esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient LoRa Routing for Smart Grids
## 1 Introduction
### Contribution  
The work presented in this paper makes the following contributions:  
* A modified LEACH (Cum_LEACH) protocol based on the cumulative energy distribution is proposed for CH selection.
* Multiple paths are created using a novel method, quadratic-kernel-exploitation-based African buffalo optimisation (QKE-ABO), which transmits the data from the transmitting node to the receiving node when they are far away from each other.
* A modified Mexican axolotls optimisation, scrambled mutation Mexican axolotls optimisation (SMax), is proposed for multi-path scenarios.  
This paper analysed the effectiveness of our proposed optimal path algorithms from different aspects by conducting extensive simulation tests using the MATLAB simulator. The different phases involved in the proposed system are shown in Figure 2: initially deploying the sensor node, then using Cum_LEACH to select the CH, followed by test packet transmission if the transmitting and receiving nodes are very far from each other using a QKE-ABO optimisation technique to create multiple paths, then from these paths, using SMAx to choose an optimised path. The detailed flow is explained in Section 3. The phases of the communication presented in this new contribution are given in Figure 2.
# CarFree: Hassle-Free Object Detection Dataset Generation Using Carla Autonomous Driving Simulator
## 5 Automatic Ground Truth Generation
### Filtering Out Occluded Objects  
This section introduces our idea for identifying occluded objects, which is depicted in Figure 2(b). For that, we make use of the semantic segmentation image, which is also available from the Carla server. A semantic segmentation image provides pixel-wise classifications where each pixel represents an object's class by a color code. For example, in the middle of Figure 2(b), the pixels that belong to vehicles are represented by blue color. It also indicates that the proposed bounding box in the left top corner is actually not a visible object since a building occludes it. Thus, it can be safely ignored.  
Algorithm 2 shows the procedure of how we can check the existence of a valid and visible object inside a given 2D bounding box. The algorithm takes a 2D bounding box, its corresponding object class, and a semantic segmentation image. Then, if the center pixel of the bounding box is identified as belonging to the given object class by checking the semantic segmentation image, it is decided as visible. Note that our algorithm may fail to identify a partially occluded object, which is a known limitation of our method. For that, please understand that our primary aim is not to discover all the fully and partially visible objects. Instead, we aim to produce as many training images as possible from the simulator. For finding partially occluded objects, Algorithm 2 can be easily extended by sampling multiple locations in addition to the center point.  
```
0:\(\{(x_{1},y_{1}),\cdots,(x_{4},y_{4})\}\)
0:\(T\): Target object class
0:\(S\): Semantic segmentation image
0: True when the object is visible, False otherwise
1:\(x\leftarrow(x_{1}+x_{2}+x_{3}+x_{4})/4\)
2:\(y\leftarrow(y_{1}+y_{2}+y_{3}+y_{4})/4\)
3:if\(S[x][y]==T\)then
4:return True
5:else
6:return False
```  
**Algorithm 2**Check the visibility of an object  
Algorithm 3 shows the fitting procedure, where the original bounding box \(\{(x_{1},y_{1}),\cdots,(x_{4},y_{4})\}\) is given as an input and its output is a fitted bounding box \(\{(\hat{x}_{1},\hat{y}_{1}),\cdots,(\hat{x}_{4},\hat{y}_{4})\}\).
# Reliability Assessment of RC Bridges Subjected to Seismic Loadings
## 4 Example of Application
### Probability of Exceeding a Certain Drift Threshold  
Fragility curves are used to assess the vulnerability of structures due to a certain environmental load. It is assumed that fragility curves follow a lognormal shape with the following expression [36]:  
\[P(D\geq d|y)=1-\Phi\Bigg{(}\frac{lnd-ln\hat{D}|y}{\sigma_{ln\hat{D}|y}}\Bigg{)} \tag{10}\]  
where \(\hat{D}_{|y}\) is the median value of the structural demand given a seismic intensity, \(y\); and \(\sigma_{ln\hat{D}|y}\) is the standard deviation of the natural logarithm of the demand for a given intensity, \(y\).  
