esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating the Feasibility of Intelligent Blind Road Junction V2I Deployments
## 2 Intelligent Perception System (IPS) Overview
### Measurement Scenarios
#### 2.1.2 Configuration 2: Fog Computing  
The second configuration of the IPS that will be evaluated, illustrated in Figure 3, is where the computational resources are located within the CIVs that connect to the IPS, also known as Vehicular Fog Computing [48, 49]. This configuration can be viewed as the inverse of Configuration 1, as it offsets the challenges associated with deploying the computational resources at the FSN(s) by transferring the burden of processing sensor data to the CIVs subscribed to the IPS. Thus, in this configuration, the sensor data are transmitted directly to the CIVs subscribed to the IPS. In this configuration, more autonomy is given to the individual connected vehicles. Specifically, the CIV does not need to blindly trust the confidence intervals of the processed data coming from the FSN(s) and may verify the content of the sensor data itself.  
This configuration has a significant advantage in terms of cost as the sensors alone should not require substantial power, climate control, or security. The primary cost associated with this configuration is the deployment cost, where the FSN(s) would only require power and a network connection. However, this configuration also has a significant disadvantage in terms of network performance, as the unprocessed sensor data from one or more FSNs must be broadcast to the subscribed CIVs. Thus, transmission costs will be higher and as a consequence larger deployment costs for communications hardware.
## 3 Ground Landing Station Design  
In this section, a robotic sensing platform for UAVs is proposed. Figure 1 illustrates the design of the GLS structure. The modular design performs landing and battery replacement for UAVs. The system is based on a waterproof Nylamid-Polymer structure with epoxy resin in combination with steel, and it is comprised of four main modules: the Battery-UAV coupling, the swapping mechanism, the storage system, and the landing surface. The details of each subsystem are given in the next subsections.  
The GLS can be adapted to a wide variety of UAVs; however, this model is designed based on the usage of a custom-made DJI Flamewheel hexacopter. The UAV is powered with a 6200 MAh LiPo battery with dimensions of \(164\times 48\times 44\) mm.
## 3 Results
### Analysis of Bacterial Communities Composition
#### 3.3.1 Microbial Composition and Dominant Species  
In the gut (Figure 2A), the top six dominant bacteria were S. praecaptivus (68.99%), Lactobacillus micheneri (17.95%), C. colinum (6.32%), Lactobacillus acettolerans (1.02%), Kurthia populi (0.61%), and Acitenobacter lwoffii (0.45%). Sodalis praecaptivus was dominant in SD (95.48%), LN (87.22%) and XJ (89.30%), while the dominant bacteria in CQ was L. micheneri (67.17%).  
In the brood provision (Figure 2B), the top six dominant bacteria were _S. praecaptivus_ (26.66%), _Acitenobacter nectaris_ (13.05%), _Lactobacillus floricola_ (4.76%), _A. lwoffii_ (7.77%), _Massilia_ sp. (4.26%), _Rosenbergiella nectarae_ (2.46%). Further analysis revealed that the dominant bacteria in SD, CQ, LN and XJ were _S. praecaptivus_ (15.90%), _A. nectaris_ (52.18%), _Massilia_ sp. (12.00%) and _S. praecaptivus_ (89.29%), respectively.  
In the nest soil (Figure 2C), the six most abundant bacteria were _Gaiella occulta_ (4.33%), _Vicinamibacter silvestris_ (3.88%), _A. lwoffii_ (3.29%), _Massilia_ sp. (3.10%), _Ideonella_ sp. (1.89%), and _Methyltolenera versatilis_ (1.73%). The dominant bacteria of sample SD, CQ, LN and XJ were _Gaiella occultas_ (11.72%), _Ideonella_ sp. (5.77%), _Methyltolenera mobilis_ (6.34%) and _A. lwoffii_ (13.13%).  
Hierarchical clustering between groups using bacterial species with a relative abundance greater than 1 % in the samples showed that the samples from the four experimental plots all exhibited similar clustering, i.e., larval gut and brood provision samples clustered together, while the nesting soil samples were clustered separately, indicating that the composition of the larval gut bacterial community was relatively close to that of the brood provision (Figure 3).  
Figure 2: Bacterial community structure and relative abundance in gut (**A**); brood provision (**B**); and nest soil (**C**). The y-axis and x-axis represent the proportional abundance of bacterial OTUs and samples, respectively.
