertial mechanization will accumulate rapidly; thus, in this work, the barometer-originated observation is adopted for drift-error constraint of altitude estimation:  
\[\delta\mathbf{z}_{h}^{n}=h_{B}^{n}-h_{INS}^{n} \tag{12}\]  
where \(h_{B}^{n}\) indicates the height of the barometer origin presented in [6] and \(h_{INS}^{n}\) is the \(z\)-axis location change estimated by inertial mechanization. To further improve the accuracy of forward inertial odometry, a global optimization module was designed in this work, aiming to reduce the cumulative and drift errors originated by the smartphone integrated sensors and the dead-reckoning algorithm. Forward-backward smoothing filters were applied for location and attitude optimization, using the forward-navigation information [12]:  
\[\mathbf{\hat{x}}_{k-1|k}=\mathbf{\hat{x}}_{k-1}+\mathbf{P}_{k-1}\mathbf{\hat{\varphi}}_{k}^{ \mathrm{T}}(\mathbf{P}_{k-1}^{-})^{-1}(\mathbf{\hat{x}}_{k}-\mathbf{\hat{x}}_{k}^{-}) \tag{13}\]  
\[\mathbf{P}_{k-1|k}=\mathbf{P}_{k-1}-(\mathbf{P}_{k-1}\mathbf{\hat{\varphi}}_{k}^{\mathrm{T}}( \mathbf{P}_{k}^{-})^{-1})(\mathbf{P}_{k}-\mathbf{P}_{k}^{-})\cdot(\mathbf{P}_{k-1}\mathbf{\hat{ \varphi}}_{k}^{\mathrm{T}}(\mathbf{P}_{k}^{-})^{-1})^{\mathrm{T}} \tag{14}\]  
where \(\mathbf{\hat{x}}_{k}\) and \(\mathbf{\hat{x}}_{k}^{-}\) represent the updated state vectors recorded during forward inertial ranging and the recorded predicted state values. \(\mathbf{P}_{k-1}^{-}\) is the predicted covariance matrix recorded during forward inertial odometry. \(\mathbf{\hat{\varphi}}_{k}^{\mathrm{T}}\) is the state-update matrix for the current moment and \(\mathbf{P}_{k-1}\) is the recorded updated covariance matrix. \(\mathbf{\hat{x}}_{k-1}\) and \(\mathbf{\hat{x}}_{k-1|k}\) indicate the smoothed state vectors for the current and last moments and \(\mathbf{P}_{k-1|k}\) represents the corresponding smoothed covariance matrix.
# FLaMAS: Federated Learning Based on a SPADE [PERSON]
## 2 Related Work
A further review is presented in [17], which discusses the opportunities and challenges of federated learning in IoT platforms, as well as how federated learning can enable different IoT applications. The paper also identifies critical challenges of federated learning in IoT platforms, highlighting some recent promising approaches to address them. Regarding specific studies, we highlight the framework presented in [18], where the authors propose the use of blockchain and federated learning approaches to build a secure architecture for privacy-preservation, oriented to smart healthcare. In this case, the idea is to enhance security and privacy by employing the two technologies in a combined manner. In a related way, the research presented in [19] makes use of blockchain and federated learning by designing a distributed architecture for data sharing between multiple parties oriented to the industrial IoT. In [20], the authors present _FedHealth_, a federated transfer learning framework for wearable healthcare, which performs data aggregation using federated learning, and then builds personalized models through transfer learning. In this case, the application area is again healthcare and the main challenges are privacy and security. Finally, another interesting study is presented in [21], where a new algorithm is proposed to implement consensus techniques for federated learning in large scale networks, which was validated on an industrial IoT scenario. Large mobile companies are not only researching federated learning, but are also using this algorithm in development; not only is _Google_ using it in the _Gboard_ mobile keyboard [22], in _Android_ messages [13], and in some features in the _Pixel_[23] mobile phone, but _Apple_ also is using it in its mobile keyboard _Quicktype_, and even in the voice classifier for \"Hey, [PERSON]\", all in its iOS 13 [24]. As can be seen, there are many recent studies that have focused on proposing frameworks or mechanisms to facilitate the use of federated learning techniques. Many of these proposals have focused on the IoT area given the close relationship between the two technologies. In most cases, the idea is to improve crucial aspects, such as security and privacy of the information obtained by IoT devices. However, most federation approaches are still rather rigid and may not perform well in situations involving frequent node changes, connectivity problems, etc. Thus, the following section presents a new flexible tool for the development of federated learning systems based on the use of the SPADE platform, which is specially designed for the creation of multi-agent systems.
