esa-sceva/satcom-chunk-collection huggingface dataset:
# General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning
## 4 Experiment and Results
### Model Interpretation  
To better understand the decision-making process of our model, we visualized the Raman spectra using a heatmap-based line plot (see Figure 6) [39]. The heatmap represents the gradients in the neural network, with the redder areas marking larger gradients, indicating the focus of the network. The \(x\)-axis represents the Raman wavelength and the \(y\)-axis represents the corresponding intensity of the signal. The model assigns heatmap values based on its interpretation of feature importance, where higher values (in red) indicate regions that contributed significantly to the classification decision, while lower values (in blue) represent less influential regions.  
Compared to other models that may treat all features equally or only few of them, our model efficiently focuses on key spectral features, as shown in the heatmap-based plot. This visualization demonstrates the model's capacity for identifying and leveraging meaningful patterns in Raman spectra, contributing to higher classification accuracy.  
This visualization by using heatmap-based plots further supports the claim that our model not only achieves higher accuracy but also provides interpretable results, which are critical for scientific applications like Raman spectroscopy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Network Framework** & **Qualitative Detection** & **Quantitative Detection** \\  & **Accuracy** & **Accuracy** \\ \hline Without attention & 96.36\% & 91.22\% \\ Fusion after two branches & 94.78\% & 90.54\% \\ Single-branch pre-fusion & 92.66\% & 89.33\% \\ Three-branch fusion & 98.45\% & 94.62\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experimental results.  
Figure 6: _Cont._
# Autonomous Driving Assistance with Dynamic Objects Using Traffic Surveillance Cameras
## Appendix A  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & \(P^{\prime}\) **(Transformed Point).** & \(P^{*}\) **(Reference Point).** & **Error (m)** \\ \hline
1. & 37.39810236995146, 127.11284371695545. & 37.39810236995146, 127.11284458460997. & 0.27 \\ \hline
2. & 37.398056022983745, 127.11284453412071. & 37.398056022983745, 127.11284459860855. & 0.06 \\ \hline
3. & 37.398014448299726, 127.11284392441756. & 37.39801444225397, 127.112846110664. & 0.41 \\ \hline
4. & 37.3981003578181, 127.11287948619685. & 37.39810034847469, 127.11288081438913. & 0.48 \\ \hline
5. & 37.398053366157214, 127.11288048901872. & 37.39805336615721, 127.11288084370179. & 0.31 \\ \hline  & & & \\ \hline
22. & 37.39805187414329, 127.11291959905732. & 37.398051865349494, 127.11291840016989. & 0.36 \\ \hline
23. & 37.39799629735647, 127.11291879882195. & 37.39799629149394, 127.11291837671978. & 0.37 \\ \hline
24. & 37.39804721105009, 127.11296406345524. & 37.39804722277515, 127.11296141945408. & 0.24 \\ \hline
25. & 37.39790503296632, 127.11296219917062. & 37.397905021241264, 127.11296124944073. & 0.44 \\ \hline
26. & 37.39794793496272, 127.11291813053069. & 37.397947934962716, 127.11291835477248. & 0.02 \\ \hline  & Avg. & 0.15 \\ \hline  & RMS. & 0.19 \\ \hline \end{tabular}
\end{table}
Table 1: Results of the Evaluation of Projection Points on World Coordinate.
# Optical Wireless Communications Using Signal Space Diversity with Spatial Modulation
## 1 Introduction
### Principle of the SSD Scheme Incorporated with SM  
The end-to-end transmission of the SSD scheme incorporated with SM in the 2 \(\times\) 1 MISO OWC system is shown in Figure 2.  
Figure 1: 2 \(\times\) 1 MISO OWC system configuration and SM implementation. (a) 2 \(\times\) 1 MISO OWC system; (b) principle of SM.  
