esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Dynamic Evolution Model and Method of Communication Network Based on Real War Game
## 4 Results and Discussion
### The impact of Different Attack Methods on the Combat Network Indicators  
(1) Change the precision \(\alpha\) with the breadth \(\beta\) fixing  
With the growth of \(\alpha\), the more likely the information of important nodes in the network will be obtained, and the higher the accuracy of the attack. It can be seen from Figure 12 that when \(\alpha\) increases, indicators 1-4 show a downward trend. The larger the value of \(\alpha\), the faster the downward trend. There is a positive correlation between \(\alpha\) and the magnitude of the downward trend. The average shortest length tends to increase with the \(\alpha\) increase. This is because the number of important nodes being attacked continues to increase when the network is continuously attacked. As more and more nodes in the  
Figure 11: Error analysis of robustness of differently distributed combat networks under \(\alpha=0.2\), \(\beta=0.5\) attack.  
network increasing, the connection needs to rely on more node relays, which causes the path length of the connection between nodes increasing with deeper attacking. There are fewer and fewer important nodes remaining in the combat network, and the remaining edge node distances have a connection relationship due to their own existence, and the entire average shortest length shows a decreasing trend.  
(2) Change the breadth \(\beta\) with the precision \(\alpha\) fixing  
Similarly, as shown in Figure 13, as the proportion of \(\beta\) increases, the various indicators of the combat network show a downward trend, and the rate of decline depends on the speed of the network collapse. Before the communication subnetwork completely collapses, the larger the \(\beta\) value, the larger the downward trend, and the higher the robustness of the network.  
Figure 12: Changes in various indicators of the same distributed combat network under \(\beta=0.5\).  
In the same way, we can analyze the trends of several other indicators, and we can see when \(\beta=1.0\), the combat network will not completely collapse. Through the horizontal comparison of different indicators, it can be found that the constructed network model is more in line with the changing trend of the robustness of the combat system in the actual fighting.
# An Analysis of the Use of Feed-Forward Sub-Modules for Transformer-Based Image Captioning Tasks
## 3 Method
### MLPs in Image Captioning
This also helps the gradients propagate through the model more easily. Since we do not just use the final output of the last decoder, but rather the final outputs of the all the encoders (multi-level encoder-decoder network), are saved for interfacing with the corresponding decoder. For the objective functions, as with many similar sequence generation works, we first optimize the cross entropy then perform CIDEr-D metric optimization [41], i.e., given \((y_{1:T}^{*})\) as a sequence of ground truth word tokens where \(T\) is the sequence length of the sequence \(y_{1},...y_{T}\). The cross-entropy loss is given by:  
\[L(\theta)=-\sum_{t=1}^{T}\log\left(p_{\theta}\left(y_{t}^{*}\mid y_{1:t-1}^{* }\right)\right) \tag{5}\]  
where \(\theta\) represents the model parameters. The CIDEr score is optimized by SCST:  
\[L_{RL}(\theta)=-E(y_{1:T\sim p\theta}[r(y_{1:T})] \tag{6}\]  
where \(r\) is the score function. The gradient is then approximated by:  
\[\
abla_{\theta}L_{RL}(\theta)\approx-(r(y_{1:T}^{*})-r(\hat{y}_{1:T}))\
abla_ {\theta}\log p_{\theta}(y_{1:T}^{*}) \tag{7}\]  
Summary: How it all comes together: The output of Faster-R-CNN model, which contains detected image features (regional proposals) is used as the input to the encoder of out transformer model. The first sub-module which is comprised of fully connected layers, learns to create the most optimum generalized representation of the input features during training. The position-wise feed-forward layers create a fixed vector representation of the input image in the model dimension, which will be the output of the encoder and one part of the decoder input for the cross-attention sub-module. A representation of the captions which are the decoder inputs and targets (outputs) is created using a deep fully connected decoder input sub-module and passed on to the cross-attention sub-module. The cross-attention sub-module detects and learns the relationships between the caption embedding representation and the image feature representation. From the output of the cross-attention layer, transformed by the position-wise linear layer, the image-caption representation is passed through a final linear layer and after a softmax operation outputs a probability distribution over the vocabulary. The first or next token in the caption sequence can be sampled from this probability distribution. During inference this is done in an auto-regressive manner until the end of sequence token is predicted.
