esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Fabric Defect Detection Model for High-Resolution Images
## 2 Proposed Model Based on Cascade R-CNN
### Backbone Improvement
#### 2.4.2 SAC Layer  
Although ResNet-50 achieved good results on the VOC [28] and COCO [29] datasets, the fabric defect dataset can be further improved. Owing to the large difference in fabric defect size, the introduction of a multiscale module can help in improving the detection accuracy. Consequently, we replaced the 3 \(\times\) 3 convolutional layer with SAC (Switchable Atrous Convolution) [30] in Stage 2.  
As shown in Figure 8, the SAC module can be divided into three parts, i.e., two global context modules and the main SAC module. The global context modules obtained global information from the global average pooling layer and added it to the feature map. The main SAC module contained three paths, namely the Switch path and two feature extraction paths. The Switch path comprised an average pooling layer with a 5 \(\times\) 5 kernel and 1 \(\times\) 1 convolutional layer. The two other paths consisted of two convolutional layers with different atrous rates [31].  
Figure 8: SAC (Switchable Atrous Convolution).  
Figure 7: ResNet-50.  
We use y = Conv(x, w, r) to denote the convolutional operation with weight w and atrous rate r, which takes x as its input and outputs y. Then, we can convert a convolutional layer to SAC as follows:  
\[\text{Conv}(\text{x},\text{ w},\text{ 1})\ \rightarrow\text{S}(\text{x})\cdot\text{ Conv}(\text{x},\text{w},\text{1})+(\text{1}-\text{S}(\text{x}))\cdot\text{Conv}(\text{x}, \text{w}+\bigtriangleup\text{w},\text{r}) \tag{1}\]  
The convolution results were obtained by fusing the defect information acquired at different atrous rates according to Equation (1). In Equation (1), S(\(\cdot\)), Conv(x; w; r), x, w, and r denote the switch operation, convolution operation, input, weight, and atrous rate, respectively.  
The input feature maps were convolved at two different atrous rates to obtain the feature information in different receptive fields. The improved backbone model could adapt to the features of different scales; accordingly, this model was used to improve the detection accuracy of multiscale defect targets.
## 1 Introduction
Convolution neural network (CNN) is a kind of feedforward neural network with convolution transformation and deep structure. It is one of the representative algorithms of deep learning. CNN extracts translation-invariant features of input data by layer-by-layer convolution and pooling operations in the architecture. For digital communication signals, there are features that can distinguish signals of different modulation modes. For example, different order quadrature amplitude modulation (QAM) signals have amplitude and phase hopping points that occur between different symbols. CNN can effectively extract such features. Besides, the convolution kernel parameter sharing and the sparseness of the inter-layer connection in the CNN architecture enable the convolutional neural network to have a grid-like topology with a small amount of computation, which is advantageous for processing a large amount of RF data. In fact, CNN has achieved excellent results in processing audio data, and this advantage is consistent when dealing with complex baseband signals. Therefore, we believe that CNN, a mature deep learning model, has great potential for application in AMR tasks. [PERSON] et al. [19] discussed the impact of deep neural network design on communication receivers. Besides, the effects of certain errors bound in detection and isolation are analyzed. It was shown that for the frequency offset and sample rate offset not covered in the training samples, there is a significant decline in the test samples. Summarizing the existing research on the application of classification on raw baseband signals, there is less consideration of the errors introduced in signal detection and isolation and the robustness of the algorithm performance in such offsets. However, in blind wide-band signal processing, these offsets caused by parameter estimation or hardware defects are difficult to avoid. In the AMR system, a technique that can reduce the influence of signal isolation and detection error needs to be proposed. In this article, we study the AMR method based on the raw IQ signals under parameter estimation errors. Firstly, the influence of parameter estimation errors on the performance of the CNN classifier is analyzed. Then, an AMR method based on a spatial transform network (STN) is proposed. By applying this improved model, the robustness of the AMR method under parameter estimation errors has been significantly improved. The remainder of this paper is presented as follows: In Section 2, we provide an overview of flow for blind wideband signal capture model. Section 3 introduces the classification method based on STN. Section 4 introduces the evaluation setup for AMR. Section 4 presents simulations and results. Finally, Section 5 concludes the paper.