At the transmitters, the transmit bits are divided into different subsets, for instance, the underlined subsets \"100\" and \"010\" in Figure 2a. Two consecutive subsets form a group for the SSD implementation. Within each subset, the first bit is the spatial bit for SM, as the blue numbers shown in Figure 2a. As introduced in Section 2.1, the spatial bit controls the activation of two transmitters, i.e., \(\mathrm{T}\times 1\) and \(\mathrm{T}\times 2\). The remaining bits in each subset, as the red numbers shown in Figure 2a, are used for signal modulation to offer SSD. Before SSD implementation, the signal modulation bits go through the quadrature amplitude modulation (QAM) mapping to create the complex-valued signals, such as the signals \"\(-1-j\)\" and \"\(1-j\),\" respectively. The subsequent SSD realization includes two steps, as shown in the red dash-dotted box in Figure 2a. The two complex-valued signals in a group are first rotated by multiplying \"\(e\)\"\(e\)\". Then, when two spatial bits differ in a group, diversity interleaving is conducted by exchanging the imaginary part of one signal with the real part of the other signal. The SSD output signals are then allocated to the corresponding transmitters according to the spatial bits. Here, carrierless amplitude and phase modulation (CAP) is conducted as the last step to enable real-valued signal transmission in the OWC channels. Such electrical CAP signals are modulated onto the optical carrier via optical modulation.  
After free-space optical propagation, the optical signals are collected and detected by a single optical receiver. The converted electrical signals are further processed, as shown in Figure 2b. The spatial bits are first recovered, followed by the SSD signals from each transmit channel being routed to their CAP-matched filters, respectively. The two CAP output signals are then summed up for the demodulation of SSD signals, including diversity deinterleaving, maximum likelihood (ML) decoding, and QAM demapping. Finally, the recovered bits from SSD and SM are combined to complete the overall signal recovery.  
Note that, in this paper, the proposed SSD scheme was mainly investigated to achieve the improved BER performance. When SM is incorporated with the SSD scheme, the  
Figure 2: Block diagram of the SSD scheme incorporated with SM. (**a**) Signal generation; (**b**) signal detection.  
system throughput can be further enhanced by introducing spatial bits as part of Tx bits. For instance, the data rate in Figure 2 can be increased by 1.5 times due to one additional spatial bit being introduced in each subset.
## 2 Materials and Methods
### Study Procedure  
The clinical work procedure for the IDBS is shown (Figure 1). Intra-oral scan data were obtained from the patients using an intra-oral scanner (Medit I-700, Medit Corp., Seoul, Republic of Korea) and converted to stereolithography (STL) files. The virtual set-up and placement of the orthodontic brackets on each tooth were performed using 3 Txer software (ver. 2.5; CENOS Co., Indeokwon, Gyeonggi-do, Republic of Korea) (Figure 2). The IDB Bracket transfer jigs were designed using CAD/CAM software and fabricated using a polyjet-type 3D printer (Projet MJP 3600, 3D Systems Co., Rock Hill, SC, USA) (Figure 3A). Ceramic self-ligation orthodontic brackets and metal tubes with Tweemac prescription were adapted into the fabricated transfer jigs. Teeth surfaces were etched with phosphoric acid gel and Transbond XT primers (Figure 3B). After the light-curing resin adhesive (Transbond(tm) XT Light Cure Adhesive, 3M Unitek, Monrovia, CA, USA) was applied to the bracket base, the IDB jig with the bracket was bonded to the tooth using a light-curing device (VALO, Ultradent, South Jordan, UT, USA (Figure 3C-E)). Premature contact spots were checked and ground after the jig removal (Figure 3G-I).  
Figure 1: Clinical workflow list for indirect bonding jig system (IDBS) procedure. It takes 10 days to receive jigs after intraoral scanning and ordering. CAD/CAM, computer-aided design, and manufacturing; 3D, three-dimensional.
