esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This avoids ambiguity and uses a more standard term to describe the data;
* Enabling intermediate routers to independently calculate data freshness and make caching decisions based on it, effectively optimizing traffic load;* Structuring data request messages to include sensor identifiers and data types, which enables efficient data retrieval management based on specific time intervals;
* Improving cache performance by ensuring it contains only current data, which increases cache hits, reduces response delay, and lowers network traffic load. The rest of the article proceeds as follows: Section 2 includes related work about the data caching method; Section 3 describes the proposed fresh-based caching method; Section 4 explains and discusses the simulation results; and Section 5 concludes the paper.
## 4 Detailed Design
### Walking Speed Measurement  
The proposed system automatically records a user's indoor location data and time in the wearable device's internal memory if a user only performs his or her daily activities in a residential space. Furthermore, the proposed system can record a user's indoor location and evaluate the user's walking speed between fixed spaces based on the location change event. Figure 4 illustrates the walking time measurement while a user moves between two indoor spaces. Spaces A and B are entry-detection spaces; each door is equipped with a stationary device, including two LF antennae (inside and outside). The user who was initially in Space A left that space and is trying to enter Space B. The moment the user enters Space B, the user's wearable device obtains and records the walking time by calculating the difference between the exit time from Space A and the entry time into Space B. Consequently, the walking speed (based on walking time) can be continuously calculated and recorded. Over time, improvement or deterioration in walking speed may be observed by checking the change in time required by the user to move between indoor living spaces.
## 4 Design of MIM-QSMS Decoder for RC-QC-LDPC Codes  
The MIM-QSMS decoder in [15] is designed for an LDPC code with a fixed code rate, where we refer to this type of decoder as the rate-dependent MIM-QSMS (RD-MIM-QSMS) decoder. Obviously, decoding LDPC codes with various code rates using the RD-MIM-QSMS decoder requires different sets of LUTs corresponding to each code rate. Thus, it not only requires multiple rounds of design process to construct the LUTs but also a large amount of memory usage for hardware implementation. Although constructing LUTs used for decoding RC-QC-LDPC codes has been proposed in [11; 14], these methods only focus on the flooding schedule and cannot be directly applied to a shuffled decoder. This is because the updating order of the exchanged messages in a shuffled decoder is different from that in a decoder with a flooding schedule. Motivated by the above observations, in this section, we propose a design method of the RC-MIM-QSMS decoder, which is capable of decoding RC-QC-LDPC codes based on only one set of LUTs with much less memory requirement.
# Multi-Access Channel Based on Quantum Detection in Wireless Optical Communication
## 4 Discussion
### Does QAM Work for MED and USD?  
The approach proposed in this paper targets M-PSK, which is easily associated with the possibility of considering the QAM approach. In fact, QAM uses amplitude and phase co-modulation, and a typical generalization of the coherent state form is shown in Figure 9, where the amplitude of \(2K\) users in the outer ring is twice the amplitude of \(K\) users in the inner ring, i.e., \(2|\alpha_{k<2K}|=|\alpha_{k\geq 2K}|\). Unfortunately, although multiple users can be represented by the quantum-state form of QAM, there is no good method for obtaining their optimal detection so far [35]. This is because the quantum state in the QAM form breaks the symmetry, which makes it very difficult to solve MED. The same tentative challenge is also true for USD [32].  
This is really an area that has not been covered by our work, and we will be looking for possible solutions to this problem in the future.  
Figure 8: BER versus SNR performance of USD and NOMA.  
Figure 9: Coherent-state signals in the QAM form for \(3K\) users.
# Metal-to-Insulator Transition in Ultrathin Manganite Heterostructures
## 6 Future Prospect  
In summary, a comprehensive study of the dead layer of LSMO reveals the important role of intimate coupling between lattice, charge, orbital, and spin, in the transport and magnetism of LSMO. These couplings are sensitive to off-stoichiometry, orbital reconstruction, change of bandwidth, and Mn valence, leading to suppressed magnetism and insulating ground state. Besides, the disorder-induced electronic localization, which is amplified by reduced dimensionality, further prevents the ultrathin film from exhibiting metallic states, setting up an ultrathin limit of the dead layer. However, some questions or debates still remain. What is the actual physical consequence of the polar catastrophe due to the polar nature of LSMO? Is it the formation of oxygen vacancies or pure charge transfer? Can the observed small orbital polarization really can destroy the ferromagnetic ordering, and is it therefore is vital to induce the dead layer?  
