esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Evaluation of Ultimate Flexural Limit State
### Beam Deflection Angle  
As mentioned earlier, the PC zone rotates as a near-rigid body. Although there is no yielding of longitudinal reinforcement occurs in the RC zone, the RC zone might also have small deformation. Thus, the beam deflection angle consists of two parts developing from the PC zone and the RC zone, which are denoted by \(R_{pc,u}\) and \(R_{rc,u}\), respectively. The rotation angle developing at the beam-column interface due to the main crack opening can be calculated from Equation (11), and considered as the beam deflection angle (\(R_{pc,u}\)) of the PC zone.  
\[R_{pc,u}=\Delta_{c,ex}/x_{n} \tag{11}\]  
On the other hand, the beam deflection angle of RC zone, \(R_{rc,u}\), is calculated from Equation (12) based on the trilinear bending moment-deflection angle relation shown in Figure 8 [62].  
\[R_{rc,u}=\frac{(M_{b,rc}-M_{c})(R_{y}-R_{c})}{(M_{y}-M_{c})}+R_{c},\text{ for }R_{c}\leq\ R_{rc,u}\leq\ R_{y} \tag{12}\]  
where \(M_{b,rc}\) is the moment force acting on the boundary between PC and RC zones at ultimate flexural state, which can be calculated based on the calculated \(M_{b,u}\) at the linearly distributed bending moment shown in Figure 7. The cracking drift angle (\(R_{c}\)), the cracking moment (\(M_{c}\)), the initial stiffness (\(K_{c}\)), the yielding drift angle (\(R_{y}\)), the yielding moment (\(M_{y}\)) and the secant stiffness (\(\alpha_{y}\)) at the yielding point are calculated by Equation (13) through (19) according to the AIJ provision [62], where \(\alpha_{y}\) was evaluated from the test results of over 200 specimens in order to estimate the yielding drift angle of RC members [63].  
\[M_{C}=0.56\sqrt{\sigma_{B}}Z_{e} \tag{13}\]\[M_{y}=0.9a_{t}\sigma_{y}d \tag{14}\]  
\[R_{c}=M_{c}/K_{c} \tag{15}\]  
\[R_{y}=M_{y}/\left(a_{y}K_{c}\right) \tag{16}\]  
\[K_{c}=3E_{c}I_{b}/\left(l-l\right) \tag{17}\]  
\[R_{y}=M_{y}/\left(a_{y}K_{c}\right) \tag{18}\]  
\[\alpha_{y}=\left\{\begin{aligned} &(0.043+1.64 np_{t}+0.043(l-l^{ \prime})/D)\cdot\left(d/D\right)^{2},&\text{when }(l-l^{\prime})/D\geqq 2.0\\ &(-0.0836+0.159(l-l^{\prime})/D)\cdot\left(d/D\right)^{2},& \text{when }(l-l^{\prime})/D<2.0\end{aligned}\right. \tag{19}\]  
where \(Z_{e}\) is the section modulus of RC zone, \(a_{t}\) is the cross-sectional area of longitudinal reinforcement, \(d\) is the effective depth, \(n\) is [PERSON]'s modulus ratio of reinforcement and concrete, and \(p_{t}\) is the longitudinal reinforcement ratio.  
As can be seen from Figure 8 and Equation (12), the case of \(R_{rc,u}\) less than \(R_{y}\) is taken into account and the case of \(R_{rc,u}\) beyond \(R_{y}\) is not considered because the longitudinal reinforcement yielding is not allowed, as mentioned earlier. It should be noted that all these equations are usually applied to predict and reproduce the lateral load-deformation curves of test results in Japan. As a result, the total beam deflection angle (\(R_{b,u}\)) can be estimated from Equation (20).  
\[R_{b,u}=R_{pc,u}+R_{rc,u} \tag{20}\]
## 5 Miscellaneous Sequentially Pd-Catalyzed Cyclizations  
Before the advent of CuAAC for regioselective triazole synthesis [63], a regiospecific synthesis of 2-allyl-1,2,3-triazole **39** by palladium-catalyzed cyclization via azidation of allyl methyl carbonate with TMSN3 at elevated temperature was reported by [PERSON] (Scheme 26) [129].  
This palladium-catalyzed three-component reaction of activated alkynes (substituted with electron withdrawing group(s)), allyl carbonate, and trimethylsilyl azide also represented a regiospecific formation of 2-allyl-1,2,3-triazole.  
