esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Factorial Design  
Assessing the typicality of the odor is a direct and efficient way to study the impact of odor categories. It is a straightforward evaluation of the positive or negative impact of the category. Thus, this experimental approach seems promising to understand more deeply the contribution of the volatile fraction to the odor of complex products ([PERSON] et al., 2004). Full factorial design was used to evaluate the notes contribution to the whole odor were shown in Figure 2. Among the six odor notes, only four were selected to keep the number of mixture in a manageable range for sensory evaluations (X1 = fruity; X2 = sour; X3 = green; X4 = woody). Fatty and floral notes were dropped due to both no significant impact of the blend. Relative contributions of main effects and interactions are shown on the Pareto charts. One important finding is the major effect of order interactions (X1X4 especially), emphasizing the existence of complex interactions occurring between odor notes.  
Figure 1: The \(\sigma/\tau\) plot of 6 different binary mixtures. 1, Fruity and Sour. 2, Fruity and green. 3, Fruity and woody. 4, Sour and green. 5, Sour and woody. 6, Woody and green.  
As shown in Figure 3, The interaction X1X4 was further investigated. The woody note (X4) has a positive effect when the fruity note (X1) is also in the mixture but tends to show a negative effect otherwise.
## 5 Experiments
### Complexity Analysis
#### 5.5.3 Scalability and Performance Under Different Settings  
To assess scalability, we evaluated the algorithm on a range of FJSPLSP-LTCC benchmark instances (MK01-MK10), covering small to large-scale problems with varying ma  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CRGPPO-TKL**} & \multicolumn{3}{c}{**CRGPPO-TKL with SA**} \\  & **Mean** & **Std** & **ODR** & **Mean** & **Std** & **ODR** \\ \hline MK01 & 99.1 & 16.6 & 2.8\% & **96.4** & 4.8 & **0.0\%** \\ MK02 & 93.5 & 10.7 & 0.7\% & **92.9** & 6.0 & **0.0\%** \\ MK03 & **259.8** & 13.7 & **0.0\%** & 260.1 & 16.8 & 0.1\% \\ MK04 & 131.8 & 1.7 & 0.1\% & **131.8** & 1.4 & **0.0\%** \\ MK05 & 198.3 & 5.3 & 0.7\% & **197.0** & 2.2 & **0.0\%** \\ MK06 & 195.6 & 6.7 & 1.1\% & **193.6** & 7.0 & **0.0\%** \\ MK07 & 218.6 & 28.8 & 5.3\% & **207.6** & 8.4 & **0.0\%** \\ MK08 & **653.6** & 14.0 & **0.0\%** & 656.0 & 10.0 & 0.4\% \\ MK09 & **551.8** & 6.7 & **0.0\%** & 555.3 & 3.8 & 0.6\% \\ MK10 & **383.3** & 14.7 & **0.0\%** & 394.7 & 9.4 & 3.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparative analysis results after parameter modification.  
chine numbers, transportation resources, and energy constraints. Experimental results confirm that CRGPPO-TKL maintains stable performance and convergence behavior across different problem sizes. This indicates that the proposed method is well-suited for real-world flexible job shop environments with limited transport and energy resources.
## 2 Optimization Challenges in V2G Systems  
Various approaches to optimizing V2G systems have been explored. These approaches can vary significantly in terms of the optimization function used, the constraints involved--which may differ depending on the features considered--and the problem size itself. Regarding objective functions, they are diverse. However, most of them center around three main areas or a combination of them. These areas include grid load control, optimizing economic cost and minimizing pollution generation. While focusing exclusively on one of these areas is possible, authors often prefer combining objectives, either through employing a multi-objective function [12] or converting multiple objective functions into a single one [13]. Additionally, in some cases, multiple objectives are correlated. For example, when considering both renewable and conventional energy generation, the cost of producing energy via sustainable sources is typically more economical than conventional energy production costs. Therefore, maximizing renewable energy generation also minimizes energy production costs.  
