esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental and Parametric Study on the Pull-Out Resistance of a Notched Perfobond Shear Connector
## 4 Finite Element Analysis
### Verification of Finite Element Model  
The numerical results resembled the pull-out failures of conventional and notched perfobond shear connectors reasonably well, as shown in Figure 12. The concrete crack initially occurred near the perfobond shear connectors and spread out across the concrete slab as the load increased. The rebar in the hole of conventional perfobond shear connectors yielded at the locations of perforation due to interlock effects. In comparison, smaller deformation was observed in the analyzed failure mode of notched perfobond shear connectors. The concrete dowels in the hole failed in shear.  
Figure 11: Material constitution of steel.  
As shown in Figure 13, the load-separation curves obtained from finite element analysis were compared with pull-out test results. Three stages were identified in the typical load-separation curves of both conventional and notched perfobond shear connectors, including an elastic stage, a nonlinear parabolic regime, and a descending branch. When the dilation angles of concrete were changed among \(30^{\circ},37^{\circ}\), and \(40^{\circ}\), the analyzed pull-out resistance of conventional perfobond shear connectors was 188.1 kN, 259.5 kN, and 292.1 kN, respectively, accounting for 71.6%, 98.8%, and 111.2% of the mean test result, which equaled 262.6 kN. For notched perfobond shear connectors with the dilation angles of \(30^{\circ},37^{\circ}\), and \(40^{\circ}\), the analyzed pull-out resistances were 179.6 kN, 264.1 kN, and 295.8 kN, respectively, taking up 63.5%, 93.4%, and 104.6% of the mean test result, which equaled 282.7 kN. Variations of the viscosity among 0, 0.1, and 0.01 showed no influences on the load-separation curves. It was indicated that the calibrated finite element model could be used to generate reasonable analysis results for both conventional and notched perfobond shear connectors. In this study, the dilation angle could be preferably set as \(37^{\circ}\) and the viscosity could be taken as zero.  
Figure 12: Tested and analyzed failure modes. (**a**) Crack in concrete slab; (**b**) deformation of perforating rebar; (**c**) shear of concrete dowel.
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Applications of DDPM in Image Processing
#### 3.3.2 Image Generation  
DDPM is a generative model, capable of generating realistic images. Its denoising diffusion process, coupled with the reversibility property, allows it to sample from the learned distribution and create new images. This application is valuable in tasks such as image synthesis for artistic purposes or as a source of diverse training data for other machine learning models [43; 44].
# 2-Methoxy-4-Vinylphenol as a Biobased Monomer Precursor for Thermoplastics and Thermoset Polymers
## 1 Introduction
Moreover, silyl-protected and epoxidized MVP were also investigated in controlled radical polymerizations, yielding polymers with a wide range of thermal and chemical properties [33; 34]. Although FA-derived monomers have shown properties that could be well suited to replace fossil-based monomers in polymerizations, their modifications and suggested end-applications have so far been limited [31; 32; 33; 34; 35]. In this work, we expand the scope and, therefore, emphasize the potential of FA as a platform monomer by creating a library of biobased monomers and exploring their structure-property relationship in thermoplastic and thermosetting applications. Initially, MVP was produced via the decarboxylation of FA, whereafter its phenolic group was modified to yield monomers that can undergo solution and emulsion polymerization, toward thermoplastic applications. Moreover, MVP was modified to synthesize divinylbenzene (DVB)-like monomers of different aliphatic chain lengths, which were thermally cured with thiol crosslinkers to produce thermosetting films. The resulting materials were characterized by means of chemical (nuclear magnetic resonance (\({}^{1}\)H-, \({}^{13}\)C-, and 2D-NMR), Fourier-transform infrared spectroscopy (FT-IR), polarity (contact angle), and chemical stability tests), thermal (differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA)), morphological (dynamic light scattering (DLS)), and mechanical (dynamic mechanical analysis (DMA)) techniques, which provided a holistic view of their final properties.
