esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiple Thermal Parameter Inversion for Concrete Dams Using an Integrated Surrogate Model
## 3 Case Study
### Finite Element Model and Boundary Conditions  
According to the construction information provided by the contractor, the 15# dam section under construction was selected to establish a three-dimensional finite element model. The coordinates were the X-axis in the transverse direction, Y-axis in the downstream direction, and Z-axis in the vertical direction. The mesh is divided by 8-node hexahedral elements, with 21,664 elements and 26,505 nodes, as shown in Figure 6. The range of mesh size along X, Y and Z directions are 2.75-3.00 m, 3.58-6.11 m and 0.33-0.75 m, respectively. The construction process of the pouring block was simulated using the birth and death element technique, which first kills all the elements, and then activates each pouring block according to the construction progress.  
The surface temperature of the dam is affected by various factors such as air temperature, solar radiation, wind, and curing measures, so the equivalent surface heat release coefficient [42] is used to reflect the heat exchange performance of concrete and air. Before being covered by concrete, the sides are supposed to be a third-type boundary condition, and an adiabatic boundary after being covered. The upstream, downstream, and top surfaces are considered third-type boundary conditions.  
The initial temperature of the concrete was assumed to be the placing temperature. The water cooling was applied immediately after the completion of concrete pouring, and the water inlet and outlet were reversed every 24 h. Air temperature and water pipe cooling parameters such as water temperature and flow rate were obtained according to the monitoring records. The ground temperature at the bottom of the dam is a constant 20 \({}^{\circ}\)C.  
The temperature coefficient \(a\), the adiabatic temperature rise model parameters \(\theta\), \(s\), and \(m_{1}\), as well as the equivalent heat release coefficient \(\beta\), are the thermal parameters that need to be inverted according to the analysis in Section 2. In addition to the above, the parameters of the cooling pipe are shown in Table 1, and the other material parameters are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameters** & **Inner Radius** & **Outer Radius** & **Thermal Conductivity** & **Length** \\  & **(m)** & **(m)** & **[k]/(m\(\cdot\)d\(\cdot\)\({}^{\circ}\)C]** & **(m)** \\ \hline Value & 0.014 & 0.016 & 39.84 & 300 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the cooling water pipe.  
Figure 6: Finite element mesh model.
## 2 Related Works
### Symmetric Cryptography  
It is a foundational pillar of modern cryptographic systems, employs a singular, shared, secret key for both the encryption and decryption processes. This mechanism ensures streamlined, efficient, and robust protection of data during transmission and storage. However, its strength is equally its vulnerability; the security of the entire system rests on the confidentiality and secure management of this single key. Consequently, while symmetric cryptography offers high speed and computational efficiency, its centralized key management demands rigorous security measures to guard against potential breaches and unauthorized access.
## 3 Research Methodology
### Feature Selection  
The objective of this step is to select features from the input dataset and choose the optimal combination that produces the best results for each model. Various methods can be employed to achieve this objective. One such method involves using feature importance scores, which assign scores to each feature, prioritizing their contribution to the prediction [36; 37; 38]. In this study, we employ a feature importance technique based on tree-based ensembles [39]. Feature importance is measured by the extent of error reduction (such as MAE or RMSE) that each feature contributes [40]. To accomplish this, we utilize the feature importance based on decision trees available in the Python scikit-learn library [41] to identify the most suitable features for the model. Finding the optimal sliding window for using lagged features in the models is an important task of this step. For each model, the best sliding window was determined to be 6 days, meaning that lagged data from the previous 6 days will be used.
## 5 Towards a More Quantitative Malaria Model for Predicting Effective Gametocytemia
### Models for the Three Observables as Function of Parameter Gametocytemia  
Having described the values of the incidence, the Hurst exponent and entropy obtained in six simulations with an agent model for malaria spreading, we now derive models for each one of these observables as a function of the central parameter in our approach, the gametocytemia level \(w_{h}\).  
Notice that, being a parameter that cannot be measured directly from empirical series of malaria incidence, a model from simulations relating the observables with this parameter will enable predicting the \"effective\" gametocytemia level (i.e., the transmissibility) in empirical cases.  
