esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Factors that Affect the Adsorption Process
### Reusability of Adsorbent  
Revival and desorption of the adsorbent is an essential parameter for industry to reduce the cost of the extraction process. Acids (HCl), H\({}_{2}\)SO\({}_{4}\), HNO\({}_{3}\)), bases (NaOH, KOH), salts (CaCl\({}_{2}\), Ca(NO\({}_{3}\))\({}_{2}\), Na\({}_{2}\)SO\({}_{4}\), Na\({}_{2}\)CO\({}_{3}\)), and chelating agents (EDTA) can all be used to desorb [120]. The regenerating agents are considered effective if they can recover the metal ions without destroying the adsorbent binding sites. For example, [PERSON] et al. (2015b) studied the desorption of mercury ions from polyacrylamide-2-amino-1,3,4-thiadiazole chelating resin (PAN-ATD) by using 30 mL of HCl and HNO\({}_{3}\) in different amounts (0.5-4.0 M) [45]. It was found that a 4.0 M solution of HCl or HNO\({}_{3}\) solution completely (100%) desorbed mercury ions. The adsorption-desorption phenomenon was carried out five times, and it hardly affected the adsorption value. Adsorption values were sustained at up to 90.8%, even after repeating this phenomenon 5 times. In one of our desorption  
Figure 11: Effect of adsorbent dosage on the adsorption of metal ions. Adapted with permission from reference [129].  
studies, the removal efficiencies of Pb\({}^{2+}\) at 0.1M HCl for cycles 1, 2, and 3 were found to be 89.3%, 63.2%, and 54.6%, respectively as shown in Figure 12[128].  
[PERSON] et al. (2017) synthesized thiol-functionalized polyacrylamide fibers by a microwave irradiation technique for the extraction of Cd\({}^{2+}\) and Hg\({}^{2+}\) ions [32]. This technique is better than conventional methods due to reduced reaction time, reduced side products formation, and an increase in the reaction yield. Moreover, the desorption of Cd\({}^{2+}\) and Hg\({}^{2+}\) ions was conducted through 0.1M HCl, and the regeneration process of metal ions, as well as fibers, was repeated 5 times (Figure 13). The effective desorption of these metal ions from the fibers could take place due to the greater attraction of H\({}^{+}\) ions towards the fibers compared with metal ions.  
Figure 12: Regeneration process of the modified polyacrylamide. Adapted with permission from reference [128].  
Figure 13: Adsorption capacities of Hg\({}^{2+}\) and Cd\({}^{2+}\) on PANMW-Thiol fiber regeneration. Reproduced with permission from reference [32].
###### Abstract
To this end, we designed and investigated K-anonymity-based secure hierarchical federated learning (SHFL). Unlike existing solutions, we consider a realistic deployment of various healthcare application-aware IoT devices in which the local client (LC) devices directly communicate with the healthcare cloud/server. Every LC is an intermediate bridge for a few end devices called intra-local clients (ILCs). This unique learning technique provides network scalability by sharing the learning models of directly and indirectly connected devices with a global model trained from rich data. SHFL provides a loose federation of directly connected clients and allows the client devices to communicate and accommodate local models of the connected sub-clients. Each local model of the client and subclient devices has a local dataset to train the learning models on the local devices without interacting with the cloud server. The significant contributions of this study are summarized as follows. 1. We propose the SHFL framework for smart healthcare systems. In particular, we added K-anonymity-based location privacy along with data privacy of FL to anonymize the identity of the participating clients. 2. The proposed SHFL framework leverages the centralized server that communicates with multiple directly and indirectly connected devices hierarchically. Moreover, the proposed SHFL formulates the hierarchical clusters of LBSs to execute hierarchical FL. 3. The performance of the proposed SHFL was evaluated through extensive simulation experiments conducted with multiple model architectures and datasets. The remainder of this paper is organized as follows. In Section 2, we briefly discuss the studies that motivated us to conduct this research. In Section 3, we explain the system models of the proposed SHFL framework. In Section 4, we propose a K-anonymity-based secure hierarchical framework and demonstrate the application of the proposed SHFL. In Section 5, we conduct simulation experiments to demonstrate the performance of the proposed SHFL in comparison with state-of-the-art approaches. Finally, we present the conclusions of this study in Section 6.
## 5 Results of Health Monitoring
### Internal Force Monitoring Results
#### 5.3.3 Tower Root Stresses  
The measured compressive stresses and change percentages of west tower roots are shown in Table 5. The variation amounts within the range from \(-1\) MPa to \(1.5\) MPa reflect the whole sectional compression status of the tower roots.
