esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
Linear free energy relationships can provide significant mechanistic insights into both solution-based and enzyme-catalyzed reactions [51; 52]. When **NP-Zn** and NINP are used to hydrolyze a series of _para_-substituted phenyl carbonates, linear relationships are observed in both cases, with the reaction constant (\(\rho\)) of 2.86 (Figure 3a) and 2.59 (Figure 3b), respectively. These numbers are similar to those reported for the hydrolysis of aromatic carbonates by hydroxide, i.e., \(\rho\) = 2.7-3.2 [48]. As discussed earlier, the hydroxide ions in the NINP catalyst are most likely those near the surface of the micelles, generated with the help of the electric potential of the cationic micelles [49; 50]. For **NP-Zn**, the zinc cofactor is proposed to use a metal-bound hydroxide for the nucleophilic attack on the carbonate substrate. Essentially, the Lewis acidic zinc helps a metal-bound water molecule to deprotonate more easily to generate a stronger nucleophile (i.e., hydroxide) in the active site. The pK\({}_{\text{a}}\) of [Zn(OH\({}_{2}\))\({}_{6}\)]\({}^{2+}\) is 9.0 [53], but the many cationic charges on the cross-linked micelles can help the deprotonation of the water, similar to what happens in many enzymes [54]. Consistent with the picture, the hydrolysis of **1b** by **NP-Zn** is less sensitive to the solution pH than the NINP control over pH 7.0-9.5 in Figure 1b. When external hydroxide ions are involved in the hydrolysis, the reaction is most sensitive to the solution pH (at least above pH 8.0), as shown by the reaction in the buffer in Figure 1b. Further support for the hydroxide-based hydrolysis in **NP-Zn** comes from the kinetic solvent isotope effects, which are often used to distinguish a nucleophilic mechanism by hydroxide from a general base catalysis [55; 56; 57; 58]. The former does not involve O-H bond cleavage in the rate-determining step, affording a kinetic solvent isotope effect of \(k_{\text{H2O}}/k_{\text{D2O}}\approx 1\). A general base mechanism, characterized by a cleavage of the water O-H bond in the rate-determining step, often exhibits a primary solvent isotope effect, with \(k_{\text{H2O}}/k_{\text{D2O}}\) = 2-3. In our hands, a \(k_{\text{H2O}}/k_{\text{D2O}}\) value of 1.04 was observed for the hydrolysis of **1b** by NP-Zn at pH 7 (Table S1), consistent with the postulation that a nucleophilic attack by hydroxide is responsible for the catalysis. In this experiment, the pD value was determined by adding 0.4 to the pH meter reading since water and D2O have different dissociation constants [56].
## 2 Coded CDMA  
In the case of multiple access systems, it is common to use Forward Error Correction (FEC) techniques to combat the effects of multiple access interference (MAI) and channel noise. In such a case, a user would pre-code their data, before interleaving and spreading with their unique spreading code. A diagram of such a scenario is shown in Figure 1.  
In Figure 1 binary data of length \(L\) for user \(k\), denoted \(b_{k}\), is presented to encoder \(C\), where \(b_{k}\in 0,1\) and \(k\in 1\ldots K\). The encoder produces any coded data \(d_{k}\) of length \(L/R\), where \(R\) is the rate of code. The data is randomly interleaved through the interleaving block \(\pi\) to produce \(\mu_{k}\) which is then mapped onto the binary phase shift keying (BPSK) constellation to produce coded, interleaved BPSK data \(x_{k}\). The BPSK data is then multiplied by the unique spreading vector \(s_{k}\) and transmitted across the additive white Gaussian noise (AWGN) channel.