To model the malaria incidence \(I\) we consider a continuous step function, varying from \(I=0\) for \(w_{h}\) to \(I=1\) for \(w_{h}=1\):  
\[I=\frac{1}{1+\left(\frac{A}{w_{h}}\right)^{\alpha}}\,. \tag{6}\]  
The dashed line in Figure 5a shows a function given by Equation (6) for \(A=0.562\) and \(\alpha=8.3\). Here, parameter \(A\) gives the gametocytemia level, which brings the malaria incidence to the level of 50%, while the value of parameter \(\alpha\) indicates how abrupt the transition from eradication to prevalence occurs when increasing the gametocytemia level.  
Through inspection of Figure 5b, we choose to model the Hurst exponent \(H\) by a power law  
\[H=Bw_{h}^{\beta}\,, \tag{7}\]  
for which the best fit yields \(B=0.56\) and \(\beta=0.58\). Here, parameters have no direct interpretation.  
Figure 5: (a) Evaluation of the Malaria incidence in time series from model simulations at different settings of human-to-mosquito transmission efficiency (\(0.420<w_{h}<0.733\)). The Hurst exponent is consistently close to 1.0 at the phase transition (when \(w_{h}\sim 0.420\)), and decreasing to values of \(\sim\)0.5 (close to a random walk stochastic state) with higher values of \(w_{h}\sim 0.700\) in higher disease transmission epidemic stage. For the same series (b) evaluation of the Hurst exponent and (c) evaluation of Shannon entropy. In each case we simulated the agent model 10 times.  
Finally, to address the abrupt transition observed for the entropy when the gametocytemia level varies, we choose a step function tuned by an exponential of \(w_{h}\):  
\[S=\frac{1}{1+C\exp\left(-\gamma w_{h}\right)}\,, \tag{8}\]  
with the best fit yielding \(C=10\),\(136\) and \(\gamma=32\). The parameter \(C\) tunes how low the entropy is for the extreme case of \(w_{h}=0\), while, similarly to parameter \(\alpha\), the \(\gamma\) controls how abrupt the transition from that minimum level to \(S=1\) occurs.
## 2 Literature Review
It is decided which forwarder node to use based on its low depth value and high residual energy. The network lifetime, throughput, and reliability are all improved due to this configuration. As only one random node is used for data transmission, the chances of data loss will increase. Hole formation occurs when the energy of a top node runs out, preventing any further data transfer. Hole creation is avoided in [16]. Dispersed energy-efficient and connectivity-aware routing methods are discussed because sink nodes are far from the region of interest. Underwater data transmission is disrupted when commonly utilized overhead lowest depth nodes create a hole in the network. These solutions prevent this problem. Multipath routes are used in this protocol. Low-depth nodes have a lower probability of going dead. Still, a simulation reveals that their lifespan improves from 50% to 70% due to lower-depth nodes bearing less data transmission load. The number of dead nodes decreases while the number of live nodes reaches a maximum. The authors in [17] proposed a delay-intolerant energy-efficient algorithm known as DIEER. This approach can increase the PDR to avoid collisions in data packet propagation delays. Except for the DIEER protocol, no other routing protocol addresses these issues. As data are retransmitted less frequently, the network uses less energy, which reduces latency. The joint optimization framework for sink mobility, hold, and forwarding mechanisms is introduced. Data aggregation and pattern matching algorithms fix the threshold's adaptive value and reduce network delay while delivering maximum data, increasing the network's life, and using the least energy necessary. An underwater platform with sink mobility and extensive distribution of nodes with different communication radii has been designed for three-dimensional operation. No retransmission of data occurs as a result of adapting the offered protocol. In-band communication where the TX and RX are required to have reliable communication. Through the TX-RX interaction, reliable communication protocols can ensure lead performance. The author of [18] designed and developed the Onion, a dependence-based communication protocol for MIMO MRC-WPT. It is the extension of the C1G2 protocol. The authors designed and developed the prototype for the implementation of the evaluation of this protocol. The experiment results show that the proposed protocol performed 40% compared to other dependency schemes in terms of communication ratio. An overview of all the schemes is illustrated in Table 1.
