esa-sceva/satcom-chunk-collection huggingface dataset:
# Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks
## 3 Preliminaries
### Q-Learning Framework
Similarly, with grid \(v_{n_{1}}\) as the source node, the route to the destination grid is \(\mathcal{RP}(v_{n_{1}},D)=\{v_{n_{1}}\to v_{n_{2}}\to v_{n_{3}}\to v_{n_{9}}\to v_{n_{1 0}}\to v_{n_{16}}\to v_{n_{17}}\to v_{n_{23}}\to v_{n_{25}}\to v_{n_{30}}\to D\}\), \(\#hops(\mathcal{RP})=10\) instead of \(\mathcal{RP}(v_{n_{1}},D)=\{v_{n_{1}}\to v_{n_{7}}\to v_{n_{13}}\to v_{n_{14}} \to v_{n_{20}}\to v_{n_{26}}\to v_{n_{22}}\to v_{n_{23}}\to v_{n_{34}} \to v_{n_{28}}\to v_{n_{29}}\to v_{n_{30}}\to D\}\), \(\#hops(\mathcal{RP})=12\). Figure 6 illustrates the broadcasting characteristics in real road conditions. Vehicle nodes B, C, D, and E can receive beacons from vehicle A, and these vehicles can also transmit data to vehicle A. When vehicle D needs to transmit a data packet, its neighbor set \(\mathcal{N}_{v}=\{\)A, B, C, E\(\}\), vehicle A is undoubtedly the best choice. For vehicle D, vehicle A can achieve the furthest transmission distance within its communication capability, thereby minimizing \(\#hops(\mathcal{RP})\). The Q-learning update mechanism ensures that vehicle D selects vehicle A, because if data require a two-hop transmission, the Q value undergoes two discounts, thereby making it smaller compared to the Q value with a single discount. Therefore, vehicle A's Q value is guaranteed to be the largest. However, for node E, vehicle A is not a good choice. Since both vehicles are moving relative to each other, they are likely to move out of communication range easily. Figure 4: A 4 \(\times\) 4 grid Q-learning update process. Unlike a simple static model, the study must deal with a highly dynamic environment. Therefore, in the process of algorithmic learning, adapting to environmental changes is of paramount importance. This adaptability enables vehicles to learn how to select appropriate routing action for different network states, thereby ensuring that data packets can be effectively guided to their destination in the ever-changing VANET environment.
## 4 Results and Discussion
### Performance of YOLO Models on RTTS Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Warmup\_Momentum** & **Warmup\_bias\_lr** & **box** & **cls** & **cls\_pw** & **obj** \\ \hline YOLOv5 & 0.8000 & 0.1000 & 0.0500 & 0.5000 & 1.0000 & 1.0000 \\ YOLOv5 + GWO & 0.8956 & 0.0991 & 0.0496 & 0.5956 & 1.1816 & 1.2008 \\ YOLOv5 + ARO & 0.8000 & 0.1000 & 0.0500 & 0.5000 & 1.0000 & 1.0000 \\ YOLOv5 + CLEO & 0.8658 & 0.0972 & 0.0425 & 0.5692 & 1.0823 & 1.0925 \\ YOLOv7 & 0.8000 & 0.1000 & 0.0500 & 0.3000 & 1.0000 & 0.0700 \\ YOLOv7 + GWO & 0.0991 & 0.8956 & 0.7956 & 0.3956 & 1.1816 & 0.0495 \\ YOLOv7 + ARO & 0.0960 & 0.8387 & 0.7303 & 0.3704 & 1.1298 & 0.0472 \\ YOLOv7 + CLEO & 0.8008 & 0.8100 & 0.0512 & 0.4000 & 1.1280 & 0.0470 \\ YOLOv9 & 0.8000 & 0.1000 & 7.5000 & 0.5000 & 1.0000 & 0.7000 \\ YOLOv9 + GWO & 0.8238 & 0.0848 & 0.0424 & 0.5238 & 1.0452 & 1.0500 \\ YOLOv9 + ARO & 0.0852 & 0.0467 & 0.5975 & 1.1680 & 1.168 & 1.0945 \\ YOLOv9 + CLEO & 0.8208 & 0.0802 & 0.0497 & 0.5680 & 1.1760 & 1.1497 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Optimized and original 7 to 12 hyperparameters in YOLOV5, YOLOv7, and YOLOv9.