# Quantum Recurrent Neural Networks: Predicting the Dynamics of Oscillatory and Chaotic Systems
## 4 Numerical Experiments
### System of Lorenz Equations  
The Lorenz equations, fundamental in chaos theory, model the dynamics of atmospheric convection and are characterized by their chaotic nature for certain parameter values. The system is described by the following set of differential equations:  
\[\begin{split}\frac{dx}{dt}&=\sigma(y-x),\\ \frac{dy}{dt}&=x(\rho-z)-y,\\ \frac{dz}{dt}&=xy-\beta z.\end{split} \tag{45}\]  
In Equation (45), \(x,y\), and \(z\) represent the system states, and the parameters \(\sigma,\rho\), and \(\beta\) are crucial for the system's behavior. The chosen values \(\sigma=10.0\), \(\beta=\frac{8}{3}\), and \(\rho=28.0\) are known to induce chaotic dynamics in the Lorenz system. These parameters represent the Prandtl number, normalized Rayleigh number, and certain physical dimensions of the convective cells, respectively. Their specific values make the system a classic example of  
Figure 14: QGRU Predictions over 10 epochs. Up: Oscillator 1, down: Oscillator 2 epoch: 5, 50, 70, 100; green dashed line: actual values; blue solid line: training predictions; red solid line: testing predictions.  
Figure 15: Experiment 2: train and test losses for the models over 100 epochs. From left to right: LSTM, QLSTM, GRU, QGRU.  
chaos, making it a compelling subject for studying the predictive capabilities of quantum-enhanced and classical neural networks. Particularly, the research on the utilization of classical neural networks is discussed in [49].  
Data Preparation and Hyperparameter Configuration  
Unlike the previous two experiments, we generated the data differently in the application of RNN models on Lorenz equations. We first randomly generated 10 datasets; we used 67% of them for training and the remaining 33% for testing. The shapes of the training and test datasets are as follows:  
* Features: torch.Size([3120, 10, 3]);
- Labels: torch.Size([3120, 3]).
* Features: torch.Size([780, 10, 3]);
- Labels: torch.Size([780, 3]).  
Regarding the hyperparameter aspect, the previous two experiments demonstrate that QRNN models rapidly catch the trend of the dataset. Consequently, we aim to explore more about these models' proficiency in learning features with shorter sequence lengths, reduced data points, and fewer epochs.  
We pick sequence length 10 for all the models and all the models will be running over 20 epochs instead of the 100 epochs in the previous two experiments.  
Hyperparameter configurations for LSTM, QLSTM, GRU, and QGRU models are presented in Table 6:
## 2 Material and Methods
### Experimental Set-Up  
Irradiations were performed at the INFN-LNS clinical proton therapy facility where a 62 \(\pm\) 0.3 MeV proton beam was available. Details on the irradiation beamline, beam characteristics, and dosimetric procedures can be found elsewhere [19; 20; 21].  
All measurements were performed by positioning the floating electrode inside a 20 \(\times\) 20 \(\times\) 20 cm\({}^{3}\) water tank, on the opposite side of the beam entrance window (see Figure 1) outside the irradiation field. The water tank was electrically isolated to avoid charge leakages. The beam spot size was circular, with a 25-mm diameter (Figure 1).  
All the experimental measures involved recording the detector signals for five repeated irradiations corresponding with the same released dose. The integrated charge values and the corresponding uncertainties were estimated as the average and standard deviation of the five consecutive measurements, respectively. Figure 2 shows an example of the detector current output in the five irradiations. Each curve corresponds to a dose of 4 Gy, released at the water-tank entrance. The huge variations in the signals are due the proton beam instabilities during the irradiations and could be the cause of the lower observed sensitivity with respect to the calculations.
