esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 A Routing Protocol for LLNs (RPL)
### RPL Overview
OF0 uses the hop count as a routing metric to select the best parent from a set of neighbours in the DODAG. Figure 5: RPL control packets and flow direction. Figure 6: RPL nodes rank. The hop count metric captures the number of hops, i.e., the number of relay nodes, involved in relaying or forwarding packets between a source and destination node. Usually, using hop counts as a routing metric provides stability to the network, as the shortest path in terms of hops often remains the same unless the node forming the path moves or runs out of battery. Another advantage of using the hop count is the simplicity of the metric, as computing and minimising the number of hops between a source node and a destination requires a simple addition. However, the hop count metric only considers the number of hops for path selection. It does not consider the routing reliability issues caused by link quality or the node energy. Selecting the shortest path with minimum hop counts may lead to (i) routing over unreliable paths in terms of link quality and (ii) heavy use of long links in terms of physical distance. As a result, links may experience high traffic loads and bandwidth consumption, resulting in higher packet loss. In addition, relying on the same nodes to forward the packets over a long period imposes extra costs on the nodes, especially in terms of energy. This often leads to unbalanced use of energy resources around the network as nodes with low battery are chosen over nodes with full battery because they form the shortest path in terms of hop counts. MRHOF uses the ETX routing metric for path selection. The ETX [88] is a link quality metric defined as the expected number of transmissions required to successfully transmit a packet through the link, including the retransmissions. Moreover, the total ETX of a path between a source and a destination is the sum of the ETX for each link forming this path. The ETX value of a link is calculated based on the links' forward and reverse delivery ratios. Each node computes the ETX for the direct neighbours and selects the node with the minimum ETX value as a parent node. Thus, MRHOF allows nodes to find the minimum ETX paths to a root in the DODAG. The main advantage of using ETX as a routing metric is achieving a high throughput compared to the hop count metric [89]. In contrast, simplistically, using the ETX may not lead to selecting the most desirable paths. A path with the smallest ETX value may contain fewer hops with long physical distances or, in the worst-case scenario, a single long hop. This usually occurs as the path with more hops tends to have a higher ETX value, even if the links forming the path have better transmission rates. The decision to select paths with fewer hops of lower transmission rates may affect the network by (i) causing bottlenecks, in the case of a single long hop, and/or (ii) consuming more energy to reach the further apart nodes.
# A Novel System of Mixed RF/FSO UAV Communication Based on MRR and RIS by Adopting Hybrid Modulation
## 3 Performance Analysis
### Average Bit Error Rate
#### 3.2.2 End-to-End Downlink  
The average BER of FSO downlink using hybrid \(L\)-PPM-BPSK-SIM is expressed as:  
\[\begin{split} P_{2}&=\int_{0}^{\infty}\dfrac{1}{2}erfc\Big{(} \dfrac{\sqrt{\gamma L\text{log}_{2}L}}{4\sqrt{2}}\Big{)}f_{\gamma_{2}}(\gamma )\,d\gamma\\ &=\dfrac{\xi^{2}2^{(\alpha_{3}+\beta_{3}-4)}}{\pi^{3/2}\Gamma(\alpha_{3}) \Gamma(\beta_{3})}G\dfrac{6,2}{4,7}\Bigg{(}\dfrac{2\alpha_{3}^{2}\beta_{3}^{ 2}}{L\log_{2}L\bar{\gamma}_{2}}\bigg{|}\theta_{1}\\ &\Bigg{)},\end{split} \tag{20}\]  
where \(\theta_{1}=1,\frac{1}{2},\frac{\xi_{2}^{2}+1}{2},\frac{\xi_{2}^{2}+2}{2}\); \(\theta_{2}=\frac{\xi^{2}}{2},\frac{\xi_{2}^{2}+1}{2},\frac{\alpha_{3}}{2}, \frac{\alpha_{3}+1}{2},\frac{\beta_{3}}{2},\frac{\beta_{3}+1}{2},0\). Substituting (17) and (20) into (16), the average BER of the end-to-end downlink is written as:
# Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix
## 4 Experiments  
### Datasets  
To evaluate the performance of the proposed method, experimental evaluations are conducted on five datasets, including Fashion-MNIST, COIL-20, COIL-100 and YouTube-Face (YTF). Figure 3 displays the visualization samples of these datasets. The detailed information of the experimental datasets is shown in Table 1.  
