esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Conceptual Reliability-Based Decision Support Framework
### Decision-Making Process  
The 'failure' during social PPPs operations in this framework is defined as the situation where the service quality perceived by the end-user is lower than the assessment of delivered service by the government, i.e., \(P_{f}~{}=~{}(P-~{}A<0)\). In light of the size of probability of this relationship, the result of RDSF could be expressed as the following three cases in Equation (1).  
\[P_{f}~{}=~{}P(P-~{}A<0)\Bigg{\{}\begin{array}{rl}\leq~{}l_{1}&\text{defined as good}\\ >l_{1}~{}but&\leq~{}l_{2}&\text{defined as acceptable},\\ >~{}l_{2}&\text{defined as poor}\end{array} \tag{1}\]  
First, if the probability of failure \(\leq l_{1}\), the service delivery performance is defined as good. In this case, it means the probability that the actual service delivery cannot meet end-user's expectation is less than \(l_{1}\). As a result, the government may be satisfied by considering there is enough resilience between the end-user's satisfaction and the assessment of actual service delivered. Second, if probability of failure \(>l_{1}~{}but~{}\leq l_{2}\), the service delivery performance is defined as acceptable. In this case, it means the probability that the actual service delivery cannot meet the end-user's expectation falls in the zone \([l_{1},l_{2}]\). The zone between \(l_{1}\) and \(l_{2}\) is the zone where the level of actual service delivery cannot meet end-user's expectation is acceptable. The government may be neither very satisfied nor unsatisfied in this situation. Last, if the probability of failure \(>l_{2}\), the service delivery performance is defined as poor. In this case, it means the probability that actual service delivery cannot meet the end-user's expectation is bigger than \(l_{2}\). The government may be unsatisfied about the social PPPs service performance as it is dangerous for the service delivered to fall below the end-user's expectation.  
This reliability-based decision support framework defines a new perspective in terms of probability to measure social PPPs service performance by analyzing the assessment of delivered service and end-user's perception of service quality. In this view point, only when the gap between \(P\) and \(A\) is larger than \((1-l_{2})\) will government be satisfied, and also support resilience management in the project to deal with unforeseen major changes. This conceptual framework can assist governments in paying attention to the end-user's perception and develop more end-user-oriented public service. In addition,the end-user would achieve value for money by engaging the public service that adequately meets their needs and expectations.
# A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning
## 4 Experimental Results and Discussion
### Generalization and Convergence Analysis  
For deep learning network models, generalization refers to the ability of the model to apply to new data after training and to make accurate predictions, while convergence refers to the stability and reliability of the model, and so testing the convergence and generalization of deep learning models is significant.  
The MeanError, RMSE, and MSELoss tests for the urban canyon and indoor datasets are shown in Figure 5. In the first 30 training sessions, the RMSE in the urban canyon scene drops from 14.2 to 1.12; MeanError drops from 13.6 to 1.02; the MSELoss drops from 68.2 to 0.42. In the indoor scene, RMSE drops from 71.3 to 0.55; MeanError drops from 7.01 to 0.64; and MSELoss drops from 23.7 to 0.18. It can be seen from the figure that the curves of the training set and the validation set are always close in three indicators; especially in the test of the urban canyon dataset, the effect of the test set is slightly better than that of the training set, and there is no overfitting phenomenon.  
Figure 5: \(Cont.\)  
We introduce other classic outstanding DNNs for comparison, namely Vit_b_16 [39], ResNet18 [18], Vgg [31], Densenet [40], Mnasnet [41], GoogLelet [19], and Mobilenet_v3 [21]. As shown in Figure 6, DECC is significantly faster than that of Vgg, ResNet18, and GoogLeNet, in terms of convergence speed. In the first 25 rounds, the convergence effect of DECC is close to vit_b_16, and superior to vit_b_16 in the last 25 rounds.  
It can be seen from the above tests that the generalization performance and convergence effect of DECC can be proven on both datasets.
