esa-sceva/satcom-chunk-collection huggingface dataset:
# Combining Fuzzy C-Means Clustering with Fuzzy Rough Feature Selection
## 3 Theoretical Background
### Rough Set Attribute Reduction  
The central notion of RSAR (rough set attribute reduction) is indiscernibility. Assume there is an information system \(I=(U,A)\), where \(U\) is a non-empty finite subset of objects (the universe), \(A\) is a non-empty finite set of attributes such as for each \(a\in A,U\to V_{a}\), where \(V_{a}\) is the value set for attribute \(a\). \(A=\{C\cup D\}\) where \(C\) is the set of condition attributes and \(D\) is the set of decision attributes.  
Definition 1 (Indiscernibility): For any \(P\subseteq A\), there is an associated equivalence relation \(IND(P):\ IND(P)=\{(x.y)\in U^{2}|\forall a\in P\ a(x)=a(y)\}\), if \((x,y)\in IND(P)\), then \(x\) and \(y\) are indiscernible from attributes of \(P\). The equivalence classes of the \(P\)-indiscernibility relation are denoted as \(\left[x\right]_{P}\).  
Definition 2 (Lower Approximation): The lower approximation of \(P\) is defined as \(\underline{P}X=\{x|\left[x\right]_{P}\sqsubseteq X\}\), \(X\sqsubseteq U\).  
Definition 3 (Positive Region): \(P\) and \(Q\) are equivalence relations over \(U\), the positive region is defined as \(POS_{P}(Q)=\bigcup_{X\in U/Q}\underline{P}X\). The positive region contains the objects of \(U\) which can be divided into \(U/Q\) class, using the knowledge of attribute \(P\).  
Definition 4 (Dependency): An important work of data analysis is finding the dependency of attributes. If all values of a set of attributes \(Q\) depend uniquely on another set of attributes \(P\), or there is a functional dependency between values of \(Q\) and \(P\), then \(Q\) depends totally on \(P\), denoted \(P\Rightarrow Q\). Dependency can be defined as: for \(P,Q\subset A\), \(Q\) depends on \(P\) with degree \(k\)\((0\leq k\leq 1)\), defined \(P\Rightarrow{}_{k}Q\), \(k=\gamma P(Q)=\frac{|POS_{P}(\underline{Q})|}{|U|}\), if \(k=1\), \(Q\) depends totally on \(P\). If \(k<1\), \(Q\) depends partially on \(P\) with degree \(k\). If \(k=0\), \(Q\) does not depend on \(P\).  
A basic idea is to calculate the dependencies of all the possible subsets of \(C\), any subset with \(\gamma(D)=1\) is a reduct. The smallest subset is the minimum reduct. However, this idea is impossible for large datasets. Algorithm 1 is called QUICKREDUCT Algorithm [20]; we do not need to calculate all the possible subsets. Start with an empty set and add attributes to the set in proper order to obtain the maximum increase of dependency until we get the maximum possible number (usually equal to 1). We need to pay attention to that the algorithm may not get the minimum set of reduct necessarily every time. In the worst situation, the complexity reaches \(n!\) for attribute dimensionality of \(n\).
## 1 Introduction
variations in ambient temperature, as this aspect had not been taken into consideration in the designs [38; 39]. This research aims to address this gap in the existing literature by studying the thermal properties of a green swale, so that an energy-efficient design of combined SuDS-SGS systems can be achieved in the future for these \"green\" SuDS. With this aim, various materials with different configurations and operating conditions (dry and wet scenarios) were evaluated to propose a new methodology to achieve an optimal design of SuDS from an energy utilization perspective. This study followed an experimental procedure used in previous research that examined the thermal properties of a blue roof to improve a building's thermal envelope [40]. Additionally, the thermal properties of materials comprising different types of green swale sections were determined. A non-destructive test was employed using the modified transient plane source (MTPS) method, also previously used by other researchers to study the thermal properties of materials used in geothermal systems [41; 42]. Furthermore, numerical models of the studied green swales were created using a hybrid numerical-experimental methodology, as employed by other authors for studying the thermal behavior of construction systems [43]. These numerical models aim to further investigate the heat transmission mechanisms when combining an SGS with a dry swale. The specific objectives to achieve this goal are described below. * Characterization of the thermal properties of the materials used in the green swale cross-sections. * Identification and determination of the effect of the use of non-conventional materials such as expanded clay and construction and demolition waste (CDW) on the thermal behavior of a SuDS. * Laboratory determination of the key thermal parameters of the different layers of the green swale sections by means of standardized tests, and analyzing their thermal behavior and their implications in a system combined with surface geothermal energy in dry and wet operating conditions. * Validation of the results obtained in the laboratory tests by means of steady-state numerical simulations, Design of Experiments (DOE), and the use of Multi-Objective Genetic Algorithms (MOGAs). * Simulation of the behavior of green swales in real operating conditions, using transient state numerical models.
