esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### MAC Based on TDMA Mechanism  
Building upon the foundational design of the ADHOC MAC protocol [19], [PERSON] et al. introduced VeMAC [20], a TDMA-based MAC protocol tailored specifically for Vehicular Ad Hoc Networks (VANETs). Capitalizing on the characteristic stability of vehicles traveling on highways and the consistent network topology in the same direction, VeMAC ingeniously partitions time slots into three distinct segments. Two of these segments are allocated for vehicles moving in opposite directions, while the remaining segment is reserved for roadside units. Each node accesses a different set of slots based on its identity and direction, thereby significantly reducing the potential for collisions. In a similar vein, to address the stringent bandwidth, data rate, and delay requirements of UAV ad hoc networks, a concurrent transmission MAC protocol was proposed in [25], which integrates millimeter-wave technology, directional antennas, and TDMA. A unifying slot assignment protocol (USAP) was proposed to manage the TDMA slot and channel assignments for optimal operation in the face of changing topologies and traffic requirements [32]. [PERSON]. [33] proposed a multi-agent DRL-enhanced USAP method to improve network utility in distributed implementation. Ref. [34] introduced a dual-cluster-head-based medium access control (DCHMAC) scheme for large-scale UAV networks, demonstrating significant throughput improvement. The inter-cluster communication and the one-hop communication uses a CSMA scheme. The nodes whose distances were beyond the one-hop range adopted TDMA.  
These protocols have successfully mitigated numerous collisions by segregating nodes based on their driving directions into separate time slot sets. However, the slot-partitioning strategies effective in VANETs are not directly transferable to UAV swarm networks. The inherent arbitrariness of UAV movement directions means that even with the VeMAC method, UAV networks still face collision issues. Furthermore, while the approach outlined in [25]demonstrates superior performance, its practical application in UAV systems is hindered by the complexities associated with millimeter waves and directional antennas.
## 3 Results
### Stand-Off and Pressure Analysis  
To study the effect of \(\gamma\) on the collapse, eight different stand-offs, in the range [0, 1.4], are simulated with \(P_{\infty}=5\) and 50 MPa. The upper limit \(\gamma=1.4\) was chosen since this study focuses on strongly deformed collapses, which are known to produce cavitation erosion [1,8]. In Figure 4, the dimensionless collapsing time and the maximal pressure at the wall are plotted for different \(\gamma\). The dimensionless time is defined as the ratio between \(t_{\gamma}\), the collapsing time obtained in the simulation for a given \(\gamma\), and \(t_{\infty}\), the collapsing time for \(\gamma\to\infty\)[17]. The dimensionless pressure is defined as the ratio between the maximal pressure at the wall and the characteristic pressure of a Rayleigh collapse [52]. The pressure at the wall is calculated at the green circle shown in Figure 1 positioned below the center of the cavity with a diameter of 0.01 mm.  
Figure 4: Dimensionless collapse time (**a**) and dimensionless pressure (**b**) for different stand-off and pressure (\(dL/R_{0}=40\)).  
Figure 3: Shape evolution with pressure and velocity field (\(dL/R_{0}=80\), \(\gamma=1.2\) and \(P_{\infty}=5\) MPa).  
The collapsing time shows a maximum for \(\gamma=1\) and a minimum for \(\gamma=0\). When \(\gamma=1\), the lower part of the cavity touches the wall, while the upper part is free to move (Figure 5c). During the collapse, the cavity loses its symmetry generating a re-entrant jet, which travels for the whole diameter of the cavity before reaching the surface. When \(\gamma=0\) the cavity reduces to a semi sphere. Therefore, the collapse is symmetric again (Figure 5h) and, in fact, \(t_{\gamma}/t_{\infty}\approx 1\) (Figure 4a) as for \(\gamma\rightarrow\infty\)[17].  
The pressure is shown in Figure 4b. The normalized maximum wall pressure is thought to scale with \(\gamma^{-1}\)[1, 6]. However, this was reported for values of \(\gamma>1.4\), which are outside the range investigated in this study. Other studies show results similar to ours in the range of investigation [9, 53].