# The Use of Attentive Knowledge Graph Perceptual Propagation for Improving Recommendations
## 4 Methodology
### Initial Embedding Representation
#### 4.1.1 Knowledge Graph Embedding  
Usually, a triple \(\langle head,relation,tail\rangle\) is used to represent knowledge. Since there are too many entities and relationships with too many dimensions, one-hot vectors cannot capture the similarity when two entities or relationships are very close. Inspired by Wrod2 Vec [45], the entities and relationships in this paper are represented by the distributed representation method. Additionally, the recommendation scenario may encounter a situation whereby user-item interactions are very sparse, in which case, the above method can be combined with user-item interaction information by introducing the knowledge graph as an auxiliary information method to improve the recommendation effect. Knowledge graphs generally contain three types of data: structured knowledge (triples), textual knowledge, and visual knowledge. This paper uses the TransD knowledge graph embedding method to model structured knowledge and learn the structured representation of each item in the knowledge graph.  
The aim of the TransD approach is to project the head and tail onto the hyperplane using the same transition matrix, as shown in Figure 3. However, the head and tail are usually not entities of the same category (e.g., [PERSON], actor, Iron Man). In the given example, [PERSON] is an actor and Iron Man is a movie, which are two different categories of entities that should be transformed in different ways. Second, the projection should be related to the entities and their relationships, but the projection matrix is only determined by the relationships. TransD uses two vectors to represent each entity and relationship. The first vector represents the meaning of the entity or relationship, and the projection vector is used to construct the mapping matrix [44]. The formula is as follows:  
\[M_{rh}=r_{p}h_{p}^{T}+I \tag{2}\]  
\[M_{rt}=r_{p}t_{p}^{T}+I \tag{3}\]  
where the mapping matrix is defined by entities and relationships, \(I\) is the unit matrix, and the generated matrix is used to modify the unit matrix. Like the classical Trans series knowledge graph embedding algorithm, the distance between entities is used to represent the score. The formula is as follows:  
\[f_{r}(h,t)=\|h_{\perp}+r-t_{\perp}\|_{2}^{2} \tag{4}\]  
Figure 3: Schematic diagram of the TransD approach. The head and tail are projected to the hyperplane through the transition matrix and connected by relationships.  
Since TransD is calculated on the hyperplane, it is obtained according to the mapping matrix.  
\[h_{\perp}=M_{rt}h \tag{5}\]  
\[t_{\perp}=M_{rt}t \tag{6}\]  
The loss function uses a max-margin function with negative sampling. The formula is as follows:  
\[L(h,r,t)=max\Big{(}0,f_{r}(h,t)_{pos}-f_{r}(h,t)_{neg}+margin\Big{)} \tag{7}\]
# Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea
## 3 Experimental Process and Result Analysis
### Model Evaluation  
In order to effectively evaluate the predicted results under different ML models, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Accuracy Coefficient (ACC) are employed as the evaluation indexes for different models.  