## 3 Wind Farm Controller
### Wake Control Strategy
#### 3.1.1 Axial Induction Control  
The main purpose of AIC is to derate the turbine by pitching, tilting, or both [10]. This directly results in changes in the power production and a wake velocity deficit. The fractional reduction in wind speed between inflow and rotor is known as the axial induction factor [17]. When AIC is in use, the power generated by the upstream turbines can be reduced by modifying the axial induction factor \(a_{n}\), thereby increasing the power produced by the downstream turbine. In this research, the axial induction factor is controlled by varying the pitch and generator torque demand. Mathematically, thrust force [17] applied on the flow field \(f\), is expressed as follows:  
\[f=\frac{1}{2}\rho AC_{T}(\beta,\lambda)U^{2} \tag{5}\]  
where \(A\) is the swept area, \(\rho\) is the air density, \(C_{T}\) is the thrust coefficient, \(\lambda\) is the tip speed ratio, \(\beta\) is the blade pitch, and \(U\) is the flow velocity. Assuming there is no wake recovery, the flow velocity in the wake is given by [17]:  
\[U_{wake}=U(1-2a_{n}) \tag{6}\]  
where \(a_{n}\) is linked to thrust coefficient \(C_{T}\) through the following relationship:  
\[a_{n}=\frac{1}{2}(1-\sqrt{1-C_{T}}) \tag{7}\]  
Figure 10 represents a schematic overview of the AIC strategy [18]. Wind turbines under normal operating conditions, i.e., without any wind farm control action, are shown in Figure 10a. The length of the arrows represents the magnitude of the wind speed, and it is demonstrated that the upstream turbine operates to extract the most power from the wind, causing the wind speed experienced by the downstream turbine to be weakened, as indicated by the small arrows. When AIC is activated, as shown in Figure 10b, the upstream turbine is derated, increasing the speed of the wind approaching the downstream turbine in comparison to normal operation. As a result, even though the power produced by the upstream turbine is reduced, the overall wind farm power production may increase.  
Figure 9: Normalized power along Horns Rev wind farm row.
## 4 Recommended Sensing Technologies for Defect Types
### Recommended Optical Sensing Techniques for Defect Types  
Optical electron source techniques can detect and observe all presented defect types. Alone, SEM technologies reportedly detect all defects except for vaporization defects. SEM is the only current reported technology to detect balling, surface roughness, and denudation surface-quality defects. Surface oxidation defects are detected by all-optical techniques using an electron source except EBSD, with SEM, TEM, and EPS producing images of surface oxidation behavior and XPS and EPMA producing surface chemical composition hinting toward surface oxidation. LOF porosities can be detected using SEM and TEM, while gas porosities can be captured using SEM. Keyhole porosities can be visually captured using SEM and TEM, while EPMA quantitative chemical analysis maps can hint at keyhole porosity formations. Liquidation cracking can be observed using SEM and chemically determined using EBSD and EDS. Solidification cracking can be observed using SEM and chemically determined using EBSD. Delamination behavior can be visually observed using SEM and chemically confirmed using EDS (EDX). Optical techniques using an X-ray source can detect almost all defects. XCT can observe vaporization surface-quality defects and all microstructural defects. Liquidation cracking can be observed using XCT. XRD can hint toward surface oxidation and surface roughness defects. XRD can also hint towards gas porosities and delamination. Optical techniques using a visible light source are reported to detect and observe all presented defect types.  
Balling defects can be visually detected using OM and CLSM. OM can also detect surface oxidation and vaporization. Surface roughness and denudation can be observed using OM, CLSM, and DSLR. OM and CLSM can observe LOF porosities. OM can observe gas and keyhole porosities. Liquidation and solidification cracking can be detected using OM and DIC. Delamination can be visually detected using CLSM and DSLR. Optical techniques using other sources are reported to detect some surface-quality defects. Surface oxidation can be detected using OES and APT. Surface roughness can be detected using APT. Tables 6 and 7 summarize the possible sensing technologies for each defect using optical techniques.