While various Pd catalyst species were not able to transform nonactivated alkynes solely, copper co-catalysis finally led to a significant extension of the methodology, tolerating nonactivated alkynes (Scheme 26) [130]. Two sets of reaction conditions were optimized for regioselective one-pot syntheses  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Entry** & **Alkyne** & **Amine** & **Hetero \(\alpha\),\(\alpha^{\prime}\)-Dimers of** & **Hetero \(\alpha\),\(\alpha^{\prime}\)-Dimers of** \\  & & & **Heterocycles 36 (Yield)** & **Heterocycles 35 (Yield)** \\ \hline
1 & R\({}^{1}=p\)-MeOC\({}_{6}\)H\({}_{4}\) & R\({}^{2}=\)Bu & & \\
2 & R\({}^{1}=p\)-Ph\({}_{2}\)NC\({}_{6}\)H\({}_{4}\) & R\({}^{2}=\)Bu & & \\  
of 1-allyl 1,2,3-triazolees **40** and 2-allyl 1,2,3-triazolees **41** employing nonactivated alkynes as substrates (Scheme 27). The optimization studies disclosed the regioselective role exerted by phosphane ligands.  
**Scheme 26.** Pd-catalyzed three-component reaction of alkynes, allyl carbonate, and trimethylsilyl azide to regiospecifically give 2-allyl-1,2,3-triazole **39**.  
**Scheme 27.** Regioselective Pd-Cu-catalyzed three-component syntheses of 1-allyl 1,2,3-triazolees **40** and 2-allyl 1,2,3-triazolees **41**.  
The [PERSON] group has further developed [PERSON]'s norbormene-mediated Pd-catalyzed _ortho_-alkylation of iodo arenes under Heck conditions [131] and published an efficient methodology accessing functionalized polycyclic heterocycles by employing a norbornene-mediated, palladium-catalyzed tandem alkylation direct arylation sequence in a one-pot fashion [132, 133, 134, 135]. After oxidative addition of the palladium(0) species to the employed iodoarene a carbopalladation of norbornene precedes the sp\({}^{2}\) CH activation that enables the alkylation. Reductive elimination of the postulated intermediary palladium(IV) complex followed by dismissal of norbornene gives rise to a second CH activation that results in the heteroannulated pyrroles **42** (Scheme 28, Table 13). Following this methodology, six- and seven-membered annulated pyrroles and pyrazoles [132], polycycliciophenes and furans [133], annulated 2-\(H\)-indazoles and 1,2,3- and 1,2,4-triazolees [134], and, even more remarkable, sterically hindered bi- and tricyclic benzonitrile [135] have been prepared.  
**Scheme 28.** Sequentially Pd-catalyzed, norbornene mediated tandem alkylation direct aylation sequence in a one-pot fashion affording heteroannulated pyrroles **42**.  
**Table 17.** Selected heteroannulated pyrroles **
## 2 Related Work
### System Model  
The system model of the proposed SDAA protocol in the UVWSN aims to solve design challenges, including reliability, packet drop, and minimizing delay in UVWSN data transmission, based upon the CBND process, which we introduced by modeling [23]. Therefore, the system model employs secure data aggregation and an optimized authentication method deployed in similar conditions to the other secure MAC protocols, including SEFSC, SDA, and EEST, without cluster-based network design (CBND) deployment. Based upon this, the SDAA protocol deploys multiple mobile sinks (MMS) (which will be explained in algorithm 4 in the next section) in CBDN. Our proposed SDAA protocol also deploys secure data aggregation and authentication-optimized methods to evaluate the network design challenges, based on the communication overhead, due to malicious node attacks (MNA) in the UVWSN. Therefore, our proposed SDAA protocol utilized the USNs (underwater sensor nodes) or channels deployed in the UVWSN. However, UVWSN has peculiar features of underwater acoustic channels that use low signal propagation speed (approximately 1.5 km/s).  
Therefore, the system model utilizes USN, CH (cluster head), GW (gateway), and base station (BS). Consequently, the system model uses UVWSN transmission monitoring  
Figure 1: Aggregation and authentication processes in CBND.  
techniques. Figure 2 depicts the proposed system model. Each USN involves at least one cluster managed by CH, and BS associates with CH to the GW through acoustic links. Acoustic links apply to the UVWSN monitoring application. Thus, GW has unlimited energy resources because of optimized secure data aggregation and authentication methods, which use perfect time synchronization of the USN information delivery in the network. The complete energy efficiency capability of the system model is also due to the multiple mobile sinks (MMS) and CBND method deployed in the proposed SDAA protocol. This can reduce overhead information in the network, thereby increasing energy efficiency. During the monitoring operation of the UVWSN application, two or more GWs can link together. This reduces any delays in the network. GWs can communicate with each other through radio frequency (RF) links.