Other variations of the problem can emerge due to the consideration of different features, which may be related to the realism or conditions of the problem itself. Realism-related features encompass a plethora of dynamic and uncertain conditions if the problem is based on a real-life scenario. Uncertain features manifest in various ways. For instance, in a scenario where a V2G system must be implemented, uncertainty arises regarding the number of vehicles connected to the grid. EV owners may retrieve their vehicles at unknown times due to emergencies or unexpected needs, affecting the system's energy demand and charging station usage. Moreover, uncertainty surrounds the energy level at which EVs arrive at the charging stations and the variability of renewable energy generation, both of which significantly impact decision-making processes. These features pose additional challenges in providing reliable solutions, necessitating the use of robust optimization methods. Dynamic features also play a crucial role in V2G system optimization problems. These features can significantly influence system optimization and complicate the process. However, considering them is essential due to their day-to-day significance in providing realistic solutions. One essential aspect to consider is the dynamic pricing of electricity, which fluctuates throughout the day and is typically divided into three sections: valley, flat, and peak periods. These pricing segments affect EV charging strategies aimed at minimizing costs. Furthermore, electricity pricing is influenced by customer demand, presenting another challenge to the problem. Customer demand is a dynamic parameter partly determined by electricity costs as discussed in [14].
###### Abstract  
The influence of intra-cavity propagation delay in message encoding and decoding using chaotic semiconductor lasers is numerically investigated. A message is encoded at the transmitter laser by a chaos shift keying scheme and is decoded at the receiver by comparing its output with the transmitter laser. The requisite intra-cavity propagation delay in achieving synchronization of optical chaos is estimated by cross-correlation analysis between the transmitter and receiver lasers' output. The effect of intra-cavity propagation delay on the message recovery has been analyzed from the bit error rate performance. It is found that despite the intra-cavity propagation delay magnitude being less, it has an impact on the quality of message recovery. We also examine the dependency of injection rate, frequency detuning, modulation depth and bit rate on intra-cavity propagation delay and associated message recovery quality. We found that the communication performance has been adequately improved after incorporating intra-cavity propagation delay correction in the synchronization system.  
chaos synchronization; intra-cavity propagation delay; secure optical communication; semiconductor lasers +
Footnote †: journal: _photonics_
# Bifunctional Heterogeneous Catalyst for Biodiesel Production from Waste Vegetable Oil
## Materials and Methods
### Product Analyses  
FAME is produced via the transesterification reaction of the WVO with methanol in the presence of a catalyst; and in this study, a bifunctional heterogeneous catalyst was used to form methyl esters of the triglycerides. The product stream was analyzed for the presence of FAME, unreacted feedstock (triglycerides), residual methanol, and glycerol. The composition and quantity of fatty acids (FAs) in the WVOs and in the synthesized FAME were determined using gas chromatograph-mass spectrometry (GC-MS). A sample was injected into the GC using a 7980B Agilent Technologies Injector with a CTC CombiPAL auto-sampler and Agilent 5975B MS. The oven was programmed at 150 \({}^{\circ}\)C (1 min), 180 \({}^{\circ}\)C (4 min), and 325 \({}^{\circ}\)C (20 min) before Ramp1 (25 \({}^{\circ}\)C/min) and Ramp2 (4 \({}^{\circ}\)C/min). Pure helium was used as the carrier gas at an injection volume of 1 \(\mu\)L (Mode ratio, 15:1), a flow rate of 1 mL/min, and an injection temperature set at 280 \({}^{\circ}\)C (40-550 \(m/z\), Energy: 70 eV). Heptadecanoic acid (C17:0; C\({}_{17}\)H\({}_{34}\)O\({}_{2}\) or margaric acid) was used as the internal standard, and the FAME yield was calculated. This proved to be a reliable technique as heptadecanoic acid methyl ester is a saturated ester and does not participate in the reaction. The FA profile was compared with the literature values to authenticate the determination. The FA was compared with the fraction of the total peak area attributed to each by the GC-MS mass spectrum libraries and normalized to the heptadecanoic acid methyl ester peak. In calculating the FAME produced, the concentration of the internal standard was multiplied by the peak ratio of the FAME to the internal standard. The yield of FAME was expressed as illustrated in Equation (4) [24]:  
\[\text{FAME Yield }(\%)=\frac{\text{Weight of FAME produced }(\text{g})}{\text{Weight of oil used }(\text{g})}\times 100 \tag{4}\]  
where the weight of oil was 50 g and the FAME produced from each run was acquired from the GC-MS results.