# Reflection Propagation Law of Electromagnetic Waves in U-Shaped Roadway
## 3 U-Shaped Roadway Model and Reflection Path Validity
### Valid Mirror Points
#### 3.1.4 Reflection Point Calculation  
The coordinates of the mirror image point were derived from Equations (1)-(5) in the previous context. When the line IR intersects any reflecting plane \(m\) with exactly one intersection point \(H\), the point \(H\) is the reflection point, as shown in Figure 5.  
Knowing that the coordinates of the mirror and receiving points are \(I(x_{i},y_{i},\,z_{i})\) and \(R(x_{r},y_{r},\,z_{r})\), respectively, those of the reflecting point are \(H(x_{h},y_{h},\,z_{h})\), and because the ratios of \(\overset{\leftrightarrow}{IH}\) and \(\overset{\leftrightarrow}{RH}\) are the same, that is  
\[\frac{x_{i}-x_{h}}{x_{r}-x_{h}}=\frac{y_{i}-y_{h}}{y_{r}-y_{h}}=\frac{z_{i}-z_ {h}}{z_{r}-z_{h}}=t \tag{6}\]  
Because the reflection point \(H\) is on both line \(IR\) and in the reflection plane \(m\), (6) and the equation of the reflection plane show that  
\[\frac{Ax_{i}+By_{i}+Cz_{i}+D}{Ax_{r}+By_{r}+Cz_{r}+D}=t \tag{7}\]  
The coordinates of reflection point \(H(x_{h},y_{h},\,z_{h})\) can be obtained by combining (6) and (7):  
\[\left\{\begin{array}{l}x_{h}=\frac{D(x_{r}-x_{i})}{A(x_{i}-x_{r})+B(y_{i}-y _{r})+C(z_{i}-z_{r})}\\ y_{h}=\frac{D(y_{i}-y_{i})}{A(x_{i}-x_{r})+B(y_{i}-y_{r})+C(z_{i}-z_{r})}\\ z_{h}=\frac{D(z_{i}-z_{i})}{A(x_{i}-x_{r})+B(y_{i}-y_{r})+C(z_{i}-z_{r})}\\ \end{array}\right. \tag{8}\]
# An Improved Hybrid Transfer Learning-Based Deep Learning Model for PM2.5 Concentration Prediction
## 2 Proposed Model
### Spatial-Temporal Features Extraction Based on DSTP
#### 2.2.3 Decoder with Temporal Attention  
The decoder with temporal attention can adaptively select the encoder hidden state most relevant to the target series by weighting the encoder hidden state. The encoder with spatial attention outputs the hidden state, and the decoder learns the temporal relations of the hidden state through the attention mechanism within a window size \(S_{W}\). Based on the hidden state \(h_{t-1}^{d}\in\mathbb{R}^{p}\) and unit state \(s_{t-1}^{d}\in\mathbb{R}^{p}\) of the decoder LSTM unit at the previous time, the attention weight of each encoder hidden state in the attention module at the moment \(t\) can be calculated. The attention weights for the temporal attention mechanism are as follows:\[d_{t}^{i}=v_{d}^{\rm T}tanh\Big{(}W_{d}\Big{[}h_{t-1}^{d}:s_{t-1}^{d}\Big{]}+U_{d}h _{i}^{s}+b_{d}\Big{)}, \tag{12}\]  
where \(v_{d},b_{d}\in\mathbb{R}^{p},W_{d}\in\mathbb{R}^{q\times 2p},U_{d}\in\mathbb{R}^{p \times p}\) are parameters to learn; \(p\) is the hidden size of the third attention module, and \(h_{i}^{s}\in H^{s}\) is the \(i\)-th encoder hidden state of the second attention module. After \(d_{t}^{i}\) is calculated, it is normalized by Softmax function to get \(\gamma_{t}^{i}\). The context vector \(c_{t}\) is defined as follows:  
\[c_{t}=\sum_{j=1}^{S_{W}}\gamma_{t}^{j}h_{j}^{s}. \tag{13}\]  
The temporal relationship between all the hidden state of the central site collection and the target series of matching sites is again learned by concatenating the target series of matching sites:  
\[\widetilde{y}_{t-1}=\widetilde{W}^{\rm T}[y_{t-1}:c_{t-1}]+\widetilde{b}+ \widetilde{H}^{\rm T}y_{t-1}^{(best)}, \tag{14}\]  
where \(\widetilde{W}^{\rm T}\in\mathbb{R}^{q+1}\) and \(\widetilde{b}\), \(\widetilde{H}^{\rm T}\in\mathbb{R}\) are the parameters that map the connection to the size of the hidden state of the decoder. Then, \(\widetilde{y}_{t-1}\) and \(h_{t-1}^{d}\) are input into the LSTM layer to update the hidden state \(l_{t}^{d}\) at the current moment. The final multi-step prediction formula is as follows:  
\[y_{S_{W}+1},\dots,y_{S_{W}+\tau}=v_{y}^{\rm T}\Big{(}W_{y}\Big{[}h_{t}^{d}:c_{ t}\Big{]}+b_{y}\Big{)}+b_{y}^{\prime}, \tag{15}\]  
