esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Fusion Network vs. Shallow Network vs. Deep Network  
The proposed fusion network comprises two networks: a shallow network and a deep network. We conducted experiments to investigate the performance improvement caused by these two networks. In the design of the deep network, all features go through the deep network, namely the right network of the fusion network, as presented in Figure 4. On the other hand, the shallow network uses the inputs with all features to go through the left network of our proposed architecture, as shown in Figure 4.  
Figure 6 shows the experimental results measured by MSE and MAE, which indicate that the shallow network achieved superior performance to the deep network. This is consistent with our intuition, as a shallow network can directly digest important features, whereas a deep network aims at learning feature embeddings from the remaining features. Although deep neural networks have achieved promising results in many applications, a deeper deep network does not guarantee better performance [41]. Furthermore, the experimental results showed that the proposed fusion network outperformed the other two networks, meaning that the proposed method benefited from the characteristics of the two networks to further improve performance. The fusion network enabled features with different characteristics to go through different networks. A deep network can use a deep neural network to perform a series of nonlinear transformations to learn important feature embeddings from raw data. On the contrary, a shallow network can retain domain knowledge or important features. The combination of these two features can initiate a fusion process by using subsequent layers, which explains why the proposed fusion network yielded promising results.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Methods** & **MSE** & **MAE** \\ \hline [PERSON] et al. [28] & 7.07 & N/A \\ [PERSON] et al. [38] & 6.62 & N/A \\ [PERSON] et al. [39] & 6.72 & N/A \\ Random Forest & 6.69 & 1.96 \\ Gradient Boosting Regressor & 6.78 & 1.96 \\ XGBoost & 6.66 & 1.94 \\
**Our proposed method** & **6.36** & **1.87** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results on the 2016 PHM dataset.  
Figure 6: Performance of different network architectures, where a lower value is better.
## 2 Materials and Methods
### Bias Minimization
#### 2.3.2 Methods for Bias Reduction  
As in many studies, all histological examinations in this study are rater blinded, i.e., the central study neuropathologist is unaware of declarations regarding assumed tissue type by surgeons and fluorescence data. The histology of biopsy samples (meningioma or normal tissue) is used as the \"truth standard\" for the determination of diagnostic performance and diagnostic accuracy of 5-ALA-induced PpIX fluorescence. Blinding of pathologists, however, will not overcome biases intentionally or non-intentionally introduced by the unsupervised surgeon.  
Therefore, this study, for the first time in the context of a study for intra-operative fluorescence imaging, employs a completely independent review panel (\"biopsy review panel\") of peer neurosurgeons who are unaware of each other's identities and who are unfamiliar with any of the study surgeons or the study's principal investigators. The reviewers, whose role it is to make their own assessments of tissues and fluorescence status, are also blinded to any of the surgeons' intraoperative assessments. Importantly, data for the pivotal analyses will be derived from the assessments of the reviewers.  
Study reviewers are supplied with various segments of the videos acquired during surgery in a random order, with video segments from a single case not being reviewed en bloc by a single reviewer but rather being split and distributed among reviewers. Regarding additional data, reviewers are acquainted with the patient's age, whether the patient has a recurrent or de novo tumor, the orientation of the patient in space, and, for each segment, the neuronavigation screenshot correlating with the video segment or any biopsies.