Fragility curves are calculated for four different drift thresholds: (a) 0.002, which is associated with a threshold lower than the design threshold, (b) 0.004, corresponding to the drift design and serviceability limit state [36], (c) 0.006, chosen as an intermediate threshold between serviceability and collapse thresholds, and (d) 0.012, which is associated with the value of collapse limit state [37]. Figure 8 shows the fragility curves for different drift thresholds. The following is observed: (a) seismic intensities less than 0.4 Sa/g produce exceedance probabilities close to zero in all thresholds; (b) regarding the threshold of 0.002, there are probabilities of exceedance of 1 when seismic intensities are greater than 1 Sa/g; (c) the serviceability threshold presents a value of exceedance probability of 0.5 at 0.95 Sa/g, approximately; (c) the serviceability threshold presents a value of exceedance probability of 0.5 at 0.95 Sa/g, approximately; (d) for the 0.006 threshold, and for intensities less than 0.8 Sa/g, there are exceedance probabilities close to zero; (e) for the threshold of 0.012, the exceedance condition is associated with a probability equal to 0.31 Sa/g.  
Figure 7: Spectral acceleration hazard curve for the fundamental period, T = 0.40 s.
###### Abstract  
This study investigated the effectiveness of Tooth Primer and cleaning agents in removing saliva contamination from zirconia as assessed using shear bond strength (SBS). A total of 175 rectangular specimens, 10 mm in diameter and 6 mm in thickness, were randomly divided into seven groups (n = 25 each): group 1, no saliva contamination (control); group 2, saliva-contaminated and not rinsed; group 3, saliva-contaminated and rinsed; group 4, saliva-contaminated, Ivoclean-treated, and rinsed; group 5, saliva-contaminated, Katana Cleaner-treated, and rinsed; group 6, saliva-contaminated, Tooth Primer-treated, and rinsed; and group 7, saliva-contaminated, Tooth Primer-treated, and not rinsed. All zirconia specimens from groups 1-7 were bonded to composite rods with Panavia SA Luting Multi cement. The bonded specimens were subjected to the SBS test using a universal testing machine. To assess the type of failure, the debonded surface was evaluated using a stereomicroscope. The SBS data were analyzed using one-way ANOVA with [PERSON]'s post-test. The SBS values of groups 2 (0.90 \(\pm\) 0.20 MPa) and 3 (1.35 \(\pm\) 0.43 MPa) were significantly lower compared with the other groups (\(p\) = 0.00). The SBS value of zirconia decontamination using Ivoclean in group 4 (18.51 \(\pm\) 3.01 MPa) was significantly lower than that of the control group (22.24 \(\pm\) 2.37 MPa) (\(p\) = 0.00). However, the SBS values of groups 5 (20.92 \(\pm\) 2.63 MPa), 6 (21.43 \(\pm\) 2.81 MPa), and 7 (20.87 \(\pm\) 2.35 MPa) did not significantly differ compared with the control group (22.24 \(\pm\) 2.37 MPa) (\(p\) = 0.369, \(p\) = 0.861, \(p\) = 0.327, respectively). Moreover, SBS values did not significantly differ among groups 5 to 7 (\(p\) = 0.984, \(p\) = 1.00, \(p\) = 0.976, respectively). Regarding failure mode, groups 2 and 3 exclusively experienced adhesive failures. Groups 1, 4, 5, 6, and 7 exhibited adhesive and mixed failures. In conclusion, Ivoclean, Katana Cleaner, and Tooth Primer effectively mitigated the adverse effects of saliva contamination on the resin cement-zirconia interface. Furthermore, Tooth Primer can be used as a cleaner for saliva-contaminated zirconia surfaces, with or without rinsing.  