* Fashion-MNIST dataset : The Fashion-MNIST dataset can be divided into 10 clusters, each sample labeled with one of the ten fashion categories. In our experiments, we utilize the original gray images as the first view and the extracted edge features as the second view. We select 200 samples in each category for training following [55]'s work.
* COIL-20 dataset: The COIL-20 dataset consists of 1440 samples such as duck and car model which belongs to 20 categories viewed from varying angles. We adopt the original grayscale images as the first view, and the second view is constructed by extracting edge features from the original images.
* COIL-100 dataset: The COIL-100 dataset comprises a total of 7200 images which distributed over 100 clusters. For each object, it is positioned on a turntable set against a black background. We also down-sample the images into \(32\times 32\). We use the original gray images as the first mode and the edge images as the second mode.
* YouTubeFace (YTF) dataset: The YouTubeFaces dataset consists of videos collected from YouTube, featuring a diverse set of individuals in various scenarios and lighting conditions. It includes a total of over 3400 different individuals. Following [55], we employ the first 41 classes as the experimental dataset and crop the images to \(128\times 128\). In this paper, the three views are the original images, the gray images and the edge images extracted from the original pictures.
# Positioning Energy-Neutral Devices: Technological Status and Hybrid RF-Acoustic Experiments
## 4 Experiments Demonstrating Opportunities and Challenges in Hybrid RF-Acoustic Positioning
### Conducted Experiments  
Three types of experiments were conducted to test the accuracy and precision of the ranging method. The first two test the distant-dependent performance between the backscatter device and the audio or RF-source. In the third test, the angle of arrival (\(\alpha\)) between the RF source and device is varied. All measurements are realized in quasi-ideal circumstances, as there is a direct LOS between the source and receiver and both audio and RF-sources and receivers are pointed towards each other. Interfering sources are kept to a minimum, but could not be totally excluded as the room was used under normal conditions with persons walking in and out and other equipment turned on and off at certain moments in time. The current setup lacks the RF wake-up system described in the previous section. This means that in the reported experiments, there was a continuous backscatter communication of the received acoustic signal. To simulate the synchronization and the limited awake time of the mobile node, a 1 ms sampling window was chosen at the end of the acoustic chirp.
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 2 Overview of WSNs
### Dedicated Applications
#### 2.7.2 Query-Driven  
In query-oriented applications (see Figure 9), the deployed sensor network tracks information only at the explicit request of a base station. This category of applications is intended for the specific needs of the user. The latter may seek, for example, to request information specific to a certain region, or to interrogate the sensors to acquire measures of interest. Generally In this case, the topological knowledge of the network and the geographical location of the sensors are necessary.
# Improved ECG Watermarking Technique Using Curvelet Transform
## 6 Experimental Results
### Kullback-Leibler divergence (KL):
The text information was first transformed into the image and this image was inserted by modifying the curvelet coefficients using a clustering approach. The benefit of embedding an image was that false-positive detection will only change the pixel intensity that only degrades the image quality. The process can be automated using deep learning [30]. However, the meaning of the entire embedded information was not changed. The proposed ECG-watermarking technique that offers not only the best invisibility of watermark as well as it also offers good robustness and secures ECG-watermarking.
# Residual Dense Swin Transformer for Continuous-Scale Super-Resolution Algorithm
## 3 Methodology  
### Network Architecture  
As can be seen in Figure 1, the RDST proposed in this paper is composed of three main parts: shallow feature extraction, multilevel feature extraction, and an upsampling module. Multilevel feature extraction consists of several RDTBs and multilevel feature fusion composition. The input of the model is a low-resolution RGB image, and the output is a continuous-scale super-resolution image.  