# Deep Learning Framework for Advanced De-Identification of Protected Health Information
## 4 Results and Discussion
### Bi-LSTM-CRF Model
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Categories** & **Precision P\%** & **Recall R\%** &
\begin{tabular}{c} **F1-Score** \\ **F1\%** \\ \end{tabular} & **Support** \\ \hline B-Age & 0.91 & 0.83 & 0.87 & 759 \\ B-City & 0.27 & 0.36 & 0.31 & 69 \\ B-Country & 0.33 & 0.14 & 0.20 & 36 \\ B-Date & 0.46 & 0.71 & 0.56 & 1440 \\ B-Doctor & 0.51 & 0.69 & 0.59 & 986 \\ B-Hospital & 0.41 & 0.50 & 0.45 & 359 \\ B-Location Other & 0.00 & 0.00 & 0.00 & 7 \\ B-Medical Record & 0.00 & 0.00 & 0.00 & 1 \\ B-Organization & 0.00 & 0.00 & 0.00 & 30 \\ B-Patient & 0.53 & 0.43 & 0.47 & 393 \\ B-Phone & 0.00 & 0.00 & 0.00 & 7 \\ B-Profession & 0.28 & 0.22 & 0.25 & 85 \\ B-State & 0.35 & 0.10 & 0.16 & 170 \\ B-Street & 0.62 & 0.67 & 0.65 & 89 \\ B-Username & 0.00 & 0.00 & 0.00 & 1 \\ B-ZIP & 0.00 & 0.00 & 0.00 & 0 \\ I-Age & 0.00 & 0.00 & 0.00 & 1 \\ I-City & 0.33 & 0.09 & 0.14 & 47 \\ I-Country & 0.00 & 0.00 & 0.00 & 7 \\ I-Date & 0.85 & 0.73 & 0.78 & 492 \\ I-Device & 0.00 & 0.00 & 0.00 & 2 \\ I-Doctor & 0.62 & 0.63 & 0.62 & 760 \\ I-Health Plan & 0.00 & 0.00 & 0.00 & 0 \\ I-Hospital & 0.83 & 0.45 & 0.59 & 634 \\ I-ID\_Num & 0.00 & 0.00 & 0.00 & 0 \\ I-Medical Record & 0.00 & 0.00 & 0.00 & 21 \\ I-Organization & 0.00 & 0.00 & 0.00 & 23 \\ I-Patient & 0.47 & 0.24 & 0.32 & 269 \\ I-Phone & 0.00 & 0.00 & 0.00 & 1 \\ I-Profession & 0.65 & 0.24 & 0.35 & 118 \\ I-Street & 0.82 & 0.61 & 0.70 & 193 \\ Other & 1.00 & 0.99 & 0.99 & 287,426 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Evaluation findings for each of the 23 PHIA categories using the Bi-LSTM-CRF model. categories like \"DATE\", \"HOSPITAL\", and \"STREET\". These entities often have more structured patterns and contain more tokens, which makes it easier for the model to capture internal tokens efficiently. Figure 6: Support in each category using Bi-LSTM-CRF. (**a**) Support in each category using Bi-LSTM-CRF. (**b**) Support in each category using Bi-LSTM-CRF except the other category. Figure 7: Evaluation results for tags with support greater than 400. For most of the 23 evaluated categories, the CRF model outperforms the Bi-LSTM-CRF model, which has shown promising performance across most of the 23 evaluated categories. However, its effectiveness is influenced by the need for a larger dataset to leverage its complex architecture and hyperparameter tuning fully. While the CRF model has a simpler architecture and is generally less prone to overfitting, the Bi-LSTM-CRF model requires a larger volume of data to maximize its potential. Although the Bi-LSTM-CRF has been carefully optimized and tuned, expanding the dataset would allow for even more effective performance improvements and better utilization of the model's capabilities.
## 2 The Mathematical Modeling of OPF Objective Function
### Objective Function
#### 2.3.4 Minimization of Fuel Cost, Active Power Loss, and Voltage Drops  
As mentioned earlier in the paper, the three mentioned objectives are all essential for solving OPF. Therefore, integrating two or more targets inside the same objective function is a desirable goal. The multi-objective function \(F_{4}\) is utilized to optimize the fuel cost, power loss and voltage drop together in order to solve the optimal power flow. The mathematical modeling of the multi-objective function is mentioned below in Equation (24):  
\[F_{4}(x,y)=Min\big{\{}F_{1}(x,y)+W_{p}F_{2}(x,y)+W_{v}F_{3}(x,y)\big{\}} \tag{24}\]  
where \(F_{1}(x,y)\) is the fuel cost function, \(F_{2}(x,y)\) is the active power loss function, \(F_{3}(x,y)\) is the voltage drop function, \(W_{p}\) and \(W_{v}\) are suitable weighting factors for power loss and voltage drop, respectively.
# A Systematic Review of the Trends and Advances in IFC Schema Extensions for BIM Interoperability
## 6 Discussion
### Summary of the Results and Suggestions for Future Work
#### 6.1.4 Research Gaps, Implications, and Future Steps  
Although a significant number of studies have addressed the needs of the industry, there are still many gaps in each domain that are not properly represented. The infrastructure domain provides a poignant example. As shown in Table 3, most developments were biased towards bridges and tunnels. Even within these sectors, only subsectors (e.g., drainage, a subsidiary facility for bridges [68; 69; 70; 71; 72; 73]) have been partially modeled. Othersectors, such as dams and harbors, have even more limited representations (e.g., 3D tiles and quay walls [18; 92]). This is also true for processes, where the extensions created for project management and construction methods are only applicable to specific subsectors. This is because the studies have been developed separately in an ad hoc and haphazard fashion. Thus, future developments need to be organized more systematically for IFCs to be accepted and used across the full AEC spectrum.  