The key to understand the properties of the ultrathin films will be advanced techniques that can probe materials at the atomic scale. Recently developed techniques such as STEM, PNR, and RXR, which can provide very local lattice, spin, orbital configuration, and electronic structure, may eventually reveal the origin of the thickness-driven metal-to-insulator transition and the dead layer. In this regard, we are optimistic to see more breakthroughs in the near-future to understand the dead layer by atomically resolving all of the orbital, spin, lattice and charge structures in the films.  
The understanding and control of the dead layer are also very attractive for technological applications, such as transistors, memory devices, and sensors. At the verge of the metal-to-insulatortransition, external stimulation, e.g., the magnetic field, field gating, elastic strain, or pressure, could switch the ultrathin layers from metal to insulator, or the other way around. A thin film with critical thickness can be very susceptible to external stimulations, and it therefore serves as a good material system for fabricating electronic devices. In this regard, more research on the control of the dead layers should be required.  
Writing-Original Draft Preparation: [PERSON] and [PERSON]; Writing-Review & Editing: [PERSON] and [PERSON].  
Research by [PERSON] was funded by Hundred Talent Program of the Chinese Academy of Science. Research by [PERSON] was funded by U.S. DOE under Grant No. DOE DE-SC0002136.  
The authors declare no conflict of interest.
## 1 Introduction  
With the rapid development of industrial technology, wastewater and emissions from factories are constantly released into the environment, energy is increasingly scarce today, and organic pollutants are found in natural water bodies [1; 2]. Dye wastewater is complex, highly colored, toxic, and difficult to degrade, which can make wastewater treatment more difficult and has a fatal impact on the environment [3]. Usually, physical or chemical methods are used to treat dye wastewater, but the cost is relatively high. Compared with the above methods, semiconductor photocatalytic technology has the advantages of non-pollution, good effect, and simple treatment methods, and has been verified by other researchers [4; 5]. Photocatalytic technology is currently an efficient method for treating environmental pollution, and further research is a very meaningful task [6; 7]. TiO\({}_{2}\) has stable chemical properties, low cost, high activity, and nontoxicity, making it the most promising photocatalyst in semiconductor photocatalysts [8; 9]. However, TiO\({}_{2}\) also has shortcomings, which are mainly manifested in three aspects: Firstly, the recombination rate of photogenerated electron-hole pairs is very fast, resulting in low photocatalytic activity of TiO\({}_{2}\)[10]. Secondly, the bandgap is wide, and the absorption range of the light is very narrow, and it can only absorb the ultraviolet range [10]. Thirdly, it is difficult to quickly separate TiO\({}_{2}\) from solution after photocatalytic degradation, which makes it difficult to recover the photocatalyst. If the photocatalyst cannot be recovered, it will also cause secondary pollution [11]. Therefore, how to improve the bandgap of TiO\({}_{2}\) to broaden the photosensitive range of light and increase the utilization rate of photocatalysts have become the focus of research in the field of photocatalysis. Currently, the main methods include metal ion doping [12; 13], non-metallic doping [14; 15; 16], and compounding with other materials [17]. Therefore, in this paper, N-doped TiO\({}_{2}\) multilayer core-shell structure photocatalysts were designed, and the preparation process of the magnetic photocatalysts Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@TiO\({}_{2}\) and Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@N-TiO\({}_{2}\) was explored experimentally. The samples were characterized by transmission electron microscopy (TEM), X-ray diffraction spectroscopy (XRD), UV-VIS diffuse reflectance spectroscopy (DRS), and X-ray photoelectron spectroscopy (XPS). The photocatalytic degradation experiments of MO were also carried out in a pure MO solution and a MO-Na\({}_{2}\)SO\({}_{4}\) mixed solution, respectively. The mechanism of photocatalytic degradation of MO and MO-Na\({}_{2}\)SO\({}_{4}\) was meticulously examined.
# Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
## 4 Experimental Validation  
### Evaluation Criteria  
To assess the precision and validity of the model prediction outcomes, three prevalently utilized metrics within the realm of aero-engine remaining useful life (RUL) prediction problems were chosen for evaluation in this paper, namely the mean absolute error (MAE), the root-mean-square error (RMSE), and the Score function (Score).
## 4 Results and Discussion
### Case Study for DMSA in Dynamic Scheduling  
4.2.1. Scenario Setting  
To verify the effectiveness of DMSA in dynamic scheduling, we set up several cases that involve emergency circumstances. In the first scenario, 30 initial point targets are randomly generated; target parameters are shown in Table 4. Moreover, another 5 high priority targets are added as emergency missions to test the dynamic scheduling algorithm, as shown in Table 5. We set a constellation of 2 orbital planes with 3 EOSs on each plane, payload types (visible light and microwave) and resolution of the EOSs are also randomly generated, as shown in Table 6.
## 2 Theory, Requirements and Synthesis
### Micro-Ring Resonator as a Bandstop Filter
#### 2.2.1 MRR Temperature Dependence Management  
Silicon photonic components exhibit large thermal drifts due to the high thermo-optic coefficient of silicon (\(1.87\cdot 10^{-4}\)/K at a wavelength of 1500 nm) [38]. This means that MRRs also exhibit significant thermal drift of their resonances. Silicon-on-insulator (SOI) MRRs typically exhibit a temperature-dependent wavelength shift of the order of tens of pm/K [39]. During our previous experimental work, we observed a degradation of the SBSR over time, which can be attributed to the described thermal drift of the MRR.  
Several approaches can be taken to tackle the temperature dependence of PIC components. We can use thermoelectric coolers to externally stabilize the temperature of the chip. However, such an approach is not very practical in terms of upscaling and for commercial applications. A standard procedure in silicon photonics is to co-integrate micro-heaters along with optical components on a PIC to precisely tune the temperature of each individual component [24]. The drawback of this approach is that it introduces additional complexity into the design and fabrication of the chip. It is also possible to exploit materials with a thermo-optic coefficient that is the opposite of Si as the WG cladding to achieve athermal operation of the components [40]. Several studies on athermal MRRs have been published, such as in [21], where the temperature dependence was reduced to 5 pm/K, as well as in [41]. The problem with this approach is that it requires the use of a different material than SiO\({}_{2}\) as the WG cladding, which also increases the complexity of the fabrication process. We can also employ different architectures that require a complex design, such as coupling an MRR with an MZI to compensate for the temperature drift and achieve athermal operation of the MRR [41].  
In the previously conducted research we chose to control this issue by manually adjusting the laser wavelength for the maximum SBSR. While this was acceptable at the experimental level, this problem needs to be tackled more conclusively for scalable application. Therefore, in this paper, we propose a novel approach to the laser carrier and the MRR's resonant-wavelength interlocking.  
Figure 1 shows a cyclic causation diagram. The primary problem (#1) is addressed by the MRR-enabled sideband suppression, causing the undesired RF power fading to reduce to an acceptable level. Unfortunately, due to the temperature dependence of a PIC and its hosted MRR (problem #2), the solution becomes ineffective over time and the primary problem is invoked once again. We can then repeat the tuning procedure we took in the first place to realign the system, but the issue will arise again. We conclude that the system is indeed unstable due to the temperature dependence of the MRR.
# AI-Driven Innovations in Software Engineering: A Review of Current Practices and Future Directions
## 3 AI Applications in Software Engineering
### Coding and Implementation  
#### 3.3.1 AI-Assisted Code Generation  
AI-assisted code generation represents a significant advancement in software engineering. Tools such as GitHub Copilot, powered by OpenAI's Codex model, have demonstrated the potential of AI to generate code snippets based on natural language prompts or partially written code.  
Developers can utilize AI to automate the creation of repetitive and structurally simple code constructs, such as loops, conditionals, or even entire functions. By providing a description or an initial code fragment, AI systems can predict and generate the subsequent code, thereby accelerating development and reducing manual coding effort.  
Although AI-generated code may not always be flawless, it serves as a valuable assistant by handling boilerplate code, enabling developers to concentrate on more complex and innovative aspects of the application. Furthermore, AI code generation can contribute to standardizing coding practices and reducing human errors.  