# Preparation of Few-Layer Graphene Dispersions from Hydrothermally Expanded Graphite
## 1 Introduction  
Owing to its outstanding physical and chemical properties [1, 2], graphene has become the most studied nanomaterial of the past decade. Until now, many top-down methods have developed for preparing graphene, such as micromechanical cleavage of graphite [3], oxidation-reduction of graphite [4, 5], electrochemical exfoliation of graphite [6], and liquid-phase exfoliation of graphite [7]. Among them, the liquid-phase exfoliation is the most commonly used method to prepare graphene by sonication at a low cost and with minimal environmental impact. However, this method mostly involves the use of high-boiling point solvents, e.g., N-methyl-pyrrolidone (NMP) [8] and a long sonication time to reach high concentrations of dispersed graphene [9].  
A viable liquid-phase exfoliation route is carried out in an aqueous solution through suitable surfactants [10] (and biosurfactants [11]) that reduce and optimize the high surface energy of water to match with highly hydrophobic graphitic surfaces [12]. Surfactants offer the steric and electrostatic repulsion that is necessary to avoid the graphene sheets from aggregation due to the sonication which breaks up graphite into few-layer graphene (FLG) flakes that are coated by the surfactant molecules [12, 13]. Surfactants, therefore, are excellent candidates in the direct exfoliation of graphite because they are used as a dispersant and a stabilizer.  
In an effort to circumvent long sonication time limitations (e.g., the induced defects in graphene structure [14]), [PERSON] et al. [15] successfully demonstrated the preparation of FLG dispersions from graphite powder, in a relatively short sonication time by combining heat-treatment in NMP and liquid-phase exfoliation in different organic solvents. The heat-treatment increases the interlayer distance of graphite, which is stabilized by adding polyvinyl pyrrolidone (PVP), followed by sonication for up to 20 h to prepare well-dispersed FLG dispersions. To our knowledge, an alternative method that combines heat-treatment of natural graphite flakes and direct exfoliation in stabilizing surfactants is unexplored, particularly, at a short sonication time.  
In this study, we propose such a method to prepare FLG dispersions in surfactant water solution using hydrothermal treatment and liquid-phase exfoliation. First, we subject pristine natural graphite to temperature to produce pretreated graphite flakes. Subsequently, pretreated graphite is exfoliated into FLG by sonication and centrifugation. The cationic surfactant, hexadecyltrimethylammonium bromide (CTAB), is selected as a dispersant and a stabilizer [16; 17]. CTAB is commonly considered as the dispersing agent to stabilize aqueous solutions of graphene [18] and it has also been used as the precursor for preparing graphene nanocomposites [19] with promising applications in electrochemical sensing and biosensing [20].
# Electrochemically Activated Screen-Printed Carbon Electrode for Determination of Ibuprofen
###### Abstract  
In this study, we present a simple, sensitive and selective analytical procedure for the ibuprofen (IBP) analysis using the commercially available screen-printed carbon electrode electrochemically activated (aSPCE) by cyclic voltammetry in 0.1 M NaOH. The quantitative determinations of IBP were carried out in 0.25 M acetate buffer solution of pH \(4.5\pm 0.1\) using the differential-pulse voltammetry (DPV). Different experimental parameters for DPV analysis were optimized, including pH and concentration of supporting electrolyte, amplitude (\(\Delta\)E\({}_{A}\)), scan rate (v) and modulation time (t\({}_{m}\)). The linear ranges of calibration curve were from 0.50-20.0 and 20.0-500.0 \(\upmu\)M. The detection and quantification limits were estimated to be 0.059 and 0.20 \(\upmu\)M. The aSPCE displayed satisfactory repeatability, reproducibility, and selectivity. Furthermore, the DPV procedure with the use of aSPCE was used to determination of IBP in pharmaceutical formulations. The results achieved by DPV show satisfactory agreement with those obtained by manufacturers (the relative errors are in the range of 3.1-4.7%).  
electrohemically activated screen-printed carbon sensor; ibuprofen determination; differential-pulse voltammetry; pharmaceutical formulations +
Footnote †: journal: Applied Sciences  
1
Footnote 1: email: _Applied_  
Footnote †: journal: Applied Sciences  
(LC-MS/MS) [5], gas chromatography-mass spectrometry (GC-MS) [1] and capillary zone electrophoresis (CZE) [9]. In the determination of ibuprofen, electrochemical methods, such as voltammetry, are also used, both with the use of traditional working electrodes [10,11] and screen-printed sensors [12, 13, 14]. Nevertheless, most of these methods suffer from poor sensitivity/selectivity or high consumption of reagents and cost of apparatus. Thus, the focus of the present paper is the use of electrochemically activated screen-printed carbon sensor (aSPCE) and voltammetric technique to develop reliable and sensitive analytical method, which can permit cost-effective, convenient, rapid and selective IBP determination.
