esa-sceva/satcom-chunk-collection huggingface dataset:
# Federated Learning-Based Security Attack Detection for Multi-Controller Software-Defined Networks
## 5 Experimental Results
### Evaluation of SDN Security  
To assess the traffic classification performance in the SDN environment, the experimental model measures several metrics, including the False Alarm Rate, error rate, and latency.  
\[\text{False Alarm Rate }(\text{FAR})=\frac{\text{FP}}{\text{FP}+\text{TN}} \tag{10}\]  
\[\text{Error Rate}=\frac{\text{FP}+\text{FN}}{\text{P}+\text{N}} \tag{11}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**SDN Security Models**} & \multicolumn{3}{c}{**Enriched NSL-KDD**} \\ \cline{2-3}  & **DR (\%)** & **Accuracy (\%)** \\ \hline Federated-based [24] & 97.67 & 96.51 \\ \hline Proposed (without FL) & 92.31 & 90.05 \\ \hline Proposed & 98.01 & 97.78 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative Federated Learning Performance in Traffic Classification.  
FP and FN refer to the number of incorrectly detected malicious and normal traffic samples, respectively. Moreover, TN denotes the number of correctly detected normal traffic, whereas 'P' and 'N' correspondingly imply the total number of malicious traffic and normal traffic samples. Also, latency measures the time the controller takes to process a single packet in the SDN, which is influenced by the efficient flow traffic verification in the data plane. In particular, data plane latency is based on the packet processing time and forward decision of packets in the SDN. The design of FedSec with feature selection does not affect the packet processing time during the inspection of the network traffic flows. The hybrid heuristic-based feature selection and federated settings leverage the optimal execution of the traffic classification and reject the malicious entry updating in the flow table without increasing the overhead.  
In FedSec, the federated modeling of MC-SDN facilitates the accurate distinguishing among the multiple attack classes in the enriched NSL-KDD dataset, leveraging the dropping of malicious entries during the flow table updating by the collective intelligence from the distributed and diversified malicious traffic patterns. By training the GCNN-GRU model with the CNN-assisted extracted feature values under the categories of the DoS, Probe, R2L, U2R, and other attacks and normal samples on the TCP, UDP, and ICMP protocols with the collaborative attack knowledge, the FAR and error rate are comparatively minimal compared to the existing SDN security research while testing on three different network traffic datasets. It is accomplished by the balanced criteria-based edge weight computation for the adjacency matrix modeling in the proposed FedSec, precisely discriminating the normal and malicious patterns even when there are inherent variations in the attack features. As a result, as mentioned in Table 7, the FedSec accurately rejects the malicious traffic flows without compromising the latency in the large-scale network.
# Estimation of Sedimentary Rock Porosity Using a Digital Image Analysis
## 2 Materials and Methods
### Mercury Intrusion Porosimetry (MIP) Tests  
A series of MIP tests were conducted following the Chinese standard GB/T 21560.1 [35] with Mercury Intrusion Porosimeter Autopore VI (Figure 1). The specimen had a block shape with a size of 1 cm\({}^{3}\). Prior to testing, the specimens were dried in an oven at 105\({}^{\circ}\) C for at least 48 h, and then cooled at a room temperature of 25 \({}^{\circ}\)C. The pressure applied and pore diameter sizes ranged from 0.001 MPa to 400 MPa and 3 nm to 900,000 nm, respectively. As mercury is a non-invasive liquid, it can only enter the pore space under the action of external pressures. Therefore, the pore diameter \(d\) (nm) can be related to the applied pressure (\(P\), MPa), as given in Equation (1).  
\[D=-4\gamma cos\theta/P \tag{1}\]  
where \(\gamma\) is the surface tension of mercury (0.45 N/m), and \(\theta\) is the contact angle of mercury (130\({}^{\circ}\)). When the sample aperture is idealised as a smooth and step-changing cylinder, the  
\begin{table}
\begin{tabular}{c c c c} \hline
**Rock** & **Description** & **Mineralogy** & **Density (g/cm\({}^{3}\))** \\ \hline Sandstone & Light grey colour, grain size fine to medium, & Quartz & \\  & massive texture, no visible cracks & Calcium Carbonate & 2.65 \\  & & Kaolinite & \\ \hline Silstone & Grey colour, fine-grained, massive texture, and & Quartz & 2.60 \\  & no visible cracks & Kaolinite & 1.30 \\ \hline Coal & Black colour, fine-grained, nonelastic, massive & Carbon & \\  & texture, and minor visible cracks & Calcium Carbide & 1.30 \\ \hline \end{tabular}
\end{table}
Table 2: Description and mineral composition of the sandstone, siltstone, and coal.  
