esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Multi-Angle VSR
#### 3.1.2 Feature Extraction  
Before conducting feature extraction, we prepare 3D CNN pre-recognition models for all possible combinations of the above five angles, i.e., models each trained only using images obtained from a single angle, such as a model from frontal images and a model from 30\({}^{\circ}\) images, as well as models each built using data of several angles, such as a model trained using both 0\({}^{\circ}\) and 30\({}^{\circ}\) data and a model using all face images. An architecture of our 3D CNN-based VSR models is shown in Figure 3. The last layer has 20 outputs, each of which corresponds to one class in our recognition task. As a result, we build 31 models in this case (\(\sum_{i=1}^{5}5C_{i}=5+10+10+5+1=31\)), as shown in Table 1. Table 1 also indicates preliminary VSR results: recognition accuracy to validation data at a certain angle, using a certain model chosen among those 31 models. For example, if we adopt a 30\({}^{\circ}\) model for 60\({}^{\circ}\) data, the accuracy is 87.55%.  
According to the angle obtained in the view classification part, we select the most reliable three models for the estimated angle, which are shown in bold in Table 1. For instance, we adopt (1) \"0\({}^{\circ}\) + 30\({}^{\circ}\) + 45\({}^{\circ}\)\", (2) \"0\({}^{\circ}\) + 30\({}^{\circ}\) + 45\({}^{\circ}\) + 60\({}^{\circ}\)\" and (3) \"0\({}^{\circ}\) + 30\({}^{\circ}\) + 45\({}^{\circ}\) + 90\({}^{\circ}\)\" models for 45\({}^{\circ}\) data. In other words, we determine suitable angle combination patterns of training data for the estimated angle. We then utilize those models as feature extractors; we remove the last layer, resulting in a new output layer generating a 48-dimensional feature vector, as indicated in Figure 3. Finally, we obtain three 48-dimensional vectors from this part.  
This strategy has two advantages. First, as shown in Table 1, models trained using data of several angles have relatively higher performance than those trained using single angle data. This result motivates us to choose such models for multi-angle data. Second, even if the view classification fails, it is still expected to obtain high performance by our scheme; for instance, in the case where a 30\({}^{\circ}\) sequence is misclassified as 45\({}^{\circ}\), the above models (1)\(\sim\)(3) are used for feature extraction, all in which 30\({}^{\circ}\) data are also used in model training. There is another reason to encourage us to choose this framework. The model trained using all data, indicated in the bottom row in Table 1, achieved good performance. On the other hand, there exists a better model in all the angle cases. This suggests using only the model with all data is not the best solution. Hence, for each angle, we prepare several models trained using multi-angle data and utilize them as feature extractors.
## 3 Detection Technology of Adverse Geological Bodies
### Borehole Multi-Source Data Fusion Analysis Method
#### 3.2.2 Analysis of Cavity Extension Range  
Because of the hyperbolic radar image generated by the hollow target, the wave velocity can be determined by the hyperbolic shape of the echo. Edge detection and curve fitting are the simplest image processing methods. As shown in Figure 9, the size and location of the hole can be roughly estimated by using the echo curve of the hole target. The steps are as follows:  
Figure 8: Schematic diagram of dip angle and ductility of structural plane.  
1 Pre-processing, edge contour extraction and low-pass filtering of radar profile image.
2 The radar wave velocity and dielectric constant of the target area are estimated using the method mentioned above.
3 The vertex of the hyperbola (t\({}_{0}\), y\({}_{0}\)) is estimated in the echo curve of the hollow target.
4 Since the target echo curve points should meet the relationship (18), the point set (t\({}_{i}\), y\({}_{i}\)) can be brought in to calculate the radius of the empty target, namely,  
\[r_{i}=\left|\frac{v}{4}(t_{i}+t_{0})+\frac{(y_{i}+y_{0})^{2}}{v(t_{i}-t_{0})}\right| \tag{18}\]  
Due to the large amount of interference and noise in the target echo, the curve may appear discontinuous or have an irregular hyperbolic shape. Therefore, a series of radius values can be calculated for the point set (t\({}_{i}\), y\({}_{i}\)). At this time, the average value of these calculated results is not taken as the radius of the actual target, but the interval with the densest radius value is taken as the estimated value of the size of the empty target, which is recorded. At this time, it is also easy to determine the location of the hole.
