esa-sceva/satcom-chunk-collection huggingface dataset:
# Record Linkage of Chinese Patent Inventors and Authors of Scientific Articles
## 2 Record Linkage Algorithm
### Generation of Features for Comparison
#### 2.1.2 Title and Abstract Comparison, Feature 6  
Due to the limited number of attributes shared between records in the database, we decided to search for similarities in titles and abstracts of both papers and patents, as neither full-text documents nor keywords were available. For that reason we have to transform the textual descriptions into numerical representations depicted below.  
Abstracts contain not only meaningful phrases but also a lot of irrelevant words. To eliminate noise, we propose an algorithm depicted in Figure 2, We use phrases that can be mapped to the knowledge base DBpedia, which structures content stored in Wikipedia. A four-step algorithm processed a list of DBpedia entries found in text: spotting surface form sub-strings in the text that may be entity mentions, selecting candidate DBpedia resources for those surface forms, choosing the most likely candidates and filtering.  
We found that some of the DBpedia resources were irrelevant. Because of this, we define and apply a set of filters aiming to remove all items that point to objects, events and concepts usually not covered by scientific papers. After filtration, there were \(266,361\) unique DBpedia resources extracted from the papers. These resources form a vocabulary. The resources found within patent titles and abstracts must be present in the vocabulary to be considered during comparison.  
We select only the relevant phrases from paper or patent, and they were converted into a numerical representation using the Bag of Words modeling, followed by TF-IDF weighting. After transformation, we represent each paper or patent as a fixed-sized vector of real numbers. We reduce the high dimensionality of this vector (266,361) using the truncated singular value decomposition (tSVD) to 300, as suggested in [33].  
The resultant vectors of length 300 created for each paper and patent were used as features in the record linkage pipeline and compared with each other using the Cosine similarity function, depicted in Equation (6), where \(D_{i}\) and \(D_{j}\) are concatenation of the titles and the abstracts of the \(i\)-th patent and the \(j\)-th paper, respectively, \(d_{i}=[w_{i,1},w_{i,2},...,w_{i,n}]\) and \(d_{j}=[w_{j,1},w_{j,2},...,w_{j,n}]\) are their vector representations obtained after the DBpedia resource extraction, TF-IDF weighting and reduction in dimensionality to \(n\) dimensions, \(W_{i}\) and \(W_{j}\) are \(L^{2}\) norms of vectors \(d_{i}\) and \(d_{j}\).  
\[sim_{cosine}(D_{i},D_{j})=1/\left(W_{i}W_{j}\right)\sum_{t=1}^{n}w_{i,t}\cdot w _{j,t} \tag{6}\]  
The Cosine similarity scores are computed between the patent vector and each paper vector related to the scholar; then the maximum of these scores is selected.
## 5 Results and Discussion
### Review of Evaluation Methods  
The study of the spatial distribution pattern of geological disasters and the evaluation of their susceptibility is a crucial study. With regard to the evaluation of the susceptibility and danger of landslide disasters, the existing landslide danger evaluation is still in the exploratory stage, and there has been extensive and in-depth research both at home and abroad, but there is no unified and complete understanding of the advantages and disadvantages of the various evaluation methods and a complete set of judgement standards has not been formed.  
The current assessment of landslide susceptibility is mainly assessed using qualitative and quantitative methods. Qualitative evaluation is fast and simple and can achieve rapid analysis and evaluation of geological disaster susceptibility. Its limitations are mainly subjectivity in determining the weights of the factors involved in the model, while the applicability of the model is also limited by geographical constraints and lacks repeatability, and the accuracy of the physical model is affected by the geotechnical parameters of individual slopes or the study area, which makes it suitable for the assessment of small scopes or individual hazards only. Mathematical modeling for geohazard risk assessment requires accurate multivariate data such as elevation, slope, slope direction, relief, distance from faults, engineering geological rock group, land use type, multi-year average rainfall, and distance from rivers. So, differences in data selection and element hierarchy have a large impact on the output. The single evaluation model has a frequency ratio model, right of evidence model, and informativeness model; among them, the informativeness  
Figure 14: ROC curve of the information quantity model.  
method obtains the evaluation factor weights by statistically analyzing the conditional probability. The evaluation is more objective, and the calculation results are easy to be understood in the evaluation of the susceptibility to geologic hazards has been widely used, and the evaluation process avoids the conversion of judgement, which is better in line with reality [32].  
