esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN
## 4 Proposed Push-Based Data Forwarding Scheme with Fuzzy Logic
### Selection of Cluster Head (CH) using Fuzzy Logic
#### 4.2.2 Speed (Mobility)  
The speed of a node/vehicle is also an important parameter for selecting the CH. If the speed is higher, it will move quickly and change its position frequently. Therefore, a node/vehicle's speed impacts the control system, especially the fuzzy logic controller. The speed and mobility of a node/vehicle can be calculated by Equation (6).  
\[speed_{vi}(m/s)=\bigg{(}\frac{speed_{vi}}{\max_{i=1}^{n}speed}\bigg{)}\times 100 \tag{6}\]  
Each vehicle's speed \(vi\) is normalized by dividing it by the maximum speed. Subsequently, the percentage of all the speed is forwarded to the fuzzy logic controller.
# Analysis of Clinical Profiles, Deformities, and Plantar Pressure Patterns in Diabetic Foot Syndrome
## 2 Materials and Methods
### Data Analysis and Statistics  
Descriptive statistics were done on the following variables, grouped as follows:  
* Passive joint mobility and alignment (all measured in degrees) [15]: maximum passive ankle dorsiflexion (TT dors); maximum passive ankle plantar flexion (TT plant); maximum passive ankle inversion (TT supin); maximum passive ankle flexion (TT pron); maximum passive first metatarsophalangeal joint extension (I MTP ext); maximum passive first metatarsophalangeal joint flexion (I MTP flex); hindfoot inclination in the frontal plane (hindfoot incl); hallmark inclination in the transverse plane (hallux incl);
* Biology and clinics: age (years); body mass index (BMI, kg/m2); years of disease (YOD, years); combined neuropathy score (NS-VPT, relative unit), which combines scores from the MNSI with the vibration perception threshold (VPT, volts) at both limbs [11]; mean glycated hemoglobin (Mean HbA1c, %) averaged over the 48-month follow-up; minimum HbA1c (MIN HbA1c, %) and maximum HbA1c (MAX HbA1c, %) recorded over the 48-month follow-up; pain visual analogue scale questionnaire (VAS, 10-points score) for each limb; Manchester Oxford Foot Questionnaire (MoX, 16 items, max score 64) [16];
* Overall gait performance: dynamic arch index (AI, relative unit), cadence (cad, steps/min), foot contact time (CT, ms), velocity (vel, m/s);
* Plantar peak pressure distribution: peak pressure during gait (PP, kPa) at the hindfoot, midfoot, forefoot, big toe, and toes; dominant (D) and not-dominant (ND) limbs were analyzed separately; and
* Plantar pressure-time integral distribution: pressure-time integral during gait (PTI, kPa-s) at the hindfoot, midfoot, forefoot, big toe, and toes; dominant (D) and not-dominant (ND) limbs were analyzed separately.  
The whole group of patients (ALL), the six clinical subgroups, and the patients with lesions' (PWL) group were examined. One-way ANOVA was implemented to highlight differences among these groups (\(p<0.05\), adjusted for Tukey post-hoc comparisons). The coefficient of variation (CV) was calculated for each variable and group, and averaged (mCV) within each group of variables. The profile of each patient with lesions was finally compared with those of the corresponding group.  
Confidence intervals (95% CIs) were calculated for each weight-bearing CT-related variable for the two subgroups of type 1 patients (with (DNT1) and without (DNNT1) neuropathy. Those intervals allowed the exploration of changes in the small subset of patients with lesions who also underwent the WBCT examination. The R Foundation environment was used for the statistical analysis.