liquid-solid interface area (\(S\)) can be expressed in relation to the length \(L\), and the changing volume (\(\Delta V\)) as given in Equation (2):  
\[S=L^{*}d^{*}\tau=4\Delta V/d \tag{2}\]  
When computing the pore size distribution, the _DV/Dd_ and _DV/Dlogd_ values are compared to the pore size on the y-axis, and thus the pore distribution and size may be assessed. _DV/Dd_ is commonly utilised for analysing minor pore size changes [36]. When the distribution of voids is wider, or the pore size is greater than tens of nanometres, the logarithmic relationship is applied (Equation (3)).  
\[DV/Dlogd=ln10^{*}d^{*}(DV/Dd) \tag{3}\]
###### Abstract  
The success rate of dental implants depends on primary and secondary stability. We investigate predictive factors for future risk stratification models. We retrospectively analyze 272 patients with a total of 582 implants. Implant stability is measured with resonance frequency analysis and evaluated based on the implant stability quotient (ISQ). A linear regression model with regression coefficients (reg. coeff.) and its 95% confidence interval (95% CI) is applied to assess predictive factors for implant stability. Implant diameter (reg. coeff.: 3.28; 95% CI: 1.89-4.66, \(p<0.001\)), implant length (reg. coeff.: 0.67, 95% CI: 0.26-10.8, \(p<0.001\)), and implant localization (maxillary vs. mandibular, reg. coeff.: \(-7.45\), 95% CI: \(-8.70\)(\(-6.20\)), \(p<0.001\)) are significant prognostic factors for primary implant stability. An increase in ISQ between insertion and exposure is significantly correlated with healing time (reg. coeff.: 0.11, 95% CI: 0.04-0.19). Patients with maxillary implants have lower ISQ at insertion but show a higher increase in ISQ after insertion than patients with mandibular implants. We observe positive associations between primary implant stability and implant diameter, implant length, and localization (mandibular vs. maxillary). An increase in implant stability between insertion and exposure is significantly correlated with healing time and is higher for maxillary implants. These predictive factors should be further evaluated in prospective cohort studies to develop future preoperative risk-stratification models.  
Keywords: dental implants; osseointegration; regression analysis; risk assessment +
Footnote †: journal: Medical Biology
# Integrating IP Mobility Management Protocols and MANET: A Survey
## 4 IP Mobility Management in MANET and Comparative Summary  
This section introduces the solutions contributed in the literature that incorporate mobility management techniques to provide mobility support and roaming capability for integrated MANET-internet networks. The proposed solutions can generally be classified based on the adopted gateway discovery approach into three main classes: Proactive gateway discovery-based mobility management solutions, reactive gateway discovery-based mobility management solutions, and hybrid gateway discovery-based mobility management solutions.  
The surveyed systems are presented and analyzed according to these categories in the following subsections. The proposals in each category are summarized in a table that covers the main features and characteristics of the schemes, the mobility protocol used, MANET routing protocol used, handover metrics used, and how the proposed systems were evaluated, including the number of mobile nodes and gateways.
## 5 Experimental Environment  
The experiment was conducted at the Industrial Engineering Lab, Room 312, Engineering Building, Cheonan Campus, Dankook University, Republic of Korea.  
In the laboratory, an RSSI-based location system was built in one-third of the room. The measurement area was 3.0 \(\times\) 3.0 m, as shown in Figure 5. The area where the wireless network was established was empty, whereas the experiment was conducted in a workspace with office equipment and a PC set up in the remaining area.  