## 3 Results and Discussion
### Measurement of the Reactance Measurement with PoC Device  
This chosen frequency was considered to be used in the microcontroller-based system for real-time applications. Figure 11 shows the measurement of different concentrations of calcium for the PoC device during wavelengths of 440-470 and the results are distinguishable.  
One of the frequencies, 450 Hz, among this range depicting the highest change with respect to the reactance values was chosen to develop the standard curve for the experimented calcium samples. Figure 12 shows the response of the sensor at the frequency of 450 Hz for the five samples.  
Figure 10: The response of the developed sensors in terms of reactance versus frequency to different calcium solutions by the LCR meter.  
The relationship between the concentration and reactance is  
\[\mathrm{y}=-1.3812\mathrm{Lgx}+0.9809 \tag{4}\]  
with the R\({}^{2}\) being 0.9719. The relatively linear relationship allows for the equation to be used by us for real-time data collection as shown in Figure 8. Figure 13 illustrates the repeatability of the developed system. It can be seen that the results were consistent and the system can be used for calcium detection.  
Figure 11: The measurement of different concentrations of calcium from the PoC device (reactance versus frequency).  
Figure 12: The calibration standard of the PoC device at a frequency of 450 Hz.
# Go Wider: An Efficient Neural Network for Point Cloud Analysis via Group Convolutions
## 3 Model Structure
### Local Feature Representation  
A trending way of representing point cloud data is to assume them as graph-like structures with nodes and edges [12], where each node and the corresponding edges on a graph are naturally defined by the data points. As a result, converting a point cloud to a directed acyclic graph and applying neural networks on the graph structure is an efficient way of learning the embedded information for the neighbourhood of each node.  
Hence, we construct a directed acyclic graph \(G=(V,E)\) for a specific point set (see Figure 2), where \(V\subseteq\ \mathbb{R}^{F}\) is the node corresponding to each point of the point cloud, and \(E\) indicates the edges connecting the central point to its neighbourhood. As shown in Figure 2, assuming that the input of a point set can be represented by an uniform distribution, we choose a \(k\)-nearest neighbour (\(k\)-NN) search to explore the neighbourhood of each point, as it can guarantee a fixed number of neighbours. It is important to stress that, for selecting the neighbouring nodes, the geometrical Euclidean distance between the central point and the neighbours is used. We introduce \(K\) as one hyper-parameter of our model, which is the number of elements in the neighbourhood of a central point. Once a neighbourhood is defined, we can also introduce an extra feature linked to the graph-like structure, which is the directed edge connecting the central point with the j-th neighbour as \(\mathbf{e}_{ij}=(\mathbf{x}_{i}-\mathbf{x}_{ij})\), where \(\mathbf{x_{i}}\in V\) are the i-th central point features and \(\mathbf{x_{ij}}\) are the j-th neighbouring point features with respect to the i-th central point \(\mathbf{x_{i}}\). Hence, we can aggregate this new set of features into an augmented feature vector by simple concatenation: \(\mathbf{\hat{x}}_{ij}=\big{[}\mathbf{x}_{i},\mathbf{e}_{ij}\big{]}^{\!\!\!T}\), having a feature dimension \(\mathbf{\hat{x}}_{ij}\subseteq\ \mathbb{R}^{2F}\). For simplicity, in the rest of the paper, we are going to keep referring to \(F\) as the number of features, but the reader should keep in mind that \(F\) has twice the dimensions when taking into consideration the local feature and using an augmented feature space.