# MT-FBERT: Malicious Traffic Detection Based on Efficient Federated Learning of BERT
## 4 Experiments
### Experiment Setup
#### 4.1.3 Evaluation Metrics and Implementation Details  
When evaluating the malicious traffic detection model, the results can be categorized as either correct or incorrect, with all possible outcomes falling into the following four scenarios: (1) True positive (_TP_): the attack samples are classified as attack, (2) True negative (_TN_): the normal samples are classified as normal, (3) False positive (_FP_): the normal samples are misclassified as attack, (4) False negative (_FN_): the attack samples are misclassified as normal. Based on the above definition, Accuracy (_AC_), Precision (_PR_), Recall (_RC_), and \(F1\) can be obtained:  
\[AC=\frac{TP+TN}{TP+TN+FP+FN}, \tag{7}\]  
\[PR=\frac{TP}{TP+FP}, \tag{8}\]  
\[RC=\frac{TP}{TP+FN}, \tag{9}\]  
\[F1=2\cdot\frac{PR\cdot RC}{PR+RC}, \tag{10}\]  
In pre-training, the batch size is 32 and the total steps is 100,000. We set the learning rate is \(2\times 10^{-5}\), and the ratio of warmup is 0.048, and the dropout rate is 0.1. We fine-tune with the SGD optimizer for 100 epochs, where the learning rate is set to \(8\times 10^{-4}\). In the simulated FL setup, we set 100 clients with 500 communication rounds and employ the Dirichlet data partition method to construct different label-skew data heterogeneity scenarios. In each communication round, we randomly select 10 clients for local fine-tuning using a linear decay of the global learning rate over rounds. We use FedAvg [38] for global model aggregation. We construct the Sub-T-Layer by compressing the intermediate 10 layers. All the experiments are implemented with PyTorch 1.9.0 and trained on a server with two NVIDIA Tesla T4 GPUs.
# Measurements and Modeling of the Nonlinear Behavior of a Guitar Pickup at Low Frequencies +
## 1 Introduction  
Physical modeling and synthesis of musical instruments has been an active research field for the last few decades [1; 2] including a guitar and an electric guitar [3; 4; 5; 6; 7; 8; 9]. One of the most important elements of an electric guitar is a pickup, a sensor that captures the string vibrations and translates them into an electric signal [10]. It is basically composed of a set of permanent magnets surrounded by an electric coil. A ferromagnetic string vibrating in the vicinity of the pickup results in a variation of the magnetic flux through the coil. According to [PERSON]'s law, an electrical voltage is then induced across the coil.  
A few models of pickup are available in the literature. Some of them are based on integral equations leading to the variation of magnetic flux at the coil location [11]. These models principally show, first, that vertical oscillations of the guitar strings produce a stronger effect than horizontal ones and, second, that there is a noticeable distortion of the electric signal generated by both oscillations. An overview of the modeling issues related to magnetic pickups is available in [12]. It concerns effects of both pickup position and pickup width on the pickup timbre, as well as the effect of the pickupinternal impedance. In [12], the magneto-electric conversion done by the pickup is modeled using static nonlinearity followed by a simple derivative (Figure 1). The static nonlinearity represents the nonlinear relationship between the string displacement and the magnetic flux, which can be evaluated using computer simulations and implemented as an exponential or \(N\)-th order polynomial [13]. A slightly different but very powerful approach based on physical-laws and on the framework of port Hamiltonian systems is used in [14; 15] to model an electro-mechanical piano (including a pickup).  
On the other hand, studies on nonlinear modeling have led to many nonparametric nonlinear models, discussed in Section 2, that have evolved through the last few decades. One of them, a Generalized Hammerstein model, has proved to be useful in modeling nonlinear systems with no physical knowledge of the system [16; 17; 18].  
The goal of this paper is to proceed with the identification of pickup linearities based on a Generalized Hammerstein representation of the pickup. For this purpose, a specific experimental set-up is used to drive the pickup in a controlled way, and a technique is carried out to get rid of nonlinearities due to the driver. One of the aims of this study is to find out if it is meaningful, or not, to use a simple Hammerstein structure given in Figure 1, as it is usually done in modeling the pickup nonlinearities [11; 12; 13], or if a more complex model, such as the Generalized Hammerstein one, is necessary. The answer is given through the measurement provided in Sections 4 and 5, and a comparison between theoretical and experimental results in the case of a realistic use of the pickup is given in Section 6.
