esa-sceva/satcom-chunk-collection huggingface dataset:
# Positioning Performance Limits of GNSS Meta-Signals and HO-BOC Signals
## 3 GNSS Receiver Signal Processing
### GNSS Code/Phase-Based RTK Positioning and CRB
\begin{bmatrix}\mathbf{n}_{\Phi}\\ \mathbf{n}_{\mathbf{e}}\end{bmatrix} ,\ \mathbf{C}_{\mathbf{n}}= \begin{bmatrix} \mathbf{C}_{\mathbf{n}_{\Phi}}&\mathbf{C}_{\mathbf{n}_{\Phi},\mathbf{n}_{ \mathbf{e}}}\\ \mathbf{C}_{\mathbf{n}_{\Phi},\mathbf{n}_{\mathbf{e}}}^{\top}&\mathbf{C}_{ \mathbf{n}_{\mathbf{e}}}\end{bmatrix} ,\ \mathbf{C}_{\mathbf{n}_{\Phi}_{\Phi}}= \begin{bmatrix}- \mathbf{1}_{M,1}\ \mathbf{I}\end{bmatrix}\begin{bmatrix}\sigma_{\{\Phi,\mathbf{e} \}_{0}}^{2}&&0\\ &\ddots&\\ 0&&\sigma_{\{\Phi,\mathbf{e}\}_{M}}^{2}\end{bmatrix} [-\mathbf{1}_{M,1}\ \mathbf{I}]^{\top}\]  
The solution to this problem (i.e., which has no closed-form solution because of the integer ambiguities) is typically obtained via a three-step decomposition and solved using the LAMBDA method [41, 44]:  
\[\underbrace{ \begin{bmatrix}\widehat{\mathbf{b}}\\ \widehat{\mathbf{a}}\\ \widehat{\mathbf{z}}\end{bmatrix} }_{\widehat{\mathbf{z}}}=\min_{ \begin{subarray} {c}\mathbf{b}\in\mathbb{R}^{3}\\ \mathbf{a}\in\mathbb{Z}^{M}\end{subarray} }\left\|\mathbf{y}-\mathbf{D} \begin{bmatrix}\mathbf{b}\\ \mathbf{a}\end{bmatrix} \right\|_{\mathbf{C}_{\mathbf{n}}}^{2}=\min_{ \begin{subarray} {c}\mathbf{b}\in\mathbb{R}^{3}\\ \mathbf{a}\in\mathbb{R}^{M}\end{subarray} }\left\|\mathbf{y}-\mathbf{D} \begin{bmatrix}\mathbf{\tilde{b}}\\ \mathbf{\tilde{a}}\end{bmatrix} \right\|_{\mathbf{C}_{\mathbf{n}}}^{2}\ +\ \min_{\mathbf{a}\in\mathbb{Z}^{M}}\left\|\mathbf{\tilde{a}}-\mathbf{a} \right\|_{\mathbf{C}_{\mathbf{n}}}^{2}\ +\ \min_{\mathbf{b}\in\mathbb{R}^{3}}\left\|\mathbf{\tilde{b}} \mathbf{\tilde{b}}\mathbf{\tilde{a}}-\mathbf{b}\right\|_{\mathbf{C}_{\mathbf{b} \mathbf{a}}}^{2}. \tag{20}\]  
We can identify: (i) a first WLS problem where the integer nature of the ambiguities is disregarded, the so-called float solution, (ii) a second ILS [11], for which an integer ambiguity solution is obtained, and (iii) a third WLS refinement or correction step, the so-called fixed solution. The CRB associated with the float solution, the so-called CRB\({}_{real}\), is given by the inverse of \(\mathbf{F}_{\mathbf{z}|\mathbf{z}}\left(\mathbf{z}^{0}\right)=\mathbf{D}^{\top} \mathbf{C}_{\mathbf{n}}^{-1}\mathbf{D}\). The mixed real/integer CRB associated with the fixed solution, the so-called CRB\({}_{real/integer}\), was discussed in [45].
