esa-sceva/satcom-chunk-collection huggingface dataset:
# Digital Pre-Distortion of Carrier Frequency Offset for Reliable Wi-Fi Enabled IoTs
## 3 Problematic RF Impairments in a Narrowband
### Combining Effects of CFO and DC Offset  
Typically, in OFDM systems, no data is transmitted on the DC subcarrier. However, injection of DC components may occur at the receiver due to impairments. OFDM systems require accurate frequency synchronization between the receiver and the transmitter because, with frequency deviation, the sub-carriers will not be orthogonal, potentially resulting in inter-carrier interference. The injection of such a DC component in conjunction with any uncertainty in the CFO may limit the performance of the communication system. The samples of the signal received by the antenna may be represented by the following formula:  
\[Y[n]=x[n]e^{i(\omega+\delta\omega)n}+D+z[n]. \tag{1}\]  
\(D\) is the DC offset introduced at the receiver. \(\omega+\delta\omega\) is the normalized (by sampling frequency) radian carrier frequency offset (CFO), where \(\omega\) is the CFO estimated by the receiver and \(\delta\omega\) is the residual unknown CFO. \(x[n]\) represents the complex-valued time-domain baseband samples of the received waveform, and \(z[n]\) identifies additive noise samples. Alone, the DC offset introduced at the receiver does not have a large detrimental effect on the receiver performance. However, the combination of the DC offset and the addition of any uncertainty as to the carrier frequency offset, when the exact transmission frequency is unknown, to the receiver tends to cause significant adverse effects in receiver functionality. To combat the distortive effects of the combination of an uncertain CFO and a high DC offset, a high pass DC component filter may be added to the receiver. The high pass filter minimizes any DC offset that may be introduced by the receiver. In the absence of CFO, DC offset can be measured and corrected in the digital domain using a training sequence included in the transmitted signal. For instance, the IEEE 802.11a standard for wireless communication provides for long and short training sequences to be included in the transmitted data. These training sequences are made up of symbols that do not include any DC component; in the absence of DC offset, the average of the samples over a period of one training symbol would be zero. Thus, averaging of received samples corresponding to symbols of either training sequence can be used to estimate DC offset. However, the CFO introduces a time dependent phase shift that causes the average of the samples over the training sequence period to be non-zero even in the absence of DC offset, rendering this technique unreliable. It would therefore be desirable to provide a more reliable technique for estimating and correcting a DC offset in the digital domain in the presence of CFO.
# Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities
## 3 Applications
### Non-Safety Applications
#### 3.2.2. Vehicle as a Mobile Service Provider  
Other potential applications beyond entertainment can be provided by utilizing the sensing, communication, and computation capabilities within the CVs and hence enable CVs to act as a mobile service provider for many entities including car riders, nearby vehicles, and third-party recipients [97; 98]. This includes providing services, such as data relaying, commercialization, and localization and data storage services [98].  
In the data-relaying services, connected vehicles act as forward data relays that enable a source vehicle to communicate with out-of-range destination vehicles through intermediate vehicles. Data relaying can take place among vehicles and between vehicles and microcells/RSUs. In this context, data-relaying services can be used as a methodology to increase network coverage. Data relaying in connected vehicles can follow one of three communication forms that include geo-casting, unicasting, and information dissemination. In geo-casting, connected vehicles relay data to a group of destination vehicles that are geographically located in specific zones of interest, and then the data is sent back to the source vehicle, for example, traffic-related data that include road conditions in a specific location, such as gas stations, where vehicles send their real-time speed indicating the current traffic at this specific location. Unicast means that vehicles relay data to a single destination vehicle. Information dissemination refers to the flooding of data to all neighboring vehicles. This takes place in cases of road-hazard situations, such as pothole detection using in-vehicle sensors. This information can be disseminated to other vehicles to warn them of the harshness and location of this pothole. Moreover, advertisement agencies can make use of the mobility features of CVs to advertise to drivers/passengers' special offers by hotels, restaurants, shopping malls, etc. Hence, commercialization is a paid service to the driver whose CV relays information about offers and advertisements while on the move [4; 98].