## 3 Probabilistic Hesitant Fuzzy Evidence Theory
### Generation Methods of Probabilistic Hesitant Fuzzy Basic Probability Assignment
#### 3.2.1 Distance-Based Generation Method  
For practical systems, obtaining sufficient, reliable training data can be challenging. To address this issue, we propose a method for generating PHFBPs based on the distance between sample data and ideal values derived from system analysis and experts' opinions. The steps are as follows:  
* Step 1: Define the ideal values for each class. The ideal value of class \(i\) is denoted as \(C_{i}=(c_{i1},c_{i2},\cdots,c_{ik})\), where \(c_{ik}\) is the ideal value of attribute \(a_{ik}\). These values can be determined based on expert analysis of the system or through the use of clustering algorithms such as KMeans. For test sample \(S=(s_{1},s_{2},\cdots,s_{k})\), if \(s_{k}\) is equal to \(C_{ik}\), then \(s_{k}\) is considered to belong to class \(i\).
* Step 2: Calculate the distance between the actual and expected values. To generate the probability assignment for test sample \(S\), we employ the Euclidean distance metric to quantify the dissimilarity. The Euclidean distance between \(S\) and each center \(C_{i}\) is computed as follows: \[d_{ik}=\mid s_{k}-c_{ik}\mid.\] (30)
* Step 3: Generate BPAs. The proximity of \(s_{k}\) to a center determines the likelihood of \(s_{k}\) belonging to that class. As \(s_{k}\) moves further away from a center, its likelihood of belonging to that class diminishes. Therefore, the following formula is given to calculate the BPAs of the \(k\)th attribute: \[\gamma(\theta_{i})=\frac{\alpha e^{-\frac{\theta_{ik}^{2}}{2}}}{\sum_{i=1}^{N} \alpha e^{-\frac{\theta_{ik}^{2}}{2}}},\] (31) where \(\alpha>0\) is an adjustment parameter representing the impact of the distance.
* Step 4: Add the corresponding probabilities to the BPAs. The probabilities \(p(\theta_{i})\in(0,1]\) are determined by experts, and they are added to the BPAs to obtain the PHFE containing a single value \(h_{p}(\theta_{i})=\{\gamma(\theta_{i})(p(\theta_{i}))\}\).
* Step 5: Select different criteria and repeat Steps 1-4. Experts may have difficulty determining the ideal value for a specific focal element due to uncertainty or variability in evaluation criteria. Thus, \(n\) ideal values \(C_{i}^{1}\cdots C_{i}^{n}\) are determined based on different evaluation criteria for class \(i\). By performing the aforementioned Steps 1-4 \(n\) times,we can obtain multiple elements \(\gamma(\theta_{i})(p(\theta_{i}))\), which collectively form the PHFBPA of attribute \(k\) for class \(i\): \[m_{P}(\theta_{i})=\{\gamma_{j}(\theta_{i})(p_{j}(\theta_{i}))\mid j=1,2,\cdots,n\}.\] (32)
## 3 Publication Structure Analysis
### Popular Research Areas  
Figure 7 depicts the primary research fields and their respective record counts from 2005 to 2024 in OT security. 'Computer Science' is the most prominent research area in the WoS, with 893 records, while 'Engineering' holds the second rank with a total of 533 records. Telecommunication' is the third most prominent, with 221 records highlighting the necessity for robust security strategies in communication networks incorporating OT. The integration of OT within the domain of 'Automation Control systems' is becoming more popular, as shown by 93 publications in the WoS database. 'Physics' and 'Chemistry', with 44 and 36 WoS publications, respectively, offer a multidisciplinary perspective and focus on industry-specific applications of OT. 'Science Technology, Other', 'Energy Fuels', and 'Materials' close the popular research list, with a relatively small number of publications, varying around 20-30. Still, these areas can be considered emerging with the potential for further publication increase.  
Figure 8 depicts the patterns in WoS publications across specific subject areas. Since 2005, Computer Science has been the leading field of research, followed by Engineering. Since 2017, there has been a significant rise in research publications in Telecommunications while the increase in other top areas (i.e., Automation Control Systems, Physics, Instruments Instrumentation, Energy Fuels, Chemistry, Science Technology, Other Topics, and Material Science) is relatively slow. This can be a sign that researchers are trying to focus on the fundamental questions of OT security without yet digging deeper into separate application areas.  
