esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network
###### Abstract  
With the continuous development of intelligent vehicles, people's demand for services has also rapidly increased, leading to a sharp increase in wireless network traffic. Edge caching, due to its location advantage, can provide more efficient transmission services and become an effective method to solve the above problems. However, the current mainstream caching solutions only consider content popularity to formulate caching strategies, which can easily lead to cache redundancy between edge nodes and lead to low caching efficiency. To solve these problems, we propose a hybrid content value collaborative caching strategy based on temporal convolutional network (called THCS), which achieves mutual collaboration between different edge nodes under limited cache resources, thereby optimizing cache content and reducing content delivery latency. Specifically, the strategy first obtains accurate content popularity through temporal convolutional network (TCN), then comprehensively considers various factors to measure the hybrid content value (HCV) of cached content, and finally uses a dynamic programming algorithm to maximize the overall HCV and make optimal cache decisions. We have obtained the following conclusion through simulation experiments: compared with the benchmark scheme, THCS has improved the cache hit rate by 12.3% and reduced the content transmission delay by 16.7%.  
vehicle edge network; cooperative cache; temporal convolutional networks +
Footnote †: journal: Sensors  
1
Footnote 1: School of Information Engineering, Henan University of Science and Technology,  
Luoyang 471000, China; [EMAIL_ADDRESS] (H.W.); [EMAIL_ADDRESS] (J.J.); [EMAIL_ADDRESS] (H.M.)
# Prediction and Analysis of the Surface Roughness in CNC End Milling Using Neural Networks
## 1 Introduction  
In metal cutting, the workpiece quality obtained by different machining parameters is also different. In the processing of the spindle, speed is fast and slow, the feed is high and low; in the processing of the chip, there are many factors affecting the final quality of the workpiece [1; 2; 3; 4]. The back propagation neural network is used to detect the surface roughness [5]. Bit diameter, cutting speed, feed and processing time are used as inputs to the neural network structure, and the surface roughness is estimated. Design of Experiment (DOE) is a method to arrange the contents of the experiment and analyze the experimental data by using optimal words. This method has a wide range of applications, including production process and equipment parameters, process methods, raw materials, or formula selection, to improve product quality or output [6; 7].  
There are many ways to predict surface roughness and analyze each machining parameter. At present, the common prediction method is to establish the prediction model by using the method of neural network [8; 9; 10; 11]. [PERSON] et al. [12] present the Artificial Neural Network (ANN) model for predicting the surface roughness performance measure in the machining process by considering the ANN as the essential technique for measuring surface roughness. In the prediction of surface roughness, different processing parameters can be used as input to neural networks [13; 14; 15; 16; 17; 18; 19; 20]. [PERSON] et al. [21] used the response surface methodology and the artificial neural networks approach for output modeling. The method of parametric analysis is also useful to analyze by many statistical methods. Surface roughness is not only an output but also an input to predict other processing parameters inneural networks. [PERSON] et al. (2022), based on the response of cutting force and surface roughness, used artificial neural network to predict the side wear of cutting tools.  
In this study, the machining method of milling was used for experiments, and the prediction was made by using the back propagation neural network, in which the input parameters were cutting depth, spindle speed, feed, milling pitch, and the output was the surface roughness of the workpiece. Linear regression was used to compare the root mean square error (RMSE) and ANOVA was used to analyze the relationship between parameters, and the influence of input parameters of output was discussed. In this study, the inverse transfer neural network was used for modeling. The input parameters were cutting depth, feed, spindle speed, milling pitch, and the output was workpiece surface roughness.  
The remainder of this study is organized as follows. Section 2 describes the prediction and analysis of the surface roughness, Section 3 presents the results and discussion, and the Section 4 offers conclusions for this study.
## 4 Fuzzy-Logic-Based Multi-Criteria Evaluation Metric for Spam Detection Approaches
### Decision Criteria for the Spam Detection Metric  
The first issue in the development of the fuzzy-logic-based metric is the identification of decision criteria. Decision criteria play a pivotal role in the decision process. The domain expert, who has complete knowledge of the problem under study, identifies the most appropriate decision criteria. Selection of inappropriate decision criteria leads to inaccurate solutions. In the context of the current study, the literature review has identified several commonly used metrics such as accuracy, recall, precision, and F-score. Note that accuracy, recall, and precision are based on the four primary level attributes given below, and F-score is a derived metric (considered as a third-level metric) based on recall and precision, and therefore is not considered as a decision criterion in this study. The four primary level attributes are as follows [15]:  
* True positive (\(T_{P}\)): a legitimate email correctly classified as a legitimate email.