## 4 Results
### Pore System and Pore Size  
Organic-rich shales contain abundant pores developed within both the matrix and organic matter. Various pore classification schemes have been proposed based on their origin, formation mechanisms, pore size, morphology, distribution, and geometric relationships between pores and surrounding grains [21; 36; 70; 71; 72; 73; 74; 75; 76]. Referring to the mudrock pore classification developed by [PERSON] et al. (2012) and [PERSON] (2014) [21; 72], the pore systems in the organic-rich Yanchang shales were divided into inorganic pores, organic pores (OPs) and fractures. The inorganic pores were subdivided into interparticle pores (interP) and intraparticle (intraP) pores.
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
### Hydrogels as Sophisticated, Biomimetic Materials
#### 1.2.1.2 Peptide-Based Hydrogels  
Peptide hydrogels have emerged as a novel class of hydrogels, which bridge the gap between natural and synthetic hydrogels. They can be synthesised easily and reproducibly using laboratory manufactured peptides, and are also deemed to be highly versatile, as their amino acid sequences can be modified to promote advantageous functional traits [20]. Peptide-based hydrogels self-assemble in solution through sequence complementarity to form 3D hydrogels [21], with hydrogen bonding, electrostatic interactions and hydrophobic interactions contributing to the self-assembly process [22]. Generation of secondary structures such as alpha helices or beta sheets can be enhanced by the ratios of certain amino acids in the peptide sequence. The resulting hydrogels display beneficial characteristics such as a biocompatible nature and an ability to mimic the structural characteristics of the native ECM [8].  
Hydrogels based on self-assembling peptides are highly versatile and bridge the gap between natural and synthetic hydrogels [23]. Generally, self-assembling peptides are synthesised using fluoromethylaccarbonyl (Fnoc) solid phase synthesis, which are between 15-25 amino acids in length. However, some can be less than seven amino acids, which are often termed ultrashort peptides [24]. Self-assembling peptides form hydrogels through a sol-gel transition mechanism, eliminating the need to use crosslinkers. The resulting self-assembling peptide hydrogels have a high-water content (>95%) and a nanofibrous network structure, suitable for cell encapsulation and cell culture applications [23]. The majority of self-assembling peptide hydrogels are made up of L-amino acids which are located and metabolised naturally by the body, increasing biocompatibility and reducing immunogenicity or inflammatory reactions. In addition to biocompatibility, self-assembling peptide hydrogels have shear-thinning properties that allow them to recover bulk properties following shear stress, making them ideal candidates for creating tissue engineering therapies that are less invasive for patients [23].
## 4 Energy Consumption of Different Transport Schemes  
Despite the fact that digitized transport is more superior in terms of dynamic range performance compared to analog transport, other factors such as energy consumption have to be taken into consideration. Telecommunication networks have been identified as one of the major contributors to the overall ICT energy consumption with wireless access network being the largest contributor to CO\({}_{2}\) emissions [35; 36; 37]. With the wireless base station contributing approximately two-thirds of the CO\({}_{2}\) emission, it is imperative to first reduce and optimize the energy consumption of wireless base station in order to minimize the carbon footprint [35; 36; 37; 38; 39]. On the other hand, the proliferation of smart devices in recent years has put a massive burden on our current mobile networks. The most straightforward upgrade is to reduce the cell size and increase the number of mobile base stations. However this will further increase the proportion of base station energy consumption, further compounding the energy crisis. To address this issue we introduce fiber-wireless technology with optical fiber backhauling the small cells with majority of the processing power located in a central location. This architecture results in a simple, compact and energy-efficient base station.
## 3 Results
In order to evaluate the performance of all methods, the RMSE of the interpolation values was calculated for all days and is given in Figure 5.  