## 2 HSI Procedure
### Hyperspectral Wavelength Selection
#### 2.3.1 Competitive Adaptive Reweighted Sampling (CARS)  
CARS, inspired by [PERSON]'s evolutionary theory of survival of the fittest, is a very effective method of variable selection, reducing data dimensionality and collinearity. The method employs Monte Carlo sampling to create spectral subsets, followed by an exponential decay function that removes less-informative wavelengths. The steps continue with ranking the rest of the wavelengths through Adaptive Reweighted Sampling, based on their regression coefficients in descending order of their predictive relevance. This cross-validation, which is usually carried out with the RMSE, helps in selecting the optimal wavelengths. Thus, CARS has a number of advantages in balancing between minimum collinearity and high robustness of models [39; 78].  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Preprocessing Method** & **Objective** & **Advantage** & **Limitation and Challenges** & **Reference** \\ \hline \multirow{4}{*}{log (1/R)} & Predicting various quality parameters during hot-air drying & Achieves high accuracy in predicting vitamin C, SSC, moisture content, and shrinkage with lower computational demands for multispectral systems & GPR models may struggle with predicting parameters  
In the work of [PERSON] et al. (2020), CARS was combined with PSO to carry out the classification of bacterial pathogens on agar plates. The reduced number of wavelengths resulted in a correct classification rate of 99.47% in the training set and 98.44% in the prediction set, which served as evidence of the capability of the technique in reducing spectral data without compromising accuracy [79]. Then, using the CARS-PLS-DA model for a fish freshness assessment distinguished fresh, refrigerated, and frozen-thawed samples with high accuracy. Furthermore, a good predictive ability was found for the storage time estimation both under room temperature and refrigerated storage, with correlation coefficients of 0.948 and 0.9319, respectively [80]. [PERSON] et al. 2024 further showed the potential of CARS when presenting that the method was applied for chicken flesh chemical composition prediction, and after processing, a high accuracy regarding protein content prediction was achieved, hence showing the non-destructive potential of the method in food analysis [16].
## 5 Challenges and Future Directions
### Automation and Data  
While UAVs offer tremendous potential for autonomous data capture and analysis, limited onboard computational resources pose challenges. The restrictive size, weight, power, and cost budgets of UAV platforms constrain the processing capabilities that can be conducted on board. This impacts the feasibility of performing intensive computations, such as high-definition video encoding, 3D modeling, AI inferencing, and other algorithms, requiring substantial computing performances in real-time during a flight. Since UAVs must react rapidly to a dynamic environment, optimal path planning algorithms are often not viable because they require heavy processing unsuitable for real-time operations. While advances are being made with onboard GPUs to expand in-flight computations [207], developing efficient drone path planning and control methods that work within tight processing constraints remains a significant challenge. As drones take on more sophisticated roles, their limited onboard computational resources require careful management through strategic workload balancing and system optimizations. Limited autonomy and artificial intelligence capabilities pose another barrier, requiring intensive human piloting and analytic efforts. Advanced computer vision and AI would enable amore intelligent target identification, adaptive sampling, and decision making by UAVs over cities. Weather-resilience shortcomings, such as wind tolerance, restrict reliable and regular UAV data capture for the climate trend analysis [208]. Ongoing improvements to automated stability and envelope protection will help operations persist in diverse conditions. Finally, the uncertainty surrounding UAV impacts on privacy and noise may limit the urban deployment potential.
# Blind Recognition of Frame Synchronization Based on Deep Learning
## 1 Introduction
Attracted by the stripes that appear when converting binary data with frame synchronization code into pictures and considering the powerful capability of CNN in image recognition, we propose a deep-learning-based blind frame synchronization identification method by combining the classical algorithms and deep learning in this paper. The binary data was firstly subjected to a flat-top interpolation operation [30]. Subsequently, it was transformed into a series of grayscale images and combined with scaling factors to generate RGB images. Then we use the image samples to train the convolutional neural network and employ the trained network to recognize the frame synchronization code in new binary sequences. Simulation results show that compared with the existing algorithms, the proposed algorithm has better recognition performance under the condition of higher bit error rate.
