esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Approach
### Whale Optimization Algorithm as a Feature Selection  
Adapting metaheuristic algorithms to handle any optimization problem requires identifying two fundamental parts, including solution encoding and evaluation (fitness) function. Employing WOA as a binary feature selection algorithm means that the potential solution (i.e., features subset) is is expressed as a binary vector with length \(n\) (see Figure 11), where \(n\) presents the number of features in the original dataset. Each cell inside the binary vector has either 1 (i.e., selected feature) or 0 (i.e., not selected).  
\begin{table}
\begin{tabular}{l l l l} \hline \multicolumn{2}{l}{**S-Shaped Family**} & \multicolumn{2}{l}{**V-Shaped Family**} \\ \hline
**Name** & **Transfer Function** & **Name** & **Transfer Function** \\ \hline S1 & \(S(x)=\frac{1}{1+e^{-2x}}\) & V1 & \(V(x)=|\text{erf}(\frac{\sqrt{1}}{2}x)|=|\frac{\sqrt{2}}{1\Gamma}\int_{0}^{( \sqrt{\Gamma}/2)x}e^{-t^{2}}dt|\) \\ S2 & \(S(x)=\frac{1}{1+e^{-x}}\) & V2 & \(V(x)=|\tanh(x)|\) \\ S3 & \(S(x)=\frac{1}{1+e^{(-x/2)}}\) & V3 & \(V(x)=|(x)/\sqrt{1+x^{2}}|\) \\ S4 & \(S(x)=\frac{1}{1+e^{(-x/3)}}\) & V4 & \(V(x)=|\frac{2}{1\Gamma}\text{arc}\tan(\frac{1}{2}x)|\) \\ \hline \end{tabular}
\end{table}
Table 1: S-shaped and V-shaped transfer functions.  
Figure 10: Transfer functions families (**a**) S-shaped and (**b**) V-shaped.  
The main objective of the FS process is to find the smallest features subset that leads to achieving the maximum classification accuracy. Accordingly, FS can be defined as a complex multi-objective optimization problem. Aggregation is deemed one of the most common prior procedures where multiple objectives are combined into a single function. Each objective is assigned a weight to decide its significance [84]. A good ratio between selected features and classification accuracy should be achieved to have a robust FS algorithm. So, the minimization fitness function used in this work is presented in Equation (19) to assess the appropriateness of the selected subset of features.  
\[\downarrow Fitness(X)=\alpha C_{ER}+\beta\frac{|S|}{|N|} \tag{19}\]  
where \(Fitness(X)\) is the fitness value of the subset \(X\), \(C_{ER}\) represents the classification error rate for the employed internal classifier using the subset \(X\). \(S\) refers to the number selected features. \(N\) refers to the total number of features in the original dataset. \(\alpha\in\)[0,1], whereas \(\beta=(1-\alpha)\) are adopted from [82; 85; 86].
## 5 Experiments and Results
### Fabric Hyperledger
#### 5.1.1 Environment  
Our testing environment was a machine equipped with an Intel i7-4790 (8) 4.000 GHz processor, 16 GB (2 \(\times\) 8 GB) of DDR3 RAM, and two SSD storages: (1) a Samsung PM85 256 GB dedicated for an operating system and a (2) Samsung 850 EVO 500 GB dedicated for blockchain storage. This machine ran the Arch Linux operating system with kernel version 5.11.8. Docker Version 20.10.5 was used for containerization.  
For simplicity, all components ran on one, mentioned above, physical machine, and all sensors were simulated using parallel programs.  
We performed a simulation where 50 sensor devices, 2 peer nodes, 1 orderer and 1 client took part. Each sensor generated one log per second; each log was sent to a peer node, which resulted in a total load of 50 transactions per second. The simulation lasted for seven days, which resulted in 604.8 k transactions per sensor or approx 30 M transactions in total. Sensors simulated readings of either temperature (odd identifier) or humidity (even identifier). Temperature reading were simulated using the following formula:  
\[\text{temp}(d,h,m,rand)=(10+\sin(\frac{\pi}{12}(h+\frac{m}{60}-10)))+\frac{1}{ 2}\sin(d*\frac{\pi}{3.5}))(1+\frac{rand-0.5}{70})\]  
Humidity reading were simulated using the following formula:  
\[\text{humd}(hour,minute,rand)=(60+\cos(\frac{\pi}{12}(hour+\frac{minute}{60}- 10)+\pi))(1+\frac{rand-0.5}{70}),\]  
where \(d\) is an integer representing the day of the week in the range \([0,6]\), \(h\) is an integer representing the hour of the day in the range \([0,23]\), \(m\) is an integer representing the minute offset of the hour in the range \([0,59]\), and \(rand\) is a random decimal number in the range \([0,1)\) and is used to add deviation to the data.