Figure 6: Trends in publications of countries from 1124 operation technology publications from the Web of Science database.  
Table 2 displays statistics on publications, organized by research area and type of manuscript. According to the data shown in Table 2, it can be seen that proceeding papers are the most common type of publication. Currently, review papers are absent in the field of Material Science, and there are only a few in Physics and Automation Control Systems, which is particularly unusual for the latter. It can also be noticed that the number of proceeding papers in Physics and Material Science is very low, as well as the number of research articles in the Energy Fuels category. The subsequent part addresses the distribution of papers on OT security within WoS-indexed categories and publications.  
Figure 8: Trends in publication counts of research areas from 1124 operation technology publications from the Web of Science database.  
Figure 7: Leading research areas from 1124 operation technology publications from the Web of Science database.
# Impact of Latency on QoE, Performance, and Collaboration in Interactive Multi-User Virtual Reality
## 4 Results and Discussion
### Subjective Results  
In this section, the results for the subjective perception of network distortions are first discussed. Next, the completion time, subjective time perception, and cybersickness occurrence are analyzed.
# Mechanical Performance Prediction Model of Steel Bridge Deck Pavement System Based on XGBoost
## 5 Results and Discussion
### Prediction Model for Maximum Longitudinal Shear Stress between Paving Layer and Steel Bridge Panel  
In the training set, a combination of the grid search method and the five-fold cross-validation method was used to optimize the parameters of the machine learning model. R\({}^{2}\) was chosen as the main evaluation index to select the optimal combination of parameters, and the final evaluation result was the mean value of the five-fold cross-validation results. The optimal combination of parameters is learning_rate = 0.15; max_depth = 10;  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MAE** & **RMSE** & **R\({}^{2}\)** \\ \hline XGBoost & 0.023 & 0.027 & 0.864 \\ KNN & 0.032 & 0.037 & 0.783 \\ SVM & 0.041 & 0.051 & 0.592 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison with other traditional machine learning models.  
Figure 16: Relationship between predicted and true values of XGBoost model on the test set. Line: predicted values, points: true values.  
min_child_weight = 4; and n_estimators = 230. The prediction performance of the model after parameter optimization was verified on the test set. The relationship between the predicted and true values of the XGBoost model on the test set is shown in Figure 18, from which it can be seen that the predicted values of most samples in the model are very close to the true values.  
The XGBoost model is compared with other traditional machine learning models. The models were evaluated using three metrics, MAE, RMSE, and R\({}^{2}\), and a grid search combined with cross-validation was used for each model to optimize its parameters. Table 8 shows the prediction results of each traditional machine learning model on the test set, and it can be seen that the XGBoost model shows good performance. The XGBoost model was compared with the KNN model, and the RMSE was reduced by 42%, the MAE was reduced by 48%, and the R\({}^{2}\) was improved by 37%. The XGBoost model was compared  
Figure 17: Importance of input feature variables.  
Figure 18: Relationship between predicted and true values of XGBoost model on the test set. Line: predicted values, points: true values.  
with the SVM model, and the RMSE was reduced by 45%, the MAE was reduced by 50%, and the R\({}^{2}\) was improved by 47%.  
The importance of the input feature variables for the interpretability analysis of the developed prediction model using the SHAP architecture is shown in Figure 19. It can be seen from the figure that the most important characteristic variables are the lower pavement elastic modulus \(E2\), and the relatively more important characteristic variables are the upper pavement elastic modulus \(E1\) and the steel bridge panel thickness \(T\).
# Automatic Extraction of Adverse Drug Reactions from Summary of Product Characteristics
## 4 Discussion  
Many studies investigated the extraction of ADRs from SPLs from the FDA in the USA, and some effective methods have been developed. However, as an equivalent in Europe, the SmPC from the European Medicines Agency receives very little attention in the field of NLP. In this study, we fill the research gap with the development of an automated ADRs extraction method for the SmPC.  
The manual experts review results demonstrate that our proposed method is effective and has the potential of being used to solve ADR related clinical problems. Specifically, our method achieves an overall recall of 0.990 and a precision of 0.932. Such high performance has never been reported in previous studies focusing on extracting ADRs from SPLs [6; 16].  
As discussed in the error analysis, there are a few ways to further improve the performance of our method. For example, noise in the extracted terms can be cleaned with regular expressions. When it comes to the unsplit multiple ADRs, we can extend the split function with more options so that it allows strings to be split by 'or', 'and', or semicolon. Furthermore, encoding extracted ADR terms into MedDRA Preferred Terms could reduce the non-ADR errors, thus further improving the performance.  