## 4 Performance Evaluation
### Localization Results in a Single Apartment Building
Localization results of 1-C-a, 1-D-a and 1-E-a are squeezed into \(y_{i}\in\mathcal{Y}_{\text{p,APT\;1}}\) and approximated to floor classification because their XY positions cannot be mapped to \(y_{i}\in\mathcal{Y}_{\text{p,APT\;1}}\) directly. If the noise sources with the same XY positions (e.g., 2-A-a and 2-A\({}^{\prime}\)-a) are assumed to be the same category, \(\{\mathbf{P}_{i_{k}},y_{i}\}\), \(y_{i}\in\mathcal{Y}_{\text{p,APT\;2}^{\prime}}\) for \(\mathcal{T}_{\text{p,APT\;2}^{\prime}|\text{APT\;2}}\) can be mapped to \(y_{i}\in\mathcal{Y}_{\text{p,APT\;2}}\) by \(f_{\Theta^{i}_{\text{p,APT\;2}}}(\cdot)\). The locators presented a clear limitation on localization of inter-floor noise transmitted through the two unlearned floor sections, 4 F \(\rightarrow\) 3 F in APT I and 5 F \(\rightarrow\) 4 F in APT II, respectively. On the other hand, the locator showed the feasibility on floor classification of inter-floor noise transmitted through the unlearned floor sections. In summary, the CNN-based locator demonstrated the feasibility and generalizability of floor classification of inter-floor noise generated on the floors above/below, and those transmitted through the learned/unlearned floor sections in actual apartment buildings. It can contribute to minimize human effort for data gathering pertaining to the floor classification problem. However, localization of inter-floor noise was available for that transmitted through the learned floor sections alone. The adapted one-dimensional CNN-based locator with \(t=0.152\) and 0.501 s exhibited significantly low F1 scores. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Task**} & \multirow{2}{*}{**CNN**} & \multicolumn{6}{c}{**\(t\) (s)**} \\ \cline{3-8}  & & **0.152** & **0.501** & **1.00** & **1.50** & **2.00** & **3.00** \\ \hline \(\mathcal{T}_{\text{p,APT\;1}}\) & VGG16 & 0.9047 & 0.9381 & 0.9438 & 0.9516 & 0.9574 & 0.9496 \\  & & 0.9607 & 0.9899 & 0.9910 & **1.000** & 0.9955 & 0.9944 \\  & SoundNet & 0.7526 & 0.6954 & 0.7338 & 0.9426 & 0.9323 & **0.9607** \\  & & 0.9786 & 0.9273 & 0.9764 & 0.9910 & 0.9865 & 0.9899 \\ \cline{2-8} \(\mathcal{T}_{\text{p,APT\;2}}\) & VGG16 & 0.8344 & 0.8785 & 0.8968 & 0.9079 & 0.9272 & 0.9328 \\  & & 0.9333 & 0.9557 & 0.9682 & 0.9813 & 0.9786 & 0.9807 \\  & SoundNet & 0.4413 & 0.5150 & 0.8787 & 0.8752 & 0.9017 & **0.9337** \\  & & 0.8512 & 0.9318 & 0.9646 & 0.9641 & 0.9750 & **0.9880** \\ \cline{2-8} \(\mathcal{T}_{\text{p,APT\;1}^{\prime}|\text{APT\;1}}\) & VGG16 & 0.6794 & 0.7401 & **0.7805** & 0.5960 & 0.7032 & 0.5596 \\  & & 0.9709 & 0.9651 & 0.9796 & 0.9818 & **0.9834** & 0.9793 \\  & SoundNet & 0.1118 & 0.3461 & 0.4336 & 0.5951 & 0.6967 & 0.5093 \\  & & 0.2038 & 0.8627 & 0.9564 & 0.9087 & 0.9276 & 0.9333 \\ \cline{2-8} \(\mathcal{T}_{
## 2 Methods
### Case Study and Data Collection  
In this research, in order to have an interesting prediction purpose, an extensive dataset was collected from a mine containing explosive operations. A main view of the granite mine is shown in Figure 2. In general, explosive operations are common in this mine and are repeated at different intervals. The production capacity of this mine is about 500-700 thousand tons per year, and explosive operations use large quantities of explosives. The values of 856 kg to 9420 kg of explosives are usually used in holes with diameters between 76 mm and 89 mm. The highly explosive operations increased the danger in the mine. In this research, 102 operations were accurately measured and evaluated with all design details. Important parameters such as the burden, spacing, hole diameter, hole depth, total charge, number of holes, stemming length, maximum charge per delay, powder factor, sub-drilling, and distance from the blast-face, which help in determining PPV values were measured