## 3 Results
### TEM/EDX Analysis
Various carbonaceous particles, ranging from a few nanometers to a few hundred nanometers, were predominantly identified in the samples closest to Mario Zucchelli Station (samples A and B). The primary type of these carbonaceous particles consisted of soot characterized by quasi-spherical carbon nanoparticles with typical diameters ranging from 10 to 100 nm: these particles aggregated into chain-like structures extending up to one micron, probably due to aggregation phenomena occurred during the evaporation step (Figure 5a,b). Some soot nanoparticles displayed an onion-like nanostructure of graphitic layers when observed at high resolution (Figure 5c). They were predominantly composed of C and O, although traces of S, Ca, and K were observed in some soot particles. Notably, one soot aggregate showed traces of Pb (Figure 5d). Soot was also observed decorating fly ash particles and mineral dust, forming large mixed aggregates and core-shell structures, as reported in Figure 3d. In addition to soot, various amorphous spheroidal carbonaceous particles, ranging from 50 to 300 nm, were identified: the most common elements included Si, Al, Fe, with additional contributions from P, Mg, Ca, and K. Based on their morphology and composition, these particles could be attributed to fly ash particles. Fly ash particles were often adorned by mineral dust and soot particles. This unique characteristic imparted complexity to their morphology, with particles having spherical shapes with round or sharp edges and sometimes exhibiting core-shell structures (Figure 3d). A high concentration of iron and titanium oxide nanoparticles, having dimensions lower than 100 nm, in either isolated or aggregated forms, was observed in all analyzed samples except for sample D, located 250 km NE from the Italian research station. These findings could be related to increased levels of contamination deriving from the presence of the research base. An aggregate containing both types of nanoparticles is presented in Figure 6a. Moreover, EDX analysis revealed the presence of Zn and heavy metals, including Cd and Pb, predominately in the samples collected near Mario Zucchelli Station (A, B, C, E). In particular, Cd was identified in the form of cadmium oxide nanoparticles (Figure 6b), whereas Pb was localized in the proximity of soot particles. The identification of these elements in Antarctic snow samples was not surprising: as reported in previous  
Figure 4: A HAADF-STEM image and the corresponding EDX maps of a mineral dust particle detected in sample A.  
studies [6; 36], snow samples collected near the Ingrid Christensen Coast of East Antarctica and Dome Concordia were characterized by the presence of several trace elements, such as Zn, Cu, Mo, Cd, As, Se, Sb, and Pb. These elements were very abundant compared with known natural sources, thus suggesting their anthropogenic origin.
# Dynamic Optimization Using Local Collocation Methods and Improved Multiresolution Technique
## 4 Numerical Examples
### Simple Chemical Reaction Problem  
We first apply the proposed method to maximize the final amount of product in a simple two-stage chemical reaction [26; 30]. The problem is expressed as follows: minimize  
\[J=-y(t_{f}), \tag{16}\]  
subject to the state dynamics  
\[\frac{\mathrm{d}x}{\mathrm{d}t}=-ux,\frac{\mathrm{d}y}{\mathrm{d}t}=ux-\rho u ^{k}y, \tag{17}\]  
the initial conditions  
\[x(0)=1,y(0)=0.01. \tag{18}\]  
and the control bounds  
\[0.1\leq u(t)\leq 0.5. \tag{19}\]  
where \(\rho\) and \(k\) are constants, \(\rho=2.5,\ k=1.5\); \(t_{f}\) is fixed at \(t_{f}=2\).  
We solved this problem using the proposed method with \(N=8\) and \(J_{\mathrm{max}}=6\). The mesh refinement tolerance was \(\varepsilon=1.0\ \times 10^{-3}\). The method stopped after seven iterations. The time history of the control \(u\) for iterations 1, 2, 6, and 7 is shown in Figure 4, where the mesh points are the discrete control solutions, and the solid lines are obtained using the \(p\)th-order ENO interpolation.  