# Establishment and Application of Crowd-Sensing-Based System for Bridge Structural Crack Detection
## 3 Development of the Crowd Sensing Crack Collection System
### Mobile App Development
#### 3.3.1 Function Design  
The mobile application for participants is designed to allow them to record and upload information related to cracks. Therefore, its main functions are as follows: (i) Crack image acquisition: Participants can use the application to capture the cracks in real time by tapping the phone's camera to record the bridge cracks. The images can be saved separately in a folder. Also, while capturing the crack images, the mobile phone also uses the GPS sensor to record the location of the smartphone; (ii) Text description: A module is added to enable participants to describe the situation more accurately and provide more information on complicated and unforeseen issues that may arise on site; (iii) Record query: Participants can query their historical records for the crack images they have saved, which are used later for potential reward applications or other incentive measures.  
Considering the extensive range of users who might use the application, it is designed to simplify the process and reduce the learning requirement of the participants. Therefore, the process of capturing cracks has been simplified to guide the participants through taking and uploading crack images in a step-by-step manner, as shown in Figure 10.
# Bloch Oscillations in the Chains of Artificial Atoms Dressed with Photons
## 2 Statement of the Problem and Calculation Technique
### Studying Observable Values  
We will study the values of two types. The first ones directly characterize the spatial-temporal behavior of electronic component and therefore are averaged over photonic state distribution. They are position-dependent, thus we will address to their densities per unit cell of the chain. From among these values we introduce:  
(i) inversion density, given by  
\[W(p,t)=\sum_{n}\Bigl{(}\bigl{|}a_{p,n}(t)\bigr{|}^{2}-\bigl{|}b_{p,n}(t) \bigr{|}^{2}\Bigr{)} \tag{9}\]  
and (ii) tunneling current density (see Appendix B)  
\[\begin{array}{ll}J_{\text{Tunneling},p}(t)=&J_{\text{ Tunneling},p}^{(a)}(t)+J_{\text{Tunneling},p}^{(b)}(t)=\\ &-i\frac{\varepsilon}{2}t_{a}\frac{\sum}{n}(a_{p-1,n}(t)-a_{p+1,n}(t))a_{p,n}^{ *}(t)\\ &-i\frac{\varepsilon}{2}t_{b}\frac{\sum}{n}\bigl{(}b_{p-1,n}(t)-b_{p+1,n}(t) \bigr{)}b_{p,n}^{*}(t)+c.c.\end{array} \tag{10}\]  
The values of the second type directly characterize the quantum-statistical properties of light and therefore are spatially averaged. From among these values we will use:  
(iii) photonic number distribution  
\[\widetilde{p}(n,t)=\sum_{p}\Bigl{(}\bigl{|}a_{p,n}(t)\bigr{|}^{2}+\bigl{|}b_{ p,n}(t)\bigr{|}^{2}\Bigr{)}, \tag{11}\]4. mean number of photons \[\langle n(t)\rangle=\sum_{n}n\widetilde{p}(n,t),\] (12) where \(\widetilde{p}(n,t)\) is given by (11),
5. photonic number variance \[\delta n(t)=\sum_{n}n^{2}\widetilde{p}(n,t)-\langle n(t)\rangle^{2},\] (13) where \(\langle n(t)\rangle\) is given by relation (12), and
6. von Neumann quantum entropy \[S=\sum_{j}p_{j}\ln p_{j}\] (14) where \(p_{j}\) are the weights of the various Fock states of the statistical light distribution [44].
# Practical Application of Mesh Opportunistic Networks
## 4 Results  
### Survey  
Of the 14 participants, 11 answered the survey (n = 11), of whom 6 were users of iPhones (6s, 7, 7 plus and SE), 2 used Honor phones (6x and 10), one a Huawei terminal, another a Samsung phone, and the last was a Nokia user. In total, six participants used an iOS system (Apple Inc., Los Altos, CA, USA) and five used Android (Google LLC, Mountain View, CA, USA). Correct device registration helps to cross the data in the communication research protocols [36]. In this case, the ratio seemed appropriate to test and experiment with a mesh network involving various mobile devices and OSs.