## 5 A Comparative Analysis
If an AP is operating with 320 MHz bandwidth, the entire available spectrum is consumed, and the nearby APs will use the same channels, resulting in interference. Hence, it is not practical for each AP to make use of this wider channel. The 5G mmWave band supports multiple carriers of 100 MHz, and carrier aggregation helps obtain wider bandwidth, enabling dense device deployment. The smart factory floor consists of several mobile devices, such as autonomous intelligent vehicles (AIVs), drones, etc. Another factor is how well these technologies support mobility. Although WiFi and 5G enable mobility, 5G provides smoother and more reliable handovers. TSN integration creates challenges if devices are highly mobile, as the network configuration maintained by the CNC would need to be updated very often. Cost is another vital factor behind the industry's adoption. WiFi is a more cost-effective solution than private 5G networks and their TSN-integrated versions. The fourth dimension we consider is the latest trends in the market. Most enterprises adopt a technology once it is relatively mature and stable. WiFi is the most used wireless technology in smart manufacturing today, whereas private 5G networks are slowly gaining adoption. Several industries are currently carrying out private 5G proof-of-concept evaluations. The current lack of availability of native private 5G devices is a concern, even though a few Release 15/16 products are available on the market. Similar scenarios exist when it comes to TSN. Several products support TSN over Ethernet; however, TSN integration over wireless (5G and WiFi) is in the early stages and is also dependent on the standardization process. While the standards for T-E, WiFi, and 5G are established, and newer releases are forthcoming, the standardization process for TSN integration into WiFi and 5G is in the early stages.
# An Enhanced Feature Extraction Network for Medical Image Segmentation
## 5 Conclusions  
In this paper, we present EFEN for medical image segmentation tasks, which is designed by adding the feature re-extraction process and using improved skip-connection based on U-Net. By adding the feature re-extraction process, the feature extraction ability of the network is enhanced. Improved skip-connection can not only help the network to identify segmentation targets, but also further help the network to reduce the interference information from shallow features. Experiments on the CVC-ClinicDB, the ISIC-2018, and the 2018 Data Science Bowl challenge datasets show that although there are still some gaps between EFEN and the most advanced method, our method is superior to U-Net and other recent excellent networks. Furthermore, from the visual analysis we can see that EFEN can improve the segmentation result of medical images with blurred boundaries or complex backgrounds. The EFEN proposed in this paper can provide doctors with auxiliary diagnosis, help doctors locate lesions and help them to diagnose and evaluate the disease more accurately. In the future, this method or its idea can be applied to cross-modal medical image segmentation, personalized medicine and automated workflows to further explore its advantages.  
Conceptualization, Y.G. and X.C.; methodology, H.X. and Y.G.; investigation, H.X. and M.B.; writing--original draft preparation, Y.G., H.X. and M.B. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by National Natural Science Foundation of China under Grant 62172184, Science and Technology Development Plan of Jilin Province of China under Grant 2020040107/GX, 20200201292 JC, Social Science Research of the Education Department of Jilin Province (JJKH20210901 SK), and Humanities and Social Science Foundation of Changchun Normal University (2020[011]).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.  
Figure 6: Visualization results of EFEN on the 2018 Data Science Bowl challenge test set. Each column includes the original input image and its corresponding mask, the result of baseline model U-Net and EFEN. The red box shows the comparisons in the same position.
# Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models
## 3 Methodology
### Modeling: ARIMA and MLR
#### 3.3.2 Multiple Linear Regression (MLR)  
The MLR model was utilized to account for multiple variables influencing demand, such as product type and customer purchasing behavior. MLR provides a comprehensive view of demand drivers by assessing the relationships between a dependent variable (demand) and several independent variables. The model's equation is expressed as:  
\[\text{Y}=\beta_{0}+\beta_{1}\text{X}_{1}+\beta_{2}\text{X}_{2}+\beta_{3}\text{ X}_{3}+\cdots+\beta_{\text{n}}\text{X}_{\text{n}}+\varepsilon\]  
where:  
* Y: Represents the sales volume, also known as the dependent variable. It is the primary outcome we aim to model or predict.
* X\({}_{1}\): Represents temperature data, which is an independent variable that may influence sales volume.