# Smart Nanocomposites for Nanosecond Signal Control: The Nano4 waves Approach
## 3 Results and Discussion
### Properties of MNPs(Magnetic Nanoparticles)@NCS(Nanocarbon Supports) Nanoinclusions
#### 3.1.2 MNPs Spatial Distribution over the NCS (Nanocarbon Supports) Surface  
The SEM and TEM inspections of three nanocomposites prepared by the solvothermal method, where the Fe/NCS weight ratios were varied from 10% to 50%, revealed that the spatial distribution over the NCS surface remains homogeneous in all cases (see Figure 8). It also showed that the MNPs stack less onto each other at lower weight ratio, forming what appears to be single layers of MNPs deposited onto the NCS surface (Figure 8c). Nevertheless, it was also observed that some areas of the support are left uncovered when engaging the lowest weight ratio value (10%).  
Figure 7: SEM image of large micrometric agglomerates of functionalized silica-coated Fe\({}_{3}\)O\({}_{4}\) MNPs (indicated by orange arrows) found during the analysis of the covalently-linked products. These were found in close contact with GO sheets.  
The MNPs spatial distribution was also affected by the presence of oxygenated functions on the NCS surface. This effect was clearly observed when decorating pristine and oxidized MWCNTs using the solvothermal method at a 10% weight ratio (Figure 9). Indeed, smaller MNPs distributed along the MWCNTs surface when these were previously oxidized contrasted with only larger particle agglomerates found within the pristine MWCNT-supported samples. These results prove that the NCS oxygenated surface functions are serving as nucleation sites during the formation and growth of MNPs.  
Figure 8: SEM (left column) and TEM (right column) images of Fe\({}_{3}\)O\({}_{4}\)@GO nanocomposites produced via the solvothermal method [16] at 50% (**a**), 30% (**b**) and 10% (**c**) Fe/NCS loading rate.
## 5 Conclusions  
In order to realize the automatic and accurate positioning of shearers in underground mines, the LASC and CUMT adopt the same technical route, which includes three key technologies, the shearer inertial navigation system, the measurement of longwall retreat and creep displacements, and the backward calibration of the shearer trajectory. With INS as the core unit, the LASC and CUMT develop SPMS and SPAS, respectively. At the end of a longwall face, a scanning laser and a FMR 250 microwave radar are used to measure the retreat and creep displacements, and a UWB radar is adopted in CUMT to measure those two displacements with consideration of the complex environment with dust and water vapor. In order to perform backward calibration of the shearer trajectory, MFS and the GOM are proposed by the LASC and CUMT, respectively. Comparisons are made through the experiment, which shows that MFS has better performance in computation load, while the GOM achieves satisfactory accuracy. Next, a high efficiency and accurate algorithm to back-correct the shearer trajectory is our research goal.  
Conceptualization, W.D., S.W. ([PERSON]), and S.W. ([PERSON]); methodology, W.D. and S.W. ([PERSON]); software, W.D. and S.W. ([PERSON]); validation, S.W. ([PERSON]); formal analysis, W.D.; investigation, W.D. and S.W. ([PERSON]); resources, S.W. ([PERSON]); data curation, W.D. and S.W. ([PERSON]); writing--original draft preparation, W.D. and S.W. ([PERSON]); writing--review and editing, S.W. ([PERSON]); visualization, W.D. and S.W. ([PERSON]); supervision, S.W. ([PERSON]); project administration, S.W. ([PERSON]); funding acquisition, S.W. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, Grant Nos. 52304180 and Nos. 51874279, the Program of Ministry of Industry and Information Technology (TC220A04W-1,167), the Shanxi Key Research and Development Program of China (2021020101003) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
# A Graph Convolution Collaborative Filtering Integrating Social Relations Recommendation Method
## 5 Experiment
### Overall Comparison  
We compare all methods in this subsection. In Table 3, we show performance comparison between SRRA and baselines. The following conclusions can be drawn:  
First, methods that incorporate social relations outperform that does not. In Table 3, for example, DSCF, DiffNet, GraphRec, DICER, and SRRA outperform LightGCN. This demonstrates that social information is effective and helpful by being incorporated into recommender systems.  