Figure 4: _Cont._  
It is seen in Figure 4a that the proposed method started with the coarsest mesh and was able to remove most of the redundant mesh points in the constant control region, as shown in Figure 4c,d. As a result, the method used only 25 mesh points from the maximum of 513 points at the finest mesh \(V_{6,\,N}\). The objective function value was 0.308132135. The total CPU time required to solve this problem was 1.73 s. In contrast, the problem was also studied in [30] where the mesh was refined using a method by minimizing the maximum discretization error. The method of [30] generated 62 mesh points and achieved an objective value of 0.308132178. It is seen that the proposed method achieved nearly the same objective value as the method of [30] but used a much smaller mesh size, which therefore has the potential to reduce the computational cost.
# Blockchain-Based UAV-Assisted Mobile Edge Computing for Dual Game Resource Allocation
## 3 Model of a Drone-Assisted Mobile Edge Computing System
### Utility Function Definition
\[\underset{s_{i},D_{i}}{max}\ U_{ULAV}=\underset{s_{i},D_{i}}{max}\sum_{i=1}^{N} \left(s_{i}\cdot D_{i}-p_{i}\cdot D_{i}-E_{i}+R_{i}\right) \tag{5}\]  
In the game between the UAV (unmanned aerial vehicle) and the user, we have applied the auction algorithm to optimize the resource allocation between the two. The following explains the auction process of the game between the UAV and the user. 1. User puts forward resource demand:  
(1) Each user submits a resource request to the UAV with a bid according to their needs and budget. (2) Remember that user j bid for task i is \(b_{ij}\). 2. The drone collects bids from all users:  
(1) The drone collects all users' bids for each task and organizes them into a bid matrix B, where \(B_{ij}=b_{ij}\). 3. The drone conducts an auction:  
(1) For each task i, the drone conducts an auction based on the user's bids, and selects the user j with the highest bid as the winning bidder for the task. (2) The drone allocates the resources of task i to user j and collects the bid \(b_{ij}\). 4. Resource allocation results:  
(1) The drone announces the auction results, including the winning user for each task and its bid. (2) The winning user obtains the required resources and pays the corresponding bids. The auction algorithm ensures that resources are allocated to the user who needs them most and is willing to pay the highest price through a competitive mechanism, thus improving the efficiency of resource utilization. In the auction algorithm, the utility function of the UAV can be expressed in Equation (6). \[U_{i}^{ULAV}=\sum_{j=1}^{M}b_{ij}-C_{i} \tag{6}\]  
The user's utility function can be expressed in Equation (7). \[U_{ij}^{User}=V_{ij}-b_{ij} \tag{7}\]  
where V\({}_{ij}\) is the value that user j obtains from task i. \(b_{ij}\) is user j's bid for task i. \(C_{i}\) is the cost of the UAV to provide resources for task i. The goal of the UAV is to maximize its utility function, which can be expressed in Equation (8). \[\underset{b_{ij}}{max}\ U_{i}^{ULAV}=\underset{b_{ij}}{max}\left(\sum_{j=1}^{M }b_{ij}-C_{i}\right) \tag{8}\]  
The user's goal is to maximize his utility function, which can be expressed in Equation (9). \[\underset{b_{ij}}{max}\ U_{ij}^{User}=\underset{b_{ij}}{max}\left(V_{ij}-b_{ ij}\right) \tag{9}\]
###### Abstract  
Accurate energy demand forecasting is vital for optimizing resource allocation and energy efficiency. Despite advancements in various prediction models, existing approaches often struggle to capture the complex, nonlinear relationships between temperature variations and electricity consumption. To address this issue, this paper introduces a novel hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks with an attention mechanism designed to forecast day-ahead electricity demand in Australia. This research aims to enhance the accuracy of electricity demand predictions by effectively modeling the impact of heating degree days (HDDs) and cooling degree days (CDDs) on energy usage. The HDDs and CDDs capture extreme weather conditions. They are critical for understanding spikes in energy consumption for heating and cooling needs. The proposed model leverages the strengths of CNNs in extracting spatial features in HDDs and CDDs, LSTMs in capturing temporal dependencies, and the attention mechanism in focusing on the most relevant aspects of the data. This study compares the CNN-LSTM-Attention model with traditional methods, including Deep Neural Networks, and demonstrates superior performance. The results show a significant reduction in both Mean Absolute Error and Mean Absolute Percentage Error, confirming the model's effectiveness. The primary contribution of this paper lies in the novel integration of CDD and HDD data within the CNN-LSTM framework, which has not been extensively explored in prior studies. This approach offers a robust solution for energy management, particularly in climates with significant temperature fluctuations.  