# Compact Sub 6 GHz Slot Multiantenna System for 5G Laptops
## 5 Parametric Study
Using the aforementioned analysis, we could adjust the low- and high-frequency resonance frequencies and their matching by modifying various parameters. These parameters included the capacitance value of the T-shaped feed-in section, the length of the left and right branches, the length of the slotted hole, and the coupling distance between them. Figure 16: Comparison of simulated reflection coefficients with changes in parameter \(d\). Figure 11:0 Comparison of simulated reflection coefficients with changes in parameter \(t\).
# Hash-Chain-Based Cross-Regional Safety Authentication for Space-Air-Ground Integrated VANETs +
## 5 Experiment
### Hash-Chain-Based Vehicle Cross-Regional Authentication Simulation
#### 5.2.3 Comparison Experiment of Hash Block Authentication and Traditional Authentication  
In the traditional authentication process, we use the ECIES and ECDSA algorithms for encryption and decryption. The average time delay of the two algorithms is shown in Table 5.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Consensus** & \multicolumn{5}{c}{**Number of Nodes**} \\ \cline{2-6}
**Algorithm** & **4** & **6** & **8** & **10** & **12** \\ \hline Kafka & 23.21\% & 24.67\% & 25.20\% & 25.94\% & 28.69 \\ PBFT & 23.20\% & 41.70\% & 78.50\% & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Memory ratio of Kafka and PBFT.  
Figure 10: Comparison of delays in block generation under two consensus algorithms.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Process** & **Average Single Processing Delay /(ms)** \\ \hline ECIES encryption, \(t_{e}\) & 0.52412 \\ ECIES decryption, \(t_{d}\) & 0.74753 \\ ECDSA signature, \(t_{s}\) & 0.51124 \\ ECDSA authentication, \(t_{v}\) & 1.13411 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Delay of traditional encryption and decryption algorithms.  
To verify the delay change based on the hash-chain structure and the traditional encryption algorithm, in the case of a virtual map scene with a vehicle speed of 30 km/h, the delay results of the traditional authentication algorithm and the hash-chain authentication algorithm are compared as shown in the Figure 9 below. It can be seen from the simulation results that the delay of the hash chain is increased compared to the traditional authentication algorithm, but the overall difference is smaller, and in some cases, the delay is lower than the traditional authentication algorithm. Moreover, the delay of the hash chain increases as the number of SM nodes increases, which is consistent with the consensus delay verification results. Comparing the two consensus algorithms of Kafka and PBFT, when the number of nodes in the same, the delay of PBFT is larger. In addition, this paper compares the proposed authentication framework with the decentralized key management mechanism for VANET with blockchain (DB-KMM) proposed by [PERSON] et al. [34]. DB-KMM has a higher delay when the number of vehicles is small, mainly from the key management overhead. As the number of vehicles increases, the delay of DB-KMM is similar to the scheme proposed in this paper.
# A Flower-Shaped Miniaturized UWB-MIMO Antenna with High Isolation
## 2 The Proposed Antenna System
### Design Evolution Stages of the MIMO Antenna
In the MIMO antenna system, increasing the independence between the antenna components has long been a desired aim. However, as a result, additional reductions in the correlation and improved isolation between the antenna components are required. Accordingly, we enhanced the shape of the metallic ground in step 4, which completed the design of the proposed UWB-MIMO system. To construct the final ground structure, the shape of the inverted L-shaped branch was slightly modified, i.e., the vertical width was extended and the triangular patches were proportionately cut out at the edges of the inverted L-shaped branches. The resonance frequencies stimulated in step 4 are shifted to the right compared with those of step 3, which are now 5.4, 6, 8, 11.2, and 14.6 GHz, respectively, as displayed in Figure 4. Although the reflection coefficient performance is worse than that of step 3, the design of step 4 still covers the frequency range of 4.3-15.63 GHz. More importantly, Figure 5 shows that the isolation in step 4 has been enhanced compared with that in step 3, reaching more than 20 dB, which suggests that the mutual coupling has been decreased. Essentially, these results imply that the improved ground structure makes a significant contribution toward improving the isolation.
## 3 History of PCEM Development
### Instrument 3--Reflection Acquisition Mode with Inverted Microscopy and LED Source  
Recently, the PCEM instrumentation transitioned to its third generation, in which an inverted microscope (Carl Zeiss Axio Observer Z1) body was chosen as the base of the system (Instrument 3, Figure 4A,B) [45,48]. While the second generation PCEM was developed specifically for scanning PC surfaces in a dry state for the detection of surface-adsorbed biomolecule patterns (such as DNA microarrays), the third generation PCEM was designed for label-free detection of cells and real-time detection of binding events in which the PC surface is exposed to liquid. In order to avoid scattering and absorption or interference from cell bodies, microfluidic components, semi-opaque liquid media, or liquid-air meniscus, bottom illumination of the PC was adopted in a reflection mode. In this system, detection of resonant reflected wavelength shifts was adopted again as the sensing approach rather than sensing changes in the resonant angle for a fixed illumination wavelength. An LED was chosen as the light source to avoid the speckles in the acquired images that may be caused by a laser illumination source. To obtain higher illumination intensity from the LED light source, a cylindrical lens was added into the illumination light path to convert the incident light from a circular spot to a more concentrated linear beam [45].  
