esa-sceva/satcom-chunk-collection huggingface dataset:
# Chitosan-Coated Nanoparticles: Effect of Chitosan Molecular Weight on Nasal Transmucosal Delivery
## 1 Introduction
To increase bioavailability after nasal delivery, polymeric nanocapsules have been investigated [28; 29; 30]. These nanocarriers are considered a type of reservoir drug delivery system [31], and can be obtained by the interfacial deposition of pre-formed polymers [32]. Their structure is characterized by an oil core surrounded by a polymeric shell stabilized by a surfactant system [31; 33]. The lipid-core nanocapsules (LNCs), developed by our research group, are composed of a dispersion of sorbitan monostearate (solid lipid) and medium-chain triacylglycerols (liquid lipids) in the core, surrounded by poly(\(\varepsilon\)-caprolactone), an aliphatic polyester as a polymeric wall, and polysorbate 80 as a stabilizing surfactant [34]. The lipidcore dispersion, i.e., sorbitan monostearate dispersed in oil, confers different properties to this system, such as controlling the drug release and increasing the encapsulation efficiency when compared to the core of the conventional nanocapsules containing only liquid lipids [34; 35; 36]. These nanocarriers showed efficient brain delivery of drugs as resveratrol [37] and curcumin [38] when administered orally and intraperitoneally, as well as the reduction of the side effects of the antipsychotic drug olanzapine [39]. Furthermore, they demonstrated improved the vitro and in vivo antitumor effectiveness of resveratrol, methotrexate, and acetyleneenol when compared to the free drugs [40; 41; 42]. [PERSON] and co-authors [43], developed a two-step process to obtain modified LNC stabilized simultaneously with polysorbate 80, and lecithin and coated with chitosan. Chitosan is a cationic biopolymer obtained by the partial deacetylation of chitin under alkaline conditions [44]. Chitosan demonstrated several interesting properties for pharmaceutical application, such as biodegradability, biocompatibility, antibacterial activity, and the controlled release of drugs [45; 46]. Furthermore, chitosan showed mucoadhesive and penetration-enhancing properties, which are particularly desirable for its application in drug nasal delivery [47; 48]. These actions are mediated by the structural reorganization of the tight junctions of the nasal epithelium, increasing the paracellular transport of drugs [49]. In the present study, LNC stabilized with lecithin and coated with chitosan was obtained by an innovative one-pot technique. Moreover, the pharmaceutical properties of formulations with chitosan of different molecular weights (MW), intended for the nose-to-brain delivery of simvastatin, were evaluated.
###### Abstract
[PERSON] et al. (2018) applied the CNN model to mapping landslide cracks in various environmental conditions. In the studies above, CNN-based methods for crack detection have shown robust performance compared to conventional IPTs. Although CNN is frequently used in the field of crack detection, CNN-based methods have some drawbacks. The training set of the CNN is usually composed of small patches. When the trained CNN network is applied to large-sized testing images, the images need to be cropped into patches so that each patch can be classified. In this process, the patches cropped from the test images are evaluated by the CNN individually, and the relationship between the patches is not of concern. In fact, cracks are always extended spatially, which indicates that the neighborhoods of a crack patch often contain cracks. Therefore, the connection between patches, especially neighboring patches, may contain important information about cracks, which provides the inspiration for improvement. The central patch and its neighboring patches can be considered as a graph structure. To take full advantage of the information between neighboring patches and improve the crack detection performance, a graph attention network (GAT) is introduced into crack detection in this study. The graph attention network is a kind of graph neural network (GNN), which has shown good performance in graph structure data. [PERSON] et al. (2018) utilized a graph attention network to learn the relationship between users and items, which developed a realistic package recommendation system. [PERSON] et al. (2018) combined the graph attention network with the residual network to capture the correlation between adjacent image elements, based on which the assessment of the urban habitat was realized. [PERSON] et al. (2018) proposed an explainable graph attention network framework to build the connection between multiple gene modules and cancer, which could provide mechanistic insights into cancer development and heterogeneity. The graph attention network works well in these diverse cases, which encourages us to apply this model to crack detection. In this study, a method combining CNN and GAT is proposed. Firstly, a CNN model is used to extract features and reduce dimensions. Next, the CNN outputs of the central patch and its neighborhoods are connected to constitute a graph structure, which is input into a GAT network to learn the relationship and update the node features. Finally, the output features of the central node are analyzed to determine whether the central patch contains cracks. This paper is arranged as follows. The basic knowledge of CNN, GAT, and the proposed framework are given in Section 2. The case study is presented in Section 3. Finally, the main contribution of this work is concluded in Section 4.