## 3 System and Link Level Simulators
The simulator enables LL simulations both for Orthogonal Frequency-Division Multiplexing (OFDM) and Filter Bank Multicarrier (FBMC) signals. In [62], an extension of the GTEC simulation testbed is presented ([63]), for LL simulations in 5G networks. It includes fully functional transmitter and receiver implementations, as well as different channel models and functions for processing the obtained results. In [64], a novel SL simulator framework is introduced, where remote radio head (RRH) and the corresponding eNodeB can have a different cell ID. In [65], a new machine learning (ML) based link-to-system (L2S) mapping technique for inter-connecting an LL simulator and an SL simulator is presented and evaluated. In this context, overall computational complexity is reduced while block error rates (BLERs) prediction is improved. In [66], a new design of the SL 5G simulation platform is presented. The described simulator can support different types of networks as well as concurrent transmissions over multiple radio-access technologies for each single user, supporting either single or multiple services. In [67], a simulation analysis algorithm for 5G systems is introduced, which combines BS dormancy technology and existing power control technology, aiming at reducing BS energy consumption and improving network energy efficiency. In [68], the current status of the ns-3 framework for simulation of mmWave cellular systems is presented. The relevant implementation code, which is publicly available at GitHub [69], is highly modular and customizable to allow researchers to test novel 5G protocols. Mathworks has released the 5G Toolbox(tm) [70] in the framework of Matlab(c), that provides standard-compliant functions and reference examples for the modeling, simulation, and verification of 5G communications systems. The toolbox supports LL simulations, golden reference verification and conformance testing, and test waveform generation. The NetTest 5G Network Emulator solution [71] from Polaris Networks simulates various aspects of a 5G network. The protocol functions and interfaces of the NetTest 5G Network Emulators are implemented based on 3 GPP specifications that allow users to simulate various network operations and procedures in the lab. The emulator includes, among others, a Graphical User Interface (GUI) as well as an Application Programming Interface (API) for test automation. Finally, device-oriented simulators, such as ANSYS 5G simulation solutions [72], can simulate antenna-to-antenna coupling and environmental effects on signal propagation to improve capacity and data rates for wireless applications, as well as advanced RF front-end designs and Electromagnetic Interference (EMI) mitigation techniques in 5G smartphones.
## 3 Methodology
### The Loss Functions
#### 3.2.3 Log-cosh Loss  
The Log-cosh is the hyperbolic cosine of the difference between the predicted value, \(y_{p}\), and the true value, \(y_{t}\). The notion of the Log-cosh loss is described as follows:  
\[Log\_cosh\ =\ log(cosh(y_{\ p}-y_{t})) \tag{5}\]  
where we have  
\[\cosh(y_{p}-y_{t})=\frac{e^{(y_{p}-y_{t})}+e^{-(y_{p}-y_{t})}}{2} \tag{6}\]  
More specifically,  
\[Log\_cosh\big{(}y_{p}-y_{t}\ \big{)}=log(\frac{e^{(y_{p}-y_{t})}+e^{-(y_{p}-y_{t}) }}{2})\]  
The Log-cosh function is smoother than quadratic loss. It works like L2, but it is not affected by substantial prediction errors, and it also mimics a smoothed version of the L1 loss that is differentiable everywhere [38].  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Description** \\ \hline \(\overset{\rightarrow}{x}\) & Input image \\ \(y_{t}\) & Target ground truth of beauty score \\ \(\overset{\rightarrow}{y}\) & The average of the ground-truth scores \\ \(y_{p}\) & Predicted beauty score \\ \(\overset{\rightarrow}{p}\) & The average of the predicted scores \\ n & Number of data samples \\ \(f0\) & Learning function or algorithm \\ \(w\) & Weight of the input data \\ \(L\) & Loss function \\ \(j\) & Number of loss functions \\ L1 & L1 loss \\ L2 & L1 loss \\ Log-cosh & Hyperbolic cosine h \\ \hline \end{tabular}
\end{table}
Table 1: The description of the pertinent variables and notations.
## 5 Conclusions  
The OFDM signal in the UHF band for various broadcasting and communication services can severely aggravate the selectivity performance of the MedRadio RF receiver through the IM2 distortion. We have presented an analytic investigation on how the OOB OFDM signal induces the IM2 distortion and leads to the SNR degradation at the RF receiver. We also have performed theoretical analysis on how the OFDM-induced IM2 distortion can be equivalently translated to a two-tone-induced IM2 distortion. As a result, we have introduced an offset parameter \(P_{\textit{offset}}\) for compensating the difference between the multi-tone and two-tone effects. The designed MedRadio low-IF RF receiver is fabricated in a 65 nm CMOS process. Two design techniques have been described, one for the IM2 calibration through the gate bias tuning at the passive mixer's FETs, and the other for the dc offset calibration through the body bias tuning at the subsequent TIA's FETs. The proposed RF receivers have shown significant OOB CIR improvements, and the measured maximum tolerable CIR performances are between \(-40.2\) and \(-71.2\) dBc for the two-tone blocker and between \(-70\) and \(-77\) dBc for the single-tone blocker. To the author's best knowledge, this work is the first to present the analytical and experimental investigations on the OFDM-induced OOB selectivity performances in the MedRadio RF receiver for the biomedical and biosensor applications. The results of this work will be essential to enhance the selectivity performance of the MedRadio RF receiver and guarantee reliable and robust wireless communication services in implanted and wearable biomedical devices.  