# Characterization of Tiled Architecture for C-Band 1-Bit Beam-Steering Transmitarray
## 5 Conclusions  
A comprehensive characterization of the tiled transmitarray architecture first proposed in [10] has been carried out in this paper. A new compact circuit model has been devised to analyze the broadband transmission and differential phase shift characteristics of the tiled transmitarray unit cells.  
The unit cell characterization at normal incidence has been carried out using the proposed circuit model and full-wave electromagnetic simulations. It appeared that the tiled and single-panel unit cells demonstrate commensurable performance within the operating frequency band, although the tiled unit cell exhibits a higher differential phase error.  
The antenna gain and radiation patterns of the fabricated tiled and single-panel transmitarrays have been measured for different beam-scan angles, as well as compared with full-wave electromagnetic simulations. The tiled transmitarray demonstrated slightly lower gain and higher beam-pointing error as compared with the single-panel transmitarray. The measured results are in a good quantitative agreement with simulations.  
In conclusion, it has been shown that the tiled transmitarrays can be effectively designed, modelled and fabricated to demonstrate the antenna performance commensurate with conventional single-panel transmitarrays. Considering the cost of manufacture and flexibility in configuring the transmitarray for various applications, the proposed tiled transmitarray architecture proves to be a feasible and economically effective solution for 5G communication systems. The future work will be carried out on advancing the analytical model by taking into account essential effects due to spillover, [21], coupling and array non-uniformity, adopting the tiled architecture for millimeter-wave applications, investigating the heterogeneous and conformal transmitarrays enabled by the tiled architecture, as well as developing hybrid approaches to beam-scanning and beam-forming by combining tiled transmitarrays with focal plane antenna arrays.  
Conceptualization, D.K., P.T., I.M. and A.S.; methodology, V.K.; validation, P.T., I.M., V.K. and D.K.; investigation, D.K., I.M., P.T., V.K. and D.Z.; Writing--original draft preparation, D.K. and A.S.; Writing--review and editing, D.K., A.S., I.M., P.T. and D.Z.; project administration, I.M. and D.Z.; resources, D.K., I.M., P.T. and D.Z.; funding acquisition, I.M., A.S. and D.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Russian Science Foundation under Project 17-79-20374 and A.S. was supported by The Royal Society under Grant IE160128 and D.Z. was supported by The Royal Society under Grant IES\(\backslash\)R1\(\backslash\)191236236. The APC was funded by MDPI.  
Not applicable.
## 3 Methodology
### Data Pre-Processing
#### 3.3.3 Feature Scaling  
To ensure that all features, regardless of how they may appear, are equally important, feature scaling is necessary. Feature scaling is an often-used process in machine learning to normalise features [46]. By permitting fair and accurate feature comparisons, feature scaling, explicitly employing the standard scaler, is an essential pre-processing step that enhances model performance and accuracy. Additionally, the features in the dataset were scaled using the StandardScaler function from the Scikit-Learn library. This scaling process ensures that the features are comparable, avoiding any potential bias arising from differences in their magnitudes.
# Integration of Multi-Branch GCNs Enhancing Aspect Sentiment Triplet Extraction
## 4 Experiments and Discussion
### Baselines  
Specifically, to demonstrate the validity of the proposed MBGCN, we make comparisons with several existing SOTA methods designed for ASTE tasks, which are shown as follows:  
* GTS-BERT [56] proposes an end-to-end tagging scheme, Grid Tagging Scheme (GTS) with cooperation with BERT, to address the extraction task;
* GTS-CNN [56] is the Grid Tagging Scheme (GTS) that cooperates with CNN;
* GTS-BiLSTM [56] is the Grid Tagging Scheme (GTS) that cooperates with BiLSTM;
* S\({}^{3}\)E\({}^{2}\)[18] exploits the syntactic and semantic relationships between word pairs in a sentence by a graph-sequence dual representation and modeling paradigm for the ASTE task;
* Peng-two-stage+IOG [56] is the combination of Peng-two-stage [12] and IOG [58];
* Peng-two-stage [12] is a two-stage pipeline model. It extracts both aspect-sentiment pairs and opinion terms in the first stage, and pairs the extraction results into triplets in the second stage;
* OTE-MTL [59] treats the ABSA task as an opinion triplet extraction work, and jointly extracts aspect terms, opinion terms, and parses their sentiment via a multi-task learning framework;
* JET-BERT [22] builds a joint model to extract the triplets using a position-aware tagging approach, which is capable of jointly extracting aspect terms, opinion terms, and their sentiment together;
* BMRC [16] transforms the ASTE task into a Multi-Turn Machine Reading Comprehension (MTMRC) task, and three types of queries are devised to handle the related inputs;
* EMC-GCN [23] transforms the sentence into a multi-channel graph by treating words and edges as nodes and edges, respectively, while ten types of relations for ASTE are defined;
* MuG-Bert [24] proposes an approach, Multi-task learning with Grid decoding (MuG), to integrate the multi-task learning framework with grid triplets decoding from GTS;
* UniASTE\({}_{BERT}\)[11] proposes an end-to-end method that decomposes ASTE into three subtasks, namely target tagging, opinion tagging, and sentiment tagging. Furthermore, a target-aware tagging scheme is introduced to identify the correspondences between opinion targets and opinion expressions;
* Dual-MRC [15] solves the ASTE task via constructing two machine reading comprehension problems, and trains two BERT-MRC models jointly with parameters sharing.