where \(W_{y}\in\mathbb{R}^{p\times(p+q)}\) and \(b_{y}\in\mathbb{R}^{p}\) are parameters that map concatenation to the size of the decoder hidden state; \(\Big{[}h_{t}^{d}:c_{t}\Big{]}\in\mathbb{R}^{p+q}\) represents the concatenation of the decoder hidden state and the context vector; \(v_{y}\in\mathbb{R}^{\tau\times p}\) is the weight and \(b_{y}^{\prime}\in\mathbb{R}^{\tau}\) is the deviation, where \(\tau\) is the time steps to predict in the future. The linear function produces the final prediction result.
## 5 III-V-on-Silicon Devices for Optical Sensing Applications
### III-V-on-Silicon Spectrometers for Optical Sensing Applications  
As discussed above, there is an interest in spectroscopic sensors in which the absorption spectrum of a sample is measured. When a broadband light source is used, a spectrometer is required to spectrally decompose the transmitted light and compare it to the light that went through a reference path that did not go through the sample under study. Single mode optical spectrometers provide the best possible resolution for a given device footprint and are therefore of great interest for these applications. Besides passive spectrometers, also integrated photodetector arrays are required. For the telecommunication wavelength range, Ge-based photodetectors are typically used. Recently, low dark current, high  
Figure 23: **(a)** The supercontinuum spectra at the output of 2 mm long waveguides of widths 800 nm, 790 nm, 770 nm, 760 nm, 700 nm and 650 nm respectively from top to bottom. Individual spectra have been displaced by 40 dB for clarity. Dispersive waves can be observed at the ends of the spectra for all the waveguides. **(b)** The simulated dispersion profiles of the waveguides showing the ZDWs on either side of the pump wavelength at 1550 nm. **(c)** The experimental octave-spanning supercontinuum for the 700 nm wide waveguide with arrows showing the position dispersive waves. **(d)** The simulated evolution of the supercontinuum spectrum along the waveguide propagation length, which matches well with the experimental spectrum shown in **(c)**. Reproduced from [73].  
responsivity devices have been demonstrated with a cut off wavelength of about 1600 nm [76]. In many spectroscopic sensing applications one wants to work beyond a wavelength of 1600 nm, because of the more pronounced absorption features at longer wavelengths. This again requires the heterogeneous integration of III-V semiconductor devices onto the silicon photonic spectrometers. In next sections, we will elaborate on such III-V-on-silicon spectrometers for operation beyond the telecom wavelength window.
## 4 Experiments and Results
### Detection Examples  
In order to compare the detection performance of CISPNet and SSD for the remote sensing images in an intuitive manner, as shown in Figure 8, we visualized some of the images. From the figure, we can clearly see that our proposed CISPNet is beneficial to the detection of targets in the remote sensing images, especially for small targets.  
In the upper two rows of images, we can see that SSD detection results show a single object with various overlapping boxes and the smaller box has partial image such as the part of a ship or the part of an aircraft. However, the detection results of the proposed CISPNet algorithm in the same picture did not show the box-in-box status. In the lower three rows of images, the dimensions of the aircrafts, ships and oiltankers are small. SSD cannot accurately detect the position of aircrafts, ships and oiltankers because of the lack of understanding of the scene. CISPNet can more fully understand the contextual information via the CISP models and FFMs so that it can better distinguish the background and the detected targets, and determine the location of the aircrafts, ships and oiltankers through the contextual information.  