Mean Absolute Error (MAE), which is used to evaluate the closeness between the predicted data and the observed data. The smaller the value is, the better the fitting result. Root Mean Squared Error (RMSE), which is used to calculate the square root of the mean of the sum of the squares of the errors between the predicted data and the observed data. The smaller the value of RMSE, the better the fitting result. Accuracy Coefficient (ACC), which is 1 minus the absolute error between the predicted data and the observed data. The larger the value of ACC, the better the fitting result. The formulas of MAE, RMSE, and ACC are expressed as follows:  
\[MAE=\frac{1}{n}\sum\limits_{i=1}^{n}{\left|{X_{i}^{p}-X_{i}^{o}}\right|}, \tag{12}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multirow{2}{*}{**Time**} & **Water Level** & **Wind Direction** & **Wind Speed** & **Atmospheric** & **Typhoon** & **Typhoon** & **Max Wind** \\  & & **(cm)** & **Direction** & **(m/s)** & **Pressure** & **Central** & **Central Pressure** & **Speed** \\  & & & **(\(\cdot\))** & **(\(\cdot\))** & **(hPa)** & **Location** & **(hPa)** & **(m/s)** \\ \hline
24 July 2014 & 0:00 & 142 & 91 & 10.8 & 995.2 & 26.1\({}^{\circ}\) N & 988 & 25 \\  & & & & & & & 26.2\({}^{\circ}\) N & 988 & 25 \\  & & & & & & & 118.1\({}^{\circ}\) E & 988 & 25 \\  & & & & & & & 26.4\({}^{\circ}\) N & 988 & 25 \\  & & & & & & & 118.1\({}^{\circ}\) E & 988 & 25 \\  & & & & & & & 118.1\({}^{\circ}\) E & 988 & 25 \\  & & & & & & & 26.7\({}^{\circ}\) N & 988 & 25 \\  & & & & & & & 118.1\({}^{\circ}\) E & 988 & 25 \\  & & & & & & & 27.0\({}^{\circ}\) N & 988 & 25 \\  & & & & & & & 118.0\({}^{\circ}\) E & 988 & 25 \\  & & & & & & & 117.9\({}^{\circ}\) E & 988 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Oceanic and meteorological sample data during Typhoon No. 1410.  
\[RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}{\left({X_{i}^{p}-X_{i}^{p}}\right)^{2}}}, \tag{13}\]  
\[ACC=1-\frac{\sum\limits_{i=1}^{n}{\frac{\left|{X_{i}^{p}-X_{i}^{p}}\right|}{{X_{ i}^{p}}}}}{n}, \tag{14}\]  
where \(X_{i}^{p}\) and \(X_{i}^{p}\) are the \(i\) th predicted value and the \(i\) th observed value of n samples, respectively.  
In practice, the peak value of the water is usually the key criterion for marine management departments to judge whether it is necessary to enter the state of alert or disaster relief. Hence, the maximum difference between the modelled and observed values of at the water level peak is taken into consideration for analysis below.
## 2 Constraint-Handling Technique Based on a Neural Network for the Differential Evolution Algorithm
### Statement of the Mechanism Synthesis Problem  
Optimization problems for the synthesis of mechanisms usually require satisfying several design objectives and constraints. Multi-objective or mono-objective optimization techniques can solve design problems. Nowadays, mono-objective optimization techniques are commonly used for solving mechanism synthesis problems, where a multi-objective problem is transformed into a mono-objective problem by the weighted sum approach. Therefore, the mechanism synthesis problem related to this work is expressed as follows.  
\[\underset{x^{*}}{\text{Min}}\ \bar{J}(x)\] (1) subject to: \[g_{j}(x)\leq 0\ \forall\ j=\{1,2,\ldots,m\} \tag{2}\] \[h_{k}(x)=0\ \forall\ k=\{1,2,\ldots,r\}\] (3) \[x_{min}\leq x\leq x_{max} \tag{4}\]  
where \(\bar{J}(x)=\sum_{i=1}^{n}w_{i}J_{i}(x)\) is the weighted objective function, \(J_{i}\) is the \(i\)-th term associated with a design performance index, \(w_{i}\) is the \(i\)-th weight attributed to each term, \(x\ \in R^{q}\) is the design variable vector, \(g_{j}(x)\) and \(h_{k}(x)\) are the inequality and equality constraints, respectively, and \([x_{min},\ x_{max}]\in R^{q}\) are the design variable vector bounds.
## 5 Results and Analysis
### Predictive Results
However, the GCNs employed in AGCRN and GWN fail to capture dynamic spatial changes at signalized intersections for their fixed architectures after training. In comparison to the previously mentioned model, CeT primarily possesses three primate advantages. Firstly, CeT transforms vehicle-type information into word encodings and embeds them into vehicle-state information. Thus, CeT can comprehend the varying impacts of different vehicle types on traffic evolution. Secondly, CeT utilizes the dynamic topology graph to reflect different traffic patterns at intersections under various phases. With sufficient training, CeT can recognize signal phase changes from the topology graph and perform corresponding traffic prediction. Thirdly, CeT extracts spatial-temporal  
Figure 6: The comparison of predictive performance between CeT and baseline models in Scenario 1 and Scenario 2.  
dependencies and correlations from the temporal attributes and spatial topology graph to guide the prediction. The experimental results in both Scenario 1 and Scenario 2 evidence the superiority of CeT.