## 3 System Model  
This section describes the system model and problem formulation. Figure 1 illustrates an IoT network environment where one or more gateways with multiple asynchronous IoT devices are distributed in each of the \(m\) IoT networks. In each network, the IoT devices send data to the gateway according to their standards regarding the network configuration, frequency channel to be used, transmission timing, and so on. Each IoT network could not know the standards and the locations of the IoT devices and gateways of the other IoT networks. Therefore, it is difficult to avoid collisions between heterogeneous IoT networks.  
Figure 2 shows the channel selection problem of one IoT network in such heterogeneous IoT networks. In the network, \(K\) channels are available. A gateway and \(M\) IoT devices are associated with the star topology. IoT device sends data to the gateway, and when the gateway receives the data properly, the IoT device will receive an acknowledgment (ACK). This paper defines communication as success when the IoT device receives ACK information. Communication is defined as failure otherwise. If the communication fails, NACK information will be obtained on the IoT device side. For example, node 1 sends data to the gateway using channel 1; communication is successful since no other IoT device is accessing that channel. Hence, an ACK can be received from the gateway. Meanwhile,  
Figure 1: System model of the heterogeneous IoT networks.  
node two and node \(M-1\) transmit data using channel three simultaneously. The transmissions of node two and node \(M-1\) are failures because of the collision between them. In addition, node M transmits using channel k also fails because the node interferes with other IoT networks. In summary, the transmission will be assumed as a failure if two or more than two IoT devices access the same channel simultaneously, no matter which IoT network the devices belong to.  
Figure 3 shows the occupied state of the channel in the time domain. If multiple nodes send data using the same channel simultaneously, a collision will occur, and the communications are assumed to fail. Assume that an IoT battery-powered node will be driven for an extended period without charging. All nodes repeat the wake-up and sleep modes. Assume the sleep time for each IoT device is \(t\). After \(t\). time sleep, a node sends data to the gateway by utilizing the selected channel \(k\) based on the MAB methods from the available \(K\) channels. It does not matter if the node performs carrier sensing before transmission as long as it complies with the communication standard. The contents of the data and data size depend on the requirement of IoT devices. A node will be in sleep mode after sending and saving the result of receiving the ACK/NACK information.  
Figure 3: Occupied state of the channels in time domain.  
Figure 2: Channel access model.
# Fingerprinting Acoustic Localization Indoor Based on Cluster Analysis and Iterative Interpolation
## 3 The Proposed Fingerprinting Acoustic Localization Approach  
In the traditional fingerprinting localization approach, the target signal needs to match with all samples in the database to select its adjacent RPs for location estimation. Therefore, the large scale positioning database that the fingerprinting localization accuracy depends on means the complexity of matching operation improvement and the efficiency of positioning reduction. Aiming at solving the contradictory in traditional fingerprinting localization approach, this paper makes some improvements. As Figure 2 shows, after the database construction in the offline sampling phase, the entire positioning database is divide into sub-databases. Then, in the online positioning phase, the matching scope can be narrowed by the adjacent sub-database matching stage, and the adjacent RPs searching can be accomplished with a small amount of matching computation.  
Figure 1: Illustration of the fingerprinting localization process.  
Meanwhile, the offline phase of the fingerprinting localization approach requires a great sampling effort as the mobile sensor has to be placed at every position marks in the location area. To reduce the initial sampling effort, the database can be constituted using the sparse samples in the target area and extended afterwards by interpolation functions. As Figure 2 shows, an iterative interpolation method is presented to further refine the interpolation scope to avoid unnecessary virtual RPs. At the same time, the estimation result of each iteration process is compared with the one of the previous iterations, and the interpolation ends when the difference of the estimation results between the two adjacent process is less than the given threshold value.  
The novel acoustic localization approach consists of three main stages:  
1. Divide the positioning database into a certain number of sub-databases by K-Means clustering algorithm after database construction in the offline sampling phase.