Conceptualization, Y.L., S.C. and H.S.; circuit design and measurements, Y.L., J.K. and H.S.; formal analysis, H.S.; investigation, Y.L. and S.C.; data curation and visualization, Y.L., J.K. and H.S.; supervision, H.S. All authors participated for all other aspects. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea under Grant No. 2020R1A2C1008484 and Kwangwoon Research Grant in 2020.  
Not applicable.  
The authors declare no conflict of interest.
Entropy_**2013**, _15_, 4993-5011; doi:10.3390/e15114993  
_Entropy_**ISSN 1099-4300**  
www.mdpi.com/journal/entropy  
_Article_  
**Optimal Entropy-Based Cooperative Spectrum Sensing for**  
**Maritime Cognitive Radio Networks**  
**[PERSON] \({}^{1}\), [PERSON] \({}^{2}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{1,*}\)**  
\({}^{1}\) Department of Information & Communication Engineering, Sejong University, Seoul 143-747, Korea;  
E-Mails: [EMAIL_ADDRESS] (W.E.); [EMAIL_ADDRESS] (N.H.)  
\({}^{2}\) Al-Khawarizmi Institute of Computer Science, University of Engineering and Technology, Lahore 54890, Pakistan; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-70-71371237; Fax: +82-2-3408-4330.  
_Received: 8 September 2013; in revised form: 21 October 2013 / Accepted: 30 October 2013 / Published: 15 November 2013_  
**Abstract:** Maritime cognitive radio networks (MCRNs) have recently been proposed for opportunistic utilization of the licensed band. Spectrum sensing is one of the key issues for the successful deployment of the MCRNs. The maritime environment is unique in terms of radio wave propagation over water, surface reflection and wave occlusions. In order to deal with the challenging maritime environment, we proposed an optimal entropy-based cooperative spectrum sensing. As the results of spectrum sensing are sensitive to the number of samples in an entropy-based local detection scheme, we first calculated the optimal number of samples. Next, a cooperative spectrum sensing scheme considering the conditions of the sea environment is proposed. Finally, the throughput optimization of the \(m\)-out-of-\(n\) rule is considered. Results revealed that although the existing schemes work well for the lower sea states, they fail to perform at higher sea states. Moreover, simulation results also indicated the robustness of the entropy-based scheme and the proposed cooperative spectrum sensing scheme at higher sea states in comparison with the traditional energy detector.  
**Keywords:** cognitive radio network; maritime communication; spectrum sensing; cooperative detection
## 6 Challenges and Future Perspectives
However, in each communication round of synchronous FL, the training latency takes up to the slowest participant, which harshly degrades the overall performance and remains doubtful for deployment. To accelerate the slowest selected participant, an enhanced co-training approach with edge-assisted nodes or partial model training per round can become supportive methods in the synchronous FL process. DRL-based edge node selection can contribute to the FL processes by jointly observing the states of the collective edge environment (e.g., remaining resources, queuing sizes, and distance-based features) and evaluating the selection action based on expected co-training delays. * _Network architecture for federated optimization_: to operate federated optimization, the network architecture in every layer (e.g., infrastructure, control, and application) mandatorily needs to increase the complexity, intelligent systems, on-device capability, management entities of internet service providers, network operators, and softwarization/virtualization-based structures [106; 107]. The constraints on IoT resources, connectivity, and stability should be taken into further consideration in future studies. Furthermore, to take the privacy-preserving framework to an advanced next level, (federated) machine unlearning can be considered by (1) not placing user information into the privacy-agnostic procedures and (2) alleviating the impact of participant's data on the final model [108; 109; 110].