# Advanced Optimization Techniques for Federated Learning on Non-IID Data
## 3 Methodology
### Data Partitioning  
The method of partitioning data among clients in Federated Learning (FL) is critical, as it directly impacts the efficiency and accuracy of the learning models. Simulating different data distribution scenarios, such as IID (Independent and Identically Distributed) and Non-IID, is essential for understanding and addressing real-world challenges in federated training.  
In IID partitioning, the data are distributed to ensure that each client receives a balanced subset of data, reflecting the overall class distribution of the entire dataset. Specifically, for the MNIST and Fashion MNIST datasets, the data are distributed among 1000 clients, with each client receiving 6 images per class, for a total of 60 images. For CIFAR-10, the partitioning involves 500 clients, with each client receiving 100 images, evenly distributed across the 10 classes, resulting in 10 images per class. This method of partitioning facilitates smoother and more balanced training, as information from all classes is available to each client.  
On the other hand, non-IID partitioning represents a more realistic approach, where the data are unevenly distributed, and each client receives data corresponding to only a few classes. For the MNIST and Fashion MNIST datasets, each of the 1000 clients receives 60 images from only two classes, with 30 images from each class. For CIFAR-10, a similar pattern is followed, with 500 clients, where each client receives 100 images from only two distinct classes, with 50 images from each class. This strategy simulates real-world scenarios where data distribution is not uniform, reflecting the diversity of devices in a federated network.  
After pre-processing and partitioning the dataset among the clients, the data are stored in Parquet format files, which are optimized for efficient storage and fast data retrieval. The data are partitioned based on the client identifier, ensuring effective data organization and management for the federated learning process.  
The choice of data partitioning in federated learning allows for the simulation of different scenarios. IID partitioning ensures uniform class distribution among clients, while non-IID partitioning reflects the reality of more complex and uneven data distribution. By analyzing and comparing federated learning models trained on both IID and non-IID data, this study aims to improve the performance of federated training by providing solutions to the challenges posed by data heterogeneity.
# A Method for Processing Static Analysis Alarms Based on Deep Learning
## 8 Threats to Validity
### Internal Threats to Validity  
One potential internal threat to validity is related to the implementation of our DL model. The model's performance heavily relies on the hyperparameters chosen during training, such as the number of filters, filter sizes, and hidden size. Suboptimal choices of these hyperparameters may impact the model's ability to detect bugs accurately. To mitigate this threat, we conducted extensive hyperparameter tuning using cross-validation techniques to find the optimal configuration. However, there is still a possibility that alternative hyperparameter settings could yield different results. Another internal threat to validity is the potential presence of bias in the training data. Our training dataset primarily consists of warning samples detected by SpotBugs, which may introduce bias towards the alarm patterns recognized by SpotBugs. This bias can affect the generalizability of our model to other ASATs. Future research could explore the integration of diverse ASATs to mitigate this bias.
# Dynamic Performance Analysis for an Absorption Chiller under Different Working Conditions
## 6 Conclusions  
Previous studies lack the dynamic performance of the absorption chiller (AC) under different working conditions, but it is significant for the operation of the whole system, of which the stabilization can be affected by the AC transient process. The steady-state and dynamic mathematical models of a single-effect absorption chiller are established in the present work, using the working fluids are H\({}_{2}\)O-LiBr. The dynamic model is applied to demonstrate the transient response to a 10 \({}^{\circ}\)C step change of the generator inlet temperature. Besides, the dynamic performance analyses are completed under different generator inlet temperatures, cooling water inlet temperatures and evaporator inlet temperatures. Furthermore, a whole system using AC in a process plant is analyzed. As a consequence, some conclusions can be drawn:  
1. Compared with the step change of the generator inlet temperature \(\Delta T_{gin}\) = 3 \({}^{\circ}\)C, the time required to reach a new steady-state (relaxation time) increases by 11.11%-35.42% when \(\Delta T_{gin}\) increases from 5 \({}^{\circ}\)C to 15 \({}^{\circ}\)C.