2. Select the adjacent RPs of target point by the two-stage RPs matching method.
3. Conduct the position estimation of the target point by the iterative interpolation method.
# Enzymatic Synthesis of Biobased Polyesters and Polyamides
## 4 Biobased Monomers for Polyester and Polyamide Synthesis
### Other Biobased Monomers  
Lactic acid, one of the most well-known organic acids occurring naturally, can be found in many carbohydrates, for example, in naturally and fermented food products, plant, human beings and animals [144]. In most living organisms, lactic acid is also identified as a principal metabolic intermediate. Lactic acid can be manufactured chemically or biologically in industry [144]. In the chemical synthesis approach, lactic acid is prepared via hydrolysis of lactonitrile, a by-product of acrylonitrile production, by concentrated hydrochloric or sulfuric acid. This process is simple, but results in a racemic mixture of d- and l-lactic acid; and the production of lactic acid depends on the acrylonitrile industry in this case [145]. On the other hand, lactic acid can be produced via fermentation of sugars by bacteria. This microbial fermentation process involves the utilization of biomass feedstocks, low reaction temperature, low energy consumption and can resulted in enantio-pure lactic acid by selecting an appropriate microbial strain [145, 146, 147]. Currently, the global demand of lactic acid is 350 kilo ton per year, with a sustainable growth in the next decade; and more than 90% of lactic acid is commercially produced via fermentation of glucose [59]. Alternatively, production of lactic acid from biobased glycerol and its derivatives is feasibly; however, this route cannot compete with the fermentation process because of the high cost.  
3-Hydroxpropionic acid (3-HPA) is a valuable biobased platform building blocks listed in \"DOE TOP 10\" and revised \"DOE TOP 10\" [18, 22]. It can be produced via chemical approaches staring from 1,3-PDO, 3-hydroxypropionaldehyde or acrylic acid, which are not cost-effective [5]. In recent years, promising biosynthetic pathways have been developed to produce biobased 3-HPA, for example, via fermentation of sugars using genetically modified microorganisms [148, 149, 150]. Currently, the commercial production of biobased 1,3-HPA is under preparation stage by several companies including Perstorp, Opxbio-Dow chemical, BASF-Cargilland-Novozymes, and Metabolic [29].  
Many long chain fatty acids and their derivatives can be produced from renewable resources such as plant oils and fats [97, 151, 152, 153], and they are good building blocks for polyester and polyamide synthesis. Examples of long chain fatty acids include oleic acid, ricinoleic acid, erucic acid, vernolic acid, and so on [9, 154].  
Moreover, there are many other biobased building blocks for polyester or polyamide synthesis, such as ethylene glycol [31], polycarboxylic acids (citric acid, tartaric acid) [8], 11-amino-undecanoic acid [31], and so on.
## 4 Analysis of the CRB for Short Observation Intervals
### CRB when \(\Delta\rho<\rho_{clip}\): Interference Case  
When \(\Delta\rho<\rho_{clip}\), we cannot process anymore the two signal separately, which was otherwise possible thanks to the correlation properties of the PRN codes. Now, the initial \(\text{FIM}\,\mathbf{\mathsf{J}}(\mathbf{\xi})\) is no longer a diagonal matrix, and the analytic expressions for \([\mathbf{\mathsf{J}}^{-1}(\mathbf{\
abla})]_{ii}\) become quite cumbersome. Thus, from now on, we will rely on numerical evaluation of \([\mathbf{\mathsf{J}}^{-1}(\mathbf{\
abla})]_{ii}\) to compute the CRB.  
In Figure 4, we show the results of computing \(\text{CRB}(h)\) as a function of the \(\Delta\rho\) for the different GNSS signals under study. Once again, we have considered a \(|\Gamma|^{2}=0.1\), and \(T_{obs}=1\) s. As expected, we have observed that when \(\Delta\rho\) increases, the bound reaches the values obtained using Equation (35). Moreover, we also observe how that the CRB reaches these values even for \(\Delta\rho\ll\rho_{clip}\). In summary, we can identify two consequences of having higher bandwidth signals: (a) the asymptotic value for the bound is reached faster; and (b) the value of the bound is lower for any \(\Delta\rho\). In the case of (a), this can be clearly related with having a sharpened peak of the ACF for higher front-end bandwidths.  