## 3 Experimental Setup
### Settings in Our Proposed Method  
The E2E framework in this paper is implemented by the ESPnet toolkit. The main encoder, namely the acoustic feature encoder, contains four BLSTM layers, in which each layer has 320 cells in both directions followed by a linear projection layer. The auxiliary encoder, namely the pronunciation prior knowledge encoder, consists of a text embedding layer and two BLSTM layers. The text embedding layer is used to map each element in the text sequence to a 15-dimensional vector space, and each BLSTM layer has 80 cells in both directions, followed by a linear projection layer. Similarly, the frame-level attention mechanism's content-based attention mechanism has 320 attention units for the main encoder and 80 attention units for the auxiliary encoder. The content-based attention mechanism used in the encoder-level has 320 attention units. The decoder is a one layer BLSTM-based network with 300 cells in both directions. For the multi-stream expanded attention mechanism, all attention modules adopt the same structure, and each attention module contains 80 units.
# Feature-Based Image Watermarking Algorithm Using SVD and APBT for Copyright Protection
## 4 Experimental Results and Comparative Analyses  
In this section, performances in terms of the watermark's robustness and the imperceptibility of the proposed method are given and analyzed. This scheme is tested on images 512 \(\times\) 512 pixels in size, and the watermark sizes applied in this scheme are 64 \(\times\) 64 and 32 \(\times\) 32 pixels, respectively. Moreover, to balance the imperceptibility and robustness of the proposed algorithm, the scaling factor \(\alpha\) is set to 5. The execution time is calculated from image reading to watermark extraction. The algorithm is tested on MATLAB R2014a (MathWorks, Natick, MA, USA) with an Intel (R) Core (TM) i3-2100 3.10 GHz CPU (Intel, Santa Clara, TX, USA), 4 GB memory computer.
# Removing False Targets for Cyclic Prefixed OFDM Sensing with Extended Ranging +
## 5 Designing Methods to Remove False Targets
### Sequential Target Detection and Removal
Run Algorithm 1, obtaining \(\tilde{\mathbf{Z}}\);
2. For \(t=1,\cdots,T\)  
1. Get \((n_{t},m_{t})\) using (17);
2. Construct \(\mathcal{I}_{t}\) and \(\mathcal{I}_{t}^{\pm}\) based on (20) and (21), respectively;
3. Construct the single-target RDM \(\mathbf{Z}_{t}\) as per (22);
4. Update \(\tilde{\mathbf{Z}}=\tilde{\mathbf{Z}}-\mathbf{Z}_{t}\);  
**Algorithm 4** Sequential target detection and removal. In Algorithm 4, we assume that the number of overall targets is known, for illustration convenience. In practice, we can perform a threshold detection to determine whether a maximum peak in the RDM is a target or not. Numerous methods are available for determining the threshold, such as the Neyman-Pearson criterion and the constant false alarm rate (CFAR) detector, etc. One may refer to classical radar texts, e.g., [39], for more details on this topic. In simulations, we will employ the cell averaging CFAR (CA-CFAR) [39] [ch.16.5] to fulfil the task. Next, we remark on the computational complexity (CC) of the two algorithms developed in this section. For Algorithm 3, the steps for nullifying CPs, namely, Steps 2-4, have negligible CC compared with other sensing steps. This is because the indexes of CPs are easily traceable and nullifying them does not involve any computations. Therefore, Algorithm 3 has a CC in the same order as Algorithm 1, which is \(\mathcal{O}\big{(}\tilde{N}\tilde{M}\log(\tilde{N}\tilde{M})\big{)}\)[32]. For Algorithm 4, we notice that Step 1 runs Algorithm 1 and hence has a complexity of \(\mathcal{O}\big{(}\tilde{N}\tilde{M}\log(\tilde{N}\tilde{M})\big{)}\). Moreover, Step 2a, which searches the peak of the RDM \(\tilde{\mathbf{Z}}\), contributes most to the CC of the overall Step 2. Thus, given \(T\) iterations, the overall CC of Step 2 is in the order of \(\mathcal{O}\big{(}T\log\big{(}\tilde{N}\tilde{M}\big{)}\big{)}\). We note that \(T\) is theoretically equal to the number of targets and hence is much smaller than \(\tilde{N}\tilde{M}\). Thus, we can conclude that Algorithm 4 also has a CC of \(\mathcal{O}\big{(}\tilde{N}\tilde{M}\log(\tilde{N}\tilde{M})\big{)}\).