## 4 Strategy Analysis  
### Optimizing Protection Strategy Using Game Theory  
In the game-theory phase of our analysis, we assume that both defenders and attackers have full knowledge of the critical infrastructure. The protection process is modeled as a zero-sum game, which means that while the defenders try to minimize the potential loss \(L\) after the attack, the attackers aim to maximize \(L\) by allocating limited resources. Given budget constraints for protection and attack of \(B_{\mathcal{G}P}\) and \(B_{\mathcal{G}A}\), respectively, the problem can be modeled as shown in Equation (6), and the protection strategy \(P\) calculated from this model will be the most effective one in light of the budgets available.  
\[\min_{P}\max_{A}L(p_{l},\,a_{k}) \tag{6}\]\[s.t.\ C_{p_{l}}\leq Bg_{P}\ \&\ C_{a_{k}}\leq Bg_{A} \tag{7}\]  
The strategies used by each of the parties involved are evaluated based on their effectiveness and costs. For attackers, the set of attacking methods \(a_{k}\) is defined as \(A\); each attacking method \(a_{k}\) includes both the equipment used and the attack's position/direction; and the cost of conducting \(a_{k}\) is defined as \(C_{a_{k}}\).  
For defenders, the set of protection strategies \(p_{l}\) is defined as \(P\), with each strategy \(p_{l}\) comprising the target and the specific method used in protecting it, resulting in a cost for conducting \(p_{l}\) defined as \(C_{p_{l}}\). Specifically, two protection methods for each of the components in the infrastructure were considered. The first consists of improving the robustness of certain components, e.g., by building concrete walls or shelters surrounding the component that will tend to reduce the level of damage to the component if an attack occurs. When a particular robustness level \(Ro_{l}\) is added to the target component of the infrastructure, a damage reduction factor \(\Delta Ro_{l}\) will be applied to the original attack consequence associated with that component, and reduces the level of damage to related edges in the attack so that the potential loss \(L(p_{l},\ a_{k})\) can be reduced.  
The second protection method consists of improving redundancy, meaning that the given infrastructural component will be replicated, for the specific purpose of allowing that component to maintain functionality despite being damaged in an attack. When a certain level of redundancy \(Re_{l}\) is applied to a target component, the damage to that target component within the redundancy level will have only minor (ignorable) consequences to its functionality, and also maintain its level of reliance to the other relative components in the network during the attack, resulting in a reduction in total potential loss \(L(p_{l},\ a_{k})\).
## 3 Design of the Asymmetric Coupled Line Balun
A simple re-scaling of the size of balun can improve its performance. For this reason, the balun's earlier dimensions of 215 \(\times\) 148 \(\upmu\)m\({}^{2}\) were modified to 175 \(\times\) 148 \(\upmu\)m\({}^{2}\), and the resulting input/output reflection coefficients, insertion loss and phase and amplitude imbalances are shown in Figure 10. As can be seen, \(S_{11}\), and \(S_{22}\) were improved over a wider frequency range of 106-266 GHz (WR-6, WR-5, and WR-4), which made the impedance bandwidth (\(S_{11}\) and \(S_{22}\)\(<\)\(-\)10 dB) close to 160 GHz. The maximum losses of the single balun including RF-pad in WR-5 and WR-4 were 2.1 dB and 3.8 dB, respectively. The phase imbalance was less than seven degrees across all frequencies. Furthermore, the amplitude imbalance was \(<\)1 dB at most of the frequencies. However, there were some limitations arising from the frequency-dependent conductor losses and asymmetry of the balun itself. These limitations resulted in higher amplitude imbalances, which were more visible at the higher frequencies. Figure 8: Simulated \(S_{11}\), \(S_{22}\) (**a**) and amplitude imbalance and phase difference (**b**) of the asymmetric coupled line balun in Sonnet and ADS momentum. Figure 9: Simulated insertion loss (**a**) and phase imbalance (**b**) of the asymmetric coupled line balun in Sonnet and ADS momentum. Table 1 shows a summary of the various state-of-the-art baluns at different frequencies. It can be clearly seen that both baluns reported in this paper outperformed, in terms of the critical parameter, the existing baluns based on similar technologies. Most of the earlier baluns either presented higher losses or limited bandwidth. Such performances of the balun are state-of-the-art, which makes it highly suitable for the future broadband differential circuits for various applications including imaging, spectroscopy, radar, and high-speed data communication. Furthermore, the balun reported in this paper can be easily re-scaled to cover other frequencies of interest. However, due to the frequency-dependent conductor losses and asymmetry of the balun, the re-scaling can cause slightly higher amplitude imbalances at some frequencies.