* True negative (\(T_{N}\)): a spam email correctly classified as spam email.
* False positive (\(F_{P}\)): a legitimate email incorrectly classified as spam.
* False negative (\(F_{N}\)): a spam email incorrectly classified as legitimate.  
Based on the above four attributes, accuracy, which measures the proportion of the total number of correct classifications [15] is mathematically represented as follows:  
\[Accuracy=\frac{T_{P}+T_{N}}{T_{P}+T_{N}+F_{P}+F_{N}} \tag{1}\]  
Similarly, recall measures the number of correct classifications penalised by the number of missed entries [15], and is represented by the following equation:  
\[Recall=\frac{T_{P}}{T_{P}+F_{N}} \tag{2}\]  
Likewise, the precision is a measure of the number of correct classifications penalised by the number of incorrect classifications, as given by the following equation  
\[Precision=\frac{T_{P}}{T_{P}+F_{P}} \tag{3}\]  
It should be noted that while the F-score reflects the impact of precision and recall, no such measure exists that combines the effect of accuracy, precision, and recall in a single function. Therefore, there is a need to develop such a measure.
# Watermarking Protocols: A Short Guide for Beginners
## 2 Requirements
### Security Requirements
#### 2.2.5 Conspiracy Problem  
As reported above, many watermarking protocols need TTPs to ensure the correct execution of the protection scheme. However, the presence of a TTP may cause conspiracy or collusion problems. More specifically, a TTP, particularly if it is a WCA, might behave dishonestly and collude (1) with a fraudulent content provider or (2) with a malicious buyer to fabricate piracy. In the former case, the collusion could cause the same effects of the unbinding problem or the customer's right problem. In the latter case, the buyer could remove or partially corrupt the watermark embedded into the purchased content, thus cheating the tracing mechanism implemented by the watermarking protocol [16].  
Even though the watermarking protocols that do without TTPs appear to be more secure [13; 14; 17], they are often affected by usability problems. Indeed, the absence of TTPs ends up complicating the participation of buyers in the protocols, forcing buyers to carry out complex actions.
## 2 Materials and Methods
### Western Blot Analyses of Cell Lysates  
After treatments with blue-violet light, as described above, in the presence of lutein, aastaxanthin either alone, or in association at a final concentration of 100 uM, cell monolayers were washed twice with PBS and harvested mechanically using a cell scraper. Cells were centrifuged at 1000 rpm for 5 min at 25 \({}^{\circ}\)C, and pellets were lysed in RIPA buffer (Calbiochem-Merck, cat. no. 20188) supplemented with protease and phosphatase inhibitor cocktails (Protease Inhibitor Cocktail Set III EDTA-Free, Calbiochem-Merck, Cat. No. 539134; Phosphatase Inhibitor Cocktail 2, Sigma-Aldrich, cat. no. P5726, St. Louis, MO, USA; Phosphatase Inhibitor Cocktail 3, Sigma-Aldrich, cat. no. P0044, St. Louis, MO, USA) by incubation for 30 min on ice. Extracts were clarified by centrifugation at 13,000 rpm for 20 min at 4 \({}^{\circ}\)C and proteins in the supernatant quantitated by the BCA protein assay (BCA kit assay, Santa Cruz biotechnology, cat. no. 10410, Santa Cruz, CA, USA). For Western blot analysis, 25 ug of proteins were loaded onto 4-20% polyacrylamide gel (Mini-PROTEAN(r) TGXTM Precast Protein Gels, cat. no. 4561096, Bio-Rad Laboratories, Segrate, Italy) followed by electrotransfer to nitrocellulose membranes (Trans-Blot Turbo Mini 0.2 um nitrocellulose, cat. no. 1704158, Bio-Rad Laboratories, Italy). Membranes were then incubated overnight at 4 \({}^{\circ}\)C with primary antibodies (all diluted 1:1000): mouse monoclonal anti-Nrf2 antibody (Abcam, cat. no. ab89443, Cambridge, UK); rabbit polyclonal anti-Keap1 (Abcam, cat. no. ab139729, Cambridge, UK); mouse monoclonal anti-Heme Oxygenase 1 antibody (Abcam, cat. no. ab13248, Cambridge, UK); rabbit monoclonal anti-Phospho-Akt (Ser473) (D9E) XP(r) (Cell Signaling Technology, cat. no. 4060, Danvers, MA, USA); rabbit polyclonal anti-Akt (Cell Signaling Technology, cat. no. 9272, Danvers, MA, USA) and rabbit monoclonal anti-GAPDH (Cell Signaling, Leiden, The Netherlands, Cat. No. 2118). Further incubation with IgG-HRP-conjugated anti-rabbit or anti-mouse secondary antibodies (Amersham, GE Healthcare, Bloomington, IL, USA, Cat. no. NA934V) diluted 1:2000 was completed for 1 h at room temperature. The immune complexes were detected by enhanced chemiluminescence (ECL Super-SignalTM West Dura Extended Duration Substrate, Thermo Fisher Scientific, cat. no. 34075, Waltham, MA, USA) with the Chemi-DocTM Touch Imaging System (Bio-Rad, Hercules, CA, USA). The intensity of protein bands was quantitated by ImageJ Software [33].