3.1.1. Shallow and Multilevel Feature Extraction  
First, we use a 3 \(\times\) 3 convolutional layer to extract the shallow features from low-resolution images \(I_{LR}\in\mathbb{R}^{H\times W\times C_{in}}\), which is expressed in Formula (1):  
\[F_{0}=H_{\text{SFE}}(I_{LR}) \tag{1}\]  
where \(H_{\text{SFE}}(\cdot)\) refers to the shallow feature extraction module, and \(H\), \(W\), \(C_{in}\), and \(C_{out}\) are the length, width, number of channels and the number of output channels of the shallow features, respectively. On the one hand, the application of the convolutional layer can make good use of the underlying features of the image to restore an image that is more in line with the perception of the human eye. On the other hand, it is conducive to subsequent global residual learning and stabilizes the training process of the network. Subsequently, we use multiple RDTBs to extract each level's features \(F_{i,LF}\in\mathbb{R}^{H\times W\times C_{out}}\), which is expressed in Formula (2):  
\[F_{iLF}=H_{RDTB-i}(F_{i-1,LF})i\in 1,2,3,\cdots,N \tag{2}\]  
where \(H_{RDTB-i}(\cdot)\) represents the ith RDTB, and \(F_{i-1,LF}\) is the feature extracted by the ith RDTB. Each RDTB block takes the output of the previous RDTB block as the input, uses the Swin Transformer layer (STL) in the block to extract image features, and uses local feature fusion. To enhance the feature interaction within the block, local residuals are introduced to connect the training process of stabilizing the network and strengthen the feature expression ability of the network. Finally, the final feature expression is obtained through the multilevel feature fusion module \(H_{MLFF}(\cdot)\), which is expressed in Formula (3):  
\[\begin{split} F_{MF}&=H_{MLFF}(F_{0},F_{1,LF},\cdots,F_{N,LF})\\ &=H_{GFF}(F_{1,LF},\cdots,F_{N,LF})+F_{0}\end{split} \tag{3}\]  
Here, \(H_{GFF}(\cdot)\) represents the global feature fusion function between blocks. Through multilevel feature fusion and the introduction of global residuals, the network makes full use of the low-level and high-level features in the image to improve the network's reconstruction effect.
# Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing
## 4 Performance Evaluation  
The overall adopted algorithms were tested as one unit to evaluate the effectiveness of the MOTSO model. We implemented the proposed model using a Java environment, conducted on an Intel(R) Core(TM) i9-8950 CPU @2.90 GHz with 32 GB RAM(Intel Corporation, Santa Clara CA, USA). We used both a synthetic dataset (SYN) with SCAWQ and a real dataset (Gowalla) to test and compare our proposed model to the state-of-the-art algorithms. We generated synthetic data with uniformly (UNI) distributed grids (200 \(\times\) 200). According to the synthetic dataset, we defined two different cases: the first case (case 1) with 600 independent spatial tasks and 600 workers, and the second case with 1200 workers and 1200 independent spatial tasks. Moreover, the Gowalla check-in dataset in our experiment [36] did not include tasks; thus, in our examination, we selected 2400 tasks and workers randomly from Gowalla venues by SCAWG with random task durations, as per [4]. The parameter settings used to implement our model are illustrated in Table 7. It should be noted that varying some of our model parameters (e.g., population number (P), iteration number (I), and duration max (D)) led to different results. Finally, we ran each experiment 10 times and calculated the average value for each factor.  
In this section, the test results include both the synthetic dataset (with the two cases mentioned previously) and the real dataset (Gowalla). Then, we compared our proposed model with several algorithms to evaluate the effectiveness of the MOTSO model. In Section 4.1, we present the results and discuss the ranking strategy in more detail; in Section 4.2, we present and discuss the results of the proposed MOTSO model based on our three conflicting objectives, as presented in Sections 4.2.1-4.2.3.  