Figure 4: Root mean square error (RMSE) values of ANN models and linear interpolation method (LIN) and inverse distance weighted interpolation (IDW) methods. Figure 5: RMSE values for ANN-LIN-IDW methods. According to Figure 5, the RMSE values of the ANN model demonstrate that an increasing distance between reference stations adversely affects the interpolation accuracy. It is found that LIN and IDW methods produce less accurate values with an increasing distance. In general, the ANN model has an RMSE of 5.2 mm for a 40-station network, 5.8 mm for a 30-station network, and 6.7 mm for a 20-station network. It is seen that LIN and IDW methods have an RMSE value of 1 cm at a 30-40 km distance in a 40-station network. It is observed that these values reached approximately 1.5 cm in 30- and 20-station networks.
## 1 Introduction
### Focus of the Study and Statement of Originality  
This investigation's primary objective is to extract and analyze the Borassus fruit's natural fibers (BNF). The evaluation of morphological and hygroscopic properties was conducted to examine the Borassus fruit fiber's engineering qualities to turn it into an economically viable and sustainable material. The ecological benefits of using Borassus aethiopum fibers are rapid growth, renewable resources, low energy consumption during manufacturing, biodegradability, abundance, and durability.  
A primary dataset was created from the experimental results. Gradient Boosting Regressor (GBR) and Decision Tree Regression (DTR) models were used to predict the hygroscopic properties of the natural fibers based on the created primary dataset. Both models (GBR and DTR) were analyzed comparatively to determine the most efficient model during the prediction.  
This study offers an original approach to making use of the natural fibers in Borassus fruit. The major features of novelty are summed down as follows: (i) Eco-friendly extraction of natural Borassus fruit fiber (BNF): the fibers were extracted manually without the use of any chemical. (ii) Pioneering the hygroscopic properties evaluation of BNF: to the authors' knowledge, previous literature studying the hygroscopic behavior of the BNF using DVS does not exist. (iii) Build a primary dataset from experimental results: most literature using ML models utilises the secondary dataset (data collected from existing literature). Meanwhile, the present dataset was obtained experimentally. (iv) Develop Decision Tree Regression (DTR) and Gradient Boosting Regressor (GBR) models to efficiently run on a small-size dataset. Then, compare the models efficiently; the outcome offers insightful information on selecting the best model for future applications. The contributions were subsequently categorized as follows:  
Theoretical contributions:  
* The study introduces BNF as a potential engineering material, expanding the scope of natural fiber research, and it comprehensively studies its hygroscopic properties using Dynamic Vapor Sorption (DVS).
* The creation, application, and comparison of GBR and DTR models for predicting hygroscopic properties based on primary and small-size experimental data.  
Practical contributions:
* Eco-friendly extraction of Borassus fruit fibres and their identification as potential sustainable materials for construction application.
* Creation of a valuable dataset for future research and informative model comparison for selecting appropriate models in similar cases.
* Application of practical tools for BNF characterization and product development.
# Research on a Support System for Automatic Ship Navigation in Fairway
## 4 Automatic Navigation System of a ship in a Fairway
### Control System
#### 4.4.2 Ship Speed Controller  
The aim of this controller is to gradually decrease the ship's speed in each segment of the planned track. By using the PID (Proportional-Integral-Derivate) algorithm, as shown in Figure 7, the controller calculates the propeller signal for the ship dynamic model to obtain ship speed. According to the PID control theory, the error between the desired and actual speed is used as the input signal for the PID controller. The control signal (\(n\)) is the ship's propeller RPM, and will be calculated and fed into the ship dynamics. By choosing suitable parameters for the controller (\(\mathrm{K_{P}}\), \(\mathrm{K_{i}}\), \(\mathrm{K_{d}}\)), PID control law ensures that the actual speed of the ship always follows the desired value.  