Figure 7: Precision-recall curves (PRCs) of the proposed CISPNet and other state-of-the-art approaches for airplane, ship, oiltanker, respectively.
# Parametric Testing of Metasurface Stirrers for Metasurface Reverberation Chambers
## 1 Introduction
### Analysis of Simulation Results
Note that \(\tau_{s}\) can be extracted from the least-square fit of the logarithm of \(C(t)\) in (3) [23; 24], thus, the TSCS can be obtained using expression (2) (signals after 100 us are the noise floor and are not used for the least-square fit). The stirrer was rotated through 12 stirrer positions (30 degrees/step). The values of TSCS for the four considered cases were found to be: 0.168 m\({}^{2}\) (metallic stirrer), 0.195 m\({}^{2}\) (metasurface stirrer #1), 0.123 m\({}^{2}\) (metasurface stirrer #2), 0.189 m\({}^{2}\) (metasurface stirrer #3). To further verify our optimization results for the metasurfaces stirrers, some standard EMC tests [25] were carried out for the four stirrers. Particularly, the E-field components were recorded by isotropic probes placed on the eight corners of the test volume. According to [25], and taking into account the LUF of our metasurfaces reverberation chamber configuration and the studied bandwidth, the simulations were repeated for 12 steps of the stirrer rotation. The uniformity and the isotropy of the E-field are defined as the standard deviation with respect to the mean value of the maximum measures obtained for each of the eight probes and for a complete rotation of the stirrer. The results are shown in Figure 6. In the figure, the acceptable limits for field uniformity decreed in [25] are within 3 dB above 400 MHz. We plotted the standard deviation calculated from the mean value of the maximum E-field by giving equal weight to each component. The average values of the standard deviation were 2.06 dB, 1.81 dB, 2.63 dB, and 1.93 dB for the metallic stirrer and metasurfaces stirrers (#1, #2, #3), respectively. Thus, metasurfaces stirrer #1 provided a greater scattering efficiency compared to the others. We note that metasurfaces stirrer #1 respected the tolerance constraint, in accordance with the results of correlation coefficient and TSCS. From all these results, we can conclude that metasurfaces stirrer #1 exhibited much better stirring efficiency.
## 4 Conclusions  
In this paper, the influence of roller parameters and subgrade properties (namely influence factors) on the correlation relationships between ICMV and ISMV were investigated combined with a construction applying IC technology in a highway subgrade project of China. Linear and 5 nonlinear regression algorithms were employed to predict ISMV based on ICMV, subgrade properties and roller parameters. Based on the analysis and discussion on results, the main conclusions can be drawn as follow:  
* From the monadic regression results of ICMV and ISMV, using mechanical method ICMV to predict ISMV was more accurate than using empirical method ICMV. R\({}^{2}\) values between ICMV and compaction degree were lower than the R\({}^{2}\) values between ICMV and \(E_{\text{LWD}}\). It meant that ICMV reflected the stiffness of subgrade better than the density of subgrade.
* According to the feature importance, subgrade properties were more significant than roller parameters in the regression of ICMV and ISMV. Particularly, when the regression target was compaction degree, the importance of \(w\) even became higher than the importance of CMV and CCV.
* The influence on correlations between ICMV and ISMV of \(k_{\text{u}}\) and \(w\) were nonlinear whereas \(v\) and Am were linear. Therefore, multiple regression algorithms should be introduced into IC technology implementation to establish the correlation between ICMV and ISMV.
* From the comparison among linear and 5 nonlinear algorithms, the results of RF had the best performance while the results of linear algorithms had the worst performance. Moreover, the combination of RF and linear algorithm could further improve the predicted accuracy.  
Conceptualization, J.Q.; Data curation, J.Y.; Formal analysis, B.Z.; Methodology, J.P.; Writing--original draft, J.P.; Writing--review & editing, J.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by: The Transportation Science and Technology Project of Jiangxi Province, China: Intelligent Compaction Technology for Integrated Subgrade and Pavement Design (No. 2020C0002), Department of Transportation of Jiangxi Province. The Transportation Science and Technology Project of Hebei Province, China: Research on Disaster Prevention and Control Technology for High Fill and Deep Excavation Subgrade of Taihang Mountain Highway (No. TH-201901), Department of Transportation of Hubei Province.  