Second, our method SRRA achieves the best performance on these four datasets. Specifically, in comparison to DICER, a GNN and DL-based social recommendation model, SRRA scores better by an average improvement of 2.27%, 2.85%, 5.58%, and 6.90%; and to GraphRec, a very expressive GNN-based social recommendation model, SRRA scores better by an average improvement of 5.55%, 6.44%, 13.42% and 13.08% on the four datasets, respectively. We guess a possible reason is that, for Brightkite and Gowalla, as they are social networks related to location, the activities and consumption preferences for users in this type of social platform is not easy to be affected; and for Epinions and LastFM, people strongly rely on social relations to acquire correct review of goods and lists of music they will listen to. It is possible to attribute our improved model over the baseline to two factors: (1) our model use GCN architecture to extract the social ties and interactive ties in a high-order way, which can leverage the relative information from multi-hop neighbors and high-order collaborative information propagated over the user interaction graph;  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Social Recommendation**} & \multirow{2}{*}{**DL**} & \multicolumn{2}{c}{**Graph-Based**} \\ \cline{3-4}  & & & **GNN** & **GCN** \\ \hline LightGCN & & & & \(\surd\) \\ DSCF & \(\surd\) & \(\surd\) & & \\ DiffNet & \(\surd\) & \(\surd\) & \(\surd\) & \\ GraphRec & \(\surd\) & \(\surd\) & \(\surd\) & \\ DICER & \(\surd\) & \(\surd\) & \(\surd\) & \\ SRRA & \(\surd\) & & & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the model’s characteristics.  
(2) we fuse all-order social and collaborative information when modeling user and item representation which generates improved user and item representation.
# Current Status, Issues and Future Prospects of Personalized Medicine for Each Disease
## 4 Alzheimer's Disease
### Establishment of Therapeutic Agents and Treatment Methods  
So far, there is no cure for AD and no way of slowing down the progression of this disease. This stems largely from our incomplete knowledge of the true pathophysiology of AD in which abnormalities of A\(\beta\) peptide and tau processing, inflammation, oxidative stress, and vascular risk factors, amongst others, are postulated to contribute [70]. Present therapeutic agents for AD consist of symptomatic therapies, which include the acetylcholine cholinesterase inhibitors (AChEIs), such as donepezil, and the N-methyl-D-aspartate (NMDA)-receptor antagonist mementine [71]. These therapies improve the symptoms of AD, such as cognitive decline and declines in the activities of daily living and behavior, but do not alter the underlying progression of the disease. Thus, there is an urgent unmet medical need for drugs that slow or prevent the progression of the disease.  
In addition, there have been many clinical trials of therapeutic drugs that radically change the progression of the disease so far, but they have not been approved as therapeutic drugs.  
However, on 7 June 2021, the U.S. Food and Drug Administration (FDA) granted accelerated approval for aducanumab (aducanumab-avwa; Aduhelm(tm); Biogen, Solothurn, Switzerland and Eisai, Tokyo, Japan) as the first and only AD treatment to address a defining pathology of the disease, by reducing A\(\beta\) plaques in the brain [72].  
On the other hand, the development of drugs targeting tau proteins is ongoing and although several clinical trials are currently underway, there are still no drugs that have been shown to be effective [73].
## 3 Results
### Stringent Conditions and Sedentary Lifestyle Signalling Molecules  
Given that the Rsm sRNAs expression and stability were increased under starvation conditions and that the bacterial stringent response, is mediated by the ppGpp, we evaluated the production of ppGpp under these culture conditions. Spectra were acquired with a time domain of 4 MW and a total number of 750 scans were accumulated (Figure 6A). As expected, the level of ppGpp is inversely proportional to the availability of nutrients (Figure 6B), while it evolves in the same way as the expression of Rsm sRNAs and in particular _rsmZ_[34].  
Moreover, since Rsm sRNAs promote sedentary state and multicellularity through biofilm formation, a process also coordinated by the bacterial second messenger, cyclic diguanylate monophosphate (c-di-GMP), we also measured its cellular level. The same trend as for ppGpp and Rsm sRNAs was observed, suggesting a link between Gac-Rsm system and these two signal molecules (Figure 6C) as previously shown for other _Pseudomonas_ species [35; 36; 37].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & & **Half-Lives (in Min)** \\ \hline
**Genes** & **GGA Motifs** & **GGA Motifs Exposed \({}^{*}\)** & **TSB/10 24 h** & **TSB 24 h** \\ \hline RsmX-1 & 6 & 2–4 & 37 \(\pm\) 4.4 & 23 \(\pm\) 5.4 \\ RsmX-2 & 6 & 3 & 40 \(\pm\) 8.6 & 16 \(\pm\) 1.5 \\ RsmY & 7 & 5–4 & \(\geq\)60 & 45 \(\pm\) 5.2 \\ RsmZ & 10 & 8–7 & \(\geq\)90 & 53 \(\pm\) 0.9 \\ \hline \hline \end{tabular} \({}^{*}\) predicted with Mfold and RNAfold softwares.  