###### Abstract  
Reed-Solomon (RS) codes are one of the most used solutions for error correction logic in data communications. RS decoders are composed of several blocks: among them, many efforts have been made to optimize the error magnitude evaluation module. This paper aims to assess the performance of an innovative algorithm introduced in the literature by [PERSON] et al. under different systems configurations and hardware platforms. Several configurations of the encoded message chosen between those typically used in different applications have been designed to be run on an FPGA (field programmable gate array) device and an MCU (microcontroller unit). The performances have been evaluated in terms of resource usage and output delay for the FPGA and in terms of code execution time for the MCU. As a benchmark in the analysis, the well-established Forney's method is exploited: it has been implemented in the same configurations and on the same hardware platforms for a proper comparison. The results show that the theoretical finding are fully confirmed only in the MCU implementation, while on FPGA, the choice of one method with respect to the other depends on the optimization feature (i.e., time or area) that has been decided as a preference in the specific application.  
error correction codes; Reed-Solomon; embedded devices; FPGA; microcontroller +
Footnote †: journal: Electronics Electronics
## 2 Materials and Methods
### Egg Production Yield and Traits
#### 3.2.2 Lipid Oxidative Stability of Eggs  
The oxidative stability of eggs is of great importance for consumers as a higher stability implies a longer shelf life. Trials have explored the potential of PFA, in comparison with synthetic antioxidants, to extend shelf life by delaying lipid oxidation. However, the literature comparing the effectiveness of PFA compared to a positive control group including Vitamin E is still very limited.  
In laying hens, dietary supplementation with RE (0.05 and 0.1% of the diet) compared with VitE (200 mg/kg) for 25 days did not affect the lipid oxidative stability of n-3-fatty acid (FA)-enriched eggs [58]. The lowest lipid oxidation rate (MDA) in eggs was observed in VitE (200 mg/kg)-fed laying hens in comparison with those consuming RP (at 0.5 and 1% of the diet) [52]. An opposite result was reported in another study, in which the greatest lipid oxidation in yolk was observed in VitE (200 mg/kg)-fed laying hens [53]. RP supplementation showed a dose-dependent antioxidant activity [52], while SP showed better oxidative stability than RP and OP administrations. On the other hand, dietary supplementation with olive leaves (OL, at 1% of the diet) compared with VitE (200 mg/kg) did not affect fatty acid composition, MDA, and lipid hydroperoxide levels in fresh eggs [59; 60]. Moreover, dietary inclusion of OL and VitE did not protect fresh eggs from lipid oxidation compared with a basal diet, while dietary supplementation with VitE compared with OLE lowered TBARS values [60]. From the above studies, it appears that the tested PFA are not always able to reduce the susceptibility of eggs to oxidative processes, and, when successful, they presented a lower potential than VitE, even if their impact could be related to the dose tested and to the diet/eggs characteristics (i.e., unsaturated fatty acids content).
## 4 Classification of MAC Protocols Based on Channel
### Distributed Multi-Channel MAC Protocols
#### 4.3.2 Scmac  
The slotted contention-based MAC protocol addresses two types of collision. First, one occurs due to nodes not being present in close proximity, and the other is due to hidden terminals. The first is overcome by the back-off scheme of IEEE 802.11p, and the latter is overcome by TDMA. Here, the time slot is divided into two periods, i.e., TP (transmission period) and RP (reception period). During the TP, a node broadcast packet is needed to transmit, whereas the channel is reserved in the RP. If there is a reservation failure, the node does not transmit the packet in the TP but dedicates another time slot for further transmission. In SCMAC, black burst-based slot reservation is used to jam signals, thus allowing only one node to broadcast the message at a particular TP. The hidden terminal problem is overcome by utilizing spatio-temporal co-ordination (STC). The road is divided into four segments with fixed lengths, and different slots are assigned to the segments.  
SCMAC outperforms other back-off schemes in terms of packet delivery ratio. The use of STC is suitable for highway scenarios but not for rapid topological changes in VANET, as it is difficult to divide the road into segments as in STC. Thus, there is some transmission delay and performance degradation even if interference is avoided.