###### Abstract  
Chinese word segmentation (CWS), which involves splitting the sequence of Chinese characters into words, is a key task in natural language processing (NLP) for Chinese. However, the complexity and flexibility of geologic terms require that domain-specific knowledge be utilized in CWS for geoscience domains. Previous studies have identified several challenges that have an impact on CWS in the geoscience domain, including the absence of abundant labeled data and difficult-to-delineate complex geological word boundaries. To solve these problems, a novel semi-supervised deep learning framework, GeoCWS, is developed for CWS in the geoscience domain. The framework is designed with domain-enhanced features and an uncertainty-aware self-training strategy. First, n-grams are automatically constructed from the input text as a pseudo-lexicon. Then, a backbone model is suggested that learns domain-enhanced features by introducing a pseudo-lexicon-based memory mechanism to delineate complex geological word boundaries based on BERT. Next, the backbone model is fine-tuned with a small amount of labeled data to obtain the teacher model. Finally, we design a self-training strategy with joint confidence and uncertainty awareness to improve the generalization ability of the backbone model to unlabeled data. Our method outperformed the state-of-the-art baseline methods in extensive experiments, and ablation experiments verified the effectiveness of the proposed backbone model and self-training strategy.  
Keywords:Chinese word segmentation; natural language processing; semi-supervised learning; Chinese geological texts +
Footnote †: journal: applied sciences
# Interval Uncertainty Optimization Method for Electromagnetic Orbital Launcher
## 6 Conclusions  
In this paper, an interval-based uncertainty optimization method for electromagnetic orbital launchers is established to realize the reliability estimation of electromagnetic orbital launcher armatures.  
1. The uncertainty of variables is described using interval numbers, and the conversion of uncertainty optimization problems into deterministic optimization problems is achieved based on interval order relations.
2. The DBN-DNN algorithm is used to build a high-precision proxy model to predict the performance of electromagnetic orbital launchers. It is also combined with the particle swarm optimization algorithm to solve the transformed deterministic optimization problem.
3. The results of the multi-objective optimization algorithm for the electromagnetic orbital launcher show that the optimized solution set obtained by the method contains  
Figure 11: Interval non-inferior solution set distribution.  
interval rather than deterministic values, it is able to handle interval optimization problems with uncertain parameters, and the performance is improved compared with that before optimization.  
The series of interval non-inferiority solution sets obtained in the optimization examples can appropriately describe the performance fluctuations caused by uncertainty in the electromagnetic orbital launcher and can consider the uncertainty in the manufacturing and service processes to improve the performance stability of the product in operation.  
Conceptualization, L.J.; methodology, L.L. and J.S.; software, J.S.; validation, L.L. and Y.Y.; formal analysis, L.L. and J.S.; investigation, Y.Y.; resources, L.L.; data curation, L.L., Y.Y. and X.Z.; writing--original draft preparation, Y.Y.; writing--review and editing, Y.Y. and X.Z.; visualization, J.S. and X.Z.; supervision, L.J.; project administration, L.J.; funding acquisition, L.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Major Research Program of the National Natural Science Foundation of China, grant number 92066206; the Project of the National Natural Science Foundation of China, grant number 51977148; and the Local Science and Technology Development Projects Guided by the Central Government, grant number 226Z4503G.  
Not applicable.  
All data, models generated or used during the study appearance in the submitted article.  
The authors declare no conflict of interest.