In Figure 5, we show the results of computing \(\text{CRB}(h)\) for different \(\Delta\rho\) and \(|\Gamma|\) values. The plots presented allow us to visually determine, approximately, the order of the best achievable precision given a \(\Delta\rho\) and \(|\Gamma|\) combination. The \(y\)-axis represents \(|\Gamma|^{2}\), which in practice is equivalent to the gain experienced by the reflected signal compared to the LOS. As expected, for \(\Delta\rho\) values higher than \(\rho_{clip}\), the CRB agrees with the one obtained with Equation (35). Figure 4 confirms that the CRB when \(\Delta\rho>\rho_{clip}\) lower bounds all of the other cases, as we hypothesized at the beginning of this section.
# Review of Smart-Home Security Using the Internet of Things
## 3 Smart-Home Ecosystem
### Ambient Living Devices
Once accessed, attackers could manipulate lighting settings to disrupt the household or use the system as a gateway to launch further attacks on more critical systems. Smart thermostats, which adjust the temperature based on user behavior and preferences, present similar risks [58]. These devices collect detailed information about a user's daily routines and home occupancy patterns, making them a rich target for cybercriminals. If compromised, such data can be exploited for burglary or more targeted attacks. Furthermore, unauthorized control over the thermostat can lead to energy waste or damage to the home environment by extreme temperature adjustments. Air-conditioning systems, increasingly smarter and interconnected, are susceptible to the same threats as smart thermostats [59]. By gaining control over these systems, attackers could cause discomfort or increase utility bills significantly. More dangerously, if integrated with other home-automation systems, compromised air-conditioning units can serve as entry points to other devices connected to the same network. Smart ovens and fridges represent a more direct physical threat, if compromised [60; 61]. A hacked oven could be turned on or off remotely, posing fire risks or other safety hazards. Smart fridges, which often include interfaces to order groceries or manage expiry dates, contain personal consumer data that, if accessed, could lead to privacy breaches. Moreover, since these appliances are central to daily living, their disruption can cause significant inconvenience and potential health risks. Smart plugs, which are used to control various electronic devices remotely, amplify the security challenges in a smart home [62]. These devices can switch appliances on or off and monitor energy usage, providing convenience and efficiency. However, if compromised, smart plugs could be used to manipulate the power supply of critical devices, such as medical equipment, security systems, or refrigeration units, leading to dire consequences. To combat these risks, robust security measures are necessary [63; 64]. These include strong and unique passwords for device access, encrypted communications to prevent data interception, and regular software updates to patch vulnerabilities. Manufacturers must also prioritize building secure devices that can resist the latest types of cyber-attacks. This involves not only securing the device software and communication channels, but also ensuring physical tamper resistance. Implementing network segmentation can significantly enhance security by isolating smart-home devices from each other and the main computing environment. This way, even if one device is compromised, the breach does not necessarily grant access to other devices or sensitive personal data stored in other network segments.
