esa-sceva/satcom-chunk-collection huggingface dataset:
# A Self-Color-Changing Film with Periodic Nanostructure for Anti-Counterfeit Application
## 3 Results and Discussion
### Validation of the Results  
To confirm the results, we compare our results with the experimental results of some related studies. From the results, we discover that the variables affecting the frequency selectivity of the film are W and L of the nanocube. The horizontal area of a cube obtained by the product of W and L is, therefore, a significant parameter that can influence the film's optical properties. In 2014, [PERSON] et al. [41] presented a prototype of nanoparticles that were made of aluminum. The results of [PERSON] et al.'s experiment also showed a significant relationship between the resonant frequency and the horizontal cross-sectional area of nanoparticles, as shown in Figure 10.  
Finding the resonant frequency is challenging when a low-pass filter characterizes our film. Because of each adjustment, the resonant frequency changes with an accompanying low-pass filter behavior. Furthermore, it is difficult to locate the resonant frequency because the characteristic does not have a prominent convex character. However, our nanocube size is approximately the same as that which was previously studied by [PERSON] et al., and they found that at resonant frequency, the transmittance was in the range of 10% to 20%. In such a range, we find that the resonant wavelength of our best nanocube size is 500 nm,  
Figure 10: Comparison of our simulation results and the experimental results from [PERSON] et al.  
Figure 9: The transmittance and the reflectance as a function of the angle of incidence.  
which is consistent with the trend of the relationship between the resonant frequency (or wavelength (\(\lambda\)) [nm]) and the horizontal area [nm\({}^{2}\)] as:  
\[Area=0.3493\lambda^{2}-258.8\lambda+57890 \tag{1}\]  
Equation (1) is derived from [PERSON] et al.'s experiment, which showed that particles of different sizes had different resonant frequencies. In our case, the horizontal area is W \(\times\) L = 110 nm \(\times\) 140 nm = 15,400 nm\({}^{2}\) and the resonant wavelength is 500 nm. Such findings show good consistency when plotted against Equation (1), shown as the red dot in Figure 10. However, [PERSON] et al.'s prototype was not immediately suitable for security film, as their nanoparticle size was not the optimum dimension. Nevertheless, the optimum values of W and H can be obtained from our studies.  
The next study is to transform the transmittance to color, in accordance with the Commission International d'Eclairage: CIE standard, which defines three primary colors standardized at the wavelengths of 700 nm (red), 546.1 nm (green), and 435.8 nm (blue), respectively [49]. The CIE chromaticity diagram is a diagram that shows all the colors that humans can perceive. It helps to pinpoint the location of color that is seen by the human eye.
# Continuous-Variable Quantum Key Distribution Robust Against Polarization-Dependent Loss
## 4 Polarization-Pairwise Coding Scheme
When the PDL is present, intuitively, after receiver I/Q deinterleaving, the \(\triangle\)SNR between two polarizations is converted into the SNR imbalance between the I and Q components of two polarizations. Then, the angular rotation can maximize the system performance and the optimal angle is \(45^{\circ}\), which is derived on the basis of the minimum-error-rate search. The trade-off for practical implementations has been considered in pertinent literature [26]. In this scheme, the total SNR is given by Appendix B and the average symbol power of QPSK modulation is 2. Then, the \(\triangle\)SNR between two polarizations is derived as \(\triangle\)SNR\({}_{dB}=|10\log_{10}(\frac{1+\gamma^{\prime}}{1-\gamma^{\prime}})|\), where \(\gamma^{\prime}\) is the power factor with \(\gamma^{\prime}\in(-1,1)\), as shown in Figure 6. Practically, \(\triangle\)SNR has a random distribution [34]. With the total PDL value \(L\), the lowest and highest possible \(\triangle\)SNRs are 0 and \(L\), corresponding to the best and worst PDLs, respectively. In the following, we focus on the effect of \(\triangle\)SNR on system performance. The performance improvement with \(\triangle\)SNR \(>\) 0 can be regarded as an increased tolerance of PDL. At the receiver, we implement the signal processing, which includes correction, clock recovery, channel compensation, and carrier recovery. The equalized pairwise symbols \(Eq_{\pi}/Eq_{y}\) are decoded, as shown in Figure 4b. More details are described in Appendix A.  