\end{table}
Table 3: Rsm sRNAs stability is correlated to the number of GGA motifs. Relationships between the number of GGA motifs and Rsm sRNAs half-lives of _P. brassicacarum_ NFM421.
## 3 Dynamics Model of the Multistage Transmission System  
### Basic Parameters of the Research Objective  
The transmission system of a multistage gear train composed of a two-stage planetary gear train and a one-stage parallel-axis gear is shown in Figure 2. In Figure 2, 1r, 1p, 1s and 1c are the ring gear, planetary gear, sun gear and planetary carrier of the first-stage planetary gear train; 2r, 2p, 2s and 2c represent the corresponding units of the second-stage planetary gear train; 3g1 and 3g2 represent the driving gear and driven gear of the parallel-axis gear. The rated input power \(P_{rate}\) of the transmission system is 2.8 MW, the rated input speed \(n_{rate}\) is 18 rpm, the impeller radius \(R\) is 50 m, the maximum output speed \(n_{out}\) is 1887 rpm, and the transmission ratio \(i\) is 104.867. The basic parameters of the transmission system are shown in Tables 1 and 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Parameter** &
\begin{tabular}{c} **Planetary** \\ **Carrier** \\ \end{tabular} & **Ring Gear** & **Sun Gear** & **Planetary Gear** \\ \hline \multirow{7}{*}{First-stage planetary gear train} & Teeth number & - & 91 & 23 & 34 \\  & Module & - & 10 & 10 & 10 \\  & Pitch radius/mm & 285 & 455 & 115 & 170 \\  & Base circle radius/mm & - & 427.56 & 108.06 & 159.74 \\  & Mass/kg & 3240 & 448.96 & 918 & 756 \\  & Moment of inertia \(I_{1}\)/kg-m\({}^{2}\) & 680 & 65.4 & 35.4 & 26.8 \\  & Pressure angle \(\alpha\)/(\({}^{\circ}\)) & - & 20 & 20 & 20 \\  & Mesh stiffness K\({}_{\mathrm{sp}}\), K\({}_{\mathrm{rp}}\)/N-m\({}^{-1}\) & - & \(1.48\times 10^{10}\) & \(1.48\times 10^{10}\) & \(1.48\times 10^{10}\) \\  & Coupling stiffness & - & - & \(7.53\times 10^{8}\) & - \\  & K\({}_{\mathrm{1 gd}}\)/N-m-rad\({}^{-1}\) & - & \(121\) & 23 & 49 \\  & Module & - & 10 & 10 & 10 \\  & Pitch radius/mm & 360 & 605 & 115 & 245 \\  & Base circle radius/mm & - & 568.51 & 108.06 & 230 \\  & Mass/kg & 3240 & 961 & 1069 & 895 \\  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ gear train & Moment of inertia \(I_{1}\)/kg-m\({}^{2}\) & 680 & 245 & 42.9 & 33.4 \\  & Pressure angle \(\alpha\)/(\({}^{\circ}\)) & - & 20 & 20 & 20 \\  & Mesh stiffness K\({}_{\mathrm{sp}}\), K\({}_{\mathrm{rp}}\)/N-m\({}^{-1}\) & - & \(1.48\times 10^{10}\) & \(1.48\times 10^{10}\) & \(1.48\times 10^{10}\) \\  & Coupling stiffness & - & - & \(7.53\times 10^{8}\) & - \\  & K\({}_{\mathrm{1 gd}}\)/N-m-rad\({}^{-1}\) & - & - & \(7.53\times 10^{8}\) & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primary parameters of the planetary gear train.  
Figure 2: Structure diagram of the wind generator transmission system. 1r, 1p, 1s and 1c are the ring gear, planetary gear, sun gear and planetary carrier of first-stage planetary gear train. 2r, 2p, 2s and 2c represent the corresponding units of the second-stage planetary gear train; 3g1 and 3g2 represent the driving gear and driven gear of parallel-axis gear.