# ARIBC: Online Reporting Based on Identity-Based Cryptography
## 5 Discussion  
### The Security of the ARIBC  
The security of the proposed scheme depends on choices made regarding the implementation on the one hand, and on the underlying cryptographic protocols and techniques on the other. The former include:  
* The choice of the public parameters and of the elliptic curve in particular. In our implementation we selected the security parameter to be 256 bits and the NIST P-256 elliptic curve [42; 45; 51].
* The measures to secure the Master (Server) Secret key. The Master (Server) Secret key needs protection analogous to that of the private key of any Certification Authority in a X.509-based PKI; this entails the use of a special hardware security module [52] and/or the KMS to be offline, as in the case of a commercial implementation of an SK-based scheme called \"Cryptify\" ([[https://www.cryptify.com/cryptifys-implementation-of-mikey-sakke/](https://www.cryptify.com/cryptifys-implementation-of-mikey-sakke/)]([https://www.cryptify.com/cryptifys-implementation-of-mikey-sakke/](https://www.cryptify.com/cryptifys-implementation-of-mikey-sakke/)) (accessed on 8 January 2021)).
* The measures to secure the sharing of the SSKs of the anonymous reporters. In our proposal, we use time-tested technologies such as the TLS protocol for confidentiality and the X.509-based PKI certificate for the authentication of the KMS of the authority. Eponymous reporters may receive their SSKs in a secure device (e.g., a USB token) when they present themselves to the authority to register.  
The cryptographic security of the employed BLMQ signature scheme is based on the Diffie-Hellman inverse (DHI) family of primitives [42]. The signature created by the scheme \"...has a security reduction to an assumption that is related to (and actually weaker than) the q-Bilinear Diffie-Hellman inverse (q-BDHI) assumption\". The created signature is the proof of possession by the signer of the SSK. On the other hand, the security of SK-IBE is based on the q-BDHI assumption and has been proved in [44; 45]. More details on the security of the underlying cryptographic mechanisms and protocols can be found in [42; 45]; security concerns about IBC are discussed in [4].
# Performance Analysis of IoT-Based Health and Environment WSN Deployment
## 4 Experimental Results and Discussion
### Modeling Complexity  
By comparing the BA and PSO algorithms, it is determined that the number of parameters needed to be set by the user for PSO is fewer than for BA. In PSO, the user is required to determine the values of three parameters before the start of the algorithm, in addition to the run number, population size and the coefficients of fitness function (\(\alpha\), \(\beta\)) which exist in both algorithms. In contrast to that, six parameters are needed for value-initialization before the BA starts (since the number of better bees depends on the number of scout bees, there are six independent parameters in BA). Although both algorithms are easy to implement, the formulation of the problem in BA is slightly more complex than in PSO. The reason is that the same operation is done for all particles of the swarm, while there are three types of bees in BA. In addition, the number of new bees (scout bees) is created in each run in BA. On the other hand, the particles are created only once at the beginning of the algorithm and their positions are updated during the algorithm. As parameter setting is not an easy task, and determining proper values for the parameters by the stakeholders affects the success of the algorithm, an algorithm with lower parameters is preferred [90]. In this way, the PSO algorithm in this study has better performance than BA for IoT-based network deployment.