## 3 Implementation and Validation of the Proposed Framework through a One-Inch Block Experiment
### Image Quality Assessment
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{\(CV\) (\%)} & \multicolumn{4}{c}{\(\Delta_{input}\) (\%)} \\ \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Index**} & \multicolumn{4}{c}{**Index**} \\ \cline{2-11}  & \(E\) & \(PSNR\) & \(SSIM\) & \(BR\) & \(NQ\) & \(PQ\) & & \(E\) & \(BR\) & \(NQ\) & \(PQ\) \\ \hline CS & 2.74 & 8.08 & 11.01 & 1.78 & 5.04 (\({}^{\text{b}}\)) & 5.19 (\({}^{\text{a}}\)) & CS & 0.96 & 4.96 & 12.66 & 89.31 \\ CLAHE & 0.86 (\({}^{\text{b}}\)) & 2.62 (\({}^{\text{b}}\)) & 4.43 (\({}^{\text{b}}\)) & 1.96 & 6.02 & 4.46 & CLAHE & 23.46 & 12.43 (\({}^{\text{a}}\)) & 8.90 (\({}^{\text{b}}\)) & 17.04 (\({}^{\text{b}}\)) \\ HE & 2.71 & 2.75 & 14.90 & 2.07 & 5.93 & 4.45 & HE & 0.19 (\({}^{\text{b}}\)) & 4.72 (\({}^{\text{b}}\)) & 10.40 & 87.04 \\ HRIO & 1.61 & 5.73 & 7.44 & 1.65 (\({}^{\text{b}}\)) & 6.61 & 1.74 (\({}^{\text{b}}\)) & HRIO & 37.88 (\({}^{\text{a}}\)) & 6.37 & 10.78 & 103.62 (\({}^{\text{a}}\)) \\ HRDC & 6.10 (\({}^{\text{a}}\)) & 15.36 (\({}^{\text{a}}\)) & 15.18 (\({}^{\text{a}}\)) & 5.25 (\({}^{\text{a}}\)) & 9.41 (\({}^{\text{a}}\)) & 3.35 & HRDC & 22.31 & 5.75 & 14.16 (\({}^{\text{a}}\)) & 83.58 \\ \hline \hline \end{tabular}  
* (\({}^{\text{a}}\)) max. (\({}^{\text{b}}\)) min.  
\end{table} 
Table 3: Coefficient of variations \((CV)\) and index change concerning input image quality \((\Delta_{input})\).
## 2 Related Work  
In the spatial domain, all pixels on the first layer correspond to their own corresponding values of the least significant bit (LSB); all pixels on the eighth layer correspond to their own corresponding values of the most significant bit (MSB). That is, the LoSBs contains the least significant \(n\) bits; the HiSBs contains the higher significant (\(8-n\)) bits. That is to say, in the eight-level bit plane, the original image pixel from the intensity value of level 1-127 is changed to 0, and the intensity value of the level 128-255 is changed to 1, and the resulting binary image is described below. The higher significant images of the higher layers are called higher significant bits and represented by \(I_{HiSB}\) images, and the images of the lower significant of the lower layers are called lower significant bits (LoSBs). In this article, \(I_{LoSB}\) images are represented. That is, the LoSBs contain the least significant \(n\) bits; the HiSBs contain the higher significant (\(8-n\)) bits. For example, a gray-scale pixel value is \(155=\left(10011011\right)_{2}\). Assuming \(n=3\), the pixel value of the original image pixel in this \(I_{LOSB}\) image is \(\left(011\right)_{2}=3\), and the pixel value in this \(I_{HiSB}\) image is \(\left(10011\right)_{2}=19\). Most of the information hiding techniques in spatial domain directly change the LSBs of the pixel value to achieve the embedding of secret data.  
Both the significant-bit-difference expansion (SBDE) method proposed by [PERSON] et al. [26] and the two-layer embedding (TLE) method proposed by [PERSON] and [PERSON] at (2020) [27] utilize the HiSBs to achieve the information embedding and make the hiding methods more robust. Both the SBDE and TLE methods consider that the distortion of the \(I_{HiSB}\) image should not be too large. It will only embed the secret message in the embedding area with low complexity. The measure of complexity complex is the variance of the pixel value on the \(I_{HiSB}\) image. The smaller the variance, the higher the smoothness, which means that embedding secret information in this area will cause less distortion, therefore increasing the image visual quality. However, the SBDE method hides the data in the bit difference, resulting in severe image distortion. Therefore, the TLE method enhances the hiding performance both in image quality and embedding capacity.