The error between the desired speed and actual one is defined as follows:  
\[\mathrm{e(t)}\ =(\mathrm{u_{d}}-\mathrm{u(t)}). \tag{15}\]  
The PID control law is given as follows:  
\[\mathrm{n}\ =\ \mathrm{K_{P}}\mathrm{e(t)}+\mathrm{K_{i}}\int_{0}^{t}\mathrm{e( \tau)}\mathrm{d\tau}+\mathrm{K_{d}}\frac{\mathrm{de(t)}}{\mathrm{dt}}. \tag{16}\]  
To determine the PID control parameters, several methods exist. The manual method, Nichols-Ziegler, genetic algorithm, etc. is one of them. Ship dynamics studied in this research has a high nonlinear in a 3 DOF system, so the manual turning method is applied to determine the PID parameters.  
In this research, \(\mathrm{K_{P}}=-1\); \(\mathrm{K_{i}}=-0.05\); \(\mathrm{K_{d}}=-0.6\) were determined by the manual method for gains of P, I, and D elements, respectively.
## 2 Data and Methods
### Experimental Design  
PM\({}_{2.5}\) concentrations are associated with meteorological conditions, such as winds, temperatures, humidity, etc., and the BP neural network provides an efficient tool to fit the nonlinear PM\({}_{2.5}\) concentrations and the meteorological variables. On the other hand, the PM\({}_{2.5}\) timeseries is temporally autocorrelated and spatially correlated, especially for those low-frequency IMFs. Therefore, in this paper we designed a set of sensitivity experiments consisting of different combinations of the EEMD, BP, CNN and LSTM algorithms. Table 1 shows the experimental design. Exp. 1 applies the BP to the original PM\({}_{2.5}\) directly. In Exp. 2, the original PM\({}_{2.5}\) was first decomposed by EEMD into IMFs, each of which was fit by individual BP. Summation of all IMF prediction is regarded as the output (d_PM\({}_{2.5}\)). Exp. 3 applies LSTM to the original PM\({}_{2.5}\) without using meteorological variables and Exp. 4 uses a hybrid CNN+LSTM model. Note that Exp. 3 and 4 apply 6 hr PM\({}_{2.5}\), rather than daily PM\({}_{2.5}\), to explore the capability of LSTM in dealing with high-frequency timeseries.
## 3 Methodology
### Privacy Amplification and Key Authentication  
Lastly, block-based privacy amplification and key authentication were performed from \(\delta_{corr}\), using Block-CHMAC-EPQH (B-CHMAC-EPQH) to enhance the security of the key and ensure the extracted key's integrity and correctness. The CHMAC-EPQH algorithm is mentioned in Section 3.1.1. Thus, the privacy amplification and key authentication of \(\delta_{corr}\) were carried out successfully. The performance of the proposed research methodologies is discussed in the section that follows.
# Reduced Overhead Routing in Short-Range Low-Power and Lossy Wireless Networks +
###### Abstract  
In this paper we present enhanced routing protocol for low-lower and lossy networks (ERPL), a reduced overhead routing protocol for short-range low-power and lossy wireless networks, based on RPL. ERPL enhances peer-to-peer (P2P) route construction and data packet forwarding in RPL's storing and non-storing modes of operation (MoPs). In order to minimize source routing overhead, it encodes routing paths in Bloom Filters (BF). The salient features of ERPL include the following: (i) optimized P2P routing and data forwarding; (ii) no additional control messages; and (iii) minimized source routing overhead. We extensively evaluated ERPL against RPL using emulation, simulation, and physical test-bed based experiments. Our results demonstrate that ERPL outperforms standard RPL in P2P communication and its optimized P2P route construction and data forwarding algorithms also positively impact the protocol's performance in multi-point to point (MP2P) and point to multi-point (P2 MP) communications. Our results demonstrate that the BF-based approach towards compressed source routing information is feasible for the kinds of networks considered in this paper. The BF-based approach results in 65% lower source routing control overhead compared to RPL. Our results also provide new insights into the performance of MP2P, P2 MP, and P2P communications relative to RPL's destination-oriented directed a-cyclic graph (DODAG) depth, i.e., a deeper DODAG negatively impacts the performance of MP2P and P2 MP communications, however it positively impacts P2P communication, while the reverse holds true for a relatively shallow DODAG.  
low-power and lossy networks (LLNs); short-range wireless networks; routing protocol; probabilistic forwarding +
Footnote †: This paper is an extended version of our paper published in \"[PERSON]; [PERSON] ERPL: An Enhanced Peer-to-Peer Routing Mechanism for Low-Power and Lossy Networks. In Proceedings of the 2018 11 th IFIP Wireless and Mobile Networking Conference (WMNC), Prague, Czech Republic, 3-5 September 2018\".