## 2 System Model and Problem Formulation
### Satellite Channel Model  
Multi-beam LEO satellites usually utilize beam-forming technology to receive signals in specified directions, while the UEs transmit signals through an omnidirectional antenna. The channel power gain between UE \(i\) and beam area \(n\) can be calculated by [20]  
\[h_{i,n}=\frac{g(\theta_{i,n})GtGr}{L_{f}} \tag{1}\]  
where \(Gt\) is the maximum transmitting antenna gain of UEs and \(Gr\) is the maximum receiving antenna gain of the LEO satellite. According to Friis formula, the free space path loss \(L_{f}\) can be expressed as  
\[L_{f}=\frac{\left(4\pi d_{i}f\right)^{2}}{c^{2}} \tag{2}\]  
where \(c\) denotes the light speed, \(d_{i}=\sqrt{H^{2}+s_{i}^{2}}\) represents the distance between the LEO satellite and UE \(i\), and \(s_{i}\) is the distance between the UE \(i\) and the LEO satellite projection point. The parameter \(g(\theta_{i,n})\) in (1) is the beam gain factor, where \(\theta_{i,n}\) denotes the angle between UE \(i\) and the center of LEO satellite beam area \(n\). The beam gain factor can be calculated by [21]  
\[g(\theta_{i,n}) =\left(\frac{I_{1}(u)}{2u}+36\frac{I_{3}(u)}{u^{3}}\right)^{2} \tag{3}\] \[u =2.70123\frac{\sin\theta_{i,n}}{\sin\theta_{3\text{dB}}} \tag{4}\]  
where \(\theta_{3\text{dB}}\) is the deviation angle of half energy attenuation, which determines the radius of beam areas. \(J_{1}(\cdot)\) and \(J_{3}(\cdot)\) are first-kind Bessel functions of order 1 and 3, respectively.  
Similar to (1) and (2), the channel power gain between UE \(i\) and the eavesdropping GEO satellite can be calculated by  
\[h_{e,i}=\frac{GtGr_{e}c^{2}}{\left(4\pi H_{e}f\right)^{2}} \tag{5}\]  
where \(Gr_{e}\) is the maximum receiving antenna gain of the GEO satellite. Since the eavesdropping GEO satellite is very far from the ground, the deviation of the receiving angle can be ignored and the communication distance can be approximated by the orbit altitude.
## 3 Preliminaries  
### Network Model  
There are \(N\) mobile nodes and one mobile data collector in the network. Every node has a unique id \(s_{i}\) and senses data \(d_{i}^{t}\) at time \(t\) from the environment. The data is stored at the node until collected by the mobile collector denoted as \(MC\). Figure 1 is an example snapshot of the data gathering field. Ordinary nodes generate the sensed data, and some are selected as proxy nodes that act as intermedia storage that receive data from ordinary nodes. Data collector is feathered as relatively high moving speed, it collects data from the proxy nodes and then return back to the \(sink\) node for data uploading. We assume the network has the following characteristics:  
1. Nodes adopt an opportunistic way for message transmission. Two nodes would establish a temporary communication link for message exchange when they are within each other's communication range. The movement of nodes displays some kind of patterns in cycles during the epochs (e.g., one day/epoch), and there are some places of interest (POI) where nodes would get together and exchange messages with each other;
2. Mobile collector \(MC\) has no constraint on storage space. It is programmed and actuated by data gathering applications. \(MC\) starts its trip at the \(sink\), moves within the network to gather the data; then the mobile collector has to return to the \(sink\) to upload the gathered data and replenish its energy. For a data gathering round, the time length of data gathering for a \(MC\) should be less than a threshold \(\Upsilon\);
3. Nodes could record their locations if needed, and they move much slower than the data collector. Data transfer from nodes to \(MC\) is only possible when they are both within each other's communication range, and it reaches stable transmission when \(MC\) stays still and nodes move within the area of communication range.  
Figure 1: Illustration of data gathering using speedy mobile elements. Network meta-data are collected and extracted, based on which a set of proxy nodes are gathered. The data collector then follows the optimized schedule to pick up the sensed data from the proxy nodes through one hop transmissions, and then returns to the sink for data uploading.  
The data gathering application is characterized by the time constraint for \(MC\) within a round of data gathering, which is denoted as \(\Upsilon\). As \(MC\) is controllable, ADG adopts optimization strategies to maximize the amount of gathered data. The proposed algorithm would calculate and set three factors for the optimization: (1) when should \(MC\) start its journey for the data gathering; (2) which nodes are going to be visited by \(MC\); and (3) where and according to which order \(MC\) is going to visit the nodes and pick up the data.
## 3 Proposed Method  
In this section, we describe our proposed method, the NDRE, in detail. The overall framework of the NDRE consists of three main components as shown in Figure 1.  