# Key Technologies for THz Wireless Link by Silicon CMOS Integrated Circuits
## 2 300 GHz Band Transceiver Using Silicon Integrated Circuits
### Integrated Circuit Realizing Terahertz Transceiver and Its Characteristics
As will be described later, it is the transmission power of the transmitter and the noise figure of the receiver that determines the communication performance, but the absence of the power amplifier and the low-noise amplifier is extremely disadvantageous in wireless communication. How can we overcome this disadvantageous condition? In the high-frequency front end of the transmitter or the receiver, the basic function is to convert the modulated baseband signal to the radio frequency signal. If an amplifier cannot be used, the mixer responsible for frequency conversion must be the last stage of the transmitter and the first stage of the receiver. As a result, performance improvement of this mixer is a key technology for realizing wireless transceiver under disadvantageous conditions. It is necessary to realize an ultrahigh-frequency circuit by a technology that does not rely on transistor miniaturization [10].
## 6 Factors Affecting the CTE of Concrete
### Influence of Supplementary Materials in Concrete CTE  
CTE was not found to be considerably impacted by using different cementitious materials such as fly ash or ground granulated blast furnace slag for Portland cement concrete combinations including the same kinds of aggregates [45]. However, the addition of fly ash in concrete was found to lower the CTE particularly at an early age. Nevertheless, in the case of cement and fly ash paste, the CTE increases over time due to the ongoing pozzolanic reaction [58].  
[PERSON] et al. carried out a study to explore the use of alternative supplementary cementing materials (SCMs) in concrete, to assess their performance compared to cement. The result showed that the CTE value would not be negatively impacted using SCM, as evidenced by the minimal variation in CTE values between the control and SCM-concrete specimens [84]. The application of superabsorbent polymers (SAPs) to regulate cementitious materials' CTE was examined by [85], where the authors showed that adding SAPs lowers the CTE rise brought on by self-desiccation in combinations with low water-to-cement ratios. [PERSON] et al. [86] studied the impact of Recycled Concrete Aggregate (RCA) on CTE using 16 cores with varying RCA percentages. Specifically, when the RCA content rose from 0% to 100%, the CTE value dropped from 7.28 \(\times\) 10\({}^{-6}\)/\({}^{\circ}\)C to 4.10 \(\times\) 10\({}^{-6}\)/\({}^{\circ}\)C. The results revealed that as the RCA percentage increased, the CTE value decreased [87]. Similar findings were obtained by [88] where it was found that the mix with no RCA had the lowest CTE, and the next lowest was the mix with only 50% coarse RCA. However, in a study carried out to understand the feasibility and performance benefits of using RCA in CRCP [89], the authors found that no substantial difference was found between the CTE of concrete using RCA and NA. Overall, although some materials and additives can affect CTE, their effects are usually mild and contingent on the conditions and composition of the concrete.  
[PERSON] et al. [90] investigated the suitability of latex-modified concrete using Styrene-Butadiene Rubber and Poly-Vinyl Acetate for repairing concrete pavements. Their findings revealed that latex-modified concretes exhibit a higher CTE compared to those with ordinary Portland cement concrete. This increase is attributed to the formation of a polymer film within the concrete, which minimizes the transition zone and improves aggregate compaction, resulting in more rapid thermal stress development with temperature fluctuations.
## 3 Problem Formulation
### Definition of Cost Functions, Energy Loss Function
#### 3.1.3 Environmental Cost  
In order to make the energy system more environmentally friendly, pollution during the energy production process is considered simultaneously. In this paper, as natural gas is burned to supply heat, pollution mainly refers to greenhouse gases emissions such as \(CO_{2}\) and \(NO_{x}\). Thus, the environmental cost can be depicted as:  
\[C_{GB}^{E}=\sum_{j=1}^{m}\left(d_{ej}v_{ej}\sum_{i=1}^{N_{GB}}H_{GBi}\right), \tag{10}\]  
\[C_{CHP}^{E}=\sum_{j=1}^{m}\left(d_{ej}v_{ej}\sum_{i=1}^{N_{CHP}}\frac{P_{CHPi}+ H_{CHPi}}{4}\right), \tag{11}\]  
where \(d_{ej}\) and \(v_{ej}\) are emission intensity and environmental value, respectively. According to the equations above, the environmental cost function can be defined as follows:  
\[C_{cost}^{E}=k_{4}C_{GB}^{E}+k_{5}C_{CHP}^{E}. \tag{12}\]  
where \(k_{4}\) and \(k_{5}\) are parameters to balance the order of magnitude between two environmental costs.  