# A Communication-Efficient Secure Routing Protocol for IoT Networks +
## 6 Performance Evaluation
### Emulation-Based Analysis for IoT Networks  
We have shown that the proposed method can improve communication efficiency while maintaining security performance. However, digital signatures tend to increase the computational load on the device because they rely on public key-based techniques. Thus, evaluating the sum of the communication and computation times to obtain the practical time required for routing is important.  
Motivated by this background, we performed an emulation-based routing overhead analysis assuming multi-hop IoT networks (i.e., low-rate communication with limited computational capability).
# Evaluation of Modern Internet Transport Protocols over GEO Satellite Links
## 3 Modern Internet Transport Protocols  
Over the years, the Transmission Control Protocol (TCP) has been the primary transport protocol in the Internet. Applications relied on TCP to provide end-to-end reliable delivery of data while networks depended on it to manage bandwidth sharing and to control congestion. On top of TCP, the Hyper Text Transfer Protocol (HTTP) has become the de facto application-layer protocol to carry most Internet services. To overcome the limitations of TCP, various transport protocols have been proposed in the past. Some of them are enhancements of TCP, others are new layer-4 protocols and some others are encapsulated in UDP to minimize the impact on current Internet infrastructures. Of the latter, the most notable example is the Quick UDP Internet Connection (QUIC) which is backed by major Internet companies such as Google and others [18]. In this section, we will describe QUIC, and the new HTTP protocols such as HTTP/2 and HTTP/3 along with their recent congestion control algorithms.
# Bootstrap-CURE: A Novel Clustering Approach for Sensor Data--An Application to 3D Printing Industry
## 6 Applications to 3D Printer Data
### Comparison of Original CURE against Bootstrap-CURE  
In order to evaluate the _bootstrap-CURE_ implementation, the algorithm was tested on eight datasets from 3D printers of different sample sizes \(n\), fixed \(p=0.5\) and \(r=10\) and the total time to execute CURE and _bootstrap-CURE_, as well as the hierarchical clustering algorithms, were computed, as shown in Table 2, and a graphical summary was provided (Figure 8).  
It is quite evident from Table 2 that as the data grows in size, the original CURE implementation [63] takes quadratically longer CPU time (_TCPU_). In fact, a quadratic regression between _TCPU_ and sample size provides a single significant coefficient (to the quadratic term) and significant goodness-of-fit coefficient \(R^{2}=0.9982\).  
The proposed bootstrap implementation processes the data quite rapidly, even for a large sample size, and appears to exceed \(7\times\) speed for bigger datasets when compared with the classical implementation of the CURE algorithm. The same appears to be up to \(40\times\) faster than the hierarchical clustering algorithm in the experiments. This property seems to be well aligned with the goals of Industry 4.0, which require quick automation and analysis of a huge amount of data.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Data Size** & **Hierarchical Clustering** & **CURE** & **Bootstrap–CURE** \\ \hline
5000 & 36.296 & 2.922 & 0.578 \\
10,000 & 68.875 & 4.125 & 1.406 \\
15,000 & 101.094 & 8.297 & 2.094 \\
20,000 & 139.625 & 14.25 & 3.156 \\
25,000 & 257.375 & 22.125 & 4.187 \\
30,000 & 262.718 & 32.422 & 5.328 \\
35,000 & 274.109 & 47.469 & 6.75 \\
40,000 & 330.391 & 62.703 & 8.062 \\
45,000 & 443.578 & 76.078 & 10.141 \\ \hline \end{tabular}
\end{table}
Table 2: CPU time (sec) comparison among hierarchical, CURE and bootstrap–CURE methods.  
Figure 7: Dendrogram of centroids.  
It is also interesting to remark that the automatic criterion to cut the dendrograms developed in [88] was introduced into the process to help automate the entire _bootstrap_-_CURE_ methodology, which is a great advantage.  
From another perspective, the quality of the clusters does not change between CURE and _bootstrap_-_CURE_ by construction, since the data used for the sampling step are the same in both algorithms; one is clustered together while the other is clustered, divided into 10 samples, and thus is only impacting on _TCPU_ savings but not on the quality of the resulting clusters.