# Tension Force Detection of Short Suspender by Using Laser Doppler Vibrometer
## 5 Conclusions  
Laser Doppler Vibrometer (LDV) was used to collect the vibration frequency of steel rod-type short suspenders with different aspect ratios, and then the calculation method for tension force based on vibration frequency was established. The following main conclusions can be drawn from this study.  
1. There is a good correlation between tension force and vibration frequency for short suspenders. With the increase of tension force and aspect ratio, vibration frequency displays an increasing trend, indicating the possibility of using vibration frequency for real-time detection of the tension force state of short suspenders.
2. The relationships between vibration frequency and tension force of steel rod-type short suspenders is influenced mainly by boundary constraints and flexibility of suspenders, leading to the inadaptability of typical tension string theory. It has been found that for steel rod-type short suspenders, there is a quadratic regression relationship between vibration frequency and tension force, controlling the error between experimental and calculated tension force within 5%.  
Due to its nondestructive, fast, and noncontact characteristics, the use of LDV can effectively improve monitoring accuracy on the service state of short suspenders, and can also achieve lifetime detection with its low-cost and simple operation.  
Funding acquisition, M.X.; investigation, Z.H.; methodology, Z.H. and Y.D.; supervision, M.X.; validation, G.Z.; writing--original draft, Z.H.; writing--review and editing, G.Z. and Y.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Research Start-up Fund of Shandong Jiaotong University, grant number BS2020011 (50004953), and Natural Science Foundation of China, grant number 41572274. The APC was funded by BS2020011 (50004953).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Acknowledgments:** The authors would like to thank the Research Start-up Fund of Shandong Jiaotong University BS2020011 (50004953) and National Natural Science Foundation of China (41572274) for providing funding to carry out this investigation.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Material and Methods  
### Presentation of the Finite Element Parametrized Model  
To build finite element models automatically with different geometric configurations, a specific numerical strategy was defined. The models are composed of an implant, its abutment, the cortical and the cancellous bone; see Figure 1a. The developed numerical chain is composed of three steps. Firstly, the geometry of the implant, the abutment, the cortical and the cancellous bone are defined. Different design strategies are used for each part. The implant is designed using CATIA V5R20 (Dassault System) with the possibility of varying the length, the diameter and the taper. An example of conical and cylindrical implant design is presented in Figure 1b. The cancellous and cortical bone geometries are generated by segmentation using Mimics (Materialise) from a DICOM (Digital Imaging and Communication in Medicine) file obtained by a CT scan. A 20-mm wide section at the insertion of the first and second premolars is extracted. The geometry of the cancellous part is fixed. To have different thicknesses of cortical bone, a scale factor is applied to the cortical part. In total, four cortical configurations are defined corresponding to four different cortical thicknesses (1.0 mm, 1.5 mm, 2.0 mm and 2.5 mm); see Figure 1c. The second step of the numerical strategy is about assembly of the different parts together. It allows configuration of the placement of the implant in the bone and its angle in the vestibulo-lingual plane; see Figure 1d. The parts are meshed with tetrahedral quadratic elements,
## 4 Experimental Results  
In this section, we illustrate the estimated performance of designed waveforms using simulated data and real data in traffic scenarios. We compare the performance of three waveforms (uniform sensing, dual-PRF under uplink-flexible, and dual-PRF under uplink-fixed mode) with the DFT-based method, the IAA method, and the OMP method.