bond strength; resin cement; saliva contamination; zirconia cleaner +
Footnote †: journal: Applied sciences
## 1 Introduction
Furthermore, the compressive strength, flexural strength, tensile strength, elastic modulus, fracture energy, and stress-strain relationship of concrete are important mechanical properties that are often used for evaluating the fire resistance of concrete structures. Numerous literatures have comprehensively reported the residual mechanical properties of steel-fiber-reinforced concrete. In most of these studies, residual unstressed tests were performed, in which samples were naturally cooled to room temperature and then continuously loaded until it was destroyed [34; 35]. There are a lot of types of steel fiber that can be used in concrete. Based on the fiber shape, steel fibers can be classified as milling steel fiber, hooked-end steel fiber, straight steel fiber, corrugated steel fiber, dumbbell steel fiber, sheet and hooked-side steel fiber, needle twisted steel fiber, and so on. Among these different types of steel fiber, the first four types are often used in concrete. The common used length varies from 20 to 40 mm, and the aspect ratio often varies from 40 to 80. The elastic modulus and tensile strength are usually determined as about 200 GPa and 800-1300 MPa. In this context, this review reports recent advances pertaining to the effects of a high temperature on the residual mechanical properties, explosive spalling behavior, and microstructure of SFRC. This review is classified into four parts. A detailed description of the effect of steel fiber on the residual mechanical properties of concrete is provided. The first part summarizes the available experimental data of residual mechanical properties of concrete subjected to an elevated temperature; additionally, the residual mechanical properties of SFRC including compressive strength, flexural strength, tensile strength, elastic properties, fracture properties, and stress-strain relationships are discussed. In the second part, the main influencing factors of the explosive spalling behavior of concrete and the mechanism of explosive spalling are introduced comprehensively and, on this basis, the effect mechanism of steel fiber for preventing the explosive spalling behavior of concrete subjected to high temperatures is discussed in detail. The third part discusses the microstructure of SFRC after it is subjected to a high temperature. Finally, based on the collected data and analysis, conclusions and future perspectives are provided.
# A Novel Metasurface Lens Design for Synthesizing Plane Waves in Millimeter-Wave Bands
###### Abstract  
With the development of communication technology has come several measurement applications requiring plane-wave conditions for wireless-device characterizations in anechoic chambers. In this paper, a metasurface lens with a 2 \(\times\) 2 feeding-antenna array is proposed and characterized to synthesize a plane wave in a near field for a fifth-generation (5G) millimeter-wave radio-frequency (RF) devices test. The metasurface lens, based on Jerusalem-cross elements printed on a printed circuit board (PCB) substrate, is used for controlling the phase-shift distribution of incident spherical waves. The lens has a size of 0.4 \(\times\) 0.4 m and is designed to operate at a range from 24.25 GHz to 27.5 GHz, and its feeding-antenna array is located at a focal plane of the lens, which is parallel to the metasurface lens. The lens is studied and verified through simulations and experiments, and a uniform amplitude and phase-field distribution at a reduced distance of 1.2 m generated by the metasurface lens throughout a QZ are achieved. The worst-case amplitude and phase variation of the designated metasurface lens are \(\pm\)0.75 dB and \(\pm\)7.5\({}^{\circ}\), respectively. The results show a plane-wave condition can be achieved in 5G millimeter bands through the proposed compact and effective metasurface lens. Moreover, the proposed metasurface lens is shown to be capable of reducing the plane-wave synthesizing distance compared to the compact antenna test range (CATR) with a significantly reduced system cost, making it an attractive alternative to antenna testing in 5G millimeter-wave frequency bands.  
antenna measurement; millimeter wave communication; metasurface lens; plane wave generator
## 2 Materials and Methods
### Image Classification Methods
#### 2.3.6 Transfer Learning  
To enhance the efficiency of the disease detection models, transfer learning [37] is implemented. This technique is particularly beneficial in scenarios with limited datasets or constrained computational resources. Transfer learning involves leveraging knowledge from a source domain with abundant labeled data and applying it to a target domain with scarce labeled data. Unlike traditional methods that train models from scratch, transfer learning reuses pre-trained models and adapts them to new tasks, inheriting valuable features and representations learned from the source domain. Research has shown that models using transfer learning achieve higher accuracy and better performance with less training time and computational resources compared to models trained from scratch.  
In this research, transfer learning was applied by further training the entire pre-trained model on the target dataset with a small learning rate--as shown in Figure 5--allowing the model to adapt its representations to the new task while retaining knowledge from the source domain. This approach was used to ensure a flexible adaptation for detecting tomato leaf diseases.  