## 3 Results and Discussions
### Total Triterpene Content  
A calibration curve with an R\({}^{2}\) value 0.9994 was obtained after plotting the recorded absorbancies at wavelength maximum 547 nm against the concentrations of respective unsolic acid standard solutions (Figure 3). The amount of unsolic acid was determined using the regression equation obtained from the calibration curve. MS Excel sheet was used for constructing the calibration curve and quantification of total triterpenoids as unsolic acid equivalents. Figure 4 shows the UV-Visible spectrophotometer absorption spectrum of the triterpenoidal extracts from _D. heterophyllum_ (A), and the total triterpenoids solution from _D. heterophyllum_ (B) authenticating the maximum absorption at 547 nm. All the absorbances of the working standard solutions and test solutions were recorded at this wavelength.  
Figure 3: Standard calibration curve of unsolic acid standard solutions.  
Figure 2: The total triterpenoids before purification (**A**) and after purification (**B**).  
The contents of triterpenoids from the plant accumulated are 24 mg unsolic acid equivalents (UA)/g or (2.4%) of dried above-ground parts of _D. heterophyllum_ and after purification was 980.03 mg unsolic acid equivalents (UA)/g or (98.03%), of the crude total triterpen fraction.
## 3 Results
### Orbit Quality  
The SISRE [42, 43] is widely adopted for the quality validation of satellite ephemerides and can also be adapted to assess the consistency of orbit and clock products from different agencies with similar precision. In this contribution, the SISRE computed between BICB2a combination, B1 CB2b combination, B1 IB3I combination, CODE and GFZ final orbits are used for orbit precision validation. Meanwhile, SLR validation is used for orbit accuracy assessment. The POD assessment spans from 1 July 2022 to 31 July 2022.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Type/Frequency**} & \multicolumn{4}{c}{**IGSOs**} & \multicolumn{4}{c}{**MEOs**} \\ \cline{2-7}  &
\begin{tabular}{c} **C/N0** \\ **(dB-Hz)** \\ \end{tabular} & **MP (m)** & **Pseudo-Range Noise (m)** & **C/N0** & **MP (m)** & **Pseudo-Range Noise (m)** \\ \hline B1I & 45.18 & 0.48 & 0.40 & 47.28 & 0.40 & 0.33 \\ B3I & 47.23 & 0.29 & 0.23 & 48.78 & 0.23 & 0.20 \\ B1C & 43.88 & 0.32 & 0.30 & 45.84 & 0.26 & 0.24 \\ B2a & 45.27 & 0.26 & 0.21 & 47.49 & 0.23 & 0.18 \\ B2b & 45.93 & 0.24 & 0.22 & 47.97 & 0.22 & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RMS statistics of observation quality for BDS-3 IGSOs and MEOs.  
Figure 6: The pseudo-range noise series of BDS-3 satellites in different frequencies.
# A Learning-Based Image Fusion for High-Resolution SAR and Panchromatic Imagery
## 2 Materials and Methods
### Methods
#### 2.2.1 Selection of Training Pixels  
In the step involving the selection of training pixels, meaningful pixels to be used for establishing the relationship should be selected. In particular, training pixels should be selected to consider the differences in imaging mechanisms. This study selects invariant pixels, that is, pixels with little difference in reflectance between the two images. In other words, the relationships are established based on invariant pixels, and the values of pixels with substantially large differences are predicted [28; 34]. The invariant pixels are acquired through image differencing, which is a method that subtracts pixel values between the SAR and panchromatic images, in accordance with Equation (1):  
\[\Delta x_{d}(i,\ j)=I_{\mathrm{S}}(i,j)-I_{\mathrm{P}}(i,j)+C \tag{1}\]  
Figure 5: Flowchart of the proposed method.  
where \(I_{S}\) is the pixel value in the SAR image, \(I_{P}\) is the pixel value in the panchromatic image, \(i\) and \(j\) represent rows and columns, respectively, and \(C\) is an arbitrary constant. Then, [PERSON]'s method is used to classify change and no-change, where the no-change region is selected as the invariant pixels.
# Configuration of Non-Pumping Reactive Wells Considering Minimum Well Spacing
## 3 Results and Discussion
### Design of Practical Configurations of NPRW Based on the Performance Curves  
The performance curve presented above can be used to determine the NPRW design factors that meet the construction constraints of the site and achieve the decontamination goal. For example, when the minimum well spacing (\(\mathrm{d_{m}}\)) is presented as a construction constraint considering site geological conditions, the performance curve of \(\mathrm{R_{e}}\) vs. \(\mathrm{d_{m}}/\mathrm{d_{1}}\), such as in Figure 8a, can be used to determine the other design element of the unit segment. To do this, it is necessary to examine the constructible well diameter \(\mathrm{d_{1}}\), determine the range of available \(\mathrm{d_{m}}/\mathrm{d_{1}}\), and then determine the N that can achieve the decontamination target.  