Finally, these improved embeddings are fed into our custom CNN-GRU-3, a DL-based hybrid classifier that can effectively model sequential and complex relationships. Multiple experimental setups are designed to evaluate the performance of the proposed framework and assess the effectiveness of graph embedding methods and classifiers. First, graph embedding techniques Node2 Vec and GraphSAGE are compared regarding their ability to extract semantic and structural features from the generated graphs. These embeddings are then evaluated using two classification approaches: (1) a custom CNN-GRU-3 hybrid model designed to understand patterns and (2) an ensemble learning method that combines the strengths of multiple classifiers to improve performance. Next, we examine GAT in different roles. As a classifier, GAT is directly used to detect malware by processing graph embeddings and utilizing its ability to understand contextual dependencies within API and DLL graphs. Additionally, GAT is evaluated as an embedding enhancer, where it improves the semantic representations of Node2 Vec and GraphSAGE embeddings by incorporating the contextual structure of the graph before feeding the improved embeddings to classifiers such as CNN-GRU-3 and ensemble learning models. Finally, GAT was used as a feature extractor, where it generated enriched embeddings from the graphs, which were then classified using CNN-GRU-3 and ensemble learning. This new comprehensive evaluation and framework not only compares the performance of different embedding techniques and classifiers but also highlights the dual role of GAT as both a classifier and a feature extractor. The results of these experiments allowed us to propose the most effective framework for malware detection by balancing semantic richness and classification accuracy. The key contributions of this research are as follows. 1. A novel framework is proposed that enhances initial embedding methods, such as Node2 Vec and GraphSAGE, with GAT by integrating API call sequences and DLL information through graph representations. This approach establishes a robust foundation for malware detection by enriching contextual semantic features. 2. A comprehensive comparative analysis is performed to evaluate the effectiveness of various graph embedding techniques, including Node2 Vec, GraphSAGE, and GAT, in the context of malware detection. 3. The dual functionality of GAT as both a classifier and feature extractor is thoroughly examined, with a detailed assessment of its impact on semantic information extraction and classification performance. 4. A real-world dataset is developed from practical scenarios, contributing to the literature while providing a reliable benchmark for future malware detection research. The rest of the paper is organized as follows: Section 2 discusses some state-of-the-art research in the literature. Sections 3 and 4 highlight theoretical backgrounds and the experimental results. Section 5 includes limitations and discussion. This study is concluded in Section 6 by addressing the research aspects.
## 3 Proposed Scheme
### Communication Round of the Proposed Scheme
#### 3.1.1 Beacon Frame  
The beacon frame of each session in the proposed scheme informs other nodes about its presence and informs nodes about the CAP duration (_CD_ ) with the help of TDMA slot duration (_SD_) as follows:  
\[CD=16\times SD \tag{1}\]  
However, SD is calculated by following the modulation scheme and symbol rates used in IEEE 802.15.4 standard [26] by allowing 4 bits/symbol with a symbol rate of 62,500 symbols/s and a data rate of 250 kbps as follows:  
\[SD(msec)=\frac{250\times 1000}{62,500} \tag{2}\]  
and the number of bits in an SD (_SDb_) is calculated as  
\[SD_{b}=\frac{62,500\times 4}{250} \tag{3}\]
## 4 Results  
### Data Description  
In this study, AVL data from eight bus routes in Shenzhen, for nearly two months (from 1 April to 26 May 2019), were selected as the raw data. To fully reflect the characteristics of bus bunching and make the analysis more representative, four different types of bus routes were selected, namely the regular route, main route, express route, and branch route, as shown in Figure 3 and Table 1. The coverage of selected lines evenly contained different kinds of land uses such as commercial, residential, etc. Furthermore, all selected routes did not pass through the construction area; thus, the bus operation was not affected by road and building construction. The principles above made sure that all selected routes could reflect the general characteristics of the bus operation.  
The AVL data mainly includes the following eleven fields, and data samples are shown in Table 2.  
1. Tripld: number of trips for each vehicle;
2. Lineld: number of bus routes;
3. LineDir: direction of bus routes, including up direction and down direction;
4. BusNum: unique ID number of each vehicle;
5. StationName: name of the bus stop;
6. StationIndex: serial number of the bus stop;
7. StationId: unique ID number of the bus stop;
8. ArrTime: the time when the bus arrives at the bus stop (accurate to seconds);
9. LeaTime: the time when the bus leaves the bus stop (accurate to seconds);  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Route Number** & **Type** & **Number of Stops** & **Departure Interval (min)** \\ \hline
1 & regular & 19 & 4–10 \\
113 & regular & 52 & 4–10 \\ E11 & express & 17 & 15 \\ E15 & express & 23 & 4–10 \\ M156 & main & 30 & 10 \\ M182 & main & 55 & 6–12 (up direction) \\ B811 & branch & 15 & 5 \\ B618 & branch & 14 & 6–12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information about selected bus routes.  
Figure 3: Map of selected bus routes.
