esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Challenges and Future Directions  
Several challenges are still existing despite the success of machine learning-based VPN classification. For example, there are difficulties in feature selection due to the dynamic nature of VPN traffic, as change may occur in the traffic patterns over time. It might be essential to regularly update the feature set and retrain the models in order to maintain accuracy of classification [2]. Furthermore, the performance of machine learning models can be adversely affected, if there is encrypted traffic present on a network, because it restricts the features that can be used to classify data [7]. Finally, privacy concerns may arise when handling network traffic data, particularly when dealing with payload content [3].  
In addition to the challenges mentioned earlier, the adoption of advanced machine learning techniques like CNNs and GNNs presents its own set of difficulties. For instance, these models often require large amounts of training data, which may not always be available for VPN classification tasks. Furthermore, the computational complexity of deep learning models can be an issue, especially in real-time or resource-constrained environments.  
Future research directions include developing more efficient CNN and GNN architectures that can handle the challenges specific to VPN classification. Additionally, exploring transfer learning techniques to leverage pre-trained models and investigating methods for combining different types of deep learning models (e.g., CNNs and GNNs) to improve classification performance may be promising avenues to pursue.
###### Abstract  
Numerous studies reported that resveratrol (RVT) exhibits strong antioxidant and cytoprotective effects in brain pathologies, but its low solubility and bioavailability limit its therapeutic potential. Encapsulation of RVT in nanoparticles offers a promising strategy to enhance its effectiveness. The aim of this study was to evaluate the in vitro cytoprotective, DNA protective, and antioxidant capacity of resveratrol-loaded Pluronic (P123/F127) micelles. The effects of micellar (mRVT, water dispersion) and pure RVT (30% hydroethanolic solution) were compared in glioblastoma U87 MG cells with H\({}_{2}\)O\({}_{2}\)-induced oxidative damage. The cells were pretreated with mRVT or pure RVT (1, 3, 10, and 30 uM) for 24 h before cell damage. The cell viability, DNA damage, acetylcholine esterase (AChE) inhibitory activity, and some biomarkers for oxidative stress like lipid peroxidation (LPO), superoxide dismutase (SOD), and catalase (CAT) were evaluated. In addition, the cellular uptake efficiency of the micelles (50 nm) was tracked using red fluorescent rhodamine B as a marker. Our findings revealed that the micelles were localized in the cytoplasm of U87 MG cells within 1 h of incubation. Empty micelles, mRVT, and RVT did not reduce the viability of U87 MG cells after 24 h incubation and protect them from H\({}_{2}\)O\({}_{2}\) exposure. The most effective treatment was with mRVT (1 and 3 uM), which significantly reduced the DNA damage index, maintained LPO levels close to the control, and normalized the activities of AChE, SOD, and CAT that were disrupted by H\({}_{2}\)O\({}_{2}\) treatment. These promising results highlight the feasibility and advantages of using resveratrol-loaded nanoparticles for therapeutic applications.  
Keywords:resveratrol; micelles; cytoprotective effects; oxidative stress; glioblastoma cells : +
Footnote †: journal: Applied Sciences
# Indoor Vehicles Geolocalization Using LoRaWAN
###### Abstract  
One of the main drawbacks of Global Navigation Satellite Systems (GNSS) is that they do not work indoors. When inside, there is often no direct line from the satellite signals to the device and the ultra high frequency (UHF) used is blocked by thick, solid materials such as brick, metal, stone or wood. In this paper, we describe a solution based on the Long Range Wide Area Network (LoRaWAN) technology to geolocalise vehicles indoors. Through estimation of the behaviour of a LoRaWAN channel and using trilateration, the localisation of a vehicle can be obtained within a 20-30 m range. Indoor geolocation for Intelligent Transportation Systems (ITS) can be used to locate vehicles of any type in underground parkings, keep a platoon of trucks in formation or create geo-fences, that is, sending an alert if an object moves outside a defined area, like a bicycle being stolen. Routing of heavy vehicles within an industrial setting is another possibility.  