# Type Hierarchy Enhanced Event Detection without Triggers
## 3 Methodology
### Model Overview  
In this section, we introduce the framework of THEED in detail. Without event triggers, there will be three challenges, as shown in the task definition. To solve the first challenge, we divided the features into context-level and word-level, where context-level captures contextual information and word-level denotes word information. Meanwhile, we remove useless words from the sentence, such as \"a\", \"the\", etc., using the attention mechanism. As for the second challenge, rather than a simple \"event-type embedding table\", we first construct the type graphs, and then assign confidence to each hierarchical concept to strengthen the semantic relationship between the input text and the event type.  
Finally, to solve the multievent problem, we convert multilabel classification into a multiple binary classification task. For example, consider the following sentence, \"We go to war in Iraq, 200,000 people start protesting in Pakistan, they put too much pressure on the government\", a perfect model should detect two different events from the sentence: an Attack event and a Demonstrate event.  
As Figure 3 shows, THEED comprises three components: (1) The instance encoding represents a sentence into hidden embeddings. (2) The type hierarchy concept modular component enhances the semantic representation of event types through an external knowledge graph. (3) The event detection task relies on instance encoding and the hierarchy concept embedding to estimate the probability of a certain event type for the sentence. The details are as follows.
# Study of the Dynamic Response of a Rigid Runway with Different Void States during Aircraft Taxing
## 1 Introduction
Most of the current studies are focused on the stress, strain, dynamic displacement, and vibration response analyses of healthy roads or runways. The void detection method needs to be performed with the help of an FWD or other instruments. However, there are not many studies focused on the detection of the void of the plate bottom by the mechanical response of the runway during aircraft taxiing. The research method, which is based on the coupled vibration analysis of aircraft-runway excited by the roughness of runway pavement, is described in the second section of this paper. The dynamic load of an aircraft taxiing is calculated using the space state method, and a method for analyzing signals using wavelet packet energy is introduced. The third section focuses on the creation of a rigid runway model with joints, the design of runway models with varying degrees of void, and the effective application of aircraft dynamic load in a finite element model. The fourth section analyzes and discusses the calculation results of runway dynamic response with various degrees of void, as well as the relationship between the wavelet packet energy characteristics of pavement dynamic displacement and vibration signals and the degree of void. Lastly, the fifth section summarizes the findings. The research results can guide the deployment of buried sensors for airport runways, which can directly sense whether the runway contains voids or not by using the interaction between aircraft and runway during nonstop operation.
# Deep Eutectic Solvents as Extraction Media for
## 2 Factors Affecting the Extraction of Flavonoids Using DES Separation Techniques
### Temperature  
The temperature in which the extraction takes place is naturally expected to affect the time of the extraction, as well as its efficiency and performance. In general, higher temperatures increase molecular mobility and allow extracted molecules to diffuse to the solvent quicker. Extractions through DES are no exception. More so, extractions with DES rely on temperature to decrease the viscosities of DES, which are significantly high and render extractions cumbersome.  
According to the literature, the desirable temperature range for extractions is from room temperature (25 \({}^{\circ}\)C) to about 60 \({}^{\circ}\)C. Higher temperatures, aside from demanding energy to sustain (moving away from the green character of extractions), might also endanger either the DES or the target substance, since many of the natural substances involved are thermally sensitive [19].  
Very high temperatures also proved to decrease yield in some cases because of a decrease in the interactability between the target compound and the solvent of choice, regardless of the thermal endurance of either one (which is still a limiting factor nevertheless) [20].
# Model-Based Design and Simulation of Paraxial Ray Optics Systems
## 4 Results and Discussion
### Paraxial Cloaking  
Paraxial optical cloaking [23] is an ingenious application of geometrical optical design. If the overall ray transfer matrix is equivalent to the transmission through a constant refractive index medium, regions enclosed by the system optics are perfectly cloaked, and objects placed inside these spaces do not affect the output light, becoming invisible. Such a condition is achieved with a four-lenses setup,  
Figure 2: Modeling refraction and reflection between two media of refractive indices \(n_{1}\) and \(n_{2}\): (**a**) ray optics representation; (**b**) transfer matrix model; and (**c**) implementation in MATLAB Simulink.  
Figure 3a, in which focal lengths and path distances must satisfy \(d_{1}=f_{1}+f_{2}\) and \(d_{2}=2f_{2}\ (f_{1}+f_{2})/(f_{1}-f_{2})\) for \(f_{1}=f_{4},f_{2}=f_{3}\), and \(d_{1}=d_{3}\).  