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 7 Discussion
### Outlook  
As this review has indicated, researchers suffer from a shortage of suitable data to base their work on in this domain. Regarding datasets, researchers will continue to fill in the gaps in industry-specific datasets as IoT devices continue to gain popularity. That is, as was the case with the IIoT datasets, [83; 87], and MedBIoT dataset [51] published in the past two years, the research will be looking to target more specialized devices and network traffic, rather than trying to encompass large general datasets. On the same note, more general datasets will continue to be published, like the ones from the CIC, though they will focus more on the common attacks or vulnerabilities identified in the most recent years. There is no indication that the more recent datasets are gaining in complexity (neither in the number of records, features, or devices captured); hence, this trend is likely to continue. This is tied to the resource-constrained nature of research institutes' current training, testing, and deployment capabilities.  
In terms of infrastructure and deployment, SDNs represent a paradigm shift in the management of IoT networks, with the flexibility and scalability required to tame the complexity of IoT ecosystems today, and are well positioned to play a central role in facilitating effortless deployment and adaptive security frameworks. We can anticipate more utilization of blockchain and edge computing paradigms in the years ahead, particularly as IoT systems grow in size and complexity. By compromising on infrastructure issues with robust data-centric approaches, researchers can continue to advance the field of intrusion detection systems.  
The fields of ML and DL are continuously evolving. Our study examined the top ML models for this particular task, but forecasting which methods will be applied in the future is still difficult. Nevertheless, it is clear that, aside from the selection of ML techniques, preprocessing of data and feature selection are important factors and will continue to improve. To simplify future research, using standardized datasets and evaluation metrics is highly recommended. Also, in terms of the IDS, it is important that the system is able to provide real-time detection, so one possible future research area is to include this in ML models in a standardized way. The development of ML algorithms could also mean easier deployment even in more constricted infrastructure environment conditions.
# Empirical Remarks on the Translational Equivariance of Convolutional Layers
## 4 Experimental Results
### On the Translational Invariance of Max-Pooling and Augmentation  
This section demonstrates the well-known property of max-pooling layers and augmentation in terms of translational invariance. For this purpose, we provide three comparative results. One is for max-pooling, another is for augmentation within 2-pixel translation to partially cover the test images of 2-pixel translation, and the last one is for augmentation within 5-pixel translation to fully cover the test images of 5-pixel translation. For the network with max-pooling, we repeat a pair of convolutional and max-pooling layers \(K\) times, and name it \(Net\)-\(K\) for simplicity. We obtained all the test results by varying translation from 0 to 5 pixels. We note that the Net-4 network with max-pooling is the deepest one for \(38\times 38\) input images.  
Figure 7 shows that the networks with max-pooling are always superior to the networks without max-pooling. The deeper the network, the greater their difference, but they suffer from the large-pixel translation. However, augmentation is much more effective for translation as shown in Figure 8. The augmentation within 2-pixel translation partially covers test images. In the case of 3-pixel translation, they achieve more than a 90% accuracy regardless of the network depth.  
Figure 7: The comparison of networks with and without max-pooling by varying network depth with the same \(3\times 3\) kernel size. (**a–d**) for the Net-1, Net-2, Net-3 and Net-4 networks, respectively.  