LoRaWAN; geolocalization; ITS 2019 11 124 10.3390/f11060124
# A Comparative Evaluation of Nature Inspired Algorithms for Telecommunication Network Design
## 4 Methods
### The Bees Algorithm  
The bees algorithm is inspired by honey bees and their way of searching for flower patches rich in nectar [16]. Each solution and its value of the objective function might be perceived as a flower patch found by a scout-bee and amount of nectar available there. The neighborhood of a flower patch rich in nectar should be explored by more bees than the other flower patches. Initially, the search space is randomly sampled according to scout bees randomly searching the surroundings of the hive. Thus \(N\) random solutions are generated. Then, a group of \(m\) best solutions is chosen from the current population. These solutions are used for local search. For the _elite_\(e\) out of \(m\) solutions \(n_{1}\) random neighbors are generated. Analogously, for the remaining _best_\(m-e\) solutions \(n_{2}\) random neighbors are generated.  
Note that \(n_{1}\) should be greater than \(n_{2}\) and the size of the neighborhood of local search is determined by value \(k\). To generate a neighbor, _change realization operator_ is used \(k\) times. At the end of each iteration \(m\) best solutions are chosen from _best_, _elite_ and newly generated neighbors. To keep the population size constant, the current populations is filled with \(N-m\) completely random solutions. The pseudo-code of the algorithm is presented in Algorithm 1. For the problem described in this article the hyperparameters \(N\), \(m\), \(e\), \(n_{1}\), \(n_{2}\), \(k\) should be relatively small.  
```
Input:\(N\), \(m\), \(e\), \(n_{1}\), \(n_{2}\), \(k\) Output: the best solution found \(\text{bees}\leftarrow\text{RandomSolutions}(N)\); whilestop condition is not metdo \(\text{best}\leftarrow\text{SelectBest}(\text{bees},\,m)\); \(\text{elite}\leftarrow\text{SelectBest}(\text{best},\,e)\); \(\text{best}\leftarrow\text{best}\) - elite; \(\text{elite}\_\text{neighbors}\leftarrow\text{RandomNeighbors}(\text{elite},\,n_{1},\,k)\); \(\text{best}\_\text{neighbors}\leftarrow\text{RandomNeighbors}(\text{best},\,n_{2},\,k)\); \(\text{bees}\leftarrow\text{elite}\_\text{neighbors}\) + best_neighbors + best + elite; \(\text{bees}\leftarrow\text{SelectBest}(\text{bees},\,m)\) + RandomSolutions(\(N-m\));  end while returnSelectBest(bees, 1);
```  
**Algorithm 1**The bees algorithm.
## 2 Materials and Methods
### Hyperspectral Correction  
In hyperspectral imaging based on mosaic filters, the use of the interference filter does not completely eliminate the crosstalk between the camera spectral sensitivities. To approach the ideal spectral sensitivity \(D^{ideal}\) (provided by the camera manufacturer), [PERSON] et al. [10] proposed to compute a linear combination of the spectral sensitivities:  
\[D_{m}^{Corr}(\lambda)=\sum_{k=1}^{25}X_{m,k}.D_{k}(\lambda) \tag{2}\]  
with \(D_{m}^{Corr}\) the corrected spectral sensibility of the spectral channel \(m\) (\(m\in[1;25]\)) and \(X\) the \(25\times 25\) correction matrix. This correction matrix \(X\) was computed with a linear least squares fitting:  
\[\underset{X}{min}\|D^{ideal}-X.D\|^{2}, \tag{3}\]  
The normalized spectral sensitivities (\(\int D(\lambda).d\lambda=1\)) are represented in Figure 2.  
As the normalized spectral sensitivity (\(\int D(\lambda).d\lambda=1\)) represents a probability density function, the Kullback-Leibler divergence can be used to quantify the performance of the spectral correction [33]:  
Figure 2: Spectral sensitivities of the hyperspectral camera XIMEA MQ022 HG-IM-SM5X5-NIR. The uncorrected spectral sensitivities are plotted in solid lines on the left side of the figure and the corrected ones on the right side. The ideal spectral sensitivities are plotted in green dashed lines.  
\[KL_{k}(D_{k}\|D_{k}^{ideal})=\int_{\lambda_{min}}^{\lambda_{max}}D_{k}(\lambda). \log\left(\frac{D_{k}(\lambda)}{D_{k}^{ideal}(\lambda)}\right)d\lambda. \tag{4}\]  
\(KL_{k}(D_{k}\|D_{k}^{ideal})\) represents the Kullback-Leibler divergence (\(KL\) divergence) computed between the spectral sensitivity of the channel \(k\) (corrected or uncorrected) and the ideal spectral sensitivity of the channel \(k\). The \(KL\) divergence represents a measure of dissimilarity between \(D_{k}\) and \(D_{k}^{ideal}\). A value equal to 0 indicates the equality of the two probability density functions. The \(KL\) divergence values computed for the corrected and uncorrected spectral sensitivities are represented in Figure 3.  