Through years of application practice, it is known that the above modeling methods have a certain degree of reliability, can accurately predict the occurrence of landslide geological hazards, and can greatly promote the construction of quantitative spatial early warning of landslide hazards, which is widely used in the evaluation of the susceptibility to geological hazards in Tekesi County and in engineering practice.
# 400 GbE Technology Demonstration Using CFP8 Pluggable Modules
## 2 400 GE Pluggable Module and Host Card  
Figure 5 below shows the 400 GE CFP8-LR8 pluggable optical transceiver and the block diagram of the physical interface card (PIC) hosting the 400 GE pluggable module. The total data rate is 425 Gbit/s, due to the overhead of the FEC block and the 64B/66B mapping in the physical coding sublayer (PCS). The data are carried with 8 optical channels modulated using PAM4 signal running at 53.125 Gbit/s (8 \(\times\) 50G PAM4 WDM optical channels). The electrical interfaces (CDAUI-16) are 16 pairs of differential lanes running at 26.5625 Gbit/s (16 \(\times\) 25G electrical NRZ signals). The CFP8-LR8 uses the distributed feedback laser (DFB) as the transmitter and the PIN photodiode (p-type, intrinsic, n-type photodiode) as the receiver. The total power consumption is less than 16 W. Within the CFP8-LR8 module, a gearbox integrated circuit (IC) multiplexes two electrical lanes of NRZ signal into one optical lane of pulse amplitude modulation (PAM) signal. Gray mapping is implemented so that there is only a one bit difference between the adjacent levels to minimize the BER.  
Juniper's PIC hosts the PFE which forwards the IP packet based on the routing table generated from the routing engine (RE). The additional interfaces, like the bridge, the media access control (MAC) and the PCS layer are implemented with a field programmable gate array (FPGA). The control and management function is implemented in FPGA as well. One PIC can host three pluggable optical modules, giving a total capacity of the PIC of 1.2 Tbit/s.  
Figure 6 shows the experimental setup to demonstrate the interoperation of CFP8-LR8 modules. We use an optical network tester (ONT, Viavi) to generate a CDAUI-16 signal. One CFP8-LR8 module  
Figure 4: Typical response curve of the continuous time-domain linear equalizer (CTLE) as defined in Reference [4] in 0.5 dB step size.  
Figure 5: (**a**) Picture of 400 GE CFP8-LR8 pluggable optical transceiver; (**b**) configuration of physical interface card (PIC). MAC: media access control, PCS: physical coding sublayer, FPGA: field programmable gate array, PFE: packet forwarding engine, CDAUI-16: 400G attachment unit interface, running at 16 lanes with 26.5625 Gb/s per lane.  
is inserted into the ONT to perform electrical-to-optical conversion. The optical signal goes through a variable optical attenuator (VOA) and is received by a second CFP8-LR8 module, which is hosted by a Juniper's PIC. Within the PIC, the 400 GE signal is electrically looped back to the transmitter of the second CFP8-LR8 module. The signal either bypasses or goes through a single mode fiber (SMF) spool. The signal is then received by the CFP8-LR8 from the first module, and the ONT counts the BER.
## 4 Analysis and Results
### Model Interpretation
#### 4.4.2 SHapley Additive exPlanation (SHAP)  
The summary plots for the random forest classifier computed using the SHAP technique are shown in Figure 9. In terms of the overall importance of the features, 'Contract', 'Number of Referrals', 'Tenure in Months', 'Monthly Charge', and 'Online Security' are the five factors that make the most contribution to the prediction outcomes of the Random Forest classifier. To be specific, high values of 'Contract', 'Number of Referrals', 'Tenure in Months', and 'Online Security' increase the possibility of customer churn predicted by the classifiers, while the low value of 'Monthly Charge' increases the possibility.  
Figure 8: Local explanation for (**a**) the first sample (**upper**) and (**b**) the second sample (**lower**) of the Random Forest classifier.  