Specifically, we recommend the following directions for future works:  
In the architectural domain, given the extensive entity definitions already in place for products, the primary emphasis should remain on process representations. However, as new technologies and advanced building systems emerge, related products and their processes will also need to be represented into the schema. Such needs should see an increasing shift towards 'entity and property extensions'. Furthermore, the extension for these new technologies would need to be validated more rigorously using 'utility-centric' validation approaches to justify their inclusion in future schemas upgrades.  
For the infrastructure domain, additional product representations are required for less addressed sectors, such as dams, harbors, plants, river facilities, etc. More importantly, a systematic and holistic approach needs to be employed, preferably by bSI's technical committee to develop a long-term strategy and roadmap to fill in the gaps of this domain. Moreover, processes related to design, project management, and inspection should also be developed for general use throughout the entire domain and independent of specific sectors as much as possible.
# A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
## 3 Advanced Infrastructure for DC Fast Charging for Electric Vehicles
### Charging Standards
#### 3.1.4 Power Level and Charging Speeds  
The power levels and charging speeds enabled by the infrastructure are defined by charging standards. DC fast charging power levels can vary from 50 kW to several hundred kW, facilitating rapid charging durations [127]. Guidelines have established the upper limits for power and voltage to ensure safe and efficient charging procedures.  
Table 4 provides a comprehensive overview of the specifications for commercial Plug-in Hybrid Electric Vehicles (PHEVs), Fuel Cell Electric Vehicles (FCEVs), and Extended-Range Electric Vehicles (E-REVs). Key details encompass a variety of vehicle models, classification by powertrain type, battery capacity measured in kilowatts (KW), driving range expressed in kilometres (KM), and connector types required for charging.
## 4 Conclusions  
Bis (2-hydroxyethyl)ammonium erucate protic ionic liquid was synthesized in this study. The relatively simple acid-base neutralization reaction at 100 \({}^{\circ}\)C for 24 h was carried out, forming tertiary amine salt. As a result, a dark yellow, very viscose liquid was obtained. The bis (2-hydroxyethyl)ammonium erucate was used as a neat lubricant and as a lubricity-enhancing additive in water. The following conclusions could be drawn:  
1. Synthesized PIL has exceptionally high viscosity at room temperature, which sharply drops with increased temperature. Therefore, a low viscosity index of 88.6 was observed.
2. Bis (2-hydroxyethyl)ammonium erucate has good solubility in water. Therefore, dissolving up to 3% of PIL in this base fluid leads to a homogeneous solution, which shows no sedimentation or cloudiness.
3. Using bis (2-hydroxyethyl)ammonium erucate as a neat lubricant or an additive resulted in a low coefficient of friction and low wear. The adsorbed layer, temperature-induced tribo-reaction layer, and metal soap formation are suggested to be responsible for that.
4. By comparing the results of previous studies, it was found that a longer alkyl chain erucic acid anion provides an ionic liquid with better tribological properties. However, the optimum concentration of bis (2-hydroxyethyl)ammonium erucate should be determined to perform best when used as an additive in water base lubricating fluids.  
Conceptualization, R.K.; Investigation, M.G., A.K. and J.T.; Methodology, R.K. and A.K.; Project administration, R.K.; Resources, E.S.; Supervision, E.S. and R.K.; Validation, R.K., M.G. and A.K.; Visualization, J.T., R.K. and M.G.; Writing--original draft, R.K. and E.S.; Writing--review and editing, R.K. and E.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by grant No. S-MIP-21-61 from the Research Council of Lithuania.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results and Discussion  
In this paper, the data of Well B-1 and Well B-2 in Tarim Oilfield were used to train, verify, and test the intelligent evaluation model of cementing quality. A total of 5500 pieces of data were used, 70% of which were randomly selected for training and verification, and 30% of which were used for model testing. The model was trained by an Adam optimizer. Compared with the traditional SGD algorithm, the Adam optimizer can adaptively adjust the learning rate during the training iteration process, significantly improving the learning speed of the model [21]. Based on the parameters set by the neural network in Section 3.2, the model was built and trained on the platform.  