# Communication Architecture in Mixed-Reality Simulations of Unmanned Systems
## 7 Experiments  
The main motivation of the work presented herein is to support the development of complex UAVs via communication simulation, employing the concept of MR simulations. Our goal is to provide the same communication characteristics of systems using only HWEs in systems using SWEs. Therefore, we designed the experiments to validate the proposed communication architecture and to show that the communication characteristics of a testbed configuration with solely HWEs present can be substituted with the characteristics of MR testbeds where some of the HWEs are modeled usingSWEs. The experiments measuring the data rates were chosen since they well illustrate the behavior of the system in various settings. This behavior is expected to be similar when other communication characteristics are measured.
## 3 Results and Discussion
### Thermo-Mechanical Properties  
Dynamic mechanical analysis (DMA) was conducted to establish the influence of E-POSS and GF on the storage modulus (E) and glass transition temperature (T\({}_{\text{g}}\)) of the cured epoxy resins. As shown in Figure 6A, the incorporation of E-POSS and GF led to the higher storage modulus of the obtained composites in comparison with the pure E51 sample, indicating that the crosslinking density of the E51 + E-POSS (10%), E51 + GF (16%), and E51 + E-POSS (10%)+GF (16%) samples may be higher. The E51 + E-POSS (10%) + GF (16%) sample showed the maximum storage modulus value. In other words, the E51 + E-POSS (10%) + GF (16%) sample exhibited a highest stiffness even in comparison with the E51 + E-POSS (10%), E51 + GF (16%), and E51 samples.  
Figure 6: DMA spectra of the different samples. (**A**) Storage modulus, (**B**) Tan \(\delta\).  
The curves of tan\(\delta\) showed the viscoelastic transition of the cross-linked polymers, and further exhibited the structural information of the cured resins [29]. Generally, there is a one-to-one relationship between the glass transition temperature (T\({}_{\text{g}}\)) and the tan\(\delta\) peak temperature (T\({}_{\text{p}}\)); T\({}_{\text{p}}\) is often designated as T\({}_{\text{g}}\)[30]. From the tan\(\delta\) curves (Figure 6B), it can be found that the incorporation of E-POSS and GF led to a slight increase in T\({}_{\text{g}}\) of the obtained samples in comparison with the pure E51 sample. It was illustrated that the T\({}_{\text{g}}\) of E51 resin can be increased by adding a proper amount of E-POSS or GF. This is caused by the reactive epoxy group of E-POSS and the hydroxyl group of GF. The reactive group of E-POSS can directly participate in the curing reaction, forming a curing composite with the PEPA, and produce a higher cross-linking density structure. With the increase in the cross-linking density, the molecular chain segments between cross-linking bonds are bound more strongly, and the glass transition temperature of the epoxy resin composite will increase correspondingly. In addition, the test curve of all samples contained one simple tan\(\delta\) peak, suggesting that the epoxy resin was homogeneous. According to DMA analysis, the higher the cross-linking density, the higher the T\({}_{\text{g}}\). These results were consistent with the results reported in the literature [31; 32].