2. The relaxation time grows with the rise of the cooling water inlet temperature \(T_{cin}\), and it increases by 5.65% when \(T_{cin}\) changes from 25 \({}^{\circ}\)C to 35 \({}^{\circ}\)C.
3. Reducing evaporator inlet temperature \(T_{ein}\) can lengthen the relaxation time, which rises by 3.95% when \(T_{ein}\) decreases from 25 \({}^{\circ}\)C to 10 \({}^{\circ}\)C.
4. The control strategy considering the AC dynamic performance under different working conditions is beneficial for the real-time operation and control of the whole system.  
The authors gratefully acknowledge the support of National Key Research and Development Program of China (No. 2016 YFB0901405).  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] provided the guidance and revised the paper. [PERSON], [PERSON] and [PERSON] made the calculation.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
\begin{tabular}{l l} \(A\) & bottom area, m\({}^{2}\) \\ \(Cd\) & discharge coefficient \\ \(Cp\) & specific heat, kJ/(kg\(\cdot\)K) \\ \(g\) & gravitational acceleration, m/s\({}^{2}\) \\ \(H\) & vertical distance, m \\ \(h\) & specific enthalpy, kJ/kg \\ \(i\) & time, s \\ \(M\) & mass storage, kg \\ \(MCp\) & product of thermal storage mass and its specific heat, kJ/K \\ \(m\) & mass flow rate, kg/s \\ \(p\) & pressure, Pa \\ \(Pa\) & state parameters \\ \(Q\) & heat exchange rate, kW \\ \(S\) & section area, m\({}^{2}\) \\ \(T\) & temperature, \({}^{\circ}\)C \\ \(V\) & volume, m\({}^{3}\) \\ \(Vol\) & volumetric flow rate, m\({}^{3}\)/h \\ \(x\) & mass concentration of LiBr \\ \(UA\) & product of heat transfer coefficient and area, kW/K \\ \(z\) & fluid storage height, m \\ \end{tabular}
# Computing the Gromov-Wasserstein Distance between Two Surface Meshes Using Optimal Transport
## Appendix B Proof of Proposition 2: Retrieving the Transport Plan from the SPA Solutions  
Let us first recall the definition of the partition function (Equation (22))  
\[Z_{\beta}(s,S_{1},S_{2})= \int_{0}^{1}\prod_{k,l}dG(k,l)\int_{-\infty}^{+\infty}\prod_{k,l} dC(k,l)\] \[e^{-\beta\sum\  
Following the same procedure as described in the main text for evaluating this modified partition function, we find,  
\[F_{\beta}(\mathbf{u}) =-\sum_{k,l}D(k,l)C(k,l)-\left(\sum_{k}\lambda(k)m_{1}(k)+\sum_{l} \mu_{l}m_{2}(l)\right)\] \[+\sum_{k,k^{\prime}}\sum_{l,l^{\prime}}D(k,l)\big{|}d_{1}(k,k^{ \prime})-s\cdot d_{2}(l,l^{\prime})\big{|}^{p}D(k^{\prime},l^{\prime})\] \[-\frac{1}{\beta}\sum_{k,l}\ln\left(\frac{1-e^{-\beta(C(k,l)+ \lambda(k)+\mu(l)-u(k,l))}}{\beta(C(k,l)+\lambda(k)+\mu(l)-u(k,l))}\right).\]  
Then, the expected transport \(\bar{G}(k,l)\) between point \(k\) in \(S_{1}\) and point \(l\) in \(S_{2}\)is given by  
\[\bar{G}(k,l)=-\left.\frac{\partial F_{\beta}(\mathbf{u},v)}{\partial u(k,l)} \right|_{\mathbf{u}=\mathbf{0},p=0,C=C^{MF},D=D^{MF}\Lambda=\Lambda^{MF},\mu= \mu^{MF},\beta=s},\]  
i.e.,  
\[\bar{G}(k,l) = \frac{e^{-\beta(C^{MF}(k,l)+\lambda^{MF}(k)+\mu^{MF}(l))}}{e^{- \beta(C^{MF}(k,l)+\lambda^{MF}(k)+\mu^{MF}(l))}-1}+\frac{1}{\beta(C^{MF}(k,l)+ \lambda^{MF}(k)+\mu^{MF}(l))}\] \[= \phi(\beta(C^{MF}(k,l)+\lambda^{MF}(k)+\mu^{MF}(l)))=D^{MF}(k,l).\]  
