esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient MAC Protocol for Blockchain-Enabled Patient Monitoring in a Vehicular Network
## 2 Related Work
### MAC Protocols  
MAC protocols are mainly categorized as contention-based and contention-free MAC protocols. In contention-based MAC protocols, there are chances of collision, and this probability increases with the rise in more data requesting nodes. In such scenarios, contention-free MAC protocols are preferred. Multiple MAC protocols are designed for different applications.  
In [22; 23], MAC protocols are proposed by adjusting duty cycles according to the adaptive data traffic. The authors in [22] proposed a Priority-based algorithm for Adaptive Superframe Adjustment and GTS Allocation (PASAGA) that allows the PAN coordinator to adjust its duty cycle according to the data priority of GTS requesting nodes on their data requests. In [23], an optimized GTS (OGTS) allocation algorithm is proposed that allocates GTS to data requesting nodes optimally to improve its GTS allocation.  
TDMA-based MAC protocols to meet the adaptive data requirements of nodes are proposed in [24; 25]. Both of these MAC protocols offered bit map-assisted TDMA-based MAC protocols and allocated the unused TDMA slots to those nodes, which required more data slots in a session to offer better link utilization. The authors in [25] proposed Bit map-assisted Shortest Job First (BS-MAC) and applied the Shortest Job First (SJF) algorithm [25]. The authors claimed that their proposed algorithm reduces network delay and offers better link utilization. In [24], Bit map assisted Efficient and Scalable TDMA-based MAC (BEST-MAC) protocol is proposed that offered a knapsack algorithm [26] to optimally allocate slots to GTS requesting nodes to improve the link utilization.  
In [27], an adaptive collision avoidance scheduling is proposed for IoT networks. The proposed scheme is based on the traffic and priority of sensor nodes. The authors claimed that their proposed scheme improves the network performance as compared to a conventional scheme. In [28], an Efficient Superframe structure for adaptive Data requirement (ESAD) is proposed to meet the adaptive data requirement of wireless nodes. The authors proposed a superframe structure with an adaptive duty cycle for IEEE 802.15.4 standard. The authors claimed that their proposed scheme offers better bandwidth utilization by accommodating more GTS requesting nodes.  
_TAPD\({}_{VN}\)_ is designed to meet the adaptive data requirements between fog computing nodes and vehicles where the fog node acts as a cluster head and the vehicles node are attached with it. A brief comparison of all these MAC protocols is shown in Table 1.  
A key weakness of the current techniques in the literature is the fixed slot sizes for data transmission, which will not be suitable for varying the frequency of data transmission scenarios such as for blockchain mining data transmission. Moreover, the priority of the data is also not considered to maximize the slot utilization for high-risk patients data.
## 2 Results and Discussion
### Expression Levels of the TaTS Genes upon _Foc_ Induction  
It is common practice to gain insights into the expression level of TS genes by RT-qPCR with the beta-tubulin (housekeeping) gene as the internal reference [23]. Considering the contribution of _TaTS_ transcriptional profiles to the biosynthesis of diverse and distinct terpenoids, the mRNA levels of 10 _Tats_ genes upon _Foc_ induction were analyzed over 5 days using the RT-qPCR approach. As shown in Figure 2, the _TaTS_ genes were differentially expressed in the presence of _Foc_ and over the days of _Foc_ induction. Similar up-down transcriptional profiles were observed for the _TaTS1_, _TaTS7_, _TaTS8_, and _TaTS15_, with the highest levels (2.03-6.16-fold) on day 3. The _TaTS2_ and _TaTS4_ showed similar transcriptional profiles, peaking on day 1 (8.06- and 4.03-fold) and declining afterwards. The _TaTS9_ showed the most significant increasing trend of transcription from 3.14-fold on day 1 to 2793.12-fold on day 4, followed by decreased transcription to 1103.94-fold on day 5. The _TaTS3_, _TaTS5_, and _TaTS6_ were suppressed or not expressed in ACCC 33109 upon _Foc_ induction. The results indicated that the transcriptional patterns of seven _TaTS_ genes are complementary in functions (isozymes) and in chronological order, which contributed to the biosynthesis of diverse anti-fungal terpen upon _Foc_ induction. Since none of the _TaTS_s from ACCC 33109 have been experimentally characterized, it will be informative to understand the function of these genes via either the heterologous expression or the gene deletions. Therefore, the _TaTS3_, _TaTS4_, _TaTS8_, and _TaTS9_ genes showing representative suppressed or highly expressed transcription profiles were selected for function verification through the deletion of target genes.