The \(KL\) divergence computed between the uncorrected spectral sensitivities and the ideal spectral sensitivities is on average four times higher than those computed between the corrected spectral sensitivities and the ideal spectral sensitivities.
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 4 The Goals and Challenges of Middleware Architectures for WSNs
### Security  
With popularity and advancements in WSNs, large chunks of sensitive information are sent over wireless networks. This information can be easily hacked by malicious intrusions and internet attacks. The integration of security parameters in the system's design is necessary to achieve protection.  
Most of the middleware focuses on resource distribution, management, and the communication efficiency of the sensor network. However, data aggregation mechanisms, security methods, and resource distribution still remain massive challenges. Security must be part of the middleware design for approaches that use multiple networks' distribution. The middleware reduces the probability of errors or failure by managing multithreads efficiently. Different security mechanisms should be increased by developers of networks during the design of middleware based on SOA. The abstraction layer, wrapping mechanism, and intelligent interfaces are used to address issues of heterogeneous datafusion. The security solutions are considered in several SOM architectures approaches. [26] Proposes a generic security service for SOM architecture frameworks that provides various independent security services such as authorization, authentication, and access control.  
The SOA based on middleware is designed for Security and Surveillance WSNs with Commercial Off-The-Shelf (COTS) used to program and deploy the data processing applications after analyzing a web service [27]. This approach provides a unique, distributed data processing application in WSNs for Mobile Ad-hoc and Sensor Systems (MASSs). The architecture provides support to complex monitor applications aimed at global security, loose coupling, auto-organization mechanism, simplified connection heterogeneity, and interoperability [27].  
In addition, the security mechanisms can be achieved by end-to-end security auditing for SOA as introduced in [28]. This solution provides two new components called Taint Analysis (TA) and Trust Broker (TB) with some advanced features that take from WS-Security and WS-Trust Standards [28]. TA monitors the interactions of services at runtime and checks information flow between them, which can detect particular events. TB is considered a trusted third party responsible for maintaining end-to-end auditing in the information flow into client requests [28]. In this architecture, the service providers should register themselves closed to TB, which allows user verification by the security of the service providers via TB.
# Investigating the Electro-Optic Response of Steroid Doped Liquid Crystal Devices
## 1 Introduction
The naturally occurring molecules that have been studied as potential chiral dopants include cholesterol and its derivatives, which are still widely used as chiral dopants in LC systems due to their HTP values, liquid crystalline properties, and ability to function as thermochromic liquid crystals [18; 19; 20]. Steroidal compounds exhibiting structural similarities to cholesterol have been probed as potential dopants. These steroids include estradiol [21; 22], which was utilized to form thin CLC films that were sensitive to temperature changes making them a potential sensor materials. The steroid estrone [22; 23] was used to synthesize high HTP chiral dopants that show minimal temperature dependence of the selective reflection making them potential dopants in bistable CLC displays. Other steroids studied include androstenalone [21] and pregnolone [21]. Non-steroidal biomolecules used as chiral dopants include the monoterpene D-limonene, which slightly reduced the switching speed and voltage in twisted nematic systems [24; 25]. Other biomolecules evaluated for their chiral properties include allobetulon [26] and bottulin [27], which were synthetically modified to produce chiral dopants with high HTP values due to the easily modified functional groups present. Other chiral molecules studied for use as chiral dopants due to their inherent chirality include amino acids [28] and cholenic acid [29]. Using naturally occurring biomolecules as chiral dopants remains an under-investigated field of research. Although numerous biomolecules have been used as chiral dopants, a plethora have not been studied in LC systems. Furthermore, a large portion of naturally occurring materials can function as synthetic substrates due to the functional groups present. In this study, a series of steroids and bile acids were doped into nematic LCs to determine if the biomolecules can be utilized as chiral dopants. The steroids studied include androsterone, \(\beta\)-estradiol, cholesterol, cholic acid, epiandrosterone, hydrocortisone, prednisone, progesterone, and stigmasterrol. The bile acid lithocholic acid was chosen as a potential chiral dopant due to its structural similarity to cholesterol and asymmetric functional groups that can be modified. The biomolecules were preliminarily screened for their solubility and miscibility in common nematic LCs. Lithocholic acid was used as a test case for synthetic modification to enhance the solubility and improve the HTP. After preliminary screening, three biomolecules were used as chiral dopants in responsive CLC devices. The three biomolecules were used in normal mode switching devices to test the responsiveness of the bulk CLC mixtures. Next, more complex polymer stabilized CLCs (PSCLCs) were formulated using the miscible biomolecules to create demo devices with dynamic electro-optical responses, including blue tuning, red tuning, and reverse mode switching of the selective reflection peak to highlight the potential applications.