## 3 Results
### Optimization of In Situ Transesterification Reaction  
#### 3.2.1 Effect of Methanol-to-Biomass Ratio on Reaction Efficiency  
In order to optimize the in situ transesterification reaction, ratios of methanol-to-biomass between 30-120 mL\(\cdot\)g\({}^{-1}\) were evaluated. The effect of methanol-to-biomass ratio on reaction efficiency is shown in Figure 1. Ratios lower than 30 mL\(\cdot\)g\({}^{-1}\) were not examined due to biomass wetting limitations. At a low methanol-to-biomass ratio of 30 mL\(\cdot\)g\({}^{-1}\), the major FAMEs only accounted for 7.41% \(\pm\) 0.08% of the total biomass. For complete conversion of lipid into FAME, excess methanol was needed since transesterification is a reversible reaction. Therefore, excess methanol contributes to the reaction favoring the formation of glycerol and FAME while reaching equilibrium within a reasonable time. Additionally, the high viscosity of microalgal lipids has a detrimental effect on the interaction of lipids and methanol. The slow contact of lipid and methanol resulted in inefficient mass transfer along with unfavorable FAME conversions. As the methanol-to-biomass ratio increased to 90 mL\(\cdot\)g\({}^{-1}\), higher total FAME yields were attained (10.04% \(\pm\) 0.14%) and the EPA yield reached its highest level (4.02% \(\pm\) 0.17% of total biomass). When the methanol-to-biomass ratio was increased to 120 mL\(\cdot\)g\({}^{-1}\), FAME yield decreased to roughly 9.27% \(\pm\) 0.02%, which suggests that some protein and carbohydrate components of the biomass may have been co-extracted in the excess methanol [26]. In addition, the free fatty acid (FFA) and triacylglycercerol (TAG) in microalgae can both be converted to FAME and less methanol is required for FFA dissolution and conversion than TAG. Thus, 90 mL\(\cdot\)g\({}^{-1}\) was found to be the optimal methanol-to-biomass ratio and was employed for the following experiments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type of FAME** & **Amount (Peak Area \%)** \\ \hline
14:0 & 3.11 \(\pm\) 0.06 \\
16:1 & 18.34 \(\pm\) 0.17 \\
16:0 & 26.60 \(\pm\) 0.20 \\
18:0 & 6.78 \(\pm\) 0.04 \\
18:1 \& 18:2 & 8.95 \(\pm\) 0.04 \\
20:5 & 34.46 \(\pm\) 0.43 \\ Others & 1.75 \(\pm\) 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of fatty acid methyl ester (FAME) produced from _Nannochloropsis gaditana_ using the HCl-catalyzed in situ procedure. The reaction conditions of the in situ method were: methanol:biomass = 90 mL/g, HCl 5% by vol., 70 \({}^{\circ}\)C, 1.5 h. Each value % (of the total peak area) is the average of two independent experiments with duplicates.
## 5 Column Streaming-Based Convolution Engine Architecture
### Processing Elements and Their Connections  
Each PE comprises a D flip-flop-based register and a multiplier, as shown in Figure 14a. The data width of the components and connections are set to be 16-bits fixed as the trend of the edge AI accelerators [7; 9; 11; 13; 14; 22], already proving that DNN can be represented with 16-bit fixed-point numbers with fewer logic gates.  
Figure 14: Processing element and its connection. (**a**) A processing element; (**b**) Programmable bi-directional connections.  
Figure 13: The preloaded filter columns of a 4 \(\times\) 4-sized filter in the PE (_method_ = 2, Col\(i\) = Col10).  
The PEs shown in the green dashed lines in the above data mapping figures, such as Figure 9, are designed to be bi-directional. For selecting the input data from the required direction, a two-to-one multiplexer (mux) is added ahead of the input of the D flip-flop in the bi-directional PEs. Figure 14b shows how the bi-directional PEs broadcast their output to the two adjacent PEs by programming wire selection. If a PE's location locates at column \(x\) and row \(y\), it can be represented as PE(Col\(x\), Row\(y\)), \(\{0\leq x\leq 10,\ 0\leq y\leq 10,\ \text{and}\ (x,y)\in\mathbb{N}_{0}\}\). The coordination system can be found in Figures 7-13. PE outL represents the data that comes from the left side PE(Col\(x-1),\ \text{Row}(y+1)\)) output. For example, in Figure 7, the PE(Col6, Row\(5\)) is programmed in the left-to-right direction and accepts the input from PE(Col5, Row\(6\)).  