Appendix C Proof of Proposition 3: Monotonicity and Limits of \(F^{MF}(\beta)\) and \(U^{MF}(\beta)\)  
In Appendix A we have established that the exact free energy and internal energy defined in Equations (13) and 14, respectively, are monotonic functions of the parameter \(\beta\), and converge to \(d_{p}(S_{1},S_{2})\) when \(\beta\rightarrow\infty\). Here we consider the approximation of those quantities obtained with the saddle point approximation, namely the mean field values \(F^{MF}\) and \(U^{MF}\), and show that they satisfy the same properties.
# Efficient and Safe Strategies for Intersection Management: A Review
## 2 Safety
### Abnormal Information Warning(AIW)
#### 2.2.3 Hazardous Location Warning (HLW)  
When the HV travels to potentially dangerous conditions (for example, there is deep water under the bridge, deep pits on the road, wet roads, sharp turns in front, etc.) and exists the risk of accidents, the HLW warns the HV driver. The European Union project NordicWay (an interchange server funded by the European Union CEF-program (connected to European equipment)), allows communication between vehicles from different manufacturers to be seamless with each other and the surrounding infrastructure and road operators have crossed the border to establish, implement, and have successfully tested it [15]. The project demonstrates the functionality of the communication channel by serving road engineering warnings (TMA-trucks, vehicle attenuators), hazardous location warnings (vehicles with hazard lights activated), and wet road warnings (vehicles with activated ESP or ABS). The project demonstrates this in the border areas of Sweden and Norway, as well as in the border areas of Sweden and Denmark.  
Figure 4: RSU reminds the HV of the danger.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Usage Scenario** & **Zone of Application** & **Examples** \\ \hline Abnormal vehicle warning & Radar, laser, lidar, etc. & Intersections; roads of cities and suburban & IVMS [7] \\ \hline Control loss warning & ABS, ESP, TCS, and LDW of RV & Roads of cities and suburban & IDAS [14] \\ \hline Hazardous location warning & Potentially dangerous condition & Road bends; the bad situation of road &
\begin{tabular}{c} Interchange \\ server [15] \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Three types of abnormal information warning.
## 4 Engineering Example
### Data Preprocessing and Model Settings
#### 4.2.2 Kalman Filter Denoising  
In order to reduce the random noise interference of the sensor in the actual working conditions, after sorting according to the time order, the original data are processed for noise reduction using Kalman filtering, which minimizes the expected value of the estimation error and outputs the optimal estimation of the system state and is widely used for data noise reduction. The calculation method is shown in Equations (8) and (9).  
\[X_{i+1}=(1-K_{i+1})X_{i}+K_{i+1}Z_{i+1} \tag{8}\]  
\[K_{i+1}=(P_{i}+Q)/(P_{i}+Q+R) \tag{9}\]  
where \(X\) is the filtered output value, \(Z\) is the input data, \(K\) is the Kalman gain coefficient determined by the variance of the previous output value \(P\), the system noise \(Q\), and the measurement noise \(R\).