# Research on Transmission Task Static Allocation Based on Intelligence Algorithm
## 1 Introduction
### Contributions  
When using an intelligence algorithm to solve specific COPs, the applicability of the algorithm is a difficult problem, and the calculation results from different intelligent algorithms vary considerably. To solve the problem of TTSA, the following problems need to be solved:  
1. Establishing a mathematical model suitable for intelligent algorithm calculation;
2. Selecting an appropriate intelligent algorithm based on the characteristics of the problem;
3. Making targeted improvements to the algorithm to improve its success rate and efficiency.  
Based on the research regarding the application of GA and PSO in COPs, this paper studies the TTSA and identifies an intelligence algorithm that can obtain accurate results within a certain range through the comparison of multiple algorithms.  
The main contributions of this paper are as follows:  
1. According to the characteristics of TTSA, a mathematical model suitable for the application of intelligent algorithms is built, a matrix of evaluation values of the effect is constructed, and the input data, output data, and fitness function of the algorithm are defined;Based on GA, an improved crossover operator based on cyclic substitution grouping is proposed to avoid the loss of excellent chromosome genes due to conflict processing and to improve the execution efficiency of IGA;
3. Based on GA and PSO, two hybrid intelligent algorithms are proposed to provide more intelligent algorithm ideas for solving TTSA;
4. A DPSO is proposed. Based on TTSA, the position and direction of the particles are described again, the probability selection model is used to update the particle position, and the random disturbance strategy is added to improve the particle inertia retention.  
In Section 2, we build a mathematical model of TTSA suitable for intelligent algorithm processing. In Section 3, based on GA and PSO, the four intelligent algorithms proposed are described. In Section 4, the experimental results are presented, and the results are analyzed and discussed. In Section 5, we present the conclusion and make recommendations for future work.
## 3 Related Works
### The Benchmarks
#### 3.3.1 Transmission Power Probing (TPP)  
Our first benchmark implements the window-based technique which is common in the literature [8; 10; 24]. Nodes select and keep a transmission power and during a window certain number of packets are transmitted using the selected transmission power. This _window-based_ mechanism is dubbed as _transmission power probing_ (TPP) throughout the paper. By selecting TPP as benchmark we can check if ACTOR's probing is more efficient. Our implementation of TPP closely follows a recent work titled PC-RPL [10]. TPP was implemented by the authors since similar works were either not integrated with RPL or their implementation was not available. To the best of our knowledge, three previous works have employed variations of the window-based probing approach: PC-RPL [10] is a recent study that jointly performs transmission power control and routing. In PC-RPL, DIO packets are sent with the maximum transmission power, but for the data packets, the transmission power is decided at run-time using a window-based adaptive mechanism. When joining the network, the measured RSSI is used to initialize the transmission power. Then, at run-time, if the node succeeds in transmitting \(M\) consecutive packets, it will try reducing the transmission power by 1 level. Upon packet loss, PC-RPL increases the transmission power by 2 levels and doubles \(M\) to make future decisions more conservative. Despite the previous works, PC-RPL consolidates the transmission power control with a load-balancing mechanism in which parent nodes can inform their children to switch to another parent. The load balancing is advantageous in combination with transmission power control since the parent node will not be committed to serving the children. Unfortunately, the implementation was not available to reproduce the results.  