In this study, a Zigbee wireless network protocol was utilized. The Zigbee wireless sensor was established on the physical layer of IEEE 802.15.4 standard's low-rate wireless personal area network (LR-WPAN) communication [7; 35]. An XBee module, a commonly used product, was employed, and the network was configured by assigning a coordinator, router, and end device. To mitigate interference, channel 20 was established as the frequency of the XBee S2C sensor because it was in a different frequency band from the prevalent 2.4 GHz Wi-Fi in buildings [2].  
In the XBee S2C sensor, each FN is subordinate to the MN, which transmits signals. Signal flow was set to occur in only one direction. The Arduino Mega 2560(Arduino SRL, Via Andrea Appiani 25, 20900 Monza MB) controller in the MN, which served as a router, read the RSSI values of the four FNs and transferred the received values to the RSN.  
Four FNs were anchored to one of the four corners: (x\({}_{1}\) = 0.00, y\({}_{1}\) = 0.00), (x\({}_{2}\) = 3.00, y\({}_{2}\) = 0.00), (x\({}_{3}\) = 3.00, y\({}_{3}\) = 3.00), and (x\({}_{4}\) = 0.00, y\({}_{4}\) = 3.00). We ensured that all nodes were 1.2 m above ground, with a 3 m distance between FNs, to avoid various radio-wave interferences, such as diffraction and reflection from the floor, as shown in Figure 5. Measurements were conducted using a tripod to maintain similar variables that may occur between experiments, such as accurate measurements from the device and the state of the device. Signals received from the four FNs to the MN were sent to a computer to estimate the MN's location. Table 2 lists the compensated locations established in Section 3 for each zone of the proposed test field. As discussed in the previous section, additional segmentation processes can be employed to improve estimation accuracy. However, the scale of the network influences this, increasing computational complexity. Hence, obtaining an optimal segmentation step is difficult and requires further study.  
As explained in Section 2, the RSSI was transformed into a distance measure for the indoor location using PLE. The methodology for estimating the PLE in the test field includes monitoring the RSSI data at various distances using a single transmitter and an RSN. In the test field, the receiver collected 8000 samples of the RSSI data at different distances from each FNs. Figure 6 shows the resulting data as a logarithmic plot of the average RSSI in dBm versus distance in meters. The experimental PLE for the environment was calculated using linear regression analysis.
# A 5 Gb/s Optoelectronic Receiver IC in 180 nm CMOS for Short-Distance Optical Interconnects
###### Abstract  
This paper presents a CMOS-based optoelectronic receiver integrated circuit (CORIC) realized in a standard 180 nm CMOS technology for the applications of short-distance optical interconnects. The CORIC comprises a spatially modulated P\({}^{+}\)/N-well on-chip avalanche photodiode (P\({}^{+}\)/NW APD) for optical-to-electrical conversion, a dummy APD at the differential input for enhanced common-mode noise rejection, a cross-coupled differential transimpedance amplifier (CCD-TIA) for current-to-voltage conversion, a 3-bit continuous-time linear equalizer (CTLE) for adaptive equalization by using NMOS registers, and a \(f_{T}\)-doubler output buffer (OB). The CTLE and \(f_{T}\)-doubler OB combination not only compensates the frequency-dependent signal loss, but also provides symmetric differential output signals. Post-layout simulations of the proposed CORIC reveal a transimpedance gain of 53.2 dB\(\Omega\), a bandwidth of 4.83 GHz even with a 490 fF parasitic capacitance from the on-chip P\({}^{+}\)/NW APD, a dynamic range of 60 dB that handles the input photocurrents from 1 \(\mu\)A\({}_{\text{pp}}\) to 1 mA\({}_{\text{pp}}\), and a DC power consumption of 33.7 mW from a 1.8 V supply. The CORIC chip core occupies an area of 260 \(\times\) 101 \(\upmu\)m\({}^{2}\).  
APD; CTLE; \(f_{T}\)-doubler; optoelectronic; TIA Article
# Extending Battery Lifetime by Avoiding High SOC
## 5 Results
### Synthetic Drive Cases  
The result from the pure synthetic cases which were presented in Table 4 are given in Figure 8 as function of FCEs. The well known fact that increasing C-rate and increasing DOD are increasing the ageing can be seen. It also shows that the SOC level has a huge impact on the ageing, where higher C-rates in the lower SOC levels even generate less ageing than the lower C-rates in the higher SOC levels. As mentioned before, extrapolation was used for the lower C-rates seen in Figure 8a. The results produced are reasonable, although it would have been desirable to have experimental 0.5C cycling data.