# Review of the Main Mechanical Testing Methods for Interlayer Characterization in Laminated Glass
## 2 Test Methods for LG Interlayers Characterization
### Uniaxial Tensile Tests of Interlayers  
The uniaxial tensile test is a simple method in which a dog-bone specimen of the interlayer is subjected to direct tension according to ASTM D638 [22]. Three different displacement rates were adopted in the simulation: (1) 5 (2) 50 (3) 500 mm/min. It evaluates Young's modulus, and the shear modulus is calculated by assuming a value of the Poisson's ratio. However, it does not consider the effect of delayed deformation and of the temperature. An example of the stress distribution is shown in Figure 1a, where the specimen was designed following existing literature [20], and the thickness of the specimen was 1.52 mm. The shell element (S4R) was selected to model the behavior of interlayers under tension. The specimen was fixed at one end and loaded under the constant displacement rate of 5 mm/min (with the strain rate of \(1.7\times 10^{-3}\) s\({}^{-1}\)) at the other  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test** & **Specimen** & **Load** & **Stress** & **Duration** & **Temperature** \\  & **(I/LG)** & **(S/D)** & **(T/S)** & **(I/C)** & **(A or M)** \\ \hline Modal test [38] & & & & & \\  & LG & D & S & I & M \\  & & LG & D & S & I & M \\ \hline Dynamic torsional test [39; 40] & & & & & \\  & LG & D & S & I & M \\ \hline Dynamic mechanical thermal analysis & & LG & D & T & I & M \\
[25; 39; 41; 42; 43] & & & & & \\ \hline Specim: I: tests on interlayers, LG: tests on laminated glass, Load: S: static or quasi-static tests, D: dynamic tests, Stress: T: tensile tests, S: shear tests, Duration: I: instantaneous, C: creep, temperature: A: ambient temperature, M: different temperature. & & & \\  & & LG & D & T & I & M \\ \hline \hline \end{tabular} Specim: I: tests on interlayers, LG: tests on laminated glass, Load: S: static or quasi-static tests, D: dynamic tests, Stress: T: tensile tests, S: shear tests, Duration: I: instantaneous, C: creep, temperature: A: ambient temperature, M: different temperature.  
\end{table}
Table 1: _Cont._end in this study. The load can be obtained and the stress can be derived by dividing the load by the area of the cross-section. The results show that, in the elastic range, the stress distribution is homogeneous in the gauge length (Figure 1a). The uniaxial tensile test is the most widely applied test method to characterize the stress-strain relationship of interlayers under tension.  
In addition, the influence of strain rates (\(10^{-3}\) to \(10^{2}\) s\({}^{-1}\)) and temperature (\(-40\) to \(80\)\({}^{\circ}\)C) on the tensile behavior of interlayers are also studied [44].  
Figure 1: Stress distribution in the different test setups.
###### Abstract  
This study aims to develop an integrated wireless monitoring system named MANTiS-32, which leverages an open-source platform to enable autonomous modular operation, high-speed large-volume data transmission via Wi-Fi, and the integration of multiple complex sensors. The MANTiS-32 system is composed of ESP32-based MANTiS-32 hubs connected to eight MPU-6050 sensors each via RS485. Four MANTiS-32 hubs transmit data to a main PC through an access point (AP), making the system suitable for real-time monitoring of modal information necessary for structural performance evaluation. The fundamental performance of the developed MANTiS-32 system was validated to demonstrate its effectiveness. The evaluation included assessments of acceleration and frequency response measurement performance, wireless communication capabilities, and real-time data acquisition between the MANTiS-32 hub and the eight connected MPU-6050 sensors. To assess the feasibility of using MANTiS-32 for performance monitoring, a flexible model cable-stayed bridge, representing a mid- to long-span bridge, was designed. The system's ability to perform real-time monitoring of the dynamic characteristics of the bridge model was confirmed. A total of 26 MPU-6050 sensors were distributed across four MANTiS-32 hubs, and real-time data acquisition was successfully achieved through an AP (ipTIME A3004T) without any bottleneck or synchronization issues between the hubs. Vibration data collected from the model bridge were analyzed in real time to extract dynamic characteristics, such as natural frequencies, mode shapes, and damping ratios. The extracted dynamic characteristics showed a measurement error of less than approximately 1.6%, validating the high-precision performance of the MANTiS-32 wireless monitoring system for real-time structural performance evaluation.  