The model training results are shown in Figure 10. After 40 epochs, the identification accuracy of the intelligent evaluation model for cementing quality reached 90%, as shown in Figure 10a, meeting the engineering requirements. However, to further improve the accuracy of the model, this paper optimized the model in terms of the network structure.
# Energy-Saving Routing Protocols for Smart Cities
## 2 Materials and Methods
### Related Works
#### 2.1.1 LPWAN  
Currently, the most widely adopted wireless communication technologies for IoT and WSN applications are Low-Power Wide-Area Network (LPWAN), 3G/4G/5G cellular networks, and ZigBee. LPWANs have gained prominence compared to the alternatives, as they feature low power consumption and transmission over very long distances. The main LPWAN wireless communication technologies are LoRa (Long Range), Sigfox, NB-IoT (Narrow-Band IoT), and Wi-SUN (Wireless Smart Ubiquitous Network).
## 2 Project Overview
### Hydrogeological Conditions  
Table 1 shows the basic mechanical parameters of the soil within the site, and Figure 2 shows the distribution of soil within the foundation pit and the construction of some structures within the foundation pit. The base of the foundation pit is located in the recently deposited fine sand layer. According to the geological survey report of the project, the depth of the foundation pit project is within the depth range of 80.0 m, which mainly includes plain filled soil, clay silt, sandy silt, silty clay, clay, and a sand soil layer. The groundwater directly affected by foundation pit construction is mainly deposited in the fine sand and medium sand (partially containing gravel) layers within the depth of about 40 m. The groundwater type is diving, and the water level buried depth is 6.43\(\sim\)15.51 m. The groundwater type is confined water in the fine sand and medium sand layer within the depth of 45-50 m. The water level buried depth is 11.62\(\sim\)17.40 m. The vacuum dewatering pipe well is used to dewater the foundation pit before excavation to ensure that the soil at the bottom of the pit meets the stability requirements of bearing water resistance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Assise**} & **Elasticity** & **Natural** & **Saturated** & **Cohesion** & **Frictional** & **Modulus of** \\  & & **Modulus** & **Weight** & **Unit Weight** & **(KPa)** & **Angle** & **Unloading** \\  & & **(MPa)** & **(KN/m3)** & **(KN/m3)** & **(KNa/m3)** & **\(\Phi\)**(\({}^{\circ}\))** & **(MPa)** \\ \hline \multirow{4}{*}{**COhesion**} & **Miscellaneous fill** & 4.14 & 18.1 & 18.74 & 0 & 10 & 12.42 \\  & **clay** & 6.31 & 18.6 & 19.08 & 25 & 25 & 18.93 \\  & **Fine sand** & 40 & 18.6 & 22.16 & 0 & 28 & 120 \\  & **Silty clay** & 11.7 & 19.9 & 23.40 & 10 & 30 & 35.1 \\  & **Fine sand** & 45 & 19 & 20.12 & 28 & 25 & 135 \\  & **clay** & 14.6 & 19 & 20.00 & 25 & 22 & 43.8 \\  & **Sandy soil** & 26.2 & 19.4 & 19.84 & 0 & 30 & 78.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic physical and mechanical parameters of soil layer.  
Figure 1: Foundation pit plan and monitoring point layout.
# Formal Analysis of Ratchet Protocols Based on Logic of Events
## 2 Logic of Events Theory  
The Logic of Events theory (LoET) is a formal verification method that models protocols or specifies their required properties to determine whether these properties hold within the protocol model. It helps identify security vulnerabilities that are difficult to detect using other approaches. LoET defines core elements such as identifiers, atoms, and unpredictable data (e.g., nonce, signature, ciphertext, and encryption key), as well as event, event class, and event structure. These components support the construction of authentication theories used to prove strong authentication properties in security protocols.
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 3 GCS-Routing Design
### Routing Loops in GCS-Routing  
This section analyzes routing loops in GCS-routing. We first consider a simple example where a transient loop is formed and show how the network becomes loop-free in GCS-routing. Subsequently, we theoretically prove GCS-routing to be loop-free.
## 3 Results and Discussion  
### Experimental Results of Deformed Steel Bars  
The experimental results of the D13 deformed steel bar test showed that the maximum load was 52.2 kN when the bond length was 50 mm and 76.1 kN when it was 100 mm. In addition, the average bond strength was found to be approximately 25.4 MPa when the bond length was 50 mm. When the 50 mm bond length was tested, all three specimens exhibited pull-out failure due to cracking in the rebar and bonded surface. In contrast, the two specimens with a bond length of 100 mm exhibited tensile failure, in which the rebar yielded first, instead of pull-out failure. The experimental results of the D13 deformed steel bar are shown in Table 8.  