# Multi-Frequency Target Detection Techniques for DVB-T Based Passive Radar Sensors
## 3 Multi-Frequency Detection Schemes
### Modifications Required for Application Against DVB-T Signals
#### 3.1.1 CAF Maps Evaluation on Common Bistatic Range/Velocity Grids
Therefore, with the proposed approach, the actual integration times \(T^{\prime}_{int_{n}}\) at the different channels are given by:  
\[T^{\prime}_{int_{n}}=\left\lfloor T_{int}/T_{ss_{n}}\right\rfloor\cdot T_{ss_{n} }=\frac{\lambda_{min}}{\Delta v}+\delta T_{n}\quad n=1,..,N \tag{13}\]  
However, as for typical values \(\left|\delta T_{n}\right|\ll T_{int}\), the resulting differences in term of SNR gain are negligible. The same CAF grid in the velocity dimension is then obtained by properly oversampling the final map. This can be easily achieved by exploiting a Chirp Zeta Transform (CZT) in lieu of the standard Fast Fourier Transform (FFT). Alternatively, a simple zero-padding operation can be performed prior to applying conventional algorithms for the CAF evaluation. In fact, following the same approach of Equations (9)-(12), it can be easily verified that the quantization yield by the sampling frequency \(f_{s}\) is negligible in term of maximum relative displacement of the final velocity grid. In particular, the number of zeros to be padded has to be set in order to achieve the desired velocity spacing \(\Delta v\) at all the available frequency channels regardless of the rounding errors:  
\[Z_{P_{n}}=\left\lfloor f_{s}\left(\frac{\lambda_{n}}{\Delta v}-T^{\prime}_{int _{n}}\right)\right\rfloor=\left\lfloor f_{s}\left(\frac{\lambda_{n}-\lambda_ {min}}{\Delta v}-\delta T_{n}\right)\right\rfloor\quad n=1,..,N \tag{14}\]  
Therefore the same velocity spacing is obtained at all the considered frequency channels and the resulting CAF maps can be directly compared. However, the maps obtained at lower carrier frequencies turn out to be oversampled in the velocity dimension so that the target echo might span consecutive velocity bins and a white noise disturbance might show some degree of correlation across velocity bins. However, these effects are not expected to severely affect the possibility to control the false alarm rate provided that a reasonable number of secondary data is adopted by the CFAR detection scheme together with a proper guard interval. Moreover we observe that the oversampling factor is always smaller than 1.84 and reaches its maximum only when both the extreme carrier frequencies (at the right and at the left of the DVB-T band) are included in the MF integration.
# UAV Image Small Object Detection Based on RSAD Algorithm
## 2 Related Work
### Motivation and Contribution  
Combined with the previously mentioned, the existing object detection algorithms for UAV images do not balance detection accuracy and detection speed. They often sacrifice a large amount of detection speed in pursuit of high detection accuracy. This does not take advantage of the practical application of object detection algorithms on UAVs. Therefore, we propose RSAD, which makes a good balance between detection speed and detection accuracy. Our main contributions are as follows:  
1. By comparing backbone networks geared towards real-world applications and considering UAV resource constraints, we chose ResNet-50 as the backbone network for RSAD and continued to use multiscale detection.
2. We designed a feature fusion module based on the self-attention mechanism. Different from the traditional attention mechanism, we pay more attention to the weight relationship within the image. By reshaping and concatenating the information extracted into the image by the backbone network, the correlation between the information inside the image is established. It helps the network to better understand the information in the UAV image.
3. We introduced the Focal Loss loss function to enhance RSAD's focus on small targets. The parameters of the Focal Loss loss function are adjusted through experiments with a small number of iterations to make RSAD pay the best attention to small objects.