Data availability Statement: Data is unavailable due to project privacy.  
The authors declare no conflict of interest.
## 5 Barriers to Internet Access for Women in Developing Countries
### Exclusion from Technology Education and Design  
[PERSON], [PERSON] and [PERSON] [17] claim that \"women and girls are poorly placed to benefit from the knowledge society because they have less access to scientific and technical education specifically and to education in general\". Access to education continues to be a greater barrier for women than men and it is estimated that two-thirds of the world's illiterate population are women. In contrast, across all developing countries, 75% of women are literate compared to 86% of men. In some countries, India for example, 51% of women can read and write compared to 75% of men and without this fundamental skill, the Internet and the benefits of the online world will remain out of reach [26].  
Overcoming Internet access barriers requires action on a number of fronts. In order to reduce inequality in Internet access, we need to educate and train people to facilitate access and modify attitudes that may serve to impede access [10]. The Women and the Web report [26] revealed that one in five women in India and Egypt believe that the Internet is not appropriate for them, or that their families would disapprove, and that engaging online would not be beneficial regardless. These attitudes are presumably derived from their socio-cultural conditioning. 40% of women in this study, moreover, cited a lack of familiarity or comfort with technology as a reason for not pursuing Internet use and, typically, women who were uncomfortable with technology lacked the exposure to Internet technologies that would allow them to develop their computer and digital literacy skills.  
In addition, it has been reported that one of the consequences of having relatively few women web developers and programmers is a lack of content relevant to women's needs and interests. Moreover, 90% of online content is in English, yet only one-third of users worldwide speak it [5], which may create barriers to access for non-English speakers [27], many of whom are women living in rural and remote areas [17]. Furthermore, the content associated with new technologies, and terrestrial media generally, is largely male-centric. In 2010, the Global Media Monitoring Project (GMMP) [28] reported that only 12% of all news stories across the world's media focus specifically on women. The GMMP further noted that 46% of news stories reinforce gender stereotypes while just 6% challenge them. It is therefore unlikely that women in developing countries will be motivated to seek information online if the content is inaccessible (not available in their native languages) and irrelevant for their needs.
## 3 Methods for Closed Path Determination
### Maximum Average-Intensity Circle Extraction  
The maximum average-intensity circle (MAIC) method has been proposed to extract a closed path from an intensity sum map for TC determination [24], as is briefly explained here. The basic process is to iteratively search the local peaks from the 2D intensity sum map and to connect these  
Figure 2: A diagram to illustrate the topographic map of an image after performing watershed transformation. The red curves are the watershed lines.  
peak elements to form a closed path. The searching begins with the global maximum in the intensity sum map. The local maximum element from among several candidates of the eight neighbors of the current element is selected as the next element.
# Optimization Study on a Novel High-Speed Oil-Free Centrifugal Water Pump with Hydrodynamic Bearings
## 3 Experimental Study
### Experimental Results  
Table 3 lists the experimental results of the high-speed pump at 7500 rpm. The hydrodynamic head of the high-speed pump reached 24.78 m and that of the whole system reached 23.31 m under the designed flow rate \(Q\) = 4.0 m\({}^{3}\cdot\)h\({}^{-1}\). The efficiency of the hydraulic components was 31.13% and total efficiency was 29.29%. The head loss was 1.47 m in the flow channel of the motor, while the efficiency loss was 1.84%. At the best efficiency point, the maximum flow rate is 5.63 m\({}^{3}\cdot\)h\({}^{-1}\) and the highest head was about 32 m. When the flow rate decreased, the heads of both the whole system and the hydraulic components increased, and the total input power decreased. The efficiencies of the hydraulic components and the whole system increased to maximum values first and then kept reducing with the decrease of the flow rate. The efficiency loss in the flow channel of the motor decreased with the flow rate. Compared with pumps employing ball bearings, the noise of the high-speed pump in the current work distributed more uniformly in four directions. Here, the average noise was 53.89 dB, which was much smaller.  
Figure 5: High-speed Pump Test Rig.  
The experimental results verify the feasibility of the water-lubricated bearing in the small-sized high-speed centrifugal pump as supporting structures and the effectiveness to improve the lifetime and vibration noise of the hard-supporting structures.