We also investigated how effective the models with max-pooling and augmentation within 2-pixel translation, as shown in Figure 9. In the case of 4-pixel translation, the accuracy is almost 80%. It is about 60% by augmentation only, as shown in Figure 8. This means that using them together is synergetic when augmentation does not fully cover test images. Figure 9 demonstrates that augmentation that fully covers test images is almost translational invariant. However, we should note that it is not cost-effective or intractable to do such augmentation to cover the entire test images in real-world applications.  
Figure 8: The comparison of networks with and without augmentation within 2-pixel translation by varying network depth with the same \(3\times 3\) kernel size. (**a–d**) for the Net-1, Net-2, Net-3 and Net-4 networks, respectively.  
Figure 9: The comparison of networks with the same \(3\times 3\) kernel size. (**a**) for the Net-4 network with and without both max-pooling and augmentation (A&M) within 2-pixel translation. (**b**) for the Net-1 network with and without augmentation within 5-pixel translation.  
In Figures 7-9, we demonstrated that max-pooling provides some level of invariance in deep layers, and that augmentation is crucial to obtain translational invariance.
## 7 Conclusions  
This article presents a compact dual-band antenna operating at 25.5 and 38 GHz bands. Due to its compact design, it is suitable for smartwatch and IoT applications. The antenna structure has generated two modes at 25.5 GHz and three modes at 38 GHz, Mode 1 and 2 and Mode 1-3, which are analyzed using characteristic mode theory. Apart from these modes, the antenna generated traverse TM\({}_{10}\) and TM\({}_{02}\) modes at the respective bands. The antenna has a good measured bandwidth of 1.5 and 2.5 GHz with gains of 7.4 and 7.9 dBi. Due to traverse modes, the antenna has radiation in the broadside with single and dual beams at their respective bands. The antenna showed a sustainable \(|\)S\({}_{11}\)\(|\) level when tested with the watch case. This also improved the gain to 9.9 and 10.9 dBi. The results of SAR analysis indicate minimal RF energy penetration to human tissue, which makes it suitable for wearable applications. The link budget estimation demonstrated the highest data rate transfer of 1 Gbps for a link margin of 20 dB.  
Conceptualization, P.S.B.G., P.R.M. and T.A.; methodology, P.S.B.G.; software, P.S.B.G. and S.P.; validation, S.T.S., V.K.P. and V.K.J.; writing--original draft preparation, P.S.B.G.; writing--and editing, T.A. and P.R.M.; supervision, S.T.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not Applicable.  
All the data are contained within the manuscript.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref** & **Ant Type** & **Dim (mm2)** & \
\begin{tabular}{c} **Res** \\ **(GHz)** \\ \end{tabular} & **BW (GHz)** & **Gain (dBi)** & **SA (W/kg)** & **Radiation** &
\begin{tabular}{c} **LM** \\ **Analysis** \\ \end{tabular} \\ \hline
[11] & Mono & \(0.37\lambda_{0}\times 27\lambda_{0}\) & \(27/38/60\) & \(\begin{array}{c}22-30/38-\\ 39.5/56-61\end{array}\) & \(\begin{array}{c}5.29/7.49/9\\ \end{array}\) & \(\begin{array}{c}0.9/0.62/0.74\end{array}\) & Bi-Dir & No \\ \hline
[29] & Mono & \(1.1\lambda_{0}\times 0.86\lambda_{0}\) & \(28.5/38\) & \(26\)–\(40.5\) & \(3.8\) & NA & Bi-Dir & No \\ \hline
[30] & Mono & \(2.41\lambda_{0}\times 2\lambda_{0}\) & \(60\) & \(58.2/60.5\) & \(9.9\) & NA & Multi-Beam & No \\ \hline
[31] & Mono & \(1.36\lambda_{0}\times 1.22\lambda_{0}\) & \(28/38\) & \(22\)–\(29/37\)–\(40\) & \(5\) & NA & Omni & No \\ \hline
[32] & Mono & \(0.75\lambda_{0}\times 1.24\lambda_{0}\) & \(37\) & \(36.7/37.4\) & NA & NA & Broadside & No \\ \hline
[33] & Mono & \(1.12\lambda_{0}\times 1.12\lambda_{0}\) & \(28/40\) & \(24\)–\(32/38\)–\(41\) & \(4.5\) & NA & Bi-Dir & No \\ \hline Proposed & Patch & \(0.51\lambda_{0}\times 0.51\lambda_{0}\) & \(25.5/38\) & \(\begin{array}{c}25-26.5/\\ 37–39.5\end{array}\) & \(7.4/7.9\) & \(0.063/0.0206\) & Broadside & Yes \\ \hline \hline \end{tabular} Note: Ref. reference; Ant: antenna; Dim: dimension; Res: resonance; LM: link margin; BW: bandwidth.  