# Leveraging Digital Twin Technology for Sustainable and Efficient Public Transportation
## 1 Introduction
### Abbreviations and Literature Overview  
Table 1 presents the abbreviated terms used in the paper. For this review, relevant papers and articles were sourced from trusted scholarly literature platforms like Google Scholar, ResearchGate, and Scopus. The quality of the papers was filtered by identifying reputable journal publications, peer-reviewed papers, conferences, seminars, as well as articles and books from reputable publishers like IEEE, Springer, MDPI, Taylor & Francis, and Elsevier, amongst others. Specific and relevant keywords like \"Digital Twin\", \"Artificial Intelligence\", \"Public Transportation\", \"Smart Cities\", \"Machine Learning\", \"Traffic Data\", and \"Simulation\" were used with the operator \begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviations** & **Descriptions** \\ \hline Digital Twin & DT \\ American Public Transportation Association & APTA \\ Intelligent Transportation System & ITS \\ Adaptive Traffic Signal Control & ATSC \\ Autonomous Vehicle & AV \\ Electric Vehicle & EV \\ Artificial Intelligence & AI \\ Machine Learning & ML \\ Digital Twin Network & DTN \\ Radio Frequency Identification & RFID \\ Internet of Things & IoT \\ Global Positioning System & GPS \\ Automated Guided Vehicle & AGV \\ Internet of Vehicles & IoV \\ Vehicular Ad Hoc Network & VANET \\ Mean Absolute Percentage Error & MAPE \\ Root Mean Squared Error & RMSE \\ Artificial Neural Network & ANN \\ Mean Squared Error & MSE \\ Vehicle to Vehicle & V2V \\ Vehicle to Cloud & V2C \\ Vehicle to Pedestrians & V2P \\ Vehicle to Infrastructure & V2I \\ Traffic Accident & TA \\ Long Short-Term Memory & LSTM \\ Convolutional Neural Network & CNN \\ Asset Administration Shell & AAS \\ General Data Protection Regulation & GDPR \\ Department of Transportation & DOT \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of abbreviated terms. AND. To further understand the major focus of these papers, the themes of the publications were categorized into six broad topics. A total of 97 papers were reviewed to prepare the Relevant Literature section. Table 2 presents the bibliography of the reviewed papers, covering various aspects of digital twins, including their challenges, limitations, and real-world applications based on the identified six themes. The methodology for extracting relevant information for this review was based on the themes presented in this table. The \"Citation Number\" reflects the studies that have been cited in the References section.
# A Model to Study the Creep Behavior of Carbon Fiber/Epoxy Resin Composites Under Temperature
## 1 Introduction  
Carbon fiber-reinforced composites are commonly utilized materials in many engineering sectors because of their exceptional qualities. These characteristics dictate how these materials are used in a variety of fields, including medical equipment, electronics, automobile engineering, aerospace engineering, and the chemical industry. The materials are mostly utilized in applications that demand a high performance because of their greater manufacturing cost. This study focuses on composites composed of a carbon fiber-reinforced epoxy resin matrix.  
The authors of this study provide an interpolator for the temperature behavior of a composite under axial force in order to quickly and effectively characterize the material's creep behavior. The authors' experimental data for varying temperatures and applied axial loads serve as the basis for the proposed interpolator. The material used for the experiment was Fibredux 6376 (known as HexPly(r) 6376) [1, 2]. The properties of this material and the work conditions are described by standards ISO R527 [3] and 1 ISO 178 [4]. There is a rich literature that presents results concerning this material. We will present some basic research used in this study. Carbon fiber is a very widely known and used material in engineering applications, made primarily of carbon atoms bonded together in a crystalline structure. The main property is the strength-to-weight ratio, which makes carbon fibers incredibly strong, lightweight, and durable. These fibers are extremely stiff, which means they can withstand significant forces without breaking or bending. They are stronger than steel and much lighter (lighter than aluminum). At the same time, they have high thermal conductivity, so they can effectively dissipate heat. Carbon fibers do not rust or corrode.  
The authors of this study provide an interpolator for the temperature behavior of a composite under axial force in order to quickly and effectively characterize the material's creep behavior. The authors' experimental data for varying temperatures and applied axial loads serve as the basis for the interpolator. Overall, the authors' contributions consist of the following:  
* Development of an interpolator model for the study of the influence of temperature on the behavior of carbon fiber-reinforced composite materials;
* Experimental measurements for the analysis of the method and the study of a material frequently used in engineering applications;
* Creation of an experimental basis that can also serve as the basis for verifying other models.