The system model is implemented using eight optical elements as depicted in Figure 3b, assuming 50 mm diameter, thin lenses for the sake of simplicity. As proposed in [23], for \(f_{1}=f_{4}=200\) mm and \(f_{2}=f_{3}=50\) mm, distances \(d_{1}=d_{3}\) and \(d_{2}\) must be 250 and \(\sim\)167 mm, respectively. For visualization purposes, the model also includes free-space transmissions of length \(d_{0}=d_{4}=200\) mm for input and output rays. Static simulations carried out by scanning the input height within \(-20\leq y_{0}\leq 20\) mm provides the ray tracing profile shown in Figure 3c, which evidences the cloaked region for \(200\leq z\leq 800\) mm. As the rays are vertically confined to \(-5\leq y_{0}\leq 5\) mm, objects strategically placed between lenses \(f_{1}\) and \(f_{4}\) do not block the passage of light, i.e., \(y_{8}\) and \(\theta_{8}\) remain unchanged in relation to \(y_{1}\) and \(\theta_{1}\), respectively, and the optical system approximates a transmission element. Results are comparable to the ray tracing simulations conducted with CODE V software depicted in [23], but differ because the latter uses achromatic doublets instead of thin lenses and also computes aberrations. Nevertheless, function blocks can be simply permuted in the system model to simulate practical optical elements and achieve a more realistic analysis.
## 3 Results
Figure 2: (**a**) Dependence of intensities of the pump beam (\(n=0\)) and the n-th-order sidebands (\(n=\pm 1,\pm 2,\pm 3\)) on \(\xi\). (**b**) Spectrum for \(\xi=1\) when the pump beam frequency was \(\omega_{0}\) and the modulation frequency was \(\Omega\). Figure 4 shows contour curves plotted at \(\xi=0.25,0.5,1\) as a function of hydrogen pressure in the HC-PCF \(\rho\) (atm), the length of the HC-PCF \(l\) (m), and the input power of the pump beam \(P\) (W). The parameters ranged from 0 to 10 atm for \(\rho\), from 0 to 10 m for \(l\), and from 0 to 1 W for \(P\). \(\rho\) was assumed to be proportional to the molecular density \(N\) (cm\({}^{-3}\)). \(\xi=1\) was sufficiently achievable for the parameter ranges in this calculation, e.g., a \(\sim\)25-m HC-PCF length enabled \(\xi=1\) for \(\rho=1\) (atm) and \(P=2\) (W). The parameters ranges were experimentally feasible with commercially available equipment, indicating that the proposed idea was promising. In the MOM system based on a dispersion-compensated optical cavity reported elsewhere [10], \(\xi\) was estimated to be 0.17 at a pump power of 0.35 W. The results for the MOM based on a HC-PCF shown here implies that the HC-PCF system has a potential to overcome the optical-cavity-based system in the efficiency of the generation of the sidebands for a pump power less than 1 W.
# A Novel Approach to Enhance the Energy Efficiency of a NOMA Network
## 5 The Sub-Optimal Solution
### Power Allocation by DC Programming
Then, the rate sum for the sub-channel \(v\) is:  
\[R_{v}=B_{v}log_{2}(1+\frac{\alpha P_{v}H_{1,v}}{1+(1-\alpha)P_{v}H_{1,v}})+B_{ v}log_{2}(1+(1-\alpha)P_{v}H_{2,v}) \tag{32}\]  
Therefore, as in (16), the maximum energy efficiency for the sub-channel problem can be expressed as:  
\[E_{v}^{max} =\max_{\alpha\in(0,1)}\frac{B_{v}log_{2}(1+\frac{\alpha P_{v}H_{1,v }}{1+(1-\alpha)P_{v}H_{1,v}})+B_{v}log_{2}(1+(1-\alpha)P_{v}H_{2,v})}{P_{fixed} +P_{v}}\] \[=\max_{\alpha\in(0,1)}\left\{\frac{B_{v}log_{2}(1+\frac{\alpha P_ {v}H_{1,v}}{1+(1-\alpha)P_{v}H_{1,v}})}{P_{fixed}+P_{v}}+\frac{B_{v}log_{2}(1+ (1-\alpha)P_{v}H_{2,v})}{P_{fixed}+P_{v}}\right\} \tag{33}\]  
We transform the maximization problem into the minimization problem via deploying the DC programming approach as follows:  
\[\min_{\alpha\in(0,1)}-\frac{B_{v}log_{2}(1+\frac{\alpha P_{v}H_{1,v}}{1+(1- \alpha)P_{v}H_{1,v}})}{P_{fixed}+P_{v}}-\frac{B_{v}log_{2}(1+(1-\alpha)P_{v} H_{2,v})}{P_{fixed}+P_{v}} \tag{34}\]  