## 3 Results and Discussion
### Molecular Imprinting Stir Bar Sportive Extraction Conditions Optimization
#### 3.4.3 Influence of Stirring Speed and PH  
In order to shorten the time required to reach adsorption equilibrium, the molecular mass transfer rate can be accelerated through stirring. This experiment investigated the effect of stirring speed on the adsorption efficiency of BPA within the range of 500-900 rpm. The results are shown in Figure 8. The target analyte BPA in this experiment contains phenolic hydroxyl groups, and the pH of the solution can affect its chemical form, thereby impacting the adsorption efficiency of the molecularly imprinted coating towards the target analyte. Therefore, this experiment examined the effect of solution pH on the adsorption efficiency of BPA within the range of 4-8. The results are shown in Figure 9. It can be observed that regarding the adsorption efficiency of the target analyte BPA, the effect of agitation speed on the adsorption efficiency is not significant. The situation is similar with regard to PH, so PH has little effect on the adsorption effect.  
Figure 8: The effect of stirring speed on adsorption efficiency.  
Figure 9: The effect of adsorption pH on adsorption efficiency.
###### Abstract  
The Information-Centric Network (ICN), designed for efficient content acquisition and distribution, is a promising candidate architecture for the future Internet. In-network caching in ICN makes it possible to reuse contents and the Name Resolution System (NRS) makes cached contents better serve users. In this paper, we focused on the ICN caching scenario equipped with an NRS, which records the positions of contents cached in ICN. We propose a Popularity-based caching strategy with Number-of-Copies Control (PB-NCC) in this paper. PB-NCC is proposed to solve the problems of unreasonable content distribution and frequent cache replacement in traditional caching strategies in ICN. We examine PB-NCC with a large number of experiments in different topologies and workloads. The simulation results reveal that PB-NCC can improve the cache hit ratio by at least 8.85% and reduce the server load by at least 11.34% compared with other on-path caching strategies, meanwhile maintaining a low network latency.  
information centric network; caching strategy; popularity-based; name resolution Article
## 4 Stability Analysis of GCI Controlled by Different Domains
### Impact of PLL Bandwidth \(BW_{\text{PLL}}\)  
To investigate the impact of PLL bandwidth \(BW_{\text{PLL}}\) on the stability of grid-connected inverters under various domain controls, the DC voltage loop control bandwidth \(BW_{\text{v}}=30\) Hz, current loop control bandwidth \(BW_{\text{ic}}=600\) Hz, and grid side inductance \(L_{\text{g}}=4\) mH are specified. The Nyquist diagrams depicting changes in the PLL bandwidth \(BW_{\text{PLL}}\) are displayed in Figures 6 and 7.  
Figure 5: Nyquist diagram when Lg changes without considering voltage feedforward control. (**a**) PI control. (**b**) PR control.  
Figure 6: Nyquist diagram with \(BW_{\text{PLL}}\) changes when voltage feedforward control is considered. (**a**) PI control. (**b**) PR control.  
In Figure 6a, the Nyquist diagrams for the positive sequence impedance ratio \(ZR_{\text{p}}\)(s) and negative sequence impedance ratio \(ZR_{\text{n}}\)(s) do not encircle the point \((-1,\,\text{j0})\) when the PLL bandwidth \(BW_{\text{PLL}}\) is set at 40 Hz, 90 Hz, and 130 Hz, confirming system stability. However, as the PLL bandwidth increases to 180 Hz, the Nyquist diagram for the positive sequence impedance ratio \(ZR_{\text{p}}\)(s) encircles the point \((-1,\,\text{j0})\), indicating potential system instability.  
In Figure 6b, the Nyquist diagrams for the positive sequence impedance ratio \(ZR_{\text{p}}\)(s) and the negative sequence impedance ratio \(ZR_{\text{n}}\)(s) do not encircle the point \((-1,\,\text{j0})\) at PLL bandwidths \(BW_{\text{PLL}}\) of 40 Hz, 90 Hz, 130 Hz, and 180 Hz, indicating that the system remains stable.  
In Figure 7a,b, when the PLL bandwidth \(BW_{\text{PLL}}\) is 40 Hz, 90 Hz, 130 Hz, and 180 Hz, the Nyquist diagrams for \(ZR_{\text{p}}\)(s) and \(ZR_{\text{n}}\)(s) do not encircle the point \((-1,\,\text{j0})\). This suggests that the system is stable.  
Figures 6 and 7 demonstrate that, without implementing voltage feedforward control, the stability of the grid-connected inverters under both control modes declines as the PLL bandwidth increases. However, the stability of the time-domain PR-controlled GCI is marginally better than that of the \(dq\)-domain PI-controlled GCI, though their performances are nearly equivalent.  
In addition, when voltage feedforward control is introduced, the stability of grid-connected inverters using both time-domain PR control and \(dq\)-domain PI control is reduced, particularly at high PLL bandwidths, which may lead to instability.