Figure 6: Signal-to-noise ratio (SNR) difference between two polarizations due to PDL.
## 3 Results and Discussion
### Establishment of Collision Impact Force Model and Regression Analysis
#### 3.3.2 Average Contact Stress and Contact Area of the Seedlings  
As shown in Figure 10, as the drop height increased, the contact areas were linearly related between different contact materials, and the coefficients of determination (\(R^{2}\)) were all greater than 0.95. When the drop height was constant, the collision contact area between the oil sunflower plug seedlings and the contact materials ranged from large to small: soil, steel, and ABS plastic. The soil was compacted before the test in the test soil bin, and there were still many pores in the soil. During the test, when the oil sunflower seedlings collided with the soil, the contact area was higher, and the matrix loss of the oil sunflower seedlings and the deformation of the pot were reduced. When the plug seedlings collided with the steel plate, the plastic deformation of the steel plate was small, and the deformation was  
Figure 8: Contact stress distribution of oil sunflower seedlings of three contact materials under different drop heights. (**a**) Steel; (**b**) soil; (**c**) ABS plastic. The experiment was carried out in the Agricultural Biological Environment and Energy Engineering Laboratory, College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, on 7 August 2022.  
Figure 9: Typical stress distribution of oil sunflower plug seedlings of different contact materials under different drop heights.  
mainly concentrated on the pot, which increased the pot damage and matrix loss. When the oil sunflower seedlings collided with the ABS plastic, the contact area was small, and the plastic had a certain elasticity, which buffered the collision and deformation of the seedlings. The variation mechanisms of the contact area of oilseed sunflower plug seedlings on different contact materials were different, indicating that there was a certain relationship between the contact area and pot damage.  
As shown in Figure 11, the average contact stress between the plug seedlings and the steel plate was relatively large, ranging from 19.4 to 22.8 kPa, and the average contact stress tended to increase significantly with increasing drop height. The contact stress between the seedlings and the soil was in the middle level, ranging from 19 to 22 kPa, and the average contact stress fluctuated slightly. The average contact stress of the seedlings on the ABS plastic was significantly smaller than that on the steel plate and the soil. With increasing drop height, there was an obvious increasing trend, and the average stress range was 18.8-21 kPa. The variation mechanisms of the average contact stress of oil sunflower plug seedlings on different contact materials were different, indicating that there was a certain relationship between the average contact stress and pot damage.
# Whitening CNN-Based Rotor System Fault Diagnosis Model Features
## 2 CNNs and Normalization Layers  
Convolutional neural networks (CNNs) typically consist of convolutional layers, pooling layers, normalisation layers and a few fully connected layers. In vibration-based fault classification, the final fully-connected layer can be considered as the classifier in the fault recognition step. The other fully-connected layers, convolutional layers, normalisation, and pooling layers are used for the feature extraction step.
## 2 Study Area and Materials
### Study Area  
The study area is located in the Chi River Basin (NE-Thailand) and is characterized by having agriculture cultivations for almost % of the land use. In particular, the dominant crops are: rice, sugarcane, para rubber and cassava [28]. The Chi River Basin covers an area of 49,132 km\({}^{2}\), accounting for 29% of this region (between 15\({}^{\circ}\)10\({}^{\prime}\) to 17\({}^{\circ}\)50\({}^{\prime}\)N and 101\({}^{\circ}\)00\({}^{\prime}\) to 105\({}^{\circ}\)50\({}^{\prime}\)E) (Figure 2). The Chi River Basin is characterized by having plains and highlands topographical features, and the height above mean sea level differs from 0 to 1324 m. In particular, the central and western parts of this region are part of the Dong Phayayen Mountains, a place known as the starting point for the Chi river and several tributaries such as Nam Phong, Lam Pao and Nam Yang. All of these tributaries are very important for local agricultural cultivations and livelihoods since they represent the water sources of the area. From a climatic standpoint, the study region is classified as tropical semi-humid wet/dry-savanna (Koppen climate, classification: Aw), characterized by high cloudiness and inclement weather, as well as three different seasons of summer, rainy, and winter, ranging from February to May, May to October, and October to February, respectively. Finally, considering temperature, the long-term temperature (average 10 years) is about 27 \({}^{\circ}\)C and the average annual precipitations are about 1690 mm. The relative humidity in this region is approximately 72% [29].  