## 3 Modulation Signal Generation
### Generation of Samples and Time-Frequency Analysis
The shorter length provides high time resolution but sacrifices frequency resolution and increases leakage. Furthermore, the fact that the window length is smaller implies that each frame contains a reduced number of sample points. This, in turn, leads to the existence of a greater number of frames and, consequently, necessitates more convolution operations. Larger overlap enhances temporal resolution, smoothens STFT results, and helps capture subtle changes yet leads to high computational complexity. We adopted STFT with a window length of 20 and an overlap of 0.99 to enhance temporal resolution and emphasize rapid signal changes, crucial for TFI-based AMR. In the proposed scheme, we concentrated on extracting time-frequency features from signals without evaluating the intensity of the signal. To attain this goal, we transformed \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Value** \\ \hline \multirow{2}{*}{Modulation type} & BPSK, QPSK, 8 PSK, 16 QAM, 64 QAM, PAM4, \\  & GFSK, CPFSK, B-FM, DSB-SM,SSB-AM \\ SNR range & \(-20\)–\(18\) dB, \(2\) dB interval \\ Number of samples & \(88\),\(000\) \\ Sample rate (\(f_{s}\)) & \(200\) kHz \\ Maximum sample rate offset & \(50\) Hz \\ Maximum carrier frequency offset & \(500\) Hz \\ Samples per symbol & \(16\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specific parameters of the dataset. the color time-frequency images into binary images because binary images consume low memory and computational overhead. Figure 1 presents the TFIs of several modulations at high SNR. The time-frequency features are distinctly different for different modulations, and these are also the recognized features extracted by the neural network.
## 4 Measurement and Results/Discussion  
To conduct fiber optic sensing readings, the necessary components included a controller, a 120 V AC power supply, a sensing cable with spliced connectors, an ethernet cable, and a computer equipped with specialized software for the controller. The controller was connected to the 120 V AC power supply; this research utilized a large 1000 W lithium-ion battery for I-180 slope, and a 2000 W pure sine wave generator for Highway 84 slope to provide power in the field. The connector ends were examined for obstructions using a fiber scope and cleaned with alcohol wipes, if necessary, before being connected to the controller. It was imperative that each connector be placed into the same port for each reading because the calibration of each strand of cable was individualized. An ethernet cable was used to connect the controller to a computer. Once plugged into the controller, an IPV4 connection between the controller and the computer was established for the devices to share information. The computer software for the controller (Charon 4) was finally utilized to calibrate the device to each cable strand, take readings, and manipulate data.