# Real-Time Anomaly Detection in Cold Chain Transportation Using IoT Technology
## 3 Materials and Methods  
In order to minimise the loss of perishable food products during transportation, a Real-Time Anomaly Detection (RTAD) system is proposed to automatically monitor real-time environmental parameters, such as temperature and relative humidity. Additionally, this paper aims to demonstrate that a wireless network system based on LTE-M communication can operate properly under the conditions of flexibility, signal loss resistance and power autonomy enough to be used in the cold supply chain. The designed system should be able to communicate in real time the measured variables with a power consumption capable of providing maximum battery autonomy. In this sense, a trip detection algorithm was also created to reduce the power consumption of the IoT devices under analysis.  
The architecture of the RTAD system follows the standard 4-layer approach often employed by IoT systems. These are (1) the perception layer, (2) the network layer, (3) the processing layer, and (4) the application layer.  
1. The perception layer, sometimes referred to as the sensing or data acquisition layer, is responsible for collecting data from the environment based on the sensor technologies employed in the sensor setup.  
2. The network layer, sometimes called the communication layer, is responsible for receiving the obtained data and transferring it to the cloud storage service via a gateway. The gateway connects to the network layer using internet connection.  
3. The processing layer, sometimes called the storage and control layer, consists of data storage and analytics. This layer is responsible for the retrieval of the data from the network layer, transforming it, and storing it accordingly. Additionally, this layer handles the processing of the data for alerting and intelligent analytics.  
4. The application layer provides a front end in the form of an online dashboard to allow viewing of the data in real time. The application layer also consists of an alerting system, which operates as an SMS/email service to the user's mobile phone. The final component of the application layer is business reports which are prepared by the analytics team to give further insights into the recorded data.  
Figure 1 provides a visualisation of the architecture employed for the IoT cold chain anomaly alerting system based on these 4 layers. The following sections provide more detail as to the components in each layer.
# Nanosheets of CuCo\({}_{2}\)O\({}_{4}\) As a High-Performance Electrocatalyst in Urea Oxidation
## 3 Results and Discussion
### Electrocatalytic Activities
As observed, the polarization curves were overlapping each other, indicating the little impact that urea had on the electrochemical activity of the CuCo\({}_{2}\)O\({}_{4}\) electrode in the HER region. The CuCo\({}_{2}\)O\({}_{4}\) electrode required a potential of 168 mV to generate 10 mA/cm\({}^{2}\). The Tafel slope for the CuCo\({}_{2}\)O\({}_{4}\) electrode in 1 M KOH without and with urea was observed to be 113 and 112 mV/dec, respectively, suggesting no change in the kinetics for the HER process after addition of urea (Figure 6b). [PERSON] et al. have reported a Tafel slope of 172 and 180 mV/dec for Ni\({}_{3}\)N and Ni(OH)\({}_{2}\) nanosheets on carbon cloth in 1 M KOH + 0.33 M urea solution [11]. The efficiency (n) for the urea electrolysis can be estimated by the following expression [14]:  
\[\eta=\frac{i_{t}-i_{OER}}{i_{t}}\times 100\% \tag{2}\]  
where \(i_{t}\) is the overall current density during electrolysis of urea (1 M KOH + 0.33 M urea) and the \(i_{OER}\) is the current density of OER process in 1 M KOH at 250 s (Figure S4). An efficiency of 95% was observed for the CuCo\({}_{2}\)O\({}_{4}\) electrode. Figure 6: **(a)** Linear sweep voltammetry (LSV) curves of the CuCo\({}_{2}\)O\({}_{4}\) electrode toward HER in 1.0 M KOH with 0.33 M urea, and **(b)** corresponding Tafel plots. The electrocatalytic activities of the CuCo\({}_{2}\)O\({}_{4}\) electrode were further examined in a two-electrode electrolyzer configuration. The CuCo\({}_{2}\)O\({}_{4}\) electrode was used as the anode for the UOR and the cathode for the HER process in alkaline media. Figure 7a shows polarization curves of the two-electrode electrolyzer in 1 M KOH, without and with 0.33 M urea. The electrolyzer required a cell voltage of 1.45 V to deliver a current density of 10 mA/cm\({}^{2}\) in urea solution, which was 260 mV less than the cell voltage required without urea in the alkaline solution. The observed cell voltage was better than many other reported systems, such as Ni(OH)\({}_{2}\)-Ni(OH)\({}_{2}\) and Pt/C-RuO\({}_{2}\)[11]. In addition, it had a performance in urea solution, and the CuCo\({}_{2}\)O\({}_{4}\) electrode showed outstanding electrochemical durability (Figure 7b). The chronopotentiometry test, performed for porous pomegranate-like Ni/C in the urea oxidation reaction made by [PERSON] et al., had a stable performance over 12 h [30].