The experimental results of the D16 deformed steel bar tests show that the maximum load was 64.5 kN when the bond length was 50 mm and 95.0 kN when it was 100 mm. The average bond strengths were approximately 23.7 and 18.4 MPa when the bond lengths were 50 and 100 mm, respectively. Two specimens with a 50 mm bond length exhibited splitting failure of concrete, and one specimen showed pull-out failure. All three specimens with a 100 mm bond length exhibited concrete splitting failure, not pull-out failure. The experimental results of the D16 deformed steel bar test are shown in Table 9.  
Figure 4: Fabrication of specimens for unbonded sections [2]. (**a**) Hose insertion for an unbonded section; (**b**) Specim fabrication.  
Figure 5: GFRP hybrid rebar specimens and experiment setup [2]. (**a**) GFRP hybrid rebar specimens; (**b**) Experiment setup.  
The experimental results of the D19 deformed steel bar tests showed that the maximum load was 65.3 kN when the bond length was 50 mm and 103.7 kN when it was 100 mm. In addition, the average bond strengths were approximately 25.0 and 15.8 MPa when the bond lengths were 50 and 100 mm, respectively. In the case of a 50 mm bond length, two specimens exhibited concrete splitting failure, while one specimen showed rebar pull-out failure and splitting failure simultaneously. Similarly, for the 100 mm bond length steel bars, two specimens exhibited splitting failure and one specimen showed both pull-out and splitting failure. The experimental results of the D19 deformed steel bar are shown in Table 10.
# Classification of Marine Vessels with Multi-Feature Structure Fusion
## 3 Multi-feature Structure Fusion Based on Linear Discriminant Analysis
### Structure Fusion Mechanism  
Structure fusion [24] means that the structures (the similarity measure is used to represent the internal structure of the feature) of different features are merged by the algebraic optimization. The combined feature is mapped onto a new structure-fusion feature by the mapping matrix under consideration with a fusion structure. Therefore, the literature [24] has proposed a structure fusion method based on a locality-preserving projection (SFLPP).  
\(X_{1}=[x_{11},x_{12},\ldots,x_{1m}]\) and \(X_{2}=[x_{21},x_{22},\ldots,x_{2m}]\) are the high-dimensional feature sets of the multi-feature, where \(x_{1i}\in R^{D_{1}}\) and \(x_{2i}\in R^{D_{2}}\)\((i=1,2,\ldots,m)\). These feature matrixes are then combined into \(X_{3}=[x_{31},x_{32},\ldots,x_{3m}]\) and \(x_{3i}=\left[\begin{array}{c}x_{1i}\\ x_{2i}\end{array}\right]\in R^{D_{1}+D_{2}}\). The internal structure of the features \(W_{k}=\left\{W_{kj}\right\}\)\((k=1,2;i=1,2,\ldots,m;j=1,2,\ldots,m)\) is measured by the similarity measure, and they are calculated by the \(\chi^{2}\) metric distance as formula (11); the specific formula of the \(\chi^{2}\) metric is described in the literature [24].  
\[W_{kij}=\left\{\begin{array}{l}e^{-d(x_{ki};x_{kj})/\alpha_{k}},\quad x_{ki }\text{ and }x_{kj}\text{ is neighbor}\\ 0\,,\quad x_{ki}\text{ and }x_{kj}\text{ is not neighbor}\end{array}\right. \tag{11}\]  
\(W_{1}=\left\{W_{1 ij}\right\}\) or \(W_{2}=\left\{W_{2 ij}\right\}\) respectively is a single-feature structure. The structure of the combined feature \(X_{3}\) can be represented as \(W=W_{1}+W_{2}\). The literature [24] demonstrates that \(W\) has the same characteristics as \(W_{1}\) and \(W_{2}\) due to their linear relationship, so \(W\) can indirectly represent the internal structure of the combined feature \(X_{3}\).  
By performing a specific optimization solution on the combined feature \(X_{3}\) and its internal structure \(W\), the combined feature can be mapped into a new structure fusion feature. More details can be found in [24] and [25], the former of which refers to the optimized formula of LPP. Since LPP is an unsupervised method, the category information is not integrated into the feature structure fusion process. Therefore, the recognition performance of SFLPP needs to be further improved by considering the category information.