In Table 7:  
* N is the inertia weight;
* P is population number;
* I is iteration number;
* D is duration max;
* C1 and C2 are acceleration coefficients;
* r1 and r2 are random numbers;
* S is the speed of workers;
* No.w is the number of workers;
* No.t is the number of tasks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Parameter Value** \\ \hline \hline  & P & 50 \\ \cline{2-3}  & I & 80 \\ \cline{2-3}
**MOPSO** & N & [0,1] \\ \cline{2-3}
**configuration** & C1, C2 & 2.00, 2.00 \\ \cline{2-3}  & r1, r2 & [0,1] \\ \hline \hline  & & Case1 = 600 \\  & No.w & Case2 = 1200 \\  & & Gowalla = 2400 \\ \cline{2-3}
**SC** & & Case1 = 600 \\ \cline{2-3}
**configuration** & No.t & Case2 = 1200 \\  & & Gowalla = 2400 \\ \cline{2-3}  & S & 80 \\ \cline{2-3}  & D & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experimental settings.
## 4 Implementation and Results
### Emotion Classification and Performance Evaluation
The highest-performing model used a combination of nine attributes from the time domain, seven from the frequency domain, two from the time-frequency domain, and 16 from the location domain, achieving an accuracy of 0.96 when using ANN. This suggests that combining selected features from different domains can result in a superior performance. SVM and KNN showed a lower performance than ANN for all the hybrid vectors. Moreover, features from the time-frequency domain consistently yielded a high accuracy across all machine learning models, emphasizing their importance. The location-domain features initially performed poorly but showed significant improvement when combined with features from other domains, highlighting the effectiveness of hybrid models.
# Assessment of Word-Level Neural Language Models for Sentence Completion +
## 1 Introduction
We conducted the performance analysis of our RNN LM and multilingual BERT models on this dataset. In summary, our contributions are following:  
* We demonstrate that, when properly trained, simple RNN LMs are highly competitive for the sentence completion. Our word RNNs achieved results beyond the previous best reported on the MSR and SAT datasets. * We verify that the transfer learning approach that pre-trains a transformer-based LM from large data and fine-tunes the model for the target task is also viable for the sentence completion. Our experiments compared various pre-trained networks along with different settings for fine-tuning, showing that the performance varied significantly with different networks, and we were able to obtain state-of-the-art results for both datasets under certain configurations. * The new cloze-style dataset written in Korean was collected from the government's official examinations. Experimental results show that the models that were effective for the English datasets underperformed on the Korean dataset, leaving space for further investigation. * The PyTorch implementation code ([[https://github.com/heevery/sentence-completion](https://github.com/heevery/sentence-completion)]([https://github.com/heevery/sentence-completion](https://github.com/heevery/sentence-completion))) for experimentation is made available to encourage subsequent studies on neural approaches in machine comprehension. The remainder of this paper is organized as follows. In the next section, we begin with discussions on related work. In Section 3, we delineate word-level RNN LMs, formalize how to apply these LMs to sentence completion, and describe a fine-tuning approach that employs transformer-based models. Sections 4 and 5 show the results of experiments on the MSR and SAT sets, respectively. Then,we present the new dataset and performance analysis on this set in Section 6. Finally, the paper is concluded in the last section.