# HSVNet: Reconstructing HDR Image from a Single Exposure LDR Image with CNN
## 3 Method  
We propose a method to generate an HDR image from an LDR image with a deep learning architecture. Our proposed method first (1) pr3 processes the data by converting color space into HSV, normalizing \(H\), and a gamma correction on \(V\) channel of ground truth for the normalized range in \([0,1]\), (2) creates a mask for each input LDR image, (3) learns the U-net based network, HSVNet, to generate an HDR image by uncovering the relationship between \(H\), \(S\), and \(V\) color components, and (4) postprocesses the prediction to recover the original range of \(V\) with an inverse gamma correction(\(f^{-1}\)) on the \(V\) channel,  
\[H_{i,v}=f^{-1}(\hat{y}_{i,v}) \tag{1}\]  
where \(H\) is the reconstructed HDR, \(i\) is the index of image, \(v\) is \(V\) channel, and \(\hat{y}\) is the network output. The overall process of our framework is shown in Figure 1. First, we convert the input and the corresponding ground truth image to HSV color. Then, we normalize all values which are out of the range \([0,1]\) and create a mask of the normalized input. We transfer the normalized input, mask, and ground truth to our network. The mask is used as a weight in a loss function. Finally, we map the values of the estimated image to the original range of the ground truth to obtain a genuine HDR predicted image.
## 1 Motivation  
**Ventilated lung parenchyma is well known as a total acoustic absorber** **[**1**]** **that impairs intra-pulmonary sonography of consolidations, lung tumors, and central lymph nodes, vessels, and bronchi. Despite these limitations, lung ultrasound (LUS) is widely used in clinical praxis for the exclusion of pneumothoraxes** **[**2**]****, monitoring of extravascular water content** **[**3**]****, or diagnosis of malignancies in contact with pleural cavity** **[**4**]****. Sonography has shown a high precision comparable to CT for detection and characterization of lung nodules in contact with chest wall** **[**5**]****. These applications reflect acoustic changes of the pleural cavity and their direct adjacent lung parenchyma. Approaches use shear wave elastography showed feasibility of nodule detection in pleural proximity** **[**6**]****. However, their sonographic morphology and location to sensitive structures such as vascularity remains unknown.**  
**Unlike with other organs, the gas content of lung parenchyma can be manipulated and therefore acoustic access is generated. Adelectatic lung can achieve sonographic access, but the resulting random collapse is associated with loss of anatomic orientation.**  
**In contrast, instillation of liquids keeping the lung expanded overcomes this problem. Such liquid administration using PerFluoroCarbon (PFC)** **[**7**]** **or saline** **[**8**]** **are used for diagnostic or therapeutic purposes in pneumology for decades. The fundamentals of lung filling for the use of LUS regarding their sonographic findings have never been summarized and classified. These liquids have different physical and physiological properties, which impacts upon ultrasound imaging and clinical applicability. Therefore, this work was motivated to review the literature regarding lung filling methods and the sonographic findings. Furthermore, the future impact on clinical applications and need for further research will be discussed.
# Privacy and Security in Federated Learning: A Survey
## 3 Security in Federated Learning
### Common Attacks in FL
#### 3.2.1 Poisoning Attacks  
A Poisoning Attack is one of the most common techniques used in the FL. This type of attack can be of a different nature, but the principle remains essentially the same. A malicious client will send rigged data to affect the global model [22; 25]. Such an attack can be conducted by two means:  
* Data poisoning: The aim is to incorporate malicious data points to create bias in the global model. To be undetectable, attackers slightly modify the model parameters they send but repeat this operation over several training iterations or rounds. This makes it difficult to determine whether a local parameter set is poisoned or not. With several model updates of such a corrupted client, the global model becomes biased enough to degrade its task performance [26].
* Model poisoning: This type of poisoning is more direct; it seeks to manipulate the global model without going through the insertion of malicious data points. It is generally permitted thanks to an intrusion at the server level.