wireless measurement system; real-time data acquisition system; performance-based monitoring system; modal analysis; bridge performance evaluation; structural dynamics +
Footnote †: journal: Applied sciences  
Article
## 3 The Clustering Protocol
### CHs Election
#### 3.4.2 CHs Election in Large Clusters  
For large clusters, before the death of the first node, we implement the D-CHs division of energy balance strategy, which involves the selection of the positive cluster head (P-CH) and the secondary cluster head (S-CH). The detail procedure is given by the pseudo-code in Algorithm 3. In this paper, a cluster with CH energy consumption greater than 1.5 times the average CH energy consumption is defined as a large cluster. According to the energy consumption formulas introduced in Sections 2.2 and 2.3, we can calculate the average energy consumption of the CH, \(\overline{E_{CH}}\).  
\[\begin{split}\overline{E_{CH}}&=\big{(}\frac{n}{k} -1\big{)}\cdot e\cdot E_{elec}+\frac{n}{k}\cdot e\cdot E_{DA}+e\cdot E_{elec}+ E[f(d_{toBS})]\\ &=\frac{n}{k}\cdot e(E_{elec}+E_{DA})+A\cdot e\end{split} \tag{29}\]  
We use \(x\) to represent the total number of nodes in the cluster, so we can use the following formula to get the CH energy consumption \(E\) of the cluster.  
\[\begin{split} E&=(x-1)eE_{elec}+xeE_{DA}+eE_{elec}+ E[f(d_{toBS})]\\ &\qquad=xe(E_{elec}+E_{DA})+A\cdot e\end{split} \tag{30}\]  
Next, we determine what the value of \(E\) is when the corresponding cluster can be defined as a large cluster. Here, we assume that \(E\) is \(\gamma\) times as \(\overline{E_{CH}}\). We compare the difference between the energy consumption of the CH in the cluster and the average energy consumption of the CH in the case of General CHs and D-CHs, respectively, to obtain the critical \(E\) value of a large cluster.  
According to Figure 6, when \(\gamma\) is less than 1.5, the energy error of General CHs  
As shown in Figure 7, the WSN consists of 5 clusters (2 large clusters and 3 general clusters). Compared to the general cluster, the member nodes (white dots) in the large cluster collect environmental information and send it to the S-CH (yellow dots); the latter receives such information and the data fusion is performed in the P-CH (green dots). The final information is sent to the BS (red pentagram) for decision making.  
Figure 6: A comparison of energy error between General CHs and dual-cluster heads (D-CHs).  
Figure 7: WSN clustering topology (D-CHs division of energy balance strategy in large clusters).
# Sand Removal Mechanism of a High-Speed Roller Bit with Helical Sealing
## 1 Introduction  
The roller bit is the tool most widely used in petroleum and geological drilling. Additionally, the roller bit has the functions of impacting, crushing, and cutting the rock stratum and can adapt to soft, medium, and hard strata. The development of long-life high-speed cone bits has become an important technical issue, which urgently needs to be addressed in petroleum drilling engineering. Improving the bit drilling speed and working life are effective ways of reducing the drilling cost. However, the sealing affects the bit drilling speed and working life and is also the weakest part of the bit. Field data have shown that the failure rate of roller bits due to early bearing damage is as high as 80%, and bearings seriously worn by early sealing failure account for 30% of all failed bearings [1; 2; 3; 4; 5; 6; 7; 8]. The sealing has become a technical bottleneck that seriously affects the drilling efficiency and cost. There is thus an urgent need for a solution to this problem in petroleum engineering.  
The entry of a slurry abrasive medium is the most critical factor affecting the sealing life of a high-speed roller bit [9; 10; 11]. All existing sealings are passive [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26] and suffer serious aging and wear, limiting improvements in the bit speed and working life. In addition, poor working conditions (i.e., a high temperature, high abrasive medium, high load, and narrow sealing chamber) aggravate the sealing failure, leading to great difficulties and challenges in research [27; 28]. The working life of a sealing can therefore be improved and the fundamental sealing problem can be solved by designing a sealing structure having the function of active sand removal.  
Figure 1 shows that helical sealing is noncontact and hydrodynamic. As the roller bit rotates at high speed, the helical sealing structure has the characteristics of helical pumping and sand removal that prevent abrasive slurry from entering and wearing the bearing. Some mud can enter when the bit is stationary but can be removed as the bit rotates. The helical sealing structure can realize active sand removal without using the traditional O-ring and thus overcomes the aging and wearing problems caused by passive sealing and improves the bit speed and operation. A historical change in roller bit sealing can thus be realized.