However, it is essential to consider the limitations and ethical implications associated with AI-assisted code generation. Issues such as code correctness, security vulnerabilities, and potential intellectual property concerns must be carefully managed [50].
# Malware Classification Based on Shallow Neural Network
## 5 Conclusions and Future Work  
In this paper, we proposed a malware classification method called SNNMAC, which uses a shallow neural network to learn from the assembly instruction n-gram dataset, which was generated by an improved n-gram algorithm based on control transfer instructions. We used the SNNMAC algorithm to complete a malware classification experiment for 10,868 samples from 9 malware families, and it achieved 99.21% precision and a recall rate of 99.18%. We also made a malware family classification experiment for comparison with other related works, and the SNNMAC model outperformed most of the other works with 99.21% precision and reduced training time greatly, compared with the related work. In order to reflect the objectivity of the experiment, two datasets were used in the experiment. It is shown that our approach bridges the gap between precise but slow methods and fast but less precise methods in existing work. In addition, it is more suitable for scenes with huge amounts of malicious code.  
The model proposed in this paper uses a single feature and does not perform well in an extremely unbalanced class. At the same time, this model relies on the assembly instructions obtained by disassembly, and it cannot effectively detect executable files processed by technologies such as packing.  
In future work, multidimensional features will be selected to express code features more comprehensively and further improve the classification effect of malicious code. Additionally, we plan to evaluate the SNNMAC model against other, larger datasets.  
Conceptualization, P.Y., Y.Z. and L.L.; methodology, P.Y. and L.Z.; software, L.Z., and Y.Z.; validation, H.Z., L.Z. and L.L.; formal analysis, P.Y., L.Z. and L.L.; investigation, P.Y., H.Z. and Y.Z.; resources, H.Z., L.Z. and L.L.; data curation, H.Z.; writing-original draft preparation, P.Y.; writing--review and editing, P.Y., L.Z. and L.L.; visualization, Y.Z. and H.Z.; supervision, P.Y. and L.Z.; project administration, P.Y. and L.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Method** & **Precision (\%)** & **Recall (\%)** & **Time (min)** & **F1 Value** \\ \hline \multirow{4}{*}{Common Machine Learning Classifier} & NB & 70.21 & 70.06 & 16 & 0.9838 \\ \cline{2-6}  & LR & 71.42 & 67.38 & 17 & 0.6934 \\ \cline{2-6}  & SVM & 54.84 & 28.75 & 18 & 0.3772 \\ \cline{2-6}  & RF & 84.46 & 82.34 & 16 & 0.8338 \\ \cline{2-6}  & XGB & 85.13 & 72.02 & 22 & 0.7803 \\ \hline \multirow{4}{*}{Methods in Related Works} & Kaggle Winner & 99.63 & 99.07 & 269 & 0.9920 \\ \cline{2-6}  & MalNet & 99.14 & 97.96 & 164 & 0.9865 \\ \cline{2-6}  & [PERSON] & 92.13 & 90.64 & 24 & 0.9138 \\ \hline SNNMAC & SNNMAC & 99.21 & 99.18 & 27 & 0.9886 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The results of the comparative experiments.
# 3-D Printed Fabry-Perot Resonator Antenna with Paraboloid-Shape Superstrate for Wide Gain Bandwidth
## 5 Conclusions  
For the FPRA, the wide bandwidth is an attractive property in many applications. A novel technology of designing a paraboloid-shape superstrate to enhance the gain bandwidth of the FPRA is proposed. Compared to a traditional planar superstrate, this cambered superstrate is able to produce multiple resonant heights, and thereby satisfy the resonant condition in a much wider frequency range. In this work, a sample FPRA working at 6 GHz is designed and tested, and the 3-D printing technology is employed for fabricating the prototype antenna. For this 3-D printed antenna, its measured results agree well with the simulated ones, which demonstrate the high fabrication precision of the 3-D printing technology. Moreover, in comparison with the traditional planar superstrate, the proposed paraboloid-shape superstrate significantly improves the FPRA's 3-dB gain bandwidth from 6% to 22.2%.  
This work was supported by the China NSAF Fund under Grant U1230112.  
[PERSON] proposed the idea and designed the antenna in the study and [PERSON] performed the experiment measurements and data processing. [PERSON] and [PERSON] wrote and revised the paper. All authors read and approved the manuscript for publication.  