Label-free imaging of surface-absorbed live cells (including cell attachment, chemotaxis, and apoptosis) [45] and nanoparticles [48] has been performed using the third generation PCEM. Fluorescence-labeled imaging is also enabled in this system, in which the PC can be excited by a laser illumination source that can couple with the resonant PC mode to obtain an electric field enhancement effect. This enhancement is capable of increasing fluorescence detection sensitivity (which has been validated previously [20,76,83,85,86,87,88,89,90,91,92,93,94,95]) and enabling estimation of the distance of fluorescence emitters from the PC surface [96]. The most recently adopted PC surface design and PCEM detection instrument configurations have already been described in detail in section 2.
# Study on the Disturbance of Existing Subway Tunnels by Foundation Sloping Excavation
## 2 Engineering Background  
The starting point of the channel project across the river of the Xiangya Road in Changsha, Hunan Province, China, is located about 280 m west of the junction of Tongzipo Road and Yuehua Road, and the end point is located at the intersection of Xiangya Road and Furong Middle Road. Figure 1 shows the overall direction and distribution of the project. The pit studied in this paper crosses Changsha Metro Line 1 (already in operation) at the intersection of Huangxing North Road, Xiangya Road, and connects with Wenchangge Station of Metro Line 6. The surrounding environment is almost simple and far from the existing buildings.  
The foundation pit is approximately 7.6 m deep, 21.3 m wide and 41.3 m long. The enclosure structure adopts the support form of an underground diaphragm wall with internal support, and the thickness of the underground diaphragm wall is 1.0 m. The construction is carried out by the open excavation method. The average distance between the base plate of the upper span structure of the channel across the river and the vault of the shield tunnel of Line 1 is 2.7 m, and the minimum distance is only 2.6 m. The base is located in the powder clay layer, and the existing tunnel is located in the medium weathered slate stratum, which is approximately orthogonal to the long axis of the foundation pit. The outer diameter D of the tube sheet of the existing tunnel is 6.0 m and the thickness is 0.3 m. The reinforced concrete tube sheet of C30 strength is used. One ring consists of a capping block, two adjacent blocks, two standard blocks and one bottom block, with 10 longitudinal connection bolts. The relationship between the excavation area and the existing tunnel location is shown in Figure 2.  
According to the surface exposure and the disclosure of preliminary drilling survey, the stratum within the pit area is mainly composed of quaternary artificially deposited soil,  
Figure 1: The overall direction and distribution of the project.  
Figure 2: Relationship between foundation pit and existing tunnel location.  
lacustrine material, river alluvium and residual powder clay. The underlying bedrock is the Middle Yuanguijie Slate Group and Lengjiaxi Group Slate. Table 1 shows the proposed values of geotechnical material parameters. The formation parameters in Table 1 are determined according to the geological survey report and laboratory experiments.  
During the survey, no groundwater was encountered in the borehole of this pit node site. The foundation excavation stratum is mainly powder clay, and its permeability coefficient is only 0.001 m/d, so the permeability is very low. Therefore, there is less precipitation in this foundation excavation project, and the influence of foundation precipitation is negligible in theoretical calculation and numerical simulation.
## 4 Conclusions  
The thermal analysis of the HIXs with Cu\({}^{0}\) particles showed that they contained anomalously little hygroscopic water, with amounts as low as about 4%. The HIXs with Cu\({}_{2}\)O particles contained about 10% of hygroscopic water, less than HIXs with CuO particles (13%), and considerably less than pure resin (15%). This means that Cu\({}^{0}\) in the polymer matrix was a stronger water repellent than Cu\({}_{2}\)O and CuO.  
Both Cu\({}^{0}\) and Cu\({}_{2}\)O particles were shown to accelerate the decomposition of the anion exchanger in air, while slightly slowing down the decomposition of the anion exchanger in N\({}_{2}\). Under air both the inorganic particles transformed into CuO, whereas under N\({}_{2}\) the solid residue contained metallic copper. The oxidation state of the copper atom in the deposit was found to have an increasing effect on the amount of forming char, and metallic copper was found to contribute to the formation of a greater amount of char than in the case of the pyrolysis of the pure resin.  