# Low-Pass Filtering Method for Poisson Data Time Series
## 2 Method
### Quasi-Gaussian Filter Synthesis Procedure
Let us calculate the vector of factors \(a^{\circ}\), form the synthesized quasi-Gaussian filter FR \(H_{g}(w,w_{cg},a^{\circ})\) and define the cutoff frequency \(w_{cg}=0.175\). For the comparison, let us synthesize a common FIR filter using the fir1.mat module [3]. For the dimension \(r_{0}=8\) and the assigned cutoff frequency \(w_{c}=0.1\) we find out the final cutoff frequency \(w_{cf}=0.275\); let us denote the FR as \(H_{f}(w,w_{cf})\). On Figure 2, the FR plots for \(H_{0g}(w,w_{c})\), \(H_{g}(w,w_{cg},a^{\circ})\), \(H_{f}(w,w_{cf})\) are displayed. It is seen that, in case of low \(r_{0}\), the quasi-Gaussian filter FR was characterized by a better approximation to the prototype filter FR than the one of the common FIR filter. Figure 1: Plots of descent procedures—minimization of functionals \(S(m,I)\), \(I=1,2,\ldots,I_{0}\), \(m=1,2,\ldots,m_{0}\). Note that the proposed FIR filter, with the same dimension as the common FIR filter, made it possible to provide a lower value of the cutoff frequency than the realized cutoff frequency for the common FIR filter. The calculated cutoff frequencies of resulting FRs for common FIR filters synthesized using frequency sampling method and Fourier transforms [3,4] insignificantly (by \(\approx\)5-7% ) differ from the cutoff frequency \(w_{cf}=0.275\). This gives a reason to make a conclusion about the advantages of a quasi-Gaussian filter over standard FIR filters.
# A Novel Equivariant Self-Supervised Vector Network for Three-Dimensional Point Clouds
## 5 Limitations and Future Work  
In this study, the self-supervised learning equivariant vector network proposed by us can effectively handle various tasks under point cloud rotations and demonstrate excellent performance across different tasks. However, the datasets we use are relatively clean. Although some datasets, such as the HumanBody dataset, contain some degree of noise, it is mild, and the network can still robustly reconstruct them. But, in some complex scenarios or directly collected raw point cloud data, there is a large amount of noise unrelated to the objects themselves, disrupting the semantic structure of the point cloud, which may reduce the network's effectiveness in processing the point clouds. In the future, our method can be combined with denoising techniques by training diffusion models [66] to learn the noise in chaos point clouds and then denoise the point clouds to enhance the network's robustness to the different data source. This will enable the network to handle more realistic point cloud data and expand its applicability.
# Access Control and Pilot Allocation for Machine-Type Communications in Crowded Massive MIMO Systems
Several papers have proposed other mechanisms to alleviate the congestion caused by MTC communications. In [20], the traffic-aware ACB is proposed to reduce the congestion; however, ACB factor is just adjusted based on a function of the number of arrivals. Unlike the traditional access control approaches, the context-aware access control approaches [21; 22; 23; 24] consider a large amount of context information such as location, time and system resources. Therefore, they require that MTC devices have a large CPU capacity and large storage. Nevertheless, we would like to propose an access control protocol based on traditional access control architecture in which even the MTC devices with the moderate CPU capacity and small storage are able to access systems. In this paper, we propose a strategy to improve the access success rate between MTCDs and BS based on SUCRe protocol with access control that is implemented by the ACB scheme. We also propose a new ACB scheme that is called the dynamic ACB scheme. With the proposed dynamic ACB scheme, BS can adjust the barring factor dynamically after estimating the number of MTCD arrivals in the next time slot, therefore the system performance can be improved efficiently. Moreover, we investigate two different ACB schemes as the fixed ACB scheme and the traffic-aware ACB scheme for SUCRe protocol and RA method to confirm that ACB schemes can improve the performance significantly. A numerical simulation is realized to verify the performance of the proposed DACB-SUCR method. The rest of this paper is organized as follows. System model and preliminaries are presented in Section 2. The proposed solutions are discussed and explained in Section 3. In Section 4, we provide simulation results to illustrate the performance of our proposed method as well as the comparison with different existing approaches, and finally conclusions are given in Section 5.