# Novel Dual Beam Cascaded Schemes for 346 GHz Harmonic-Enhanced TWTs
## 2 HHTWT
### SWS Design Methodology of HHTWT  
#### 2.2.1 SWS Design  
Two sections of the HHTWT, i.e., the modulation section and the radiation section, have different operating bands. The modulation section works at E band, and the radiation section operates at THz band. The dispersion curve and interaction impedance are obtained by 3D EM simulation. For the modulation section, the phase shift at the center frequency is set to \(1.41\pi\), which ensures enough bandwidth. Then, interaction impedance should be set as high as possible. For the radiation section, the operating voltage should be the same with that in modulation section. The dispersion curve should also be flat to broaden bandwidth. Hence, the phase shift at the center frequency is set to \(1.49\pi\). Figure 2 shows the dispersion curve, associated electron beam line, and interaction impedance of the modulation and the radiation sections, respectively. The structural parameters of the HHTWT are shown in Table 1, in which a, b, h, p, and r are the width of the broad edge of the waveguide, the length of the narrow side of the waveguide, the height of the straight rectangular waveguide, the axial period length, and the radius of the electron beam channel, respectively. Between the modulation section and the radiation section, the drift tube is adopted to decrease the risk of self-oscillation and realize the transition from velocity modulation to density modulation. The length determination of two sections and drift tube are discussed later.  
Figure 1: The slow wave structure (SWS) of high-harmonic traveling wave tube (HHTWT).
# Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review
## 5 AI-Powered VANET Solutions
### Routing  
Routing is essential in VANETs since all supported services depend on multi-hop network architecture for transferring data [157]. Fluctuations in speed in urban and highway settings might lead to regular breakdowns in the network connectivity, presenting a significant obstacle to sustaining uninterrupted communication [158]. To establish effective interactions in routing protocols, especially ones that include multiple hops, it is necessary to choose relays that are quasi-optimal. The utilization of Random Forest, SVM classification, and LSTM algorithms can enhance routing protocols in the vehicle environment [159]. LSTM, in particular, plays a vital role in guaranteeing the dependability of vehicles of decisions regarding routing by making stochastic predictions of traffic movement [160]. Researchers conducted a study on driver behavior and discovered that CNN [161] is highly efficient at forecasting driving routes and preventing sudden alterations. Ant Colony Optimization (ACO) is an effective approach for finding quasi-shortest paths in data routing [107]. Additionally, the DT [48] and NB [129] methods are also valuable in this context. ACO exhibits self-organizational properties and demonstrates resilience in the face of errors. Particle Swarm Optimization is a vital component of geocast protocols since it directs the migration of particles toward the most optimal places [162].
# Experimental Study on Shear Mechanical Properties of Pile-Soil Interface Under Freezing Conditions
## 1 Introduction
The influence of factors (such as normal stress, moisture content, roughness, and boundary conditions) on the soil-structure interface has been analyzed. Although the shear mechanical properties and deformation mechanism of the unfrozen soil-structure interface have been extensively investigated, we still lack a thorough understanding of the distribution and evolution of the freezing strength at the soil-pile interface along the pile. This study focused on the shear mechanical properties of the pile-soil interface under freezing conditions. A measuring system for determining the freezing strength was developed and a series of experimental studies on the freezing strength at the interface were carried out under different temperature and water content conditions. Then, the mechanical behavior of the interface was discussed and the strength parameters for along the pile-soil interface in permafrost regions were proposed.