\end{table}
Table 2: Comparative analysis of proposed antenna with existing designs.
## 1 Introduction  
Microspheres with a diameter of tens of micrometers to several millimeters are increasingly used in the field of precision machinery and scientific research. With the rapid development of micro-nano manufacturing technology, such as micro-coordinate measuring machines (micro-CMMs) [1; 2], microspheres with enhanced accuracy are urgently required. For a microsphere used in the probe of micro-CMM, its diameter should be hundreds of microns, and sphericity should be controlled within tens to hundreds of nanometers to ensure measurement accuracy [3; 4; 5].  
Methods and technologies for microsphere measurement have yet to be developed, even though spheres with a diameter of over 1 mm can be measured easily with high accuracy in accordance with ISO 3290-1-2008. According to the guideline of ISO 3290-1-2008, three perpendicular equatorial circles of a ball are examined to acquire the corresponding roundness, and the maximum of three roundness values is designated as the sphericity of the measured ball. However, this guideline is unsuitable for the measurement of microspheres. As such, the development of microsphere measurement technologiesfor microspheres is in development, and some novel solutions have been proposed. For example, [PERSON] et al. proposed a 3D roundness measurement method for microspheres [6]. In this method, three ruby spheres with the diameter of 1 mm are calibrated each another simultaneously with a self-developed ultraprecision micro-CMM, and the standard deviation of measurement repeatability is less than 5 nm. Nevertheless, the calibration of microspheres with a diameter of hundreds of micrometers has not yet to be described by this method. [PERSON] et al. developed a modified atomic force microscope system with a precise homebuilt air-bearing spindle. The system can fabricate and measure nanostructures on the surface of a micro ball [7; 8]. [PERSON] et al. designed a measurement method with two developed miniature Michelson interferometers and showed that the run-out of the resolution can reach 1 nm [9]. These contact-type measuring methods mentioned above have good reliability and high accuracy, but they are limited by key equipment. In addition to contact-type measurement methods, optical methods are used to measure microspheres. [PERSON] et al. measured a microsphere with a spherical Fizeau interferometer [10]. [PERSON] et al. determined the size of microspheres with a spherical Fizeau interferometer and an image splicing technology [11]. [PERSON] et al. evaluated the sphericity parameters of a CMM probe through optical image measurement and surface reconstruction [12]. Optical methods are simple and have a high measurement speed, but their accuracy is in the level of micrometer level only.  
In this study, a measurement method for microsphere is proposed, and a measurement system is developed. By this method, cross-sectional profiles of a microsphere are scanned with two scanning probes. The obtained data are designated to estimate the dimensions of the microsphere.
# Characterization of Deburring by Abrasive Flow Machining for AL6061
## 2 Abrasive Flow Machining  
### Deburring Mechanism  
According to the study of [PERSON], deburring by abrasive flow progresses in three major steps [19]. First, burrs are forced to bend in one or two directions and cracks may initiate at the roots. Second, the cracks ultimately cause large burrs to fall off due to fatigue failure. Finally, the remaining burrs wear off to rounded or chamfered edges. As the burrs disappear and flow resistance decreases, the material removal rate also decreases on nearing the completion of the process.  