## 3 Materials and Methods
### Adsorption Study  
The adsorption process is highly affected by several parameters, such as temperature, adsorbent dosage, particle size of adsorbent, solution pH, temperature, contact time, and stirring, among others [29]. Previous contributions developed by the authors have suggested the optimal operating conditions for lead uptake onto the selected biomasses [14; 26; 30; 31]. In this sense, adsorption tests were performed on batch mode under 200 rpm, 30 \({}^{\circ}\)C, using bioadsorbents of 1 mm particle size at pH = 6. An orbital shaker was used, the MAXQ 4450, and the pHmeter Start A221 (both provided by Thermo Fisher Scientific, Bogota, Colombia) to reach the correct stirring speed and adjust the solution pH. The 100-ppm aqueous solution was prepared by adding 1.598 g of lead nitrate- Pb (NO\({}_{3}\))\({}_{2}\) to 1000 mL of deionized water. After adsorption, the remaining concentration was measured by atomic absorption spectrophotometry at 217 nm. A central composite design of the experiments was selected to conduct adsorption tests according to the random starts point provided by the Statgraphics Centurion Software 16.1.15 (developed by Statgraphics Technologies, Inc.). The removal yield was calculated by Equation (1), where \(C_{0}\) is the initial concentration and \(C_{f}\) is the final concentration after adsorption [32].  
\[\%\;\text{Removal}\;(\text{R})=\frac{C_{o}-C_{f}}{C_{o}}\times 100 \tag{1}\]
## 1 Introduction
Recently, the introduction of Variable Refresh Rate (VRR) technology [12] in mobile display systems has added another layer of complexity. VRR dynamically adjusts the display update signals to optimize power consumption and visual performance, but it creates a non-static display noise environment, further complicating the design of touch sensing circuits. While frequency hopping based on three selective TSP driving frequencies was proposed [13], the uniformity of the capacitance (Cs) across the TSP channels varied depending on the selection of hopping frequency, making baseline calibration more challenging and necessitating advanced filtering and noise cancellation algorithms within the TDDI controller. Figure 1: (**a**) Stack-up of an in-cell LCD display panel and (**b**) a conceptual diagram of a dot-matrix self-capacitance (C\({}_{\text{S}}\)) touch screen panel. In this paper, we propose a reconfigurable selectivity Gaussian filter and a highly accurate Cs compensation circuit to effectively address the two aforementioned challenges. First, the 4-bit coefficient Gaussian filter is implemented using a discrete-time (DT) analog circuit within the analog front-end (AFE) of the TSP readout system. This approach eliminates the need for a complex and area-intensive multiplier logic typically required in digital implementations of Gaussian filters [13]. Second, the C\({}_{\text{S}}\) compensation is achieved through a charge-boosting scheme with an Operational Transconductance Amplifier (OTA) and reference integrated capacitor. The paper is organized as follows: Section 2 presents the proposed system architecture with detailed circuit implementations. Simulation results are provided in Section 3. A comparison of the proposed design with existing TSP readout systems is discussed and the conclusion is presented in Section 4.
# Study on Underwater Target Tracking Technology Based on an LSTM-Kalman Filtering Method
## 3 Simulation
### Construction of Array Signals and Generation of Datasets
#### 3.1.1 Generation of Dataset Samples for the Target Detection Model  
There are both transmitters and receivers in an active sonar system. The transmitter transmits signals continuously. The receiving array element starts to work after the transmitting signal has been transmitted for 1 s to reduce the impact of reverberation and the receiving time lasts for 20 s. The number of receiving array elements was 16 in this experiment, which were arranged as a half-wavelength linear array. At first, we conducted beamforming to enhance the received signal and then the short-term time spectrum could be obtained using the Fourier transform, which was used as the dataset for the target detection model. According to the different characteristics (propeller blade number, shaft frequency, motor noise frequency, signal-to-noise ratio, and target orientation) of targets to be classified, the sample number of each type of target was 2000. We added the background noise of the marine environment as a class of samples and the number of samples was 2000. Assuming that we simulated five types of targets in this experiment, the total number of samples in the final dataset was 12,000. Some of the training samples are shown in Figure 5.
## 3 System Model
Depending on the matching between the target quality level \(\bar{q}_{n}\) and the quality level \(q\) of the \(p_{m}\)-th segment to be received, \(t^{k,n}(p_{m})\) can have three values:  
* If the quality \(q\) is higher than the target quality \(\bar{q}_{n}\), the download time corresponds to the latency between the source \(k\)th fog node to the transcoding node plus the transcoding time from quality \(q\) to quality \(\bar{q}_{n}\) plus the latency from the transcoding node to the requesting user (case 1.A and 1.B in Figure 1). * If the quality \(q\) of the stored video and the target quality matches the latency, the download time corresponds to the time between the \(k\)-th fog node and the requesting user (case 2 in Figure 1). * If the quality \(q\) is lower than the target quality \(\bar{q}_{n}\), the system allows to gain the enhancement from the cloud to target the requested quality level. This results in the latency from the \(k\)th node to the requesting user of the \(q\) level segment plus the latency of the missing part from the cloud to the requesting node (case 3.A and 3.B in Figure 1). As an additional case, we consider when the segment is not present in any node. In that case, we suppose that it can be entirely downloaded from the cloud facility. Figure 1: The Proposed Fog-assisted DASH Architecture.