This can be rewritten as:  
\[\min_{\alpha\in(0,1)}(f(\alpha)-g(\alpha)) \tag{35}\]  
where \(f(\alpha)=-\frac{B_{v}log(1+\frac{\alpha P_{v}H_{1,v}}{1+(1-\alpha)P_{v}H_{1, v}})}{P_{fixed}+P_{v}}\) and \(g(\alpha)=\frac{B_{v}log(1+(1-\alpha)P_{v}H_{2,v})}{P_{fixed}+P_{v}}\). Since the second derivatives of function \(f(\alpha)\) and function \(g(\alpha)\) are both greater than zero, that is, \(\
abla^{2}f(\alpha)>0\) and \(\
abla^{2}g(\alpha)>0\), these two functions are convex functions of \(\alpha\) that satisfy the conditions to apply the DC programming method. Accordingly, we can use the DC programming approach to allocate power to different sub-channel users. The principle in deploying Algorithm 2 is to convert a non-convex problem into convex sub-problems by using successive convex approximations. The specific algorithm is demonstrated as follows:  
Figure 2: Two users transmitting on the same channel. In the algorithm, \(\xi\) is the difference tolerance and \(\alpha\) presents the allocated powers of the sub-channels. The \(g(\alpha)\) objective function in (27) is substituted by the \(g(\alpha^{(i)})+\
abla g^{T}(\alpha^{(i)})(\alpha-\alpha^{(i)})\) function in (36), where \(\
abla g^{T}(\alpha^{(i)})\) is the partial derivative of \(g(\alpha^{(i)})\) for \(\alpha^{(i)}\). Therefore, the convex problem in (37) can be determined utilizing the standard algorithm of convex optimization theory [44], such as the interior point and sequential quadratic programming methods. In the simulation part of this article, we use sequential quadratic programming.
# ExtractEO, a Pipeline for Disaster Extent Mapping in the Context of Emergency Management
## 2 Materials and Methods
### ExtractEO Architecture  
#### 2.3.1 EOReader  
The assumption was made that all the spectral bands from optical sensors could be mapped between each other, in addition to the natural mapping between SAR bands, as shown in Figure 1. This is why SERTIT decided to decouple the sensor handling from the extraction algorithms: they should be able to ingest semantic bands (i.e., RED or VV), without worrying about how to load the specific sensor band, or determine what unit it is. This brings a lot of benefits:  
* the algorithm (and its developer) can focus on its core tasks (such as extraction), without needing to account for the sensor characteristics (how to load a band, which band corresponds to which band number, etc.),
* the addition of a new sensor is effortless and requires no algorithm modification,
* maintenance is simplified and the code quality is significantly improved,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Constellation** & **Product Type** \\ \hline COSMO-Skymed 1 st GEN & SCS, DGM, GEC, GTC \\ COSMO-Skymed 2 nd GEN & SCS \\ \hline ICEYE & SLC, GRD \\ \hline RADARSAT-2 & SLC, SGF, \\  & SGX, SCN, SCW, \\  & SCF, SCS, SSG, SPG \\ RADARSAT-Constellation Mission & SLC, GRD, \\  & GRC, GCC, GCD \\ \hline Sentinel-1 & SLC, GRD \\ \hline SAOCOM & SLC, ID, GEC, GTC \\ \hline TerraSAR-X, & \\ TanDEM-X, & SSC, MGD, GEC, EEC \\ PAZ SAR & \\ \hline \hline \end{tabular}
\end{table}
Table 2: SAR constellations handled in EEO as of June 2022.  
* testing is simplified, as the sensor-related parts are tested in the EOReader library.  
The python open source library EOReader [15] has been designed according to these previous principles, and to answer to CEMS-RM activation challenges, which are the multiplicity of sensors and CEMS products that need to be rapidly delivered. The EOReader handles both optical and SAR sensors, as well as loads and stacks spectral and radar bands, cloud bands, Digital Elevation Models (DEMs), and spectral indices in a sensor-agnostic manner. Of course, the EOReader follows software engineering best practices, such as code documentation, code reviews, python coding standards, and unit testing.