# Performance of the 5 th Generation Indoor Wireless Technologies-Empirical Study
## 2 Materials and Methods
### Theoretical Performance of 5G and Wi-Fi 6 Links
First, 5G NR operates on dedicated bands that are reserved solely on the specific purpose, while Wi-Fi 6 operates mainly on the Industrial, Scientific and Medical (ISM) radio bands, where there may also be other operating devices. Secondly, 5G NR uses scheduled access where the medium access control (MAC) algorithm determines the usage of the RBs, while Wi-Fi 6 is based on contention where data can be transmitted only after certain contention criteria are met. For 5G, regulators have assigned frequency in three broad ranges: high bands (e.g., mmWave), mid bands (e.g., 1-10 GHz), and low bands (e.g., below 1 GHz). These serve different purposes. The high bands support the fastest throughput, the mid bands offer a good mixture of coverage and capacity, and the low bands help to provide strong wide area and in-building coverage [27]. So far, the focus has been on the 3.5 GHz range to support initial 5G launches, followed by the mmWave awards in the 26 GHz and 28 GHz bands. For example in Finland, the mmWave band in 26.1-27.5 GHz has been divided between the three operators [28]. For Wi-Fi 6, the frequency landscape looks different from that for the 5G. Wi-Fi 6 is a term that covers IEEE 802.11 ax-2021 standard operating around 2.4 and 5 GHz carrier frequencies, and Wi-Fi 6E refers to the same standard operating at 6 GHz carriers [29]. The ISM band at 24 GHz remains vacant from Wi-Fi systems, but it has been considered as a candidate band for Broadband Wireless Access (BWA) systems in the USA and Canada [30]. The competitor for the 5G operating in the mmWave region is Wi-Fi standard IEEE 802.11 ad from 2009, which operates at 60 GHz carrier frequency, offering data rates up to 7 Gbit/s.
###### Abstract
The joint MAP algorithm is implemented based on the conditional probabilities of two consecutive transmitted CAP sample-level distributions in a symbol for active transmitter detection. However, as the distributions of CAP sample levels can be complicated at different constellation rotation angles for offering SSD, this algorithm exhibits a relatively high computation complexity. In this paper, instead of using the conditional probabilities of transmitted CAP sample levels, we propose a novel active transmitter detection scheme in SM based on the observed CAP signal characteristics under various channel gain distributions. The transmitter detection scheme consists of the transmitted signal pre-distortion technique and a novel received power-based statistical detection method. Experimental results show that, using the proposed transmitter detection scheme, the previously proposed SSD technique can be effectively realized with an improved system throughput brought by SM. Therefore, spatial modulation, as a low RF-chain complexity spatial reuse technique, can boost the data rate under such channel gain imbalance, where an effective detection of active transmitter enables SM implementation. Together with the signal space diversity technique, the performance of the OWC system can be further enhanced, which is exciting for achieving a reliable high-speed wireless transmission for 6G networks and beyond.
## 3 Optimization Strategy
### Iterative Optimization Method for Low Reynolds Number Propeller with Gurney Flap
Using polynomial interpolation method, the rotational velocity (\(n_{\text{s-req}}\)), torque (\(Q_{\text{req}}\)), and efficiency (\(\eta_{\text{req}}\)) corresponding to the required thrust (\(T_{\text{req}}\)) of each sample could be calculated by the CFD method. 3. Multi-island genetic algorithm was used to evaluate the existing samples and generate new samples from the design space that potentially had higher efficiencies. 4. Once the relative difference in efficiency (\(\eta_{\text{req}}\)) of two adjacent samples was less than \(\varepsilon_{2}\) for 20 consecutive times, the optimization was finished and the sample with the maximum \(\eta_{\text{req}}\) was considered as the optimal solution. Otherwise, (2) and (3) needed to be repeated. Step 5. Judge whether the iterative optimization converges. If the relative difference in \(\eta_{\text{req}}\) obtained in Step 4 between two adjacent iterations was less than \(\varepsilon_{3}\), the whole optimization was finished and the propeller with maximum efficiency \(\eta_{\text{req}}\) in the last iteration was considered as the final optimal solution. Otherwise, we proceeded to Step 6. Step 6. Update the constraints for the cross-sectional airfoils by BEMT method. We constructed an aerodynamic response surface of the cross-sectional airfoils by polynomial interpolation of CFD data. Subsequently, according to the new chord, the pitch distributions and rotational velocity of the optimized propeller were obtained from step 4, and the lift coefficient (\(C_{\text{J-target}}\)) and working Reynolds number (\(Re_{\text{target}}\)) of each cross-sectional airfoil were updated. Then, we returned step 3 for the next iteration optimization. The optimization procedure is summarized in Figure 14.  
Figure 14: Flowchart of iterative optimization strategy for low Reynolds number propeller.