## 3 Beam Steering/Beam Forming Using Reconfigurable Components
### PIN Diodes
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Ref.** & **Unit Cell Technique** & \begin{tabular}{c} **Phase Control** \\ **Device** \\ \end{tabular} & \begin{tabular}{c} **Frequency** \\ **(GHz)** \\ \end{tabular} & **Polarization** & \begin{tabular}{c} **Phase** \\ **Range** \\ \end{tabular} & **Gain (dBi)** & \begin{tabular}{c} **Aperture** \\ **Efficiency** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Band** \\ **Width** \\ \end{tabular} &
\begin{tabular}{c} **Beam Scanning** \\ **Capacity** \\ \end{tabular} \\ \hline
[32] &
\begin{tabular}{c} PCB \\ stacked \\ patch \\ \end{tabular} & PIN diode & 5.4 & LP, CP & 1-B1T & 17 & 28.5 & 8.5 & \(\pm\)50\({}^{\circ}\) E and H plane \\
[73] &
\begin{tabular}{c} O-U slot \\ patches \\ \end{tabular} & PIN diode & 29 & CP & 1-B1T & 28.5 & 9.5 & 14.6\% & \(\pm\)60\({}^{\circ}\) E and H plane \\
[74] &
\begin{tabular}{c} O-slot \\ patches \\ \end{tabular} & PIN diode & 29 & LP & 2-B1T & 19.8 & 15.9 & 16.2\% & \(\pm\)60\({}^{\circ}\) E and H plane \\
[76] & \
\begin{tabular}{c} microstrip \\ Vivali \\ coupled \\ slot \\ \end{tabular} & PIN diode & 13.6 & LP & 1-B1T & 22.3 & 25.6 &
\begin{tabular}{c} 1.9\% \\ (1-dB) \\ \end{tabular} & \(\pm\)40\({}^{\circ}\) E and H plane \\
[80] & \begin{tabular}{c} Square ring \\ patch \\ \end{tabular} & PIN diode & 12.5 & LP & 1-B1T & 17 & 14 & 9.6\% & \(\pm\)50\({}^{\circ}\) E and H plane \\ H and I & \
\begin{tabular}{c} shape \\ coupling \\ slot \\ \end{tabular} & PIN diode & 12.5 & LP & 1-B1T & 17 & 14 & 9.6\% & \(\pm\)50\({}^{\circ}\) E and H plane \\ C-shaped &
\begin{tabular}{c} probe-fed \\ patch \\ \end{tabular} & PIN diode & 12.1 & LP & 1-B1T & 22.1 & 22.2 & 16\% & \(\pm\)60\({}^{\circ}\) E and H plane \\
[85] & \
\begin{tabular}{c} circular \\ rings \\ \end{tabular} & 5 & LP & 1-B1T & 16.8 & 18.4 &
\begin{tabular}{c} 17\% \\ (1-dB) \\ \end{tabular} & \(\pm\)40\({}^{\circ}\) E and H plane \\
[86] &
\begin{tabular}{c} annular \\ ring \\ patches \\ \end{tabular} & PIN diode & 14 & LP & 1-B1T & 20.4 & 33.4 & 33\% & \(\pm\)50\({}^{\circ}\) E and H plane \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of beam steering reconfigurable transmitarrays with PIN diodes. are obtained, respectively, at 5.5 GHz. The aperture efficiency of the polarization rotation transmitarray is 18.4%. In [86], the authors have presented a 10 \(\times\) 10 elements 1-bit reconfigurable transmitarray (RTA) with linear polarization at Ku-band. This transmitarray has the ability of beam steering for a wide range of frequencies. The reported unit cell consists of multiple-layer circular rings and substrates. Every element of the RTA has the capability to electronically control phase shift path difference for 0 and 180 states. The 3 dB gain bandwidth of the structure lies at 33% (12.5 GHz-17.5 GHz) at 15 GHz frequency. The effective aperture efficiency of the annular rings RTA is achieved up to 33.64% at 15 GHz. From 14 GHz to 17 GHz, two-dimensional beam steering with \(\pm\)60 degrees may be realized.