## 4 Discussion
### Data Partition Characteristics
#### 4.2.1 Horizontal Federated Learning (HFL)  
The horizontal federated learning (HFL) data partition, shown in Figure 6, is recommended in the case of limited sample size variability when developing a model. In this data partition setting, the nodes could be different health institutions or health data application providers. The HFL aims to develop a global model by integrating patients' sample data from different institutions without affecting patient privacy. Each node shares different patients' index \(\mathcal{I}\) but has the same features \(\mathcal{X}\) and labels \(\mathcal{Y}\) information [26]. HFL is denoted as:  
\[\mathcal{X}_{j}=\mathcal{X}_{k},\ \ \mathcal{Y}_{j}=\mathcal{Y}_{k},\ \ \mathcal{I}_{j}\
eq \mathcal{I}_{k},\ \ \forall\mathcal{D}_{j},\ \ \mathcal{D}_{k},\ \ j\
eq k \tag{3}\]  
where \(D_{i}\) represents the dataset held by client \(i\). For instance, two healthcare providers of the same business located in different countries would like to develop an AI model. User features of these two healthcare providers will mostly be the same because both operate the same business. However, the patient samples held by the two healthcare providers are different due to geographic locations. In this regard, we can use HFL to increase the total training sample by aggregating both of the healthcare providers' user samples in a privacy-preserving manner to enhance the model's performance. Therefore, the HFL data partition resolves the lack of sample size in data training because it combines all healthcare institutions' sample data.  
Figure 5: Federated learning framework for healthcare application based on aggregation schema. (**a**) Centralized FL: the central server selects the nodes, aggregates the updates, and sends back the updated global model parameters; (**b**) decentralized FL: to develop a global model, there is no central server to orchestrate all nodes.  
HFL data partition is quite common in FL applied for medical applications. More than half of FL studies on medical applications implemented horizontal medical data partition in their experiment [18, 19, 21, 37, 39, 40, 41, 52, 54, 55]. Unlike FL applied for nonmedical applications where training is carried out across many nodes, FL studies in medical applications only handle limited nodes from 2 to 100, as listed in Table 2. For instance, [PERSON] et al. [18] experimented with four medical institutions in different places for the autism spectrum disorder (ASD) prediction scenario. Each medical party shares the same user features generated by medical equipment and combines all patient samples from four medical nodes.
# Evaluation of Different Chemical Mechanisms on O3 and PM2.5 Predictions in Alberta, Canada
## 1 Introduction
To the best of our knowledge, all of these studies used the default value for KZMIN, so they did not consider the impact on O\({}_{3}\) formation due to the variation in KZMIN employed in this paper. Alberta is Canada's largest oil-producing province and the heart of the country's oil and gas industry. Greenview Municipal District (MDGV) is in northwest Alberta and is well-known for its oil and gas rich geology. In 2019, the MDGV area had 391 wells and produced 3.6 million m\({}^{3}\) of oil and 37.3 billion m\({}^{3}\) of natural gas. There has been considerable growth in the area, with an increase of 64.0% production of natural gas over the previous five years [37]. Due to the region's extensive development of the oil and gas industry, the regional ambient air quality, particularly NO\({}_{2}\), O\({}_{3}\), and PM\({}_{2.5}\), is of concern. The Canadian Council of Ministers of the Environment [38] recommends that air quality be managed through an air zone management framework (AZMF). As shown in Table S1, AZMF categorizes stations based on pollutant concentrations and assigns a suitable color to each one. An examination of historical monitoring station data in this area (Figures S1-S3) reveals that some stations require active air quality management, as defined by Canadian Ambient Air Quality Standards or CAAQS [38]. Given the possible variation in modelling results attributable to differences in chemical mechanisms, sensitivity to photolysis rates and temperature, and the interplay between organic and inorganic chemistry, it is important to select the correct model that involves the relevant precursors, in order to properly assess policy options when developing mitigation scenarios to meet federal and provincial O\({}_{3}\) and PM\({}_{2.5}\) standards. This discussion must also be made in the context of any variation in the bias for O\({}_{3}\) formation attributable to the choice of KZMIN. This paper examines the differences in predictions of O\({}_{3}\), some gas-phase O\({}_{3}\) precursors, and PM\({}_{2.5}\) between three chemical mechanisms, CB6R3, SAPRC07, and RACM2, as implemented in the Community Multi-Scale Air Quality (CMAQ) model [29]. The predictions are evaluated against monitoring station data for different locations in the MDGV as well as relative variation in O\({}_{3}\) and PM\({}_{2.5}\) predictions across the region during the summer and winter months. The primary goal is to identify a chemical mechanism that can be used to model secondary pollutants in the MDGV region and potentially be applied more broadly to Alberta, which also takes into consideration the impact of variation in the model's vertical diffusivity due to land use.