e +
Footnote †: journal: applied sciences  
_Article_
## 3 Method
### Loss Function  
In this paper, the same loss function as [15] is adopted. Ground truth bounding boxes (GT boxes) and anchor boxes are represented as \((x,y,z,w,l,h,\theta)\), where \(x,y,\) and \(z\) represent the coordinates, \(w\), \(l\), and \(h\) represent the width, height, and length, respectively, and \(\theta\) indicates the angle offset. Therefore, the offsets of each parameter between the GT boxes and anchor boxes are calculated accordingly:  
\[\Delta x=\frac{x^{gt}-x^{a}}{d^{a}},\ \Delta y=\frac{y^{gt}-y^{a}}{d^{a}},\ \Delta z=\frac{x^{gt}-x^{a}}{d^{a}}\]  
\[\Delta w=\log\frac{w^{gt}-w^{a}}{d^{a}},\ \Delta l=\log\frac{l^{gt}-l^{a}}{d^{a}},\ \Delta h=\log\frac{l^{gt}-h^{a}}{d^{a}} \tag{12}\]  
\[\Delta\theta=\sin\bigl{(}\theta^{gt}-\theta^{a}\bigr{)}\]  
In Equation (12), the superscripts \"gt\" and \"a\" indicate the GT boxes and anchor boxes, respectively, and \(d^{a}=\sqrt{\left(w^{a}\right)^{2}+\left(l^{a}\right)^{2}}\). Consequently, the localization loss is  
\[L_{loc}=\sum_{b\in(x,y,z,w,l,h,\theta)}SmoothL1\ (\Delta b) \tag{13}\]  
The classification loss adopts focal loss [30]. The classification loss is calculated as below, where \(p^{a}\) represents the predicted class score for an anchor box. According to [30], \(\alpha_{a}=0.25\) and \(\gamma=2\).  
\[L_{cls}=-\alpha_{a}\ (1-p^{a})^{\gamma}\log p^{a} \tag{14}\]  
Based on [9], a SoftMax function is utilized as the angle classification loss (\(L_{cls}\)) to enable the learning of object orientations. Consequently, the overall loss is shown in Equation (15):  
\[Loss=\frac{1}{N_{pos}}\ (\beta_{1}L_{loc}+\beta_{2}L_{cls}+\beta_{3}L_{dir}\ ) \tag{15}\]  
In the equation above, \(N_{pos}\) represents the number of positive anchor boxes. \(\beta_{1}\), \(\beta_{2}\), and \(\beta_{3}\) are the weights for the three types of losses, where \(\beta_{1}=2\), \(\beta_{2}=1\), and \(\beta_{3}=0.2\)[9].  
Figure 5: Structure of RPN. Three consecutive convolutional blocks are employed to obtain feature maps of three different resolutions. Subsequently, these feature maps are up-sampled to match a common resolution, and then concatenated to obtain the final feature output.