* X\({}_{2}\): Represents the product type, another independent variable contributing to the model.
* X\({}_{3}\): Represents promotional activity, an independent variable likely affecting sales.
* \(\beta_{0}\): The intercept term representing the baseline sales volume when all independent variables are zero.
* \(\beta_{\text{n}}\): Coefficients of the independent variables, representing their contribution to changes in Y.
* \(\varepsilon\): The error term, capturing the variance in Y not explained by the model's variables. The independent variables were chosen based on their potential to influence sales, as determined during the data exploration phase:  
1. Temperature data (X\({}_{1}\)): Indicates environmental conditions relevant to cold chain logistics.
2. Product type (X\({}_{2}\)): Captures variability across different categories of products (e.g., frozen vs. chilled goods).
3. Promotional activity (X\({}_{3}\)): Tracks discounts or marketing campaigns affecting demand.
## 3 Preliminaries  
### Channel State Information (CSI)  
The CSI represents detailed information about the state of a communication channel being impacted by various environmental causes during the signal transmission from the transmitter (TX) to the receiver (RX). The radio transmission is a multi-path propagation of Wi-Fi signals that travels through several paths facing line-of-sight (LOS) and non-line-of-sight (NLOS) contacts from TX to RX in an indoor environment. During a multi-pathpropagation, emerging signals have different time delays and attenuate differently with varying phase shifts due to reflection, scattering, diffraction, fading, and interference produced by the surrounding objects. In systems that support multiple-input multiple-output (MIMO) antennas, the CSI can efficiently use the spatial diversity of orthogonal frequency division multiplexing (OFDM) and can carry multiple copies of the same signal characterized by sub-carriers. The channel impulse response of the estimated CSI at the receiving end is defined as [33]  
\[H_{x,y,z}=\sum_{i}^{N}A_{i}\exp^{-i\times 2\pi\times d_{x,y,z}\times f_{z}/c} \tag{1}\]  
Also,  
\[H_{x,y,z}=\underbrace{|H_{x,y,z}|}_{magnitude}\exp^{-i\angle H_{x,y,z}}_{pulse -airy} \tag{2}\]  
where \(N\) is the total number of propagation paths, out of which the \(i\)-\(th\) path length from the \(x\)-\(th\) transmit antenna to the \(y\)-\(th\) receiver antenna is \(d_{x,y,i}\), bearing an amplitude of \(A_{i}\); \(f_{z}\) is the \(z\)-\(th\) sub-carrier frequency; and \(c\) is the speed of radio waves in a vacuum, which is approximately \(3\times 10^{8}\) m/s. Equation (1) is the simplified form of the estimated CSI, neglecting the impacts of cyclic shift diversity (CSD), sampling time offset (STO), sampling frequency offset (SFO) and beamforming due to signal modulation and demodulation; TX and RX hardware imperfections; and software errors.  
When all the objects in an indoor environment are static, an occupant movement can be traced through a characteristic change in the CSI magnitude and phase-shift over multiple sub-carriers caused by the target movement. These characteristic variations create a principle background on how deep models are trained for device-free sensing. Suppose \(a\) is a transmitted radio signal. Upon reception, it is estimated to be \(b=H\times a+n\), where \(n\) is the noise vector, \(b\) is the received signal, and \(H\) is the CSI matrix. The estimated CSI is a 3-\(D\) matrix of complex values that variate along the time axis. The time axis is the fourth dimension that refers to the knowledge of the target movement with respect to time. In a Wi-Fi-based MIMO system having \(x\) transmit antennas and \(y\) receive antennas divided among \(z\) sub-carriers, the 4-\(D\) CSI matrix is denoted as  
\[H_{x,y,z}=\left[MATRIX_{(CSI)}\right]^{x\times y\times z\times f} \tag{3}\]  
where \(x\) and \(y\) represent the spatial diversity, \(z\) shows the diversity in the frequency domain, and \(t\) is the time diversity in terms of the data packets sent to the receiver.