# Attention-Based LSTM Algorithm for Audio Replay Detection in Noisy Environments
## 5 Experiments and Results
### Experimental Setup
#### 5.2.1 Baseline  
The baseline system of the ASVspoof 2017 Challenge used CQCC features [10] with a standard binary GMM classifier for genuine and spoof utterances [3]. For each utterance, the log-likelihood score was obtained from the two GMM classifiers and the final classification decision was based on the log-likelihood ratio. For the experiments in noisy environments, in order to be consistent with the given baseline in the clean background environment [3], we still used CQCC-GMM as the baseline.
## 2 State of the Art
Torsional resonant column tests were performed to measure \(G\) and \(D\) of the small strain rate of 0.0001% to 0.1%. Their values at a large strain rate (0.1% to 10%) were measured through cyclic triaxial test. The results showed that shear modulus and damping ratio of the mixtures were strongly influenced by the percentage of rubber inclusion. Shear modulus decreased with an increase in tire crumb inclusion for all confining pressures, whereas damping ratio increased with the increase in rubber content in STCM. For any percentage of tire crumbs inclusion, shear modulus increased and damping ratio decreased with increasing confining pressure. [PERSON] et al. [21] also conducted torsional resonant column and dynamic triaxial experiments of sand-granulated rubber mixtures. They examined the effect of rubber content and ratio of mean grain size of rubber solids versus soil solids (\(D_{50,r}/D_{50,s}\)) on dynamic response of these mixtures, in a range of low to high shearing strain amplitude, from about \(4\times 10^{-4\%}\) to 2.7%. Similar conclusions, as in the case of previously cited studies, were drawn. It is worth emphasizing that a decrease in \(D_{50,r}/D_{50,s}\) caused the mixture to exhibit more rubber-like behavior. Regardless of immense work performed by various researchers to determine physical, chemical, mechanical and, sometimes as well, dynamic properties of RCA and rubber-soil mixtures, the authors were unable to find the detailed study of \(V_{S}\) of RCA improved with rubber grains, covering the wide range of strains, from small to moderate strains (0.0001% to 0.1%). Therefore, such a complete study on RCA with rubber chips will help to fill this gap in the geotechnical engineering knowledge, provide new information about this modified anthropogenic soil and may be very useful for selecting the shear modulus and damping values associated with field events, e.g., machine foundations, motion characteristics or earthquake motion. Nowadays in Poland, in the field of transportation and/or road investments, anthropogenic soils can be easily located. The decision either to leave them, or to exchange for a better material, must result from studies of their behavior. In addition, the rapid development of modern civil engineering constructions as well as EU directives force engineers to use anthropogenic soil nevertheless. It must be supported by the extensive knowledge of their engineering properties, including dynamic ones.
# 3 GPP 5G V2X Scenarios: Performance of QoS Parameters Using Turbo Codes
## 1 Introduction and Related Work
Therefore, the main contributions of this paper are summarized as follows:  
* Specific 3 GPP 5G V2X scenarios of Release 16 are simulated applying the LTE turbo coding scheme, considering also different vehicle densities, vehicle speeds and frame sizes. * For the implemented 3 GPP V2X scenarios an optimization analysis was conducted for specific SNR values based on the channel decoding algorithm (log-MAP, max-log-MAP and SOVA). The aim is specific QoS specifications to be satisfied such as reliability, end-to-end latency and throughput. * Finally, the simulation results of this paper can be used as reference for the training of a future dynamic physical layer channel coding selection scheme (which emphasizes on selecting the appropriate turbo coding parameters). This scheme will do the selections on a multi\(-\)level QoS parameter indicator depending on the observed traffic conditions using a machine learning (ML) procedure.
# Clone Node Detection Attacks and Mitigation Mechanisms in Static Wireless Sensor Networks
## 3 Cybersecurity Attacks in Wireless Sensor Networks
### Clone Node Attack (Replication Attack) in Static Wireless Sensor Networks  
Wireless sensor networks (WSNs) are essentially categorized into two types, _static_ and _mobile_ WSNs. As stated in some previous pages, in static WSNs, once the sensor nodes are deployed, their location remains the same, i.e., remains fixed for data distribution, compared to mobile WSNs, where nodes can shift and move freely after deployment. Both of these types are subject to clone node attacks and, hence, replication attacks.  
A replication attack is considered one of the most dangerous attacks on wireless sensor networks. In this attack, after the adversary captures the legitimate nodes and extracts their credentials, he then deploys them to many positions in the network to perform some additional types of attacks. The attacker may isolate several legitimate nodes and replace them with his illegitimate ones. This facilitates him in obtaining higher control of the network.  