In addition, to provide a global explanation, SHAP values can also be used to explore the prediction made for an individual sample. Therefore, this paper computes the SHAP explanation for the same two samples, as investigated using LIME in Section 4.4.1, and conducts a comparison.  
According to Figure 10, the most significant factors for the first customer sample that push the prediction toward churning are 'Payment Method', 'Age', and 'Monthly Charge', and the ones that push the prediction toward not churning are 'Contract', 'Online Security', 'City', and 'Number of Referrals'. Although 'Age' is not included in the top 10 groups of LIME, LIME and SHAP are able to corroborate each other's judgment about the significance and direction of the factors for the first sample. For the second customer sample, as shown in Figure 10, SHAP shows that the 'Number of Referrals', 'Contract', 'Number of Dependents', and 'Monthly Charge' are the most significant factors that push the prediction toward churning, which is consistent with LIME explanation as well.  
Figure 10: SHAP explanation for (**a**) the first sample (**upper**) and (**b**) the second sample (**lower**) of the Random Forest classifier.  
Figure 9: Summary plots for Random Forest classifier.  
In summary, the results of the model explanations presented by the LIME and SHAP techniques, respectively, are consistent. By showing decision-makers in the telecom industry how customer-related factors affect customer retention through these interpretation figures, decision-makers can understand how AI makes predictions in a way they understand, even if they do not understand the complex algorithms of AI.
# A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data
## 2 Data
For model training and testing, the processed data are divided into a training set, validation set, and test set. Among them, the temperature data from 01:00 on 1 January 2012 to 24:00 on 31 December 2020 local time are the training set and validation set with random assignment in a ratio of 8:2, and the temperature data from 01:00 on 1 January 2021 to 24:00 on 31 December 2022 local time are the test set. After removing 14 invalid Macau real temperature values, the training set contains 63,119 samples, the validation set contains 15,780 samples, and the test set contains 17,519 samples. Figure 1: Map of Macau surrounding areas. The location of Macau (the red point), and the coverage of ERA5 reanalysis data (the black rectangle).
# Reliable Positioning and mmWave Communication via Multi-Point Connectivity +
## 2 System Model
### Performance Metrics
#### 2.3.2 Effective Throughput  
It should be noted that, as each BS is equipped with a single baseband processing circuit, only one analog beam can be generated in a given direction. Furthermore, the beam remains fixed for the complete data reception phase \(T_{d}\). Therefore, aligning the directional beam towards one particular user will results in comparatively poor SNR to all other active users. However, to efficiently utilize the full-cooperation gain, we need to provide comparable SNR to all active users. To do that, we first obtain a compromise receive beam \(\bar{\mathbf{w}}_{b}\)\(\forall b\) by the superposition of best receive beams of each user and that can obtained by applying appropriate phase-shifts and amplitude scaling to each antenna element, given by  
\[\bar{\mathbf{w}}_{b}=\sum_{k}\mathbf{w}_{b}^{(k)}. \tag{5}\]  
where \(\mathbf{w}_{b}^{(k)}\) denotes the best receive beam for \(k\)th user from \(b\)th BS measured during the training phase \(T_{t}\). It should be noted that, forming such superposition beam in the analog domain requires both amplitude and phase scaling per antenna element. Therefore, unlike in the plain single beam DFT beamforming, the superposition beams, in general, may not satisfy the uni-modulus constraints on beamforming coefficients. However, uni-modulus constraint can be imposed using the Kronecker decomposition [26], which is left for the future work. The received signal of \(k\)th user is given by  