## 1 Introduction
An experimental study showed that an actuator can achieve an angular velocity of 24 rad/s and a linear speed of 80 mm/s. However, the clamping problem of the actuator, as well as the preloading and wearing problems of the slider, are not solved. [PERSON] et al. introduced a dual-function rotary--linear actuator consisting of a cylinder type transducer with slanted piezo ceramic plates [22]. Linear or rotational motion of the cylindrical transducer is excited employing vibrations of the first longitudinal mode or of the first torsional vibration modes of the cylinder using saw-tooth type signal. The angular velocity of 3 rad/s and linear velocity of 5 mm/s was obtained. It must be noted that the range of the linear motion of the transducer is limited by the length of the cylinder. [PERSON] et al. reported on the rotary--linear actuator that has a piezoelectric tube with helical electrodes [23]. The actuator operation principle is based on the excitation of the first longitudinal and the first torsional vibration modes. A saw-tooth waveform electric signal is used for actuator driving. The actuator provides a linear velocity of 2.4 mm/s and angular velocity of 9.9 \(\times\) 10\({}^{5}\)\(\upmu\)rad/s. However, the range of linear motion is limited. Literature review showed that most of the dual-function actuators provide rotary and linear motion of cylinder-type sliders. Also, no found planar locomotion actuator or piezorobot that can rotate payload and operate as 5 DOF actuators were found. Therefore, this research aimed to propose and investigate a novel design of the 5 DOF self-motion piezoelectric actuator that can provide unlimited locomotion in the plane and rotary motion of the spherical payload. The main novelty of the actuator is that mechanical translation and rotation are induced using a single piezoelectric ring. Such a design principle allows achieving planar or angular motion using a particular resonant frequency, while the direction of motion is controlled by switching harmonic electric signals between electrodes of the ring. The novel design of the cylindrical frame allows efficiently decoupling vibrations of the particular zones and to achieve planar motion or rotation of the spherical payload independently. The rest of the paper is organized as follows. Section 2 describes the structure, dimensions, and operation principle of the novel actuator. Also, excitation schematics of the electrodes are described. Section 3 presents the results of numerical modeling. Section 4 provides measured characteristics of the actuator, i.e., the dependence of the angular and linear velocity from applied voltage and payload. Finally, Section 5 concludes this work.
## 2 Image Quality Evaluation Results
### Image Quality Degradation Effects of Multiple Droplet Contamination
Figure 5: (**a**) Image capture setup for measuring droplet contamination area; images of cover glass surface for each contamination level with different spray volumes (0.1, 0.5, and 1.0 mL) on (**b**) hydrophobic and (**c**) hydrophilic surfaces. Figure 6: Contamination area measurement process: (**a**) Real image with droplets on the cover glass surface. (**b**) Convert color image to 8-bit grayscale; (**c**) Highlight the boundaries of droplets using the threshold function. (**d**) Find the edge of the droplets. (**e**) Fill the area within the edge of the droplets with white color. (**f**) Measure the droplet area over the total area of the glass. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Contamination Area (\%)**} \\ \hline \multirow{2}{*}{**Surface Type**} & **Slight** & **Moderate** & **Severe** \\  & **Contamination** & **Contamination** & **Contamination** \\ \hline Hydrophobic & 12.92 & 36.75 & 44.12 \\ Hydrophilic & 22.27 & 45.88 & 54.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Percentage of droplet contamination area by cover glass surface type and contamination level. Figure 7: Contamination area measurement results on hydrophobic and hydrophilic cover glass under different contamination levels. Figure 8a,b show images of the slanted-edge target captured when cover glasses were contaminated. The multiple droplets on the cover glass caused a complex refraction and scattering of light, which blurred and changed the color of the image. At high levels of contamination, particularly on hydrophilic cover glasses under severe contamination conditions, the boundaries of the black-and-white contrast pattern collapsed, making it unrecognizable, as shown in Figure 8(b4). Using these images, the results of calculated image degradation are shown in Figure 8c. The MTF50\({}_{\text{loss}}\) increased as the contamination level approached severe contamination. Especially, the MTF50 approached almost zero at moderate and severe contamination on the hydrophilic surface. These results show that large contamination areas cause significant image quality degradation. Furthermore, the degradation is greater on the hydrophilic surface than on the hydrophobic surface. Figure 8: Slanted-edge target images taken at each contamination level (slight, moderate, severe) with (**a**) hydrophobic cover glass and (**b**) hydrophilic cover glass; (**c**) MTF50\({}_{\text{loss}}\) caused by multiple droplet contamination. Note that the minimum MTF50 value observed was 0.005, indicating a significant decrease in image quality due to the contamination.
## 3 System Model and Problem Formulation
### MEC Architecture  
The user device layer includes a variety of mobile devices, a Graph Parser (GP), and a scheduler for the offloading tasks. In the MEC layer, multiple servers act as its computing resources. In the cloud layer, the task graph pool of DAG and the module responsible for training the offloading decision scheduler are included.  
The architecture can implement the following process: (1) collect multiple tasks with dependencies from the mobile device and input these tasks to the graph parser; (2) computation-intensive tasks are converted into DAG, and then these DAGs are put into the scheduler training module of the cloud for DRL training; (3) the trained network parameters are sent back to the mobile device; (4) the trained neural network runs forward propagation to make offloading decisions for the task. The task can be processed by the MEC or executed by the local processing unit.