Figure 1: The implemented workflow for monitoring crop types/Land cover (LC) changes in the Chi River Basin, Thailand from 2001–2021 (over 20 years) using multi-temporal Landsat image datasets together with the random forest (RF) classifier based on Google Earth Engine (GEE).
# An Effective Hybrid Routing Algorithm in WSN:
## System Model
### Energy Consumption Model  
In WSNs, a node's energy consumed for data communication is much higher than other energy consumption, such as data sensing and data processing. That is to say, the major portion of a node's energy is consumed for data communication including data transmission and data receiving. So, our research mainly focuses on the energy dissipation for data communication. We use the model discussed in [PERSON] and [PERSON] [28] for the radio hardware energy dissipation, where the transmitter dissipates energy to run the radio electronics and the power amplifier, and the receiver dissipates energy to run the radio electronics. Thus, to transmit a k-bit data packet to the receiver at distance \(d\), it takes the energy cost as shown in the following Formula (1):  
\[E_{T\gamma}(k,d)=E_{T\gamma-elec}(i,j)+E_{T\gamma-amp}(k,d)=\begin{cases}kE_{ elec}+k\epsilon_{fg}d^{2},&d<d_{0}\\ kE_{elec}+k\epsilon_{mp}d^{4},&d\geq d_{0}\end{cases} \tag{1}\]  
The energy consumption for data transmission consists of two parts: the electronics energy consumption, denoted as \(E_{T\gamma-elec}\) and the power amplifier energy consumption, denoted as \(E_{T\gamma-amp}\). The power amplifier energy consumption is related to transmission distance and the environment, divided into the free space model (\(d^{2}\) power loss) and multipath fading (\(d^{4}\) power loss) channel models, depending on the distance between the transmitter and receiver. Power control can be used to invert this loss by appropriately setting the power amplifier--if the distance is less than a threshold \(d_{0}\), the free space model is used; otherwise, the multipath model is used.  
On another hand, to receive these data, the cost is:  
\[E_{Re}(k)=E_{Re-elec}(k)=kE_{elec} \tag{2}\]  
The Formula (2) contains only the electronics energy consumption. \(E_{elec}\) is the energy dissipation of per bit for transmitter or receiver. Based on Formulas (1) and (2), energy consumed for data transmission is higher than that of data reception. The energy consumption for data transmission relies mainly on the distance squared whereby the farther between the sender and the receiver, the higher the energy is consumed to transmit the data.
## 4 Experiment and Results
### Experiments Settings  
This section focuses on explaining the experiments that were performed to implement this research. First, this section presents the tools and devices used to implement the proposed approach and model. It then provides a conceptual description of each scenario and experiment that has been used to prove our claims. The details of these experiments are presented in the next sections in more detail, along with each experiment's results.  
This research was carried out using a Lenovo desktop device using anaconda, Jupiter notebook, and network, and the installed Python programming language, explicitly, version 3.8. It also requires configuring and preparing the environment from anaconda and installation of the libraries in this environment.  
The primary purpose of the second part of this section is to provide a general description of the experiments carried out in this research, which consists of three groups of experiments. Firstly, extracting the topics of interest for each user and clustering interesting topics into different groups. Secondly, finding the relationships, similarities and differences between users along with the semantic similarities between topics. Thirdly, representing both users and interesting topics for each user in an interactive, heterogeneous graph.  
The primary objective of the first group of experiments was to create a topics model representing each user's interest topics. The first experiment applied LDA topic modelling to produce interest topics. The second experiment was based on clustering interesting topics into different groups.  
The main aim of the second set of experiments was to find similar users based on the proposed approach. The similarity between users depended on three essential factors: interesting topics, big five personality traits [48], and demographic data. These experiments calculated the similarity between users based on each factor separately, while the last experiment calculated similarity based on the three factors. The second investigation aimed to find semantic similarities between the topics.  
The objective of the third set of experiments was to build a heterogeneous graph with two types of node: users and topics. These experiments created a user graph and a topic graph, and combined them to create a user-topic graph. Additional functionalities were then added to ensure that the visualisations are interactive.