\[y_{k}^{d}(t) =\sum_{b\in\mathcal{B}}f_{b,k}\bar{\mathbf{w}}_{b}^{\mathrm{H}} \mathbf{h}_{b,k}x_{k}^{d}(t-\tau_{b,k})+\sum_{b\in\mathcal{B}}\sum_{k^{\prime} \in\mathcal{K}\backslash k}f_{b,k}\bar{\mathbf{w}}_{b}^{\mathrm{H}}\mathbf{h} _{b,k^{\prime}}x_{k^{\prime}}^{d}(t-\tau_{b,k^{\prime}})+\sum_{b\in\mathcal{B} }f_{b,k}\bar{\mathbf{w}}_{b}^{\mathrm{H}}\mathbf{n}_{b}(t)\, \tag{6}\] \[=\mathbf{f}_{k}^{\mathrm{H}}\bar{\mathbf{h}}_{k}x_{k}^{d}(t-\tau_{ b,k})+\sum_{k^{\prime}\in\mathcal{K}\backslash k}\mathbf{f}_{k}^{\mathrm{H}} \bar{\mathbf{h}}_{k^{\prime}}x_{k^{\prime}}^{d}(t-\tau_{b,k})+\mathbf{f}_{k}^{ \mathrm{H}}\bar{\mathbf{n}}(t)\, \tag{7}\]  
Figure 2: Communication over the frames of duration \(T_{f}\), each with a training phase of duration \(T_{t}\). where the effective channel \(\tilde{\mathbf{h}}_{k}=[\tilde{h}_{1,k},\tilde{h}_{2,k},...,\tilde{h}_{B,k}]^{\rm T}\), in which \(\tilde{h}_{b,k}\in\mathbb{C}\) is obtained by concatenation of analog beamformer and actual channel. Entries of the effective channel are defined as  
\[\tilde{h}_{b,k}=\tilde{\mathbf{w}}_{b}^{\rm H}\mathbf{h}_{b,k}\,\ \forall k,\ \forall b. \tag{8}\]  
Similarly, \(\tilde{\mathbf{n}}(t)=[\tilde{n}_{1}(t),\tilde{n}_{2}(t),...,\tilde{n}_{B}(t) ]^{\rm T}\), in which \(\tilde{n}_{b}(t)=\tilde{\mathbf{w}}_{b}^{\rm H}\mathbf{n}_{b}(t)\,\forall b\). Finally, \(\mathbf{f}_{k}=[f_{1,k},f_{2,k},...,f_{B,k}]\) is user specific digital post-combiner.
## 3 Validation and Numerical Analysis
### Time-Varying Effect of the Rotating Shift Material and Outer Diameter  
In order to analyze the time-varying effect with different rotating shaft materials, three cases of shaft material are discussed in this section. Case 1 is for steel, case 2 is for copper, and case 3 is for aluminum alloys. Table 6 lists the corresponding rotating shaft material parameters. Figure 7 presents the time histories for the helix angle, transverse pressure angle, gear pair center distance, and the total contact ratio. The difference values between the time-varying values with different rotating shaft materials and the constant values can be observed. If the rotating shaft is softer, the difference values between the time-varying values and the constant values are enhanced. This indicates that the time-varying effect has more influence on soft rotating shaft material than on hard rotating shaft material.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Initial Helix Angle (Degree)** & **15** & **20** & **25** & **30** & **35** \\ \hline \multicolumn{4}{c}{**The Difference Values between Time-Varying and Constant**} \\ \multicolumn{4}{c}{**Conditions**} \\ \hline Helix angle & 0.061 & 0.0793 & 0.0954 & 0.1092 & 0.1203 \\ Transverse pressure angle & 0.666 & 0.671 & 0.675 & 0.681 & 0.686 \\ Gear pair center distance & 0.2218 & 0.2243 & 0.2265 & 0.2295 & 0.2332 \\ Total contact ratio & 0.0125 & 0.01696 & 0.02173 & 0.02689 & 0.03264 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The difference values between the time-varying and the constant conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Steel** & **Copper** & **Aluminum Alloys** \\ \hline Modulus of elasticity (Gpa) & 207.8 & 115 & 73 \\ Density of material (kg/m\({}^{3}\)) & 7806 & 8900 & 2800 \\ Poisson’s ratio & 0.3 & 0.35 & 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Corresponding rotating shaft material parameters.  
Figure 7: Time-varying and constant (**a**) helix angle, (**b**) transverse pressure angle, (**c**) gear pair center distance, and (**d**) total contact ratio under different rotating shafts.  
The same issues as Figure 7 under three different outer diameters of the shaft are obtained and shown in Figure 8. From Figure 8, it can be seen that the difference values between the time-varying values of these dynamic characteristics and the constant values are reduced with the outer diameter of the shaft enhanced. It may be noted that the time-varying effect has a more important effect on the thinner shaft.