## 3 Materials and Method
### Definition of the Main Design Parameter and Design of Experiment
With this respect, 27 different gear geometries were created through the combination of the 3 parameters and 3 levels. These levels are:  
* \(m_{n}=2;5;8\) mm in compliance with the standard range [14];
* \(\alpha_{n}=15;20;25^{\circ}\) to include a relatively small angle (\(15^{\circ}\)), the European standard (\(20^{\circ}\)) [14], and the U.S. standard (\(25^{\circ}\)) [1]. * \(x^{*}=0;0.2;0.4\) to study the effects of standard gears (\(x^{*}=0\)) and positive shift (which improves tooth strength [1]) in a machining range common in many applications [1]. In the definition of the 27 geometries (listed in Table 1) the other parameters (i.e., \(z\), \(b\), \(h_{fp}^{*}\), \(h_{ap}^{*}\), \(\rho_{fp}^{*}\)) have been kept constant within the limits of the feasibility of the profiles. More specifically, \(z=24\), \(b=10\), \(h_{aP}^{*}=1\) for all the gear geometry. \(h_{fp}^{*}=1.25\) for all geometries except gear geometry number 10 and 19 (Table 1) where, due to profile manufacturability, \(h_{fp}^{*}\) has been selected equal to 1.30. Moreover, for the same reasons mentioned above, \(\rho_{fp}^{*}\) has been selected 0.380 except for geometries with \(\alpha_{n}=25\) (7-9; 16-18; 25-27 in Table 1) where \(\rho_{fp}^{*}=0.317\). The values of \(h_{ap}^{*}=1\), \(h_{fp}^{*}=1.25\), and \(\rho_{fp}^{*}=0.380\) are standard values according to [14]. It is worth mentioning that in the studied geometries the profile is constant along the whole width \(b\), i.e., there is no profile modification [70]. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \cline{2-10}
**Gear** & \(m_{n}\) & \(\alpha_{n}\) & \(x^{*}\) & \(z\) & \(b\) & \(h_{fp}^{*}\) & \(h_{ap}^{*}\) & \(\rho_{fp}^{*}\) \\ \cline{2-10}
**Geometry** & **mm** & \({}^{\circ}\) & **-** & **-** & **mm** & **-** & **-** & **-** \\ \hline
1 & 2 & 15 & 0 & 24 & 10 & 1.25 & 1 & 0.380 \\
2 & 2 & 15 & 0.2 & 24 & 10 & 1.25 & 1 & 0.380 \\
3 & 2 & 15 & 0.4 & 24 & 10 & 1.25 & 1 & 0.380 \\
4 & 2 & 20 & 0 & 24 & 10 & 1.25 & 1 & 0.380 \\
5 & 2 & 20 & 0.2 & 24 & 10 & 1.25 & 1 & 0.380 \\
6 & 2 & 20 & 0.4 & 24 & 10 & 1.25 & 1 & 0.380 \\
7 & 2 & 25 & 0 & 24 & 10 & 1.25 & 1 & 0.317 \\
8 & 2 & 25 & 0.2 & 24 & 10 & 1.25 & 1 & 0.317 \\
9 & 2 & 25 & 0.4 & 24 & 10 & 1.25 & 1 & 0.317 \\
10 & 5 & 15 & 0 & 24 & 10 & 1.30 & 1 & 0.380 \\
11 & 5 & 15 & 0.2 & 24 & 10 & 1.25 & 1 & 0.380 \\
12 & 5 & 1 In Figure 4, it is possible to see the 27 tooth profiles created according to the properties listed in Table 1. Each tooth profile has been associated with the number of the geometry to which it corresponds. In order to simplify visibility, all the profiles have been made homogeneous through a scale factor inversely proportional to the \(m_{n}\). In the figure, it is possible to notice how the profiles vary according the three design parameters studied.
## 2 Preliminaries
### Federated Learning
The further exploration of trade-offs in healthcare-specific use cases is necessary to determine the best approach for balancing privacy with performance. Privacy preservation is a critical aspect of federated learning, especially in telemedicine, where sensitive data like patient records must remain secure. Techniques such as differential privacy (adding noise to model updates to protect individual data), homomorphic encryption (allowing encrypted computations without decryption), and secure multi-party computation (enabling collaborative computations without sharing private data) are key methods used to enhance privacy in federated setups. While these techniques offer robust privacy protection, they come with trade-offs in computational complexity and model performance. Each technique's effectiveness depends on balancing privacy with efficiency, making them essential for ensuring secure, real-time applications in patient care.