[PERSON] and [PERSON] conducted studies on elbow erosion by abrasive flow. According to these studies, the location where the streamlines hurtle against the wall of the elbow pipe coincides with the region wherein a lot of erosion occurs in real [20; 21].  
In addition, [PERSON] reported that erosion occurs when the direction of abrasive flow is suddenly changed [22]. In the study of [PERSON], it was suggested that the shear stress at the burr edge caused by abrasive flow has a great effect on the deburring performance [9].
## 4 Results and Application
### Validation of Coherent Integration Time Optimization Model
#### 4.1.2. Effective Incoherent Average Number  
It can be seen from Equation (19) that the effective incoherent average is a nonlinear function of the coherent integration time. The variations of \(N_{\text{I}}\) and \(N_{\text{eff}}\) with the coherent integration time are compared, as shown in Figure 7. It reveals that when \(T_{\text{I}}\) is constant, \(N_{\text{I}}\) and \(T_{c}\) are inversely proportional. However, \(N_{\text{eff}}\) and \(T_{c}\) are non-linearly related. The difference between \(N_{\text{eff}}\) and \(N_{\text{I}}\) will gradually decrease as the coherent integration time increases. If there were no correlation between the waveforms i.e., \(C_{y}(\vec{n}T_{c},\tau)=0,\vec{n}\
eq 0\), then \(N_{\text{eff}}\) would be equal to \(N_{\text{I}}\). \(N_{\text{eff}}\) also varies with the length delay \(c\tau_{n}\) as shown in Figure 8. It is a hybrid effect of SNR and Doppler bandwidth at different delays [35].  
Figure 6: A comparison of the reciprocal value of altimetric sensitivity between measured (red) and simulated data (black).  
Figure 7: Comparison of the effective incoherent average number (black) and the incoherent average number (red).
# Faudistic Platooning: Subdivide Platoons, Unite Networks, and Conquer Efficiency and Reliability
## 3 Vehicular Communication
### Ad Hoc Network  
Build on top of the IEEE 802.11 specification, the IEEE 802.11p standard forms the basis for vehicular ad hoc communication within the 5.9 GHz band, and this was already published in 2010. Communication within the ad hoc network is decentralized, meaning that no entity is governing the communication. As it is not bound to any infrastructure, it is available everywhere and therefore cost-effective. The communication range is very limited when a high packet reception rate (PRR) is required, e.g., data exchange on the vehicle layer. Considering small data packets of 100 bytes, a communication distance below 50 m is required to reach a PRR above 90%. Increasing the packet size significantly decreases the PRR [25]. Messages are usually broad- or geocasted. Multiple communication channels are available for this purpose [26]. Of course, for successful communication, receiver(s) and transmitters need to use the same channel, which they should agree before the data exchange.  
Transmitting nodes need to follow the CSMA/CA (carrier sense multiple access/collision avoidance) concept. This means that once a node wants to send a message, it listens on the channel for already ongoing communication. If none is detected, the node can start to transmit immediately, otherwise it waits for a random back-off time. This simple \"listen before talk\" principle prevents some collisions. However, packet collisions may occur due to phenomena such as the _hidden node_ problem. Furthermore, the channel utilization is non-optimal as nodes may wait where it would not be necessary, causing an accumulation of data that still need sending. For example, this occurs in _exposed station problem_ scenarios.  
Reliability is lost once collisions occur, and timeliness is harder to achieve once the network is non-optimally utilized. This fact worsens once the load on the network increases. The load on a network is increased by increasing the number of transmitting nodes, the payload size, and the number of packets. The latter may grow exponentially in the case of packet forwarding, e.g., to reach nodes outside of direct communication range, or in case retransmissions are necessary. An already battered network harms itself by requiring retransmissions.