To improve efficiency and reduce computational costs for adaptation to low-cost IoT devices, MobileNetV2 was chosen as the core pre-trained model for this study. Developed  
Figure 5: Application of transfer learning on ImageNet.  
by Google researchers, MobileNetV2 is an evolution of the original MobileNet architecture, designed to enhance performance while maintaining low computational complexity. This model employs techniques like depth-wise separable convolutions [38], linear bottlenecks [39], and inverted residuals [39] to make the model lightweight and efficient, ideal for deployment on IoT devices with limited computational resources. Its architecture allows for fast inference speeds without compromising performance, making it a suitable choice for this research in detecting tomato leaf diseases.
## 3 Results
### Influence of Slag and Graphene on the Healing Properties of the Asphalt Mixtures  
The results of the healing test are shown in Figure 13. The values represent the average Healing Rate (HR) of 3 samples, and the error bars represent the standard deviation. A one-way analysis of variance (ANOVA) was performed to evaluate the effect of the temperature on the healing rate of the asphalt mixtures. Normality and homogeneity of variances assumptions were checked. The analysis showed that the effect of the temperature was significant, F(3,68) = 46.96, \(p\)-value = 0.000. Post hoc comparisons using the Tukey HSD test indicated that all the means were significantly different from each other (\(p\)-value \(<\) 0.05). Therefore, the effect of the temperature was an increment in the healing rate of the asphalt mixtures. In fact, as described in Section 1, by increasing the temperature, the bitumen reduces its viscosity and flows through the open cracks easier, healing them. At 100 \({}^{\circ}\)C, for all the mixtures under study, the average HR is 67% (SD = 3.9), while the average HR for the mixtures heated at 40 \({}^{\circ}\)C is 44% (SD = 2.9). These results are consistent with those obtained by other authors [20; 40; 42].  
Figure 12: Effect of slag (a) and graphene (b) on the electrical resistivity.  
Figure 13: Effect of slag (a) and graphene (b) on the Healing Rate (HR)  
A one-way analysis of variance (ANOVA) was performed to evaluate the effect of the additive content on the healing rates of the asphalt mixtures. Normality and homogeneity of variances assumptions were checked. The analysis showed that the effect of the additive content was not significant, F(5,66) = 0.15, \(p\)-value = 0.98. Therefore, the addition of slag or graphene did not produce substantial benefits in terms of the healing rate of the asphalt mixtures. However, the benefits in terms of energy savings are important. In this sense, Figure 14 shows the relationship between the total energy consumption during the heating and the healing rate of the mixtures. These curves should be interpreted as an indicator of the healing efficiency of the energy consumed by the microwave heating technique. The greater the slope of the curves, the higher the healing efficiency. In the case of slag, considerable benefits can be obtained even with the addition of 3% (o/aggregates), while with higher contents, no improvements are achieved. Similarly, in the case of graphene, the improvement of the healing efficiency is obtained with the addition of 2% (o/binder). For example, in order to obtain HR = 60%, the healing efficiency of the mixtures with 3% of slag and 2% of graphene is about double compared to the reference mixture. In this way, approximately half of the energy for pavements maintenance operations would be saved.
## 3 Proposed Design
### Proposed Solution
Moreover, if the bit-error-rate (BER) is high then the probability of packet loss rate is also high which will cause high retransmissions. To resolve this issue, the block acknowledgement mechanism is used to support the A-MPDU frame. The receiving node receives the A-MPDU and only sends a collective acknowledgement against those MPDUs which are received correctly. The sending node will only retransmit the specific MPDU. The use of block acknowledgement mechanism supports the frame aggregation mechanism in high BER environment. Figure 7 shows an example of a block acknowledgement mechanism.
## 6 Conclusions  
This paper adopts a model-centric based Weighted federated learning model to detect and mitigate the LR-DDoS attack in the SDN control plane, and the model's performance is evaluated using the CAIDA dataset. The proposed WFL intrusion detection system is mainly based on the local training of data to extract the weights of the trained model using ANN and then share the trained weights with the federated server for aggregation. The WFL model has shown high prediction accuracy i.e., 98.85%, sensitivity 98.13%, and F1-Score 94.21%, while a very low misclassification rate of 1.15% by optimally adjusting each locally trained model at the federated server. The federated server assigns a unique preference to each locally trained model and aggregates all the local models to form a global model, and the global model is shared back to the end user device or local network for further attack detection and mitigation. The WFL model also brings pertinent benefits in terms of a smaller number of elements to transmit over the network, storage, and process by only sharing the weights of the model, not the local device or network data, which also guarantees the privacy of the end-user data. The prediction time per record is 0.019 ms, making the proposed system highly suitable for intrusion detection and highly stable. The WFL has shown its true potential for intrusion detection systems, especially for the SDN-based IoT network. The comparison with the existing approaches also validates the ability of the WFL model.