Therefore, based on this fact, it is imperative that nodes are detected as soon as possible, which is a tough task. The Figure 5 illustrates the steps of how a replication attack in WSNs works. The convenient aspect in this matter is that, luckily in static WSNs (as its name says), sensor nodes have fixed positions, which makes the detection procedure easier than in mobile WSNs.
# A Location and Velocity Prediction-Assisted FANET Networking Scheme for Highly Mobile Scenarios
## 1 Introduction  
With the advent of huge numbers of small-size and lightweight gadget devices for remote wireless communications, ad hoc networking encounters multiple far reaching applications, such as military, crisis scenarios, logistics and transportation, etc. [1]. Possessing the merits of high mobility and high adaptability, the ad hoc network can be utilized in those disaster scenarios or restricted places without wireless signals. Furthermore, multiple nodes are arranged as a swarm to cooperate with each other to accomplish more complicated tasks such as rescuing, searching, etc. Generally speaking, the ad hoc networks can be classified into several types based on their different applications, such as the mobile ad hoc network (MANET) for cellular phone scenarios, vehicular ad hoc network (VANET) for vehicle scenarios, wireless sensor network (WSN) for sensor scenarios, and the flying ad hoc network (FANET) for unmanned aerial vehicle (UAVs) scenarios, etc. [2]. Currently, the FANET is attracting increasing interests from academia, industrial and military fields with advantages of high mobility and flexibility.  
In FANET, multiple nodes are organized cooperatively and collaboratively to complete difficult missions with high efficiency [3]. Nonetheless, the FANET encounters some new challenges such as high mobility, frequent topology variations, external disturbances and non-negligible packet loss rate, etc., especially in highly mobile scenarios for large-scale networks. The self-organized networking is the fundamental path to the intelligent ad hoc network, where nodes can manage themselves and are grouped into several clusters even after being disturbed by the external interference [4]. Usually, the network management of a FANET is highly related to nodes' movement velocities and positions and it aims to formhighly adaptive self-organized groups with low message exchange overheads. However, the high mobility leads to severe challenges. On one hand, highly mobile node-to-node links experience severe Doppler frequency shift caused by relative velocity difference, increasing packet loss rate. On the other hand, nodes with high velocities are more vulnerable to external disturbances such as winds, unsteady airflow, control error, etc. As shown in Figure 1, all nodes are grouped into two clusters, and two cluster heads (CHs) are elected. For highly mobile FANETs such as UAV swarms and missile swarms, nodes are influenced by external disturbances and deviate from predefined routines when proceeding, which may cause the new selection of CHs. Consequently, a novel FANET networking scheme for highly mobile scenarios is pivotal for further routing protocol design and network performance optimization.
# Attosecond Time Delay in Photoionization of Noble-Gas and Halogen Atoms
## 3 Results and Discussion
### Phase Shift and Time Delay in the Vicinity of Giant Resonance and Cooper Minimum
For example, in the \(5p\rightarrow\varepsilon d\) channel, the phase shift shows a wider Cooper minimum at 150 eV than at 50 eV, thus the time delay sink at 150 eV is broader. The time delay in ionization from xenon and iodine 5s subshell is determined by a single ionization channel \(5s\rightarrow\varepsilon p\). The 5s time delay shows a deep narrow negative sink at 30 eV, and a shallow broad negative sink at 130 eV, which are both determined by the phase shift at the Cooper minima. However, as mentioned previously, the LDA approximation is unable to reproduce the \(\beta\) parameter deviations at the Cooper minima [67; 68; 69], which raises concerns about the qualitative accuracy of current phase shift calculations for \(s\) subshell. In order to reproduce the correct \(\beta\) parameter and phase shift, one needs to include the initial-state correlations and relativistic effects. The cross section, \(\beta\) parameter, phase shift and time delay calculations for krypton, bromine, argon and chlorine are presented in Figures 2 and 3. When the atoms become lighter, the dependence of these quantities on photon energy becomes less structured compared to xenon and iodine. Except the Kr 3\(d\) cross section shows a very broad giant resonance, ionizations from other subshells just contain a single Cooper minimum. The behavior of time delay near the vicinity of giant resonance and Cooper minimum is similar to the case of xenon and iodine.