Then, we describe our proposed method, the NDRE, in detail: (1) A sentence encoder which includes two parts: the input representation and encoding layer. The input representation takes the combination of word embedding and position embedding as a vector representation of each sentence in the package. The encoding layer consists of three modules: convolution, multi-head self-attention and piecewise max-pooling, which are used to extract semantic features implicit in sentence representation; (2) A noise-detection strategy  
Figure 1: The architecture of our proposed method (the noise detection-based relation extraction model, NDRE) used for distant supervised relation extraction. MLP: multi-layer perceptron.  
that can automatically detect noisy labels during the training process. We introduce the embedding of entities to evaluate whether the sentences in the package can express the target relation. Sentences that cannot express the target relation will be regarded as noise, and we remove the noise by modifying their labels; (3) A bag aggregation method, which first obtains the bag-level representation of the corpus and the web tables through selective attention, and then combines these two bags in a balanced manner to obtain the final sentence-bag representation.  
In the following, we first introduce the task definition and notation, and then provide a detailed formalization of the NDRE.
# DGBL-YOLOv8s: An Enhanced Object Detection Model for Unmanned Aerial Vehicle Imagery
## 4 Experiment and Result Analysis  
### VisDrone2019 Dataset  
To demonstrate the efficacy of the enhanced DGBL-YOLOv8s model for UAV aerial image detection, experiments were conducted using the VisDrone2019 dataset as the benchmark. This dataset, created by the AISKYYE team at Tianjin University, is one of the mainstream datasets for UAV aerial imagery. The dataset comprises images captured under various environmental conditions, including different scenarios, object densities, weather conditions, and lighting conditions. The dataset comprises 8599 images, distributed across training, validation, and test sets with 6471, 548, and 1610 images, respectively. It encompasses 10 distinct categories: pedestrian, people, bicycle, car, van, truck, tricycle, awning-tricycle, bus, and motor.
# Online Detection and Fuzzy Clustering of Anomalies in Non-Stationary Time Series +
###### Abstract  
We consider the challenge of detecting and clustering point and collective anomalies in streaming data that exhibit significant nonlinearities and seasonal structures. The challenge is motivated by detecting problems in a communications network, where we can measure the throughput of nodes, and wish to rapidly detect anomalous traffic behaviour. Our approach is to train a neural network-based nonlinear autoregressive exogenous model on initial training data, then to use the sequential collective and point anomaly framework to identify anomalies in the residuals generated by comparing one-step-ahead predictions of the fitted model with the observations, and finally, we cluster the detected anomalies with fuzzy c-means clustering using empirical cumulative distribution functions. The autoregressive model is sufficiently general and robust such that it provides the nearly (locally) stationary residuals required by the anomaly detection procedure. The combined methods are successfully implemented to create an adaptive, robust, computational framework that can be used to cluster point and collective anomalies in streaming data. We validate the method on both data from the core of the UK's national communications network and the multivariate Skoltech anomaly benchmark and find that the proposed method succeeds in dealing with different forms of anomalies within the nonlinear signals and outperforms conventional methods for anomaly detection and clustering.  
Keywords:signal processing; real time; nonlinear; SCAPA; NARX; long term +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 4 Application of UAV-Aided Non-Terrestrial Networks in UAIS
### Connected and Automated Vehicles in UAV-Aided NTNs  
Connected and automated vehicles (CAVs) and autonomous driving are popular subject in the realm of smart cities, where there have been significant advancements in both academic and industrial settings in the past few years. CAVs are systems that are equipped with advanced perception, cognition, planning, and control capabilities, allowing them to operate autonomously by sensing traffic via onboard sensors and exchanging information with roadside unit devices. Autonomous driving is a popular field of research that builds the foundation of intelligent transportation system (ITS). In complex road traffic and challenging weather conditions, the collection of multiple pieces of information from various sensors may be ambiguous, resulting in a failure to accurately acquire environmental information. Therefore, it is increasingly important for CAVs to communicate with other vehicles, pedestrians, and roadside facilities in order to establish a more intelligent and safe transportation system [130; 131].