and recorded. Additionally, the Vibra ZEB seismographer was used for PPV recording. The distance from the seismograph to the explosion was about 280 m-530 m. According to the content given in Section 1 about the influence of various parameters, the importance of this parameter was determined by the effect of this phenomenon. In addition, previous research also made recommendations on the selection of effective parameters, the importance of each item, and design issues, which can be consulted for further information. Finally, the input data collected for the prediction and simulation purposes with the mathematical and probabilistic models are summarized in Table 3. Using this table, different ranges of recorded data can be seen. The relationships between the PPV and other input indicators are demonstrated in the correlation matrix plot (see Figure 3), which can be observed as the pairwise relationship between two indicators with corresponding correlation coefficients for each indicator. In the remaining sections, after input selection through the FS technique, five ML methods, i.e., RF, CART, CHAID, ANN, and SVM, are applied to predict PPV, and then the best PPV prediction model is selected and introduced.
## 3 Proposed Neural-Network-Based Decoding Scheme
### LSTM-Based Equalizer of Proposed Neural Network  
The proposed equalizer based on LSTM is shown in Figure 6, which comprised an vector input layer, a hidden layer, and a classification layer. In this work, LSTM is  
Figure 8: Sketch map of the architectures of residual unit and recalibration unit in basic block.  
Figure 7: The structure of channel-attention-Net-based encoder which comprised of two basic blocks in this case.  
introduced to decode a vector descriptor generated by the proposed encoder and equalize channel distortion, which is equal to a time-series classification problem. In the \"one-to-one\" form, LSTM takes an input, a time-series window \(t\) of descriptor vector \(x_{t}\) (one descriptor vector \(x\) at each time step of window \(t\)), and at each time step of the time-series window \(t\), it finally outputs a classification result, which is given by the softmax vector.  
The overlaps between the exposure time of symbols cause a degree of ISI happening in the sub-image that needs to be demodulated now (at time step \(t\)). The generation of an output vector of the LSTM unit at time step \(t\) is a comprehensive consideration of long-short-term memory and the latest training data vector, which means that parameters in short-term memory can be used to quickly process ISI brought by a previous one symbol, and those parameters in long-term memory are used to increase the accuracy of recognizing symbols of specific data transmitted repeatedly, which contributes to recognizing more headers of data packets so that more packets could be decoded. Due to the effective use of long-short-term memory by an LSTM unit, it will learn a pattern so that it can make specific compensation for mitigating ISI happening in the current sub-image decoding after obtaining the output vector of the previous time steps so as to reduce the classification error.
# Comparing Augmented Reality-Assisted Assembly Functions--A Case Study on Dougong Structure
## 4 Major Assisted Functions
### Three-Dimensional Viewer-Based System  
Figure 5 shows the framework design of the 3D viewer-based system. Unity was adopted as the software platform for implementing its functions. Unity is a 3D programming engine that supports Windows, macOS, and Linux operating systems and deploys applications compatible with mobile operating systems such as Android and iOS. It provides interactive programming graphical user interfaces that facilitate 3D scene construction, model rendering, and 3D manipulation. Developers can integrate third-party libraries in C# as a plug-in into their software development.  