When the constructible well diameter \(\mathrm{d_{1}}\) is limited, Figure 8b can be used to determine the number of wells to achieve the target removal efficiency. For the selected well diameter, the maximum decontamination efficiency can be expected at the point where the  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **S\({}_{\mathrm{i}}\) = 1.0 m** & **S\({}_{\mathrm{i}}\) = 1.5 m** & **S\({}_{\mathrm{i}}\) = 2 m** & **S\({}_{\mathrm{i}}\) = 2.5 m** & **S\({}_{\mathrm{i}}\) = 3 m** \\ \hline \(\mathrm{d_{1}}\) = 0.15 & 0.81 & 0.61 & 0.45 & 0.38 & 0.31 \\ \(\mathrm{d_{1}}\) = 0.25 & 1 & 0.945 & 0.74 & 0.59 & 0.50 \\ \(\mathrm{d_{1}}\) = 0.35 & 1 & 1 & 1 & 0.87 & 0.72 \\ \hline \end{tabular}
\end{table}
Table 2: Ratio of contaminant removal by triple transect NPRW (sawtooth array) according to the well diameter \(\mathrm{d_{1}}\) and the minimum well spacing \(\mathrm{d_{m}}\) (or width of unit segment, \(\mathrm{S_{i}}\)).  
Figure 8: Performance curves of (**a**) NPRW according to various configurations and well spacings (\(\mathrm{S_{i}}\)), and (**b**) triple transect NPRWs with various well diameter (\(\mathrm{d_{1}}\)) and well spacing (\(\mathrm{S_{i}}\)).  
performance curve of d1 and the line of S1 = d2 meet. If this expected efficiency is less than the target efficiency, it is necessary to increase the number of wells in the unit segment.  
The effect of the hydraulic conductivity of the reactive media is reflected through the value of \(\alpha\) in this prediction of contamination removal. By adjusting \(\alpha\), the hydraulic conductivity and heterogeneity of the aquifer can be reflected in the well configuration design. Adjustment by reducing \(\alpha\) can be a conservative design method that considers factors such as inaccuracies in site characterization and non-ideal performance of reactive materials.
## 1 Introduction
Therefore, there are several sources of uncertainty related to chloride ingress modeling. According to [PERSON] and [PERSON] [22], these uncertainties may come from not only the key parameters of the model (concrete cover depth, chloride concentration at the surface, diffusion coefficient, chloride threshold concentration) but also from the models (physical and surrogate), and measurement methods chosen. Consequently, probabilistic models that take into account the uncertainty and variability of the main parameters are more suitable for lifetime assessment [23; 24; 25]. These models also allow prediction of the probability of corrosion of the rebars and its sensitivity to the different parameters. [PERSON] et al. [16] proposed a probabilistic model of fatigue corrosion for RC structures. This model combined a simple solution of [PERSON]'s law [26], electrochemical principles, a rate competition Criterion and linear elastic fracture mechanics. It was updated recently [17] to account for a more realistic model of chloride ingress; but it still neglects the effects of loading on the chloride ingress process. Characterizing the effects of loading on the chloride ingress mechanism is a major challenge that should be addressed to improve the lifetime assessment for in-service RC structures. In this context, the main objective of this work is to propose a methodology for the probabilistic characterization of the input parameters of a chlorination model taking into account the effects of loading. The proposed methodology is based on the Bayesian network (BN) approach, which is a probabilistic tool that could be used to identify parameters by integrating experimental data. The Bayesian approach has already been used to update/identify the parameters of chlorination models, and to assess/update the reliability of concrete [27; 28] or timber [29; 30] structures. However, it has not been used to estimate the effects of mechanical loading on the chloride ingress mechanism by using experimental data. The experimental data presented in the paper comes from a previous research study on the combined effects of chlorination and cracking detailed in [4]. The paper is organized as follows. The first part of the document summarizes the chloride diffusion models in sound and cracked concrete (Section 2). Section 3 gives a general description of the experimental tests by presenting the equipment, method and inspection data that will be used for identification purposes. In Section 4, we detail the proposed Bayesian network that will be used to characterize the model parameters. Finally, Section 5 deals with the results of the identification of input variables for different loading cases and their effects the probability of corrosion initiation.