In brief: there are Cu-containing HIXs which when dried contain far less hygroscopic water than another similar composite materials. During the pyrolysis of HIXs with copper containing deposit (cupric compounds, cuprous compounds as well as metallic copper) the inorganic phase takes part in the formation of an additional amount of char and also transforms itself (reduces itself when possible). The two conclusions provide a basis for in-depth research aimed at the obtaining usable composites with a designed (organic-inorganic) composition and innovative use of this type materials (instead of synthetic carbon prepared on the basis of ion-exchange resin alone, by pyrolysis of HIX one can obtain metal-doped carbon possessing far more diverse properties).  
Conceptualization, E.K.-B.; formal analysis, I.M.; investigation, E.S. and I.M.; methodology, I.M.; resources, E.S.; software, I.M.; supervision, E.K.-B.; validation, I.M.; visualization, E.S. and I.M.; Writing--Original draft, E.K.-B. and I.M.; Writing--Review and editing, E.K.-B. All authors have read and agreed to the published version of the manuscript.  
Ministerstwo Nauki i Szkolnictwa Wyzszego: SUB.D050.21.034.
## 3 English-to-Japanese Patent Machine Translation
In 2020, the reported number of patents filed worldwide increased by 1.6% with 3.3 million patents filed, where approximately 85% of the total number of patents filed were accounted for by five national/international patent offices [22]. The National Intellectual Property Administration of the People's Republic of China (CNIPA) received upwards of 1.5 million applications, followed by the United States Patent and Trademark Office (USPTO), which received 597,172 applications. Ranked third, the Japan Patent Office (JPO) had 288,472 applications, the Korean Intellectual Property Office (KIPO) had 226,759, and finally, the European Patent Office (EPO) had 180,346 [22]. Figure 1 below demonstrates the growth of patent applications from 2006 to 2020 worldwide. MT of patents becomes an important problem as it is useful, in terms of industrial use, for countries to be able to file patents in foreign languages. The translation must also be accurate as possible since even a small variation of the intended meaning may lead to legal loopholes that will be taken advantage of to exploit intellectual properties [23]. Efficient MT of patents is a direct step towards having unified human knowledge and sharing patent information for the human race in the general domain or at least on certain predefined domains, such as efforts related to fighting climate change. Although commercially available translators such as Google Translate and Microsoft Translator offer machine translation with a high level of accuracy for generic and non-technical texts, more specialized domains/scopes rely on industry-specific training data to translate the text so that the translation may be relevant in their context [24]. While Google's Cloud Translation and Microsoft's Translator allow for custom translation and model training to counter this issue, the cost of using them risks getting expensive since more complex, and large training data as well as custom translations lead to high costs while using those products, which may be prohibitive. Custom translations with Microsoft are performed using their C2-C4 instances, where the cost of using those instances ranges from 2055 USD/month to 45,000 USD/month, respectively [25]. This incurs a large expense, especially for companies with many patent grants. For instance, IBM, ranked as 2021's most innovative company in the US IP space, had 9130 and 8682 patent grants in 2020 and 2021, respectively [26]. Since IBM offers a range of products and patents corresponding to different industries, the use cases would require multiple model training, which further increases the cost. Given the difficulty of MT and the high cost associated with it, continued research in the field of MT is extremely important, and therefore, the aim of this paper is to investigate and analyze existing machine translation models used to translate patents containing highly technical words from a range of domains while also working to increase the models' level of accuracy using training data.
## 4 Results
### Validation of GPP Estimates  
We roughly compared GPP_act with the results of [PERSON] et al.'s study [PERSON] et al. (2017), which predicted the GPP in China from 2000 to 2009 using the Eddy Covariance-Light Use Efficiency (EC-LUE) model, and validated results with measurements of 23 EC flux towers in China. Our estimates of different biomes were within the range of GPP observations shown in [PERSON] et al.'s study. Additionally, we compared our eight-day GPP estimates against MOD17 GPP for cropland, grassland, EBF, and DBF averaged on the pixels of the same land cover types on the land cover maps derived from both Landsat images and MOD12 land cover data. We found that GPP act was in agreement with MOD17 GPP for all of the vegetation types (Figure 4), but the values of GPP act were slightly higher than those of MOD17 GPP, especially for EBF and DBF.  