The hardware devices involved in this study consist of two parts. The development of Unity applications was mainly conducted in a desktop personal computer (PC). Its specifications are listed as follows: Intel Core-I5 6500 processor (3.2 GHz), 8 GB memory, 300 GB hard drive, Graphics 530, and NVIDIA GeForce GTX950. The assisted functions were deployed in a smartphone, Zenfone3 ZE520 KL, which interacted with the user during the assembly process. The phone has a 5.5\" screen with a \(1920\times 1080\) pixel image resolution. The device uses the Android 6.0 Marshmallow operating system and is equipped with a Qualcomm Snapdragon 2 GHz eight-core processor.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function** & **Paper-Based** & **3D Viewer** & **AR** \\ \hline Interactive 3D model display & no & yes & yes \\ Part identification with feedback & no & no & yes \\ Stepwise color labeling & yes & yes & yes \\ AR animation & no & no & yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Assisted functions provided by three media.  
Figure 4: Two augmented reality (AR)-assisted systems.  
The 3D viewer-based system starts by showing the first part model on the screen of a mobile phone that is set up on a work table using a tripod. Clicking on the next button, as shown in Figure 6, displays the next component to be assembled and changes the process to the next step. The user has two options at this point: (1) repeat the display of the assembly result, and (2) start assembling the actual components. The view angle of the scene can be freely changed at any time by clicking the button on the right side at the bottom of the screen. Once the current step is completed, the user will need to click the next button to enter into the next step. A similar operation repeats until the entire assembly process is completed. There is a sign at the left upper corner showing the current status of the assembly process. It is a common technique of the user interface design.
# Generative Adversarial CT Volume Extrapolation for Robust Small-to-Large Field of View Registration
## 2 Materials and Methods
### Proposed Method
#### 2.2.2 Image Metadata Retrieval  
Since our convolutional neural network (CNN) generator operates on voxel intensity information only, we needed to perform an extra-step to retrieve the metadata of the extrapolated images.  
Intuitively, the extrapolated image will have the same spacing and orientation as the thin one. However, the origin and dimension of the image changes due to the addition of synthetic information. Determining the grid dimension of the expanded volume is straight forward since we always quadruple the input field of view on the z-axis:  
\[(d\tilde{e}_{x},d\tilde{e}_{y},d\tilde{e}_{z})=(dt_{x},dt_{y},dt_{z}\times 4) \tag{5}\]  
To compute the origin of the extrapolated volume, we first needed to determine thin slab's location within the extrapolation grid. In the current work, we addressed this issue in the post-processing phase, performing an extra-registration step to determine the extent of extrapolation as further described:  
We overlapped the thin slab (sliding it across z direction) at each possible location of the extrapolated volume, calculating the voxel-wise mean squared error (Figure 6--\(d_{1\_k}\)). Next, we determined the extent extrapolation by picking the index which minimized this penalty.  
Further, the origin of the extrapolated image was calculated using the following expression:  
\[(o\tilde{e}_{x},o\tilde{e}_{y},o\tilde{e}_{z})=(ot_{x},ot_{y},ot_{z}-argmin_{i =1\_k}(d_{i})\times st_{z}) \tag{6}\]  
where \(st_{z}\) is the spacing of thin volume across z direction.  
Figure 5: Generator optimization workflow. A conditional GAN was employed in extrapolating thin input volumes, expanding their FOV with a factor of 4.  
In our tests, this simple registration step was always accurate because the extrapolation network only had to copy the thin slab's intensities into the output volume without modifying them at all, hence generating relatively few errors.
## 3 Results and Discussion  
### Method Development and Optimisation  
Many trials were made on different C18 columns, such as (Econosil silica (250 \(\times\) 4.6 mm, 10 \(\upmu\)m, Econosil C18 (250 \(\times\) 4.6 mm, 10 \(\upmu\)m) and Phenomenex LUNA C18 (250 \(\times\) 4.6 mm, 10 \(\upmu\)m); the chromatographic separation of the three analytes in the same chromatographic run could not be achieved. However, sharp peaks in short time were obtained on Alltech Apollo C18 (150 mm \(\times\) 4.6 mm, 5\(\upmu\)m). The use of acetonitrile in the presented ion-pair HPLC method gave better chromatographic and analytical outcomes over methanol. Initial attempts at pH (7-7.5) close to the pKa of the phosphate (to have a good buffering capacity) did not give satisfactory chromatographic separation of the ternary mixture, although optimisation for individual analytes seemed to be achievable.  