## 8 Conclusions  
In this work, we have presented a communication system based on the DASH7 Alliance Protocol by utilizing GNU Radio and Software-Defined Radios. The complete communication system works in simulation, which makes the software suitable as a learning, analysis, and validation tool for DASH7 packets. Furthermore, the demodulation system was deployed and examined in a cabled setup and in an indoor and suburban environment. The obtained data sets are open-source and can be accessed on Zenodo. The cabled setup and indoor experiments showed that the software performed sufficiently, while the suburban experiment showed severe packet loss, which was also observed while using the dedicated DASH7 gateways. This was due to the nature of field experiments and was caused by multipath effects and varying (N)LOS conditions. Nevertheless, certain optimizations can be performed to decrease the packet loss within the software.  
In future work, we envision several software optimizations. Currently, a lot of parameters are defined statically, which restricts the abilities of the software. To make the system more robust, we can implement certain DASH7-specific parameters dynamically. These parameters involve correlation with a varying length of the preamble, varying Sync Word Classes and channel classes. When taking these parameters into account, other system parameters need to be changed dynamically as well, for example, filter and gain blocks. Lastly, it is needed to implement the \(\frac{1}{2}\)-FEC technique.  
On the level of digital signal processing, we find a few shortcomings in the current implementation. Firstly, an automatic Center Frequency Offset compensation block needs to be created. Secondly, the mixing stage of the demodulator should be improved to a self-centering technique. In this way, the signal is automatically centered at baseband level, which implies that it does not matter which specific channel a signal is received on. Thirdly, a phase offset measuring step and phase compensation step need to be added while maintaining the mixing stage. Furthermore, the Normal-Rate and Hi-Rate channel classes need to be implemented and validated. Penultimately, the dynamical detection of the used Sync Word Class and coding scheme for a certain packet should be implemented.  
Finally, the implementation of loading a configuration file containing several DASH7 specification-specific parameters can make the system more dynamic and less error-prone.  
Conceptualization, N.B.; Methodology, D.J. and N.B; Software, D.J. and N.B.; Investigation, D.J.; Data curation, D.J.; Writing--original draft, D.J.; Supervision, N.B., R.B. and M.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data set presented and used in this study is openly available at [[https://doi.org/10.5281/zenodo.10961310](https://doi.org/10.5281/zenodo.10961310)]([https://doi.org/10.5281/zenodo.10961310](https://doi.org/10.5281/zenodo.10961310)) (accessed on 10 September 2024).  
The authors declare no conflicts of interest.
# A Spectral-Aware Convolutional Neural Network for Pansharpening
## 2 Backgrounds  
The goal of pansharpening is to recover HR-MBO images from observed low spatial resolution ones and the connected PAN images. It usually can be formulated as minimizing the loss function of expected square error:  
\[\ell(\mathbf{\theta})=E\|\widehat{\mathbf{H}}(\ \cdot\mathbf{\theta})-\mathbf{Y}\|_{F}^{2}, \tag{1}\]  
where \(\widehat{\mathbf{H}}\) stands for the mapping from the observed MS/HS image and the associated PAN image to the high-resolution image to be predicted, \(\mathbf{Y}\) is the ideal high-resolution image, and \(\mathbf{\theta}\) denotes the parameters connected to a parametric structure. CNNs, as specific instantiations of artificial neural networks, can be used to learn the mapping \(\widehat{\mathbf{H}}(\ \cdot\mathbf{\theta})\) in an end-to-end fashion. In CNNs, postulates such as the limited receptive field and the spatial invariant weight (so-called weight sharing) are normally suggested. The response of a convolutional layer in a CNN can be given by the following:  
\[\mathbf{Y}_{l}=\varphi(\mathbf{W}_{l}*\mathbf{X}_{l}+\mathbf{B}_{l}), \tag{2}\]  
where \(*\) denotes the convolution operation; \(\mathbf{X}_{l}\) and \(\mathbf{Y}_{l}\) are the input and output of the \(l\)th layer, respectively; \(\mathbf{W}_{l}\) and \(\mathbf{B}_{l}\) are the weight and bias matrices, respectively; and \(\varphi(\cdot)\) represents the activation function, for which the rectified linear unit (ReLU) is commonly used due to its ability to mitigate gradient vanishing and computational simplicity [42].  
Owing to the 3D data arrangement of MS/HS images, two kinds of convolution operations can be involved in CNN-based pansharpening, i.e., 2D convolution and 3D convolution. When applied along spatial dimension, 2D convolution can extract spatial information in MS/HS images. However, 2D convolution is unable to effectively exploit the potential features among bands, i.e., the spectral information encoded in neighboring bands, due to the separated synthesis for each output band. In contrast, 3D convolution is realized by convolving a 3D kernel with a data cube, which is naturally suitable for extracting both the spatial information between neighboring pixels and the spectral correlation in adjacent bands simultaneously.