## 6 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Configuration**} & **Theoretical Volume of Defects (cm\({}^{3}\))** & **Theoretical Surface Area of Defects (cm\({}^{2}\))** & **SDI (\%)** & **Experimental Volume of Defects (cm\({}^{3}\))** & **SDI \({}^{*}\) (\%)** \\ \hline C1 & 22.72 & 32.71 & 40.45 & 23.81 & 42.4 \\ \hline C2 & 6.25 & 7.54 & 11.13 & 6.77 & 12.05 \\ \hline C3 & 11.37 & 18.78 & 20.24 & 12.32 & 21.94 \\ \hline C4 & 25.36 & 30.77 & 45.16 & 26.35 & 46.92 \\ \hline \hline \end{tabular} (*) Mean value.  
\end{table} 
Table 5: Theoretical values and experimental average values of the SDI for each configuration. the measurements of the SDI and the volume of defects are consistent and reliable, which enhances the validity and reliability of this study's results. This strong linear relationship implies a direct dependence between the SDI and the volume of defects. In other words, as the volume of defects increases, the SDI increases proportionally. For example, if the surface defects increase in surface area or depth, their volume will also increase proportionally, which reflects the linear relationship between the SDI and the volume of defects. The observed linear relationship between the SDI and the experimental volume of defects suggests consistency in measurements and a direct correlation between surface quality and the volume of defects and, consequently, between surface quality and the SDI. These findings are consistent with previous studies [5; 6], which revealed a strong relationship between the surface area, the depth of the defects, and the quality of surfaces. These defects may allow the accumulation of contaminant agents and promote the agglomeration of microorganisms that can compromise the structural integrity of the concrete and detract from its aesthetic appearance. According to these criteria, the SDI can be employed to classify the levels of surface quality. These classifications provide an insight into the quality of the sample surfaces, where a low SDI indicates a higher surface quality due to the low defect volume, while a high index is associated with lower surface quality due to the high defect volume. Therefore, Configuration C2, with the lowest SDI, represents the highest quality, while Configuration C4, with the highest index, indicates the lowest surface quality.
# Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles
## 11 Conclusions  
Before the introduction of any attacking nodes, the biggest difference in the results was between the Maxprop and Spray and Wait protocols. The biggest trade-off was between speed and probability of delivery. In a scenario where speed was more important than delivery and there was an unlimited number of resources, Spray and Wait would be better suited. This would most likely be in a scenario where if the data delivery was slow then it would be useless, such as information about a traffic jam on a motorway. In that scenario, if a node received the data too slowly, then the likelihood was that they would already be a part of the jam so it would be a pointless message. In scenarios where delivery probability was more important than speed of receipt, MaxProp would be the preferred protocol. The PRoPHET algorithm never outperformed the two protocols, however it could provide a happy medium between the two. This meant that it could be used in situations whether neither of the others were suitable. For example, if a protocol is needed that was faster than Maxprop but not as resource-heavy as Spray and Wait, it would be ideal, provided the use case allows for a lower delivery probability than either of the others.  
All of the protocols were affected by the introduction of black-hole nodes into the network. Spray and Wait is more vulnerable to the attacks as delivery probability reduces at a slightly faster rate than MaxProp. PRoPHET appears to deliver more messages with an increased number of black holes suggesting that it is more resistant to the attacks. This is probably due to the methodical way the protocol replicates messages, so reducing the traffic in the network is beneficial. The metric that was most altered during the experiment was the buffer time of the Spray and Wait algorithm. This was one of a few trend lines that were not linear. The quadratic curve meant that if even more black holes were introduced into the network then the rate of increase would continue to get increasingly larger, until eventually it would become unmanageable. This contrasts sharply with Maxprop and PRoPHET where the buffer time was unchanged by the presence of black holes, meaning the protocols were more resilient to black-hole attacks from both a delivery probability and (in particular) a latency perspective. As PRoPHET demonstrated the highest resilience to attacks, in an application where it was highly likely to undergo attack, it should be the protocol of choice. As in this experiment, the network is safety-critical; it is imperative that the network performs predictably. In this instance, the PRoPHET protocol should be used with messages being sent multiple times in order to increase the delivery probability. This would make the network more likely to deliver messages when performing normally and under attack.