# Effects of Adding Alkali Metals and Organic Cations to Cu-Based Perovskite Solar Cells
## 2 Computational Conditions and Experimental Methods
To form the mesoporous TiO\({}_{2}\) layer, a TiO\({}_{2}\) paste was prepared from the TiO\({}_{2}\) powder (Aerosil, Tokyo, Japan, P-25, 200 mg) with poly(ethylene glycol) (Nacalai Tesque, Kyoto, Japan, PEG #20000, 20 mg) in ultrapure water [49]. The solution was mixed with acetylacetone (Fuijifilm Wako Pure Chemical Corporation, Osaka, Japan, 20 mL) and surfactant (Sigma-Aldrich, Tokyo, Japan, Triton X-100, 10 mL) for 30 min. Then, the TiO\({}_{2}\) paste was spin-coated on the compact TiO\({}_{2}\) layer at 5000 rpm for 30 s. The resulting cell was annealed at 550 \({}^{\circ}\)C for 30 min to form the mesoporous TiO\({}_{2}\) layer. To prepare the perovskite compounds, mixed solutions of CH\({}_{3}\)NH\({}_{3}\)I (2.4 M, Showa Chemical, Tokyo, Japan) and PbCl\({}_{2}\) (0.8 M, Sigma-Aldrich, St. Louis, MO, USA) in N,N-dimethylformamide (DMF, Sigma-Aldrich, 0.5 mL) were prepared for the standard cell. Pb or MA in the perovskite structure was expected to be substituted by Cu, Na or EA, respectively. These perovskite solutions were then introduced into the TiO\({}_{2}\) mesopores by spin-coating at 2000 rpm for 60 s, which is followed by annealing in air. During the spin-coating, a hot air-blowing method was applied [50]. Temperatures of the cells during the air-blowing were set at 90 \({}^{\circ}\)C. A polysilane solution was prepared by mixing chlorobenzene (Fuijifilm Wako Pure Chemical Corporation, 0.5 mL) with decaphenylcyclopentasilane (DPPS, Osaka Gas Chemicals, Osaka, Japan, OGSOL SI-30-15, 10 mg). During the last 15 s of the third spin-coating of the perovskite precursor solutions, the DPPS polysilane solution was also spin-coated on the perovskite layer [51; 52; 53; 54]. After the spin-coating, the thin films were annealed from 90 to 160 \({}^{\circ}\)C in steps of 10 \({}^{\circ}\)C, holding each temperature for 10 min [55]. The temperature rising rate is \(\sim\)5 \({}^{\circ}\)C min\({}^{-1}\), which was not included in the annealing time. A hole-transport layer was prepared by spin-coating. A solution of spiro-OMeTAD (Fuijifilm Wako Pure Chemical Corporation, Osaka, Japan, 36.1 mg) in chlorobenzene (Fuijifilm Wako Pure Chemical Corporation, 0.5 mL) was mixed with a solution of lithium bis(trifluoromethyliumylsulfonyl)imide (Li-TFSI, Tokyo Chemical Industry, Tokyo, Japan, 260 mg) in acetonitrile (Nacalai Tesque, Kyoto, Japan, 0.5 mL) and tris(2-(1H-pyrazol-1-yl)-\(\lambda\)-_4-tert_-butyl pyridine)cobalt(III) tri[bis(trifluoromethane)sulfonimide] (FK209, Sigma-Aldrich, 188 mg) in acetonitrile (0.5 mL) for 24 h. The former solution with 4-tertbutylpyridine (Sigma-Aldrich, 0.018 mL) was mixed with the Li-TFSI solution (0.010 mL) and FK209 solution (0.004 mL) for 30 min at 70 \({}^{\circ}\)C. Then, the spiro-OMeTAD solution was spin-coated on the perovskite layer at 4000 rpm for 30 s. All procedures were performed in ambient air. Finally, gold (Au) electrodes were evaporated as top electrodes using a metal mask for the patterning.
###### Abstract
Compared with Faraday rotators and electro-optic modulators, the photoelastic modulator (PEM) has application advantages, such as a high modulation frequency, a large optical aperture, high modulation purity, and stable operation [11]. [PERSON] et al. developed a waveplate measurement system based on photoelastic modulation [12]. A single PEM is used, and the direct current (DC), the first and second harmonic terms of the photoelastic modulation are measured to obtain the waveplate parameters. However, none of the current measurement methods can realize simultaneous measurement of the two parameters of the waveplate retardance and fast-axis azimuth in a single measurement. Accurate measurement and calibration of the waveplate over the entire clear aperture are critical for the application of polarization optical systems with a large field of view and a large clear aperture. In this study, we developed a differential frequency modulation approach that leverages the advantages of the PEM. It employs dual PEMs, and digital phase-locked data processing technology is used to demodulate the signals. Moreover, a high-speed, high-precision waveplate parameters measurement method is demonstrated. The parameters over the entire clear aperture of the waveplate are measured by equipping it with a two-dimensional motorized scanning stage.