## 2 Background
### IEEE 802.15.4/ZigBee Cluster-Tree Topologies
The coordinator can allocate up to seven GTSs, and a GTS is allowed to occupy more than one slot period [5]. In the cluster-tree topology, a coordinator node (excluded the PAN coordinator) must keep the synchronisation between its own active period (acting as coordinator node) and the parent's active period (acting as child node). Also, for each cluster, low duty cycles can be activated to save energy, setting a value of SO smaller than BO. In cluster-tree networks, sending beacon frames without any special care on timing issues may result in collisions among beacons from neighbour clusters [7]. Thus, it is necessary to implement inter-cluster synchronisation mechanisms to avoid this problem. Two types of beacon collisions are possible: direct or indirect. In a direct beacon collision, two or more coordinators (in the transmission range of each other) transmit their beacon frames at the same time. In the indirect beacon frame collision, two or more coordinators are hidden-nodes from each other and send messages to an overlapped node [7]. This paper is focused on cluster-tree networks defined by the ZigBee specification [6], due to some specific features, such as: suitability to deploy wide-scale networks, energy-efficiency and time-sensitive message guarantees.
## Methodology
### Batch Adsorption Experiments  
Batch adsorption was used to determine the distribution coefficient and adsorption efficiency of the H-TTAPA resin for metal ions in natural water. H-TTAPA (1 g) was placed in a corked conical glass flask containing 10 mL (corresponding to 1 mg of metal ions) of 1000 ppm metal ion solution, and a known volume of buffer solution was then added (40 mL). For an acidic pH, sodium-acetate-acetic-acid buffer solution was used, and for a basic pH, ammonium hydroxide and ammonium chloride buffer solution were used. The solution was continuously stirred with a magnetic stirrer for 4 h at 25 \({}^{\circ}\)C. The two phases were separated using Whatman filter paper no. 42.  
AAS was used to determine the concentration of the ions in the solution before and after absorption. Air acetylene was selected as the gas to generate the flame, and the metal ions were measured in the column at various wavelengths along the main resonance line: Cu(II) at 324.7 nm, Fe(II) at 248.3 nm, Zn(II) at 213.9, Pb(II) at 283.2 nm, and Cd(II) at 228.8 nm. A series of standards for the metal ions were prepared to obtain the calibration curves, with which unknown concentrations of metal ions were determined.  
The removal efficiency (%) was determined using Equation (3), and the distribution coefficient (Kd) of the H-TTAPA resin was calculated using Equation (4) [44; 45; 46; 47; 48]:  
\[\text{Removal~{}efficiency}~{}(\%)=\bigg{(}\frac{C_{o}-C_{e}}{C_{o}}\bigg{)}100 \tag{3}\]  
Distribution coefficient \(=\) Adsorption capacity/initial concentration\(\times\)removal rate (4)  
where \(C_{o}\) (mg/L) is the initial concentration, \(C_{e}\) (mg/L) is the equilibrium concentration, and \(V\) (L) is the volume of the dye solution.
## 4 Proposed Framework
### Handling Emergency Access
\begin{table}
\begin{tabular}{|l|l|l|l|l|l|l|l|l|l|l|l|} \hline  & **Computation** & **Step** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** \\ \hline \multirow{6}{*}{**DID**} & Hash & 0,0,1 & & 1,1,1 & 0,1,0 & 0,1,0 & 1,0,0 & 0,1,0 & 0,0,1 & & \\ \cline{2-11}  & Signature or Private Key Encryption & 0,0,1 & 0,1,1 & & 0,1,0 & & 1,1,0 & 0,0,1 & & & \\ \cline{2-11}  & Signature Verification or Public key Encryption & & & 1,0,0 & & & & & & & \\ \cline{2-11}  & Symmetric Key Encryption/ Decryption & & & & & & & & & \\ \cline{2-11}  & Key Generation & 1,1,0 & & & & & & & & & \\ \cline{2-11}  & Random Number Generation & 1,1,0 & & & & & & & & & \\ \hline \multirow{6}{*}{**DID**} & Hash & 1,0,1 & 1,1,1 & 1,1,1 & 0,0,1 & & & & & & \\ \cline{2-11}  & Signature or Private Key Encryption & & 1,0,0 & 0,1,1 & & 0,0,1 & & & & & 0,1,0 \\ \cline{2-11}  & Signature Verification or Public key Encryption & 1,0,1 & 0,0,1 & & 0,0,n & & & & & 0,1,0 & \\ \cline{2-11}  & Symmetric Key Encryption/ Decryption & & & & 0,0,1 & & & & & & \\ \cline{2-11}  & Key Generation & & & & & & & & & & \\ \cline{2-11}  & Random Number Generation & & 1,1,0 & & & & & & & & \\ \hline \multirow{6}{*}{**DID**} & Hash & & & 1,0,0 & 1,0,0 & 1,1,0 & 0,1,1 & 0,0,1 & 0,1,1 & & \\ \cline{2-11}  & Signature or Private Key Encryption & & & & & & & & & & 0,1,1 & \\ \cline{1-1} \cline{2-11}  & Signature Verification or Public key Encryption & & & & 1,0,0 & 1,0,0 & 1,1 & 0,1,1 & 0,0,1 & & 0,1,0 \\ \cline{1-1} \cline{2-11}  & Symmetric Key Encryption/ Decryption & & & & & & & & & & 0,1,1 & \\ \cline{1-1} \cline{2-11}  & Key Generation & & & & & & & & & & & \\ \cline{1-1} \cline{2-11}  & Random Number Generation & & & & & & 1,0,0 & 0,1,0 & 0,1 & & & \\ \hline \end{tabular}
\end{table} 
Table 12: Computations performed in the three proposed architectures (Indy, Indy + Fabric and Indy + Fabric + IPFS). Of course, hospitals should know the contact of the trustees of [PERSON]. For this reason, [PERSON] needs to register them somewhere (e.g., hospital information systems) so that they will be found and contacted quickly. It will also be a good idea that hospitals are one of the trustees. Obviously, the emergency procedure (finding trustees, contacting them, recovering the key, and revealing vital attributes) should be automated so as to not waste a precious second.
# Personalized Federated Multi-Task Learning over Wireless Fading Channels
## 1 Introduction
In addition to the wireless channel effects, the communication is performed over bandwidth- and power-limited regime, which brings concerns about communication costs. We utilize over-the-air (OTA) aggregation to perform efficient aggregation over a shared wireless channel to support the clients on the same bandwidth by utilizing the additive nature of wireless multiple access channel (MAC) [21; 22]. In practice, when the OTA mechanism is utilized, the gradients are superposed, and it is not possible for the PS to receive individual gradients of the clients. However, the PS needs individual gradients from the clients to perform dynamic weighting. To address this issue, we modify _FedGradNorm_ with OTA in a hierarchical structure, which is called hierarchical over-the-air _FedGradNorm_, _HOTA-FedGradNorm_[23]. Hierarchical federated learning (HFL) establishes clusters of clients around intermediate servers (IS). ISs communicate with the PS instead of clients directly communicating with the PS. Some aspects of HFL have been studied in the literature, such as, latency and power analysis [24; 25], and resource allocation [26; 27]. These works demonstrate the advantage of the proximity of ISs to the clients in terms of resource consumption of clients. In addition to these advantages, we utilize hierarchical structure since it provides an efficient way of combining _FedGradNorm_ with OTA over the wireless fading channel. The main contributions of our paper can be summarized as:  
* We propose the _FedGradNorm_ algorithm. The proposed algorithm takes advantage of the _GradNorm_[19] dynamic weighting strategy in a PFL setup for achieving a more effective and fair learning performance when the clients have a diverse set of tasks to perform. * We propose _HOTA-FedGradNorm_. The proposed algorithm takes into account the characteristics of the communication channel by defining a hierarchical structure for the PFL setting. * We provide the convergence analysis for adaptive weighting strategy for MTL in PFL setting. Existing works either do not provide convergence analysis or do it in special cases. We demonstrate that _FedGradNorm_ has an exponential convergence rate. * We conduct several experiments on our framework using Multi-Task Facial Landmark (MTFL) dataset [28], and RadComDynamic dataset on the wireless communication domain [29]. We investigate the changes in task loss during training to compare the learning speed and fairness of _FedGradNorm_ with a similar PFL setting which uses equal weighting technique, namely _FedRep_. Experimental results exhibit a better and faster learning performance for _FedGradNorm_ than _FedRep_. In addition, we demonstrate that _HOTA-FedGradNorm_ results in faster training over the wireless fading channel compared to algorithms with naive static equal weighting strategies since dynamic weight selection process takes the channel conditions into account.