When considering the emission cost of the main grid, coal is considered as the main fuel of the main grid, thus the environmental cost of the main grid is depicted as:  
\[C_{grid}^{E}=\sum_{j=1}^{m}d_{ej}v_{ej}P_{grid}. \tag{13}\]  
The environmental cost function can be rewritten as:  
\[C_{cost}^{E}=k_{4}C_{GB}^{E}+k_{5}C_{CHP}^{E}+k_{6}C_{grid}^{E}. \tag{14}\]  
Emission intensities for different devices are shown in Table 1 as below:  
In Table 2, environmental values of different greenhouse gases are given as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Types of Greenhouse Gases**} & \multicolumn{2}{c}{**Emission Intensity \(d_{ej}\) (kg/MW\(\cdot\)h)**} \\ \cline{2-4}  & **CHP** & **GB** & **Grid** \\ \hline \(CO_{2}\) & 623.0000 & 742.6000 & 643.8900 \\ \(NO_{x}\) & 2.8800 & 0.2556 & 2.8800 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Emission intensities of different devices.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**Types of Greenhouse Gases**} & \multicolumn{2}{c}{**Environmental Value \(v_{ej}\) (yuan/kg)**} \\ \hline \(CO_{2}\) & 0.044 \\ \(NO_{x}\) & 8.000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Environmental values of different greenhouse gases.
# A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images
## 3 Proposed Methodology
### Tumor Localization and Segmentation Using Mask-RCNN  
Mask-RCNN is the recent DL technique that is used for both object detection and pixel-level segmentation [37]. It is the extension of Faster RCNN [56], which performs segmentation as well along with classification and localization. Furthermore, Mask-RCNN decouples the mask and class prediction. It adds a small network overhead, i.e., an FCN network to perform segmentation. In the presented work, we proposed a custom Mask-RCNN by introducing the DenseNet-41 at the feature computation layer. DenseNet [57; 58] is the latest presented approach of CNN, in which the present layer relates to all preceding layers. DenseNet comprises a set of dense blocks that are sequentially interlinked with each other with extra convolutional and pooling layers among successive dense blocks. DenseNet can present the complex transformations which result in improving the issue of the absence of the target's position information for the top-level key points to some degree. DenseNet minimizes the number of parameters, which makes them cost-efficient. Moreover, DenseNet assists the keypoints propagation process and encourages their reuse, which makes them more suitable for brain tumor classification. The structure or flow of the presented approach is illustrated in Figure 3. The custom Mask-RCNN architecture consists of different networks, i.e., a convolutional backbone network, region proposal network, RoI classifier and bounding box regressor, and the segmentation network. A detailed description of each step is discussed below.
# Overview and Perspectives for Vehicle-Integrated Photovoltaics
## 2 Results and Discussions
#### 2.1.3 Other Vehicles with On-Board PV  
Motor homes and coaches are often equipped with a PV module of about 100 Wp to keep the lead-acid battery full for auxiliaries or to start the engine.  
On coaches, trains, and trucks, PV is employed to power the auxiliaries even if no reason is given for this limitation. A few kWp of photovoltaics can be integrated on these vehicles where the mean PV area is about 20-23 m\({}^{2}\). On vehicles, the commercial offers are mainly limited to installed PV kits.  
Buses, drones, planes, and spatial vehicles have the largest available surface area for integrated PV, ranging from hundreds of square meters to even thousands of square meters on ships. The generated electricity is used for propulsion and/or auxiliaries, but no market offerings could be found.  
The increased range offered by the photovoltaics embedded on a vehicle will depend heavily on the real energy generated by the PV, as well as on the vehicle performance in terms of system and motor efficiency, drag coefficient, weight, etc.