In their optimisation of an ion-pairing method for the separation of metformin (pKa = 2.8, 11.5) and rosiglitazone (pKa = 6.1, 6.8) [51] on a similar column, [PERSON] et al. reported that good resolution between metformin and rosiglitazone peaks was obtained by decreasing the pH from 7.5 to 7.1 [54]. Thus, it was decided, in this study, to decrease the pH to 5.5, where metformin HCl is reported to be fully ionised in its cationic form [59], which enables the ionised biguanide to couple/ion pair and efficiently retained with the anionic SDS moieties adsorbed on the silica of the C18 column. Also, at pH = 5.5, which is very close to the pKa of gliclazide and glipizide, 5.8 and 5.9, respectively, around 50% of the sulfonylurea are ionised and can be retained satisfactorily with ion-pairing chromatography on the same column. We found that the concentration of SDS of 0.03% is optimal for ion-pairing metformin in a very short time without affecting the retention times of glipizide or gliclazide. Also, this low concentration of SDS did not cause the precipitation (snow-like effect) reported by [PERSON] et al. (resulting from a side interaction of SDS with KH\({}_{2}\)PO\({}_{4}\) buffer) [29]. Below 0.03%, we could not retain metformin. Also, concentrations above 0.03% resulted in delayed metformin retention and sulfonylurea peaks brought forwards (overlapped), and had a bad resolution with metformin peak; similar findings were reported by [PERSON] et al. and others [34; 35; 41; 54].  
In this paper, a very short HPLC run for the individual analytes was shown in Figure 1, and a chromatogram of the ternary mixture was presented in Figure 2, showing sharp and symmetric peaks with good separation between the three therapeutics analysed, with the optimised chromatographic conditions outlined in Table 1 and eluted within 5 min.  
Figure 1: Individual peaks for metformin 25 mg/ml. (A), glipizide 25 mg/ml. (B) and glichzade 25 mg/ml. (C).
# An Efficient Framework for Securing the Smart City Communication Networks
## 3 Description of Our Proposed Cyber Security Framework for Smart Cities
### Overview  
The purpose of our proposed cyber security framework is to enhance the communication efficiency as well as to reduce any negative impacts of security. In other words, we aim to improve the security system, which is one of the fundamental requirements for smart city systems, and also to ensure the sufficient resilience between two parties without attacks. Notably, the security requirements can vary from system to system. Hence, the planned design is required in order to overcome these challenges at an early stage.  
To deploy our proposed framework along with the key exchange Algorithm 1, two different tasks are used; namely, authentication and encryption between every smart meter and the control center in order to create a two-way secure communication.  
```
1 Procedure Exchanging a random number (x, y) Select (p, g, x, y) foruserAdo
2\(A=g^{x}\)modp foruserBdo
3\(B=g^{y}\)modp //Both users exchange keys in order to calculateK1 andK2
4\(KI=B^{x}\)modp
5\(K2=A^{y}\)modp
6 Select(t, s, m)
7//Both userschoosem as secret number \"log\"
8 foruserAdo
9\(C=log_{m}\)(t, KI)
10 foruserBdo
11\(D=log_{m}\)(s, KI)
12//Both users exchange their final keys
13\(FK_{A}=C^{*}D\)
14\(FK_{B}=D^{*}C\)
15//\(FK_{A}\)=\(FK_{B}\)=\(FK\)are shared symmetric keys
16 Endprocedure
```  
**Algorithm 1**Modifying the process of D-H keys exchange algorithm.