Our study has some limitations. First, there are no standardized ADR annotations based on the SmPC available to benchmark ADR extraction methods. It is difficult to reproduce the performance of this study. Manual reviews from different experts might present different performance scores.  
Another limitation lies in the manual review process of this study. Involving clinical experts in the manual review process is always challenging given their busy schedule. The coronavirus has made the situation even worse. Therefore, the manual process in this study only included one clinical expert. To compensate for this, we added one clinical NLP expert as another reviewer. The NLP expert is familiar with this topic and was trained for the task by the clinical expert. To address this, we plan to expand the manual review to a larger size of samples and a bigger group of clinical experts in a post-COVID world.  
The proposed ADR extraction method is developed based on data from the EMC in UK. However, due to Brexit, product labels in UK might changes. Then the method will not perform the same as shown in this study. Further development of this method should focus on using the SmPCs from the EMA.
# Emotion-Driven Music and IoT Devices for Collaborative Exer-Games
## 5 Music Recommendation and Personalization Based on Emotions
### IoT Principles and Computational Framework  
In terms of the IoT principles underlying the proposed system, the architecture leverages a distributed model, where the computational workload is divided between edge devices (wearables) and cloud-based services. This approach ensures that low-latency operations, such as emotion recognition, are processed locally on the wearables, while more computationally intensive tasks, including music recommendation and personalized stimuli generation, are handled by cloud services like Azure Durable Functions. By distributing the workload, the system minimizes delays during real-time interactions, crucial for maintaining an uninterrupted gaming experience. Additionally, the system uses MQTT (Message Queuing Telemetry Transport) as the primary network protocol due to its lightweight nature and low bandwidth consumption, ideal for IoT ecosystems with constrained devices.  
The neural network algorithm utilized for emotion recognition is a Random Forest model, trained using audio features provided by the _Spotify_ API. This model was selected for its robustness and ability to handle non-linear relationships between input features and output labels, which is essential when working with complex emotional states. The network architecture used in this work was chosen after evaluating several alternatives, including Support Vector Machines and Gradient Boosting classifiers, as discussed in [45]. Random Forest provided the best balance between accuracy and computational efficiency, particularly in scenarios involving limited real-time processing power on edge devices. Furthermore, the computational complexity of the entire system is optimized through the use of serverless computing (Azure Durable Functions), where the pay-per-use model minimizes resource waste and scales automatically with demand, thus reducing the overall cost and complexity of managing a cloud-based infrastructure.
## 3 Results and Discussion
### Comparisons with BPNN Models
#### 3.1.1 Effect of Parameters on Different Models  
First, the differences between the CNN and BPNN models were considered for studying the effect of the parameters. The four groups of training parameters for the CNN and BPNN models are listed in Table 5. In the BPNN models, the tests referred to different hidden layers, as shown in Table 5. The errors of the prediction results are shown in Figure 9 for the CNN models and Figure 10 for the BPNN models.  
Figure 9 shows that with different model parameters, the prediction results for the CNN models did not differ much, and the training effect was relatively stable. In contrast, the BPNN training results appeared to be quite variable for all the tests and were extremely unstable with respect to the different model parameters, as shown in Figure 10. Figure 10 also shows that Test 1 had the worst testing results and the highest error rate. Furthermore, the variability of the red histogram was the largest, showing that Test 2 was the most unstable. Test 3 was relatively stable and its error rate was the lowest. Furthermore, in the non-FAIS model, the training effect was encouraging, except in Test 1. Among the four tests, the most stable BPNN model was the non-SOCS model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**CNN**} & \multicolumn{2}{c}{**BPNN**} \\ \cline{2-4}  & **Kernel Size** & **Kernel Number** & **Hidden Layer Size** \\ \hline Test 1 & [2 1] [3 1] & 190 310 & 5 \\ \hline Test 2 & [2 1] [3 1] & 190 320 & 6 \\ \hline Test 3 & [2 1] [3 1] & 198 320 & 7 \\ \hline Test 4 & [2 1] [3 1] [4 1] & 190 320 322 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Four tests with different parameters.  
Figure 9: Errors of CNN predictions.  
As shown from the analyses of the CNN models and BPNN models, the parameters could be further optimized for the two kinds of models. Compared to the BPNN models, the CNN models had better accuracy and stability with different model parameters. To ensure the accuracy and stability of the prediction results, the BPNN should avoid the Test 1 parameters with a high error rate and the Test 2 parameters with poor stability.