On the other hand, the bi-directional PEs are programmed to accept data from PE outR coming out of PE(Col\((x+1)\), Row\((y-1)\)) when the _method_ equals 2. For example, PE(Col6, Row\(5\)) is programmed to propagate data from the right-to-left direction and stream the data to PE(Col5, Row\(6\)) when accepting the input from PE(Col7, Row\(4\)), as shown in Figure 9. If a PE does not need a bi-directional connection but only left-to-right propagation (from Col\(x\) to Col\(y\)\(\{0\leq x<y\leq 10,\ \text{and}\ (x,y)\in\mathbb{N}_{0}\}\)), the mux in front of the PE input is removed to save the FPGA resource.
# Hybrid TOA/RSS Range-Based Localization with Self-Calibration in Asynchronous Wireless Networks
## 4 Performance Assessment
Furthermore, in [44] it is shown that the hybrid TOA/RSS range estimator achieves a smaller MSE than the joint ML estimator (this is because of the bias-variance trade-off, i.e., it has been possible to design an estimator that is slightly biased but overall has a reduced value of MSE. ); hence, this may, in turn, impact on the corresponding localization accuracy assessed here.
## 2 Materials and Methods
### Synthesis of Granules  
Mg-WH granules were synthesized through a dissolution-precipitation reaction, as illustrated schematically in Figure 1. Calcium sulfate (CS, CaSO\({}_{4}\)) was selected as the starting material, which was prepared by heating CSD powder at 170 \({}^{\circ}\)C for 5 h. Initially, three types of precursor granules with different chemical compositions were synthesized: monophasic (R1) and mixed (R2, and R3) granules. Monophasic precursor granules R1 were prepared by thoroughly mixing 20.0 g of CS with 12 mL of deionized water. Mixed granules R2 were prepared by homogenizing 20.0 g of CS with 8.0 g of (NH\({}_{4}\))\({}_{2}\)HPO\({}_{4}\), and then mixing with 12 mL of deionized water. Mixed granules R3 were prepared by homogenizing 20.0 g of CS with 8.0 g of (NH\({}_{4}\))\({}_{2}\)HPO\({}_{4}\) and 4.0 g of MgHPO\({}_{4}\)\(\cdot\)3H\({}_{2}\)O, then mixing with 12 mL of deionized water. The R1-R3 samples were kept at room temperature for a week to allow the solidification process to occur. Then, solidified samples were crushed in an agate mortar and sieved to obtain granules with a size of approximately 200-355 \(\mu\)m.  
The synthesis of the Mg-WH product granules (see Figure 1) from R1 and R2 precursor granules was carried out as follows: 1 g of each sample was mixed with a 0.20 M magnesium acetate (Mg(CH\({}_{3}\)COO)\({}_{2}\); Mg(Ac)\({}_{2}\)) solution (0.20 g Mg(CH\({}_{3}\)COO)\({}_{2}\)\(\cdot\)4H\({}_{2}\)O dissolved in 5.0 mL of water). Then, 50 mL of 1.0 M NaH\({}_{2}\)PO\({}_{4}\) and 50 mL of 1.0 M Na\({}_{2}\)HPO\({}_{4}\) solutions were added sequentially. The mixture was placed in a capped bottle and kept in a drying oven at 80 \({}^{\circ}\)C for 24 h and 48 h. The synthesis of the Mg-WH from R3 granules followed the same procedure, except that the addition of the Mg(Ac)\({}_{2}\) solution was omitted. After 24 h and 48 h, all synthesized samples were filtered, washed several times with deionized water, and dried in the oven at 80 \({}^{\circ}\)C for 1 h. The samples were then sieved to collect granules of approximately 200-355 \(\upmu\)m in size. Granules synthesized from R1, R2, and R3 granules for 28 h and 48 h were named PR1/24h, PR2/24h, PR3/24h, PR1/48h, PR2/48h, and PR3/48h granules, respectively.