All three of the mentioned works define \"reliable\" links as those that deliver a certain number of successful packets consecutively. In a scenario in which all of the transmission power settings are less than ideal, this definition is not useful. For example, consider that setting the transmission power to \(-30\) dBm achieves 90% PDR but with 0 dBm only 50% of the packets are received (due to collision). In this case, PC-RPL [10] and active probing [24] choose 0 dBm, and ART [8] will keep changing its transmission power without converging. An example run of TPP is illustrated in Figure 2. TPP considers a window of size \(M\), which is managed in a similar way to the PC-RPL transmission power control mechanism. For a fair comparison, TPP initializes the transmission power based on RSSI from the first DIO.  
In addition to finding the \"optimal\" transmission power in a scenario in which no \"reliable\" link can be found, it is also important to quickly converge. The reinforcement learning formulation allows ACTOR to explore the alternatives efficiently. We show that fast convergence and optimal transmission power control and routing can be achieved, along with stable topology, on resource-constrained COTS devices.
# Last-Mile Optimization Using Neural Networks
## 3 Proposed Method
### Classification Methods Used
#### 3.5.1 Logistic Regression  
To predict product flows effectively within the city, a trained model utilizing multinomial logistic regression was developed, incorporating a ridge estimator [6]. This approach aims to optimize the last-mile delivery in urban areas, thereby contributing to the overall improvement of logistic processes.
## 2 Materials and Methods  
The 2D array proposed in this study is composed of 5 \(\times\) 5 edgeless diodes assembled using the \"drop-in\" tap-bonding technique patented by CMRP. Drop-in bonding allows connection of the individual diodes to a polyimide tail (Kapton) using only an aluminum flexible carrier [32]. A single edgeless diode consists of a p\({}^{+}\) top implantation and p-type substrate with a 1 \(\times\) 1 mm\({}^{2}\) area and 100 \(\upmu\)m substrate thickness. Its cross-section is shown in Figure 1. The center-to-center distance between pixels is 3 mm and 5 mm horizontally and vertically, respectively. A sketch and photo of the array are reported in Figure 2.  
The channels are identified from D1 to D25 from the bottom left side to the top right side, as illustrated in Figure 2. Notably, the central diode is identified as D13. These diodes are securely integrated within a recess housed in a Kapton substrate. The use of Kapton  
Figure 1: A schematic representation of the edgeless diode cross-section. The diode consists of a p-Si substrate encased by phosphorous implantation (n\({}^{+}\)), while an ohmic contact at the top is created through boron implantation (p\({}^{+}\)). A 250 \(\upmu\)m layer of SiO\({}_{2}\) serves as passivation, with an additional 100 \(\upmu\)m of aluminum (grey) contacts deposited on top. To protect the diode, a 500 \(\upmu\)m layer of polyimide (orange) is applied. Not to scale.  
offers mechanical protection, flexibility, and tissue equivalence of the extra-cameral material surrounding the sensitive volume.  
A multichannel electrometer is used to read out and convert the accumulated charge of the edgeless diodes to a digital signal without dead-time or the need for external synchronization [33]. A graphical user interface visualizes the 25 channels in the appropriate 2D geometry, providing an instantaneous charge for each frame of acquisition, a total integral map, and an instantaneous current generated in each pixel. For further details about the multichannel electrometer and the graphical user interface, refer to the Supporting Information.  
The data for this study were collected using linear accelerators (LINACs), specifically a Varian Clinac 21 iX at the Shoalhaven Cancer Care Centre (SCCC) in Nowra (NSW, Australia) and a Varian TrueBeam at the St George Hospital Cancer Care Centre in Sydney (SGHCC). The experiments were all performed by delivering a total of 100 MU at a nominal rate of 600 MU/min using 6 MV photon beams unless otherwise indicated.  
The central diode of the edgeless array was precisely aligned with the incoming beam on a 10 \(\times\) 10 cm\({}^{2}\) field collimated by open-field jaw Flattening Filtered (FF) beams.