Sensors_**2013**, _13_, 5220-5250; doi:10.3390/s130405220  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Identifying the Key Factors Affecting Warning Message Dissemination in VANET Real Urban Scenarios**  
**[PERSON]**\({}^{1}\)**, **[PERSON]**\({}^{1}\)**, **[PERSON]**\({}^{1,\ast}\)**, **[PERSON]**\({}^{2}\)**, **[PERSON]**\({}^{2}\)**and [PERSON]**\({}^{2}\)  
\({}^{1}\) DIIS, University of Zaragoza, Ciudad Escolar s/n, Teruel 44003, Spain;  
E-Mails: [EMAIL_ADDRESS] (M.F.); [EMAIL_ADDRESS] (P.G.)  
\({}^{2}\) DISCA, Universitat Politecnica de Valencia, Camino de Vera s/n, Valencia 46022, Spain;  
E-Mails: [EMAIL_ADDRESS] (J.-C.C.); [EMAIL_ADDRESS] (C.T.C.);  
[EMAIL_ADDRESS] (P.M.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-9-7861-8156; Fax: +34-9-7861-8104.  
_Received: 21 February 2013; in revised form: 8 April 2013 / Accepted: 12 April 2013 / Published: 19 April 2013_  
**Abstract:** In recent years, new architectures and technologies have been proposed for Vehicular _Ad Hoc_ networks (VANETs). Due to the cost and complexity of deploying such networks, most of these proposals rely on simulation. However, we find that most of the experiments made to validate these proposals tend to overlook the most important and representative factors. Moreover, the scenarios simulated tend to be very simplistic (highways or Manhattan-based layouts), which could seriously affect the validity of the obtained results. In this paper, we present a statistical analysis based on the \(2^{k}\) factorial methodology to determine the most representative factors affecting traffic safety applications under real roadmaps. Our purpose is to determine which are the key factors affecting Warning Message Dissemination in order to concentrate research tests on such parameters, thus avoiding unnecessary simulations and reducing the amount of simulation time required. Simulation results show that the key factors affecting warning messages delivery are the density of vehicles and the roadmap used. Based on this statistical analysis, we consider that VANET researchers must evaluate the benefits of their proposals using different vehicle densities and city scenarios, to obtain a broad perspective on the effectiveness of their solution. Finally, since city maps can be quite heterogeneous, we propose a roadmap profile classification to further reduce the number of cities evaluated.  
keywords: vehicular _Ad Hoc_ networks; performance evaluation; inter-vehicle communication; \(2^{k}\) factorial analysis; real roadmaps; traffic safety
## 3 Proposed CPDAE  
In this study, a CPDAE network architecture was proposed for ECG noise cancelation. The proposed structure consists of several encoder and decoder layers. The noisy ECG \(\mathbf{\hat{x}}\) is fed into the input layer first, and the clean ECG feature code \(\mathbf{z}\) is extracted in the encoding stage. The signal \(\mathbf{\widetilde{x}}\) is reconstructed in the decoding stage. Moreover, channel-wise average pooling (CWAP) between the corresponding encoder and decoder layers is added to compensate for the decreased feature content layer by layer.  
To test the denoising performance, three models were proposed and implemented in this study. The proposed CPDAE\({}_{\text{Lite}}\) attempts to minimize the amount of parameter usage and the number of multiply-accumulate operations (MACs); the proposed CPDAE\({}_{\text{Full}}\) exhibits the greatest denoising capability; the proposed CPDAE\({}_{\text{Regular}}\) is the median version between the lite and full versions. In the remainder of this paper, only CPDAE\({}_{\text{Regular}}\) is discussed for simplicity. The CPDAE\({}_{\text{Regular}}\) consists of seven encoders, seven decoders, and six CWAP blocks. The overall architecture is illustrated in Figure 5. The detailed procedures of the encoder, decoder, and CWAP are described in the following sections.