The authors declare no conflict of interest.
## 3 Real-Coding PBIL Algorithm for Multi-Satellite Scheduling
### Fitness Function  
In the PBIL algorithm, the fitness function represents the direction of evolution. It determines which individual will be chosen to learn and generate the probability of value to guide the generation of offspring. Generally, different fitness functions can be established according to different optimization goals.  
In this paper, the objective function in the mathematical model is directly taken as the fitness function.
## 5 Implementation and Results
### Experiment 2
#### 5.2.1 Comparison between Experiment 1 and Experiment 2  
In this section, we compare the results of the first experiment, which used all of the features of the datasets along with Experiment 2, which selected the most informative subset of the features using the proposed wrapper selection method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Data Type**} & \multirow{3}{*}{
\begin{tabular}{c} **Before** \\ **Optimized** \\ **FS** \\ \end{tabular} } & \multicolumn{4}{c}{**After Optimized FS**} \\ \cline{3-6}  & & \multicolumn{4}{c}{**Algorithm**} \\ \cline{3-6}  & & **GWO** & **FPA** & **BA** & **ABC** \\ \hline \hline \multirow{2}{*}{\(\mathit{PBC}\)} & 20 & 6 & 13 & 4 & 12 \\ \hline \hline \multirow{2}{*}{\(\mathit{GE}\)} & 43,931 & 15,392 & 21,714 & 21,556 & 21,469 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Final optimal features.  
Figure 8: ROC curves of the proposed models on the PBC dataset.  
According to Figures 9 and 10, and Tables 6 and 7, we observed the following: From Figure 9, we can see that all classifiers' accuracies were enhanced after using the GWO, FPA, BA, and ABC-based wrapper methods. The best model that obtained the best accuracy on the PBC dataset was GWO-SVM (99.66%) in Experiment 2, while the DT classifier gave the highest accuracy of 95.5% in Experiment 1.  
On other hand, Figure 10 shows that all classifiers' accuracies were enhanced after using the GWO, FPA, BA, and ABC-based wrapper methods on the GE dataset. The best accuracy achieved in Experiment 2 was 99.34% for GWO-SVM, while the best accuracy obtained in Experiment 1 was 86.6% for the DT classifier.  
Moreover, the size of the features was reduced to 6 after using GWO for the PBC dataset and 15,392 for the GE dataset. Regarding AUC, F1 score, precision, and recall, they were also enhanced after using the GWO, FPA, BA, and ABC-based wrapper selection methods for the two datasets.
## 3 Underwater Channel Characterization
### Electro-Magnetic Properties of Water
#### 3.2.3 Electrical Conductivity of the Medium  
The conductivity of medium (\(\delta\)) is the overall effect over EM waves when they pass through the medium. It is calculated in Siemens per meter (S/m). When EM waves propagate through a conductive medium, they will reflect the EM waves. Conductivity of any medium depends on the number of ions present. When we consider water as a medium of propagation, which is conductive in nature, it will reflect EM waves. However, water medium itself can be categorized into three types with respect to its conductivity as shown in Table 3.  
The conductivity of the Red Sea is 8 S/m, whereas it is only 2 S/m in the Arctic. Commonly, for sea water, the conductivity \(\delta=4\) S/m [4; 22; 23]. This is 400 times higher than the conductivity of freshwater, which is typically around 0.001 S/m [11]. If EM waves will propagate through such mediums, they will be attenuated. Thus, conductivity of a medium has a direct relationship to the propagation of EM waves, _i.e._, with the increase in conductivity, attenuation will be increased and EM waves will travel less distance. The velocity of EM waves is also reduced because of the change of medium from air to water. The rate of reduction of EM waves is dependent on the relative dielectric permittivity \(\epsilon_{r}\), the relative magnetic permeability \(\mu_{r}\) and the electrical conductivity \(\delta\) and given by:  
\[v=\frac{c}{\sqrt{(\epsilon_{r})(\mu_{r})(\frac{1+\sqrt{1+(\frac{\delta}{( \omega\omega)^{2}})}}{2})}} \tag{4}\]  
Due to the factors of water listed above, the fundamental physical behavior of EM waves changes in UW environments. We further extend our discussion on EM propagation in fresh and sea water.