Figure 3: The coefficient of determination (R\({}^{2}\)) and root mean square error (RMSE) of the relationship between the synthetic normalized difference vegetation index (NDVI) and the NDVI derived from the original Landsat images in four test regions in the study area (\(p<0.001\) for all of the studied years).
## Appendix B Calculation of Coefficients in the Expansion (39)  
The scalar product \(\langle\psi_{q,m}|\Psi_{n,m}(t)\rangle\) can be calculated with the help of Equation 7.414.4 from [4],  
\[I = \int_{0}^{\infty}x^{a}e^{-bx}L_{n}^{(a)}(\lambda x)L_{q}^{(a)}( \mu x)dx \tag{16}\] \[= \frac{(n+q+a)!(b-\lambda)^{n}(b-\mu)^{q}}{n!q!b^{n+q+a+1}}F\bigg{[} -n,-q;-n-q-a;\frac{b(b-\lambda-\mu)}{(b-\lambda)(b-\mu)}\bigg{]}.\]The Gauss hypergeometric function in the right-hand side is a polynomial of the order \(n_{<}=\min(n,q)\). One of possible forms is the expression in terms of the Jacobi polynomial (see Equation 4.22.1 from [59]):  
\[P_{n}^{(\alpha,\beta)}(x)=\frac{(2n+\alpha+\beta)!}{n!(n+\alpha+\beta)!}\left( \frac{x-1}{2}\right)^{n}F\left(-n,-n-\alpha;-2n-\alpha-\beta;\frac{2}{1-x} \right). \tag{54}\]  
Then, Formula (53) can be written as follows (assuming \(q\geq n\)),  
\[I=\frac{(q+|m|)!(b-\mu)^{q-n}(\lambda+\mu-b)^{n}}{q!b^{q+|m|+1}}P_{n}^{(q-n,|m|) }\left(\frac{b(\lambda+\mu-b)-2\lambda\mu}{b(b-\lambda-\mu)}\right). \tag{55}\]  
In our case, we have \(x=r^{2}\) and \(\lambda=|\omega|/\hbar\). Other parameters are as follows,  
\[b=\frac{1}{2\hbar}\left(|\omega|-\frac{i\dot{\omega}}{\epsilon}\right)=\frac{| \omega|u_{+}e^{i\phi}}{\hbar z},\quad\mu=\frac{|\omega|}{\hbar|z|^{2}},\qquad z =u_{+}e^{i\phi}+u_{-}e^{-i\phi},\]  
\[b-\lambda=-\frac{|\omega|u_{-}e^{-i\phi}}{\hbar z},\qquad b-\mu=\frac{|\omega |u_{-}^{-}e^{i\phi}}{\hbar z^{*}},\qquad\lambda+\mu-b=\frac{|\omega|u_{+}^{*}e ^{-i\phi}}{\hbar z^{*}}.\]  
The phase \(\phi\) was defined in Equation (21). Consequently, the argument of the Jacobi polynomial in Equation (55) equals  
\[\frac{b(\lambda+\mu-b)-2\lambda\mu}{b(b-\lambda-\mu)}=\frac{1-|u_{-}|^{2}}{1+ |u_{-}|^{2}}.\]  
Combining all terms in the Formulas (3), (6) and (55), we obtain the following expression for the complex coefficient \(\langle\psi_{q,m}|\Psi_{n_{r}m}(t)\rangle\) with \(q_{r}\geq n_{r}\):  
\[\langle\psi_{q,m}|\Psi_{n_{r}m}(t)\rangle = \left[\frac{(q_{r}+|m|)!n_{r}!}{(n_{r}+|m|)!q_{r}!}\Big{(}\frac{z }{z^{*}}\Big{)}^{2q_{r}+|m|+1}\right]^{1/2}e^{i\chi(t)-i\phi(t)(2n_{r}+|m|+1)} \tag{56}\] \[\times\frac{(u_{-}^{*})^{q_{r}-\hbar_{r}}\big{(}u_{+}^{*}\big{)} ^{n_{r}}}{u_{+}^{q_{r}+|m|+1}}P_{n_{r}}^{(q_{r}-n_{r},|m|)}\left(\frac{1-|u_{ -}|^{2}}{1+|u_{-}|^{2}}\right).\]  
Similar formulas (in a slightly different context) were found in paper [5]. All phase factors disappear when one calculates the module squared \(|\langle\psi_{q,m}|\Psi_{n_{r}m}(t)\rangle|^{2}\). Moreover, this quantity becomes symmetric with respect to quantum numbers \(q_{r}\) and \(n_{r}\). The result is given by Equation (40) of the main text.