## 2 Results and Discussion
### Changes in the Composition of Volatile Compounds in Strawberries
Thus, CO\({}_{2}\) treatment may inhibit acetaldehyde formation and help maintain the aroma quality of strawberries during storage, regardless of maturity. A previous study reported that the acetaldehyde content was lower in strawberries (_Fragaria vesca_ L.) stored in a controlled atmosphere with a higher CO\({}_{2}\) concentration compared to that in the untreated control [38]. High CO\({}_{2}\) treatment inhibited pyruvate decarboxylase activity that decarboxylulates pyruvate to yield acetaldehyde during storage [39]. 2,3-Butanedione is responsible for the pungent off-odor of strawberries [40]. The 2,3-butanedione concentration of bright-red strawberries (B0h, 92 \(\upmu\)g/kg) was 2.9-fold higher than that of half-red strawberries (H0h, 32 \(\upmu\)g/kg). The 2,3-butanedione concentration was lower in CO\({}_{2}\)-treated strawberries during storage compared to untreated strawberries at the same storage time, regardless of the ripening stage. Thus, CO\({}_{2}\) treatment may improve the aroma quality of strawberries during storage regardless of the maturity based on lower concentrations of 2,3-butanedione compared to the control during storage. Hexanoic acid is also related to a rancid off-odor in strawberries [40]. The concentration of hexanoic acid of half-red strawberries increased from 26 \(\upmu\)g/kg (H0h) to 370 \(\upmu\)g/kg (H9D) and 162 \(\upmu\)g/kg (HT9D). In bright-red strawberries, the concentration of hexanoic acid increased from 87 \(\upmu\)g/kg (B0h) to 664 \(\upmu\)g/kg (B9D) and 502 \(\upmu\)g/kg (BT9D). Thus, hexanoic acid concentrations increased during storage regardless of the ripening stage; however, CO\({}_{2}\) treatment may inhibit hexanoic acid production during storage. Similar to our findings, the hexanoic acid concentrations of (untreated) 'Sweet Charlie' strawberries increased during 9 d storage at 4 \({}^{\circ}\)C [10]. However, the hexanoic acid concentrations of 'Pajaro' strawberries decreased during 12 d storage in 20% CO\({}_{2}\) at 0 \({}^{\circ}\)C [41]. In addition to hexanoic acid, various fatty acids, including linoleic acid and linoleic acid, were identified in nine strawberry cultivars in a previous study [42]. Some ester compounds, such as methyl butanoate, ethyl butanoate, and methyl hexanoate, are formed by the \(\upbeta\)-oxidation of fatty acids in strawberries [18]. In this study, the ester compound production may have been inhibited during storage by CO\({}_{2}\) treatment possibly owing to inhibited fatty acid oxidation during storage.
## 5 Simulation Experiments  
In this section, we compare the performance of the proposed LFDC algorithm in terms of cache hit efficiency, energy consumption, and HE gain with centralized DRL ([38]), distributed DRL ([13]), the classical federated learning algorithm FedAvg ([39]), and traditional caching algorithms, including first in first out (FIFO, [18]), least frequently used (LFU, [19]), and least recently used (LRU, [17]). Furthermore, we evaluate the performance of the proposed algorithm under different hyperparameters. The following are three comparison algorithms also based on DRL:  
1. Centralized DRL: A DDQN model is deployed in the cloud to train and make decisions on global dynamic caching.
2. Distributed DRL: A DDQN model is deployed in each BS, which is trained and makes decisions based on local data.
3. FedAvg: A DDQN model is deployed in each BS and aggregated and distributed through the federated averaging method.
# A Fully Integrated Low-Power Multi-Mode RF Receiver for BDS-3/GPS
## 4 Receiver Design  
### Chip Architecture of the Receiver  
The diagram of the receiver applicable to BDS-3/GPS, working in the L1 band at 1575.42 MHz, is shown in Figure 2, where all blocks are integrated into the chip. The receiver adopts a single conversion low-IF structure, rather than a zero IF structure, to reduce the impact of a higher flicker noise [4; 7]. LNA is the first stage connected to the antenna, so it dominates the noise behavior of the entire receiver [5]. The input of LNA matches 50 \(\Omega\) in the narrow band near the navigation frequency of 1575.42 MHz. The subsequent ACTBA converts the single-ended RF signal into differential signals to avoid the common-mode noise. Compared with the structure of a passive balun plus a differential amplifier, a certain gain is provided by ACTBA to reduce the gain pressure of other RF stages and save the area with comparable power. A passive switching mixer with a smaller area is used to down-convert the RF signals to IF signals using differential LO signals from the frequency synthesizer (FS). The maximum gain of a passive switching mixer is only \(2/\pi\). Therefore, Gm, the passive switching mixer, and Tia form the GMT circuit to compensate for RF gain. The down-converted IF signal is then filtered by a third CBPF with a programmable bandwidth of 2 or 4 MHz, providing image rejection, and it is easy to integrate. An RC calibration is used in CBPF to avoid the influence of process variation on the center frequency and bandwidth. A two-stage PGA with a digital AGC loop is employed to ensure that the signal magnitude at the ADC input is constant, regardless of the received signal strength. Due to the image rejection of CBPF, only the in-phase path signal is amplified and quantized to reduce the area and power by half. The DC offset correction (DCOC) circuit is adopted at the last stage of PGA to avoid signal saturation. Finally, the IF signals are digitized by a four-bit ADC. DCOC, AGC, and RC calibration are all implemented digitally, effectively reducing the chip area. Also, they are automatically shut down after convergence to effectively reduce power.