# Research on Chinese Named Entity Recognition Based on Lexical Information and Spatial Features
## 4 Experiments
### Data Sets and Hyperparameters  
This research employs four Chinese named entity recognition datasets to substantiate the enhanced performance of the proposed SISF method in the domain of NER. These datasets encompass OntoNotes 4.0 and MSRA datasets originating from the news domain, as well as Resume and Weibo datasets sourced from online repositories within China. Specifically, the Resume dataset was constructed from resumes on Sina Finance, while the Weibo dataset was built from information on China's social media platform, Sina Weibo.  
The Weibo dataset comprises four distinct entity types: Person Names (PER), Organization Names (ORG), Location Names (LOC), and Geographic/Social/Political Entities (GPE). On the other hand, the Resume dataset encompasses eight entity categories: Countries (CONT), Education Levels (EDU), Location Names (LOC), Person Names (PER), Organization Names (ORG), Professions (PRO), Racial Backgrounds (RACE), and Job Titles (TITLE). In contrast, the MSRA dataset comprises three entity types: Organization Names (ORG), Person Names (PER), and Location Names (LOC). Lastly, OntoNotes 4.0 includes four entity types: Person Names (PER), Organization Names (ORG), Location Names (LOC), and Geographic/Social/Political Entities (GPE).  
A statistical enumeration of entities within the four datasets is provided in Table 1. 'Train' indicates the size of the training set, 'Dev' is the size of the validation set, and 'Test' is the size of the test dataset. 'Entity Types' refers to the types of entities in the datasets, and 'Charavg', 'Wordavg', and 'Entityavg' are the average number of words, words, and entities annotated by dictionaries and entities in the instance, respectively. Conflict lexicon denotes the number of matched vocabulary conflicts in the test set. The hyperparameters for each dataset are listed in Table 2.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Ontonotes4.0** & **MSRA** & **Resume** & **Weibo** \\ \hline Train & 15,778 & 46,675 & 3821 & 1350 \\ Dev & 4301 & 4376 & 463 & 270 \\ Test & 4346 & 4376 & 477 & 270 \\ Entity Types & 4 & 3 & 8 & 4 \\ Charavg & 36.92 & 45.87 & 32.15 & 54.37 \\ Wordavg & 17.59 & 22.38 & 24.99 & 21.49 \\ Entityavg & 1.15 & 1.58 & 3.48 & 1.42 \\ conflict lexicon & 48,766 & 38,809 & 7583 & 2368 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data set statistics.  
Given that Flat-Lattice uses experiments conducted on NVIDIA GeForce RTX 2080 Ti cards, this paper uses NVIDIA GeForce RTX 3090 cards in order to ensure the rigor of the experiments, as well as to demonstrate the superiority of this paper in optimizing the vocabulary enhancement method for enhancing Chinese named entity recognition. This paper conducts the corresponding experiments on the NVIDIA GeForce RTX 3090 card.
## 3 Results
### NDACC Rapid Delivery Demonstration Service
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multirow{2}{*}{**Institute**} & \multirow{2}{*}{**Total O\({}_{3}\)**} & \multirow{2}{*}{**Tropo**} & \multirow{2}{*}{**Tropo**} & \multirow{2}{*}{**Strato**} \\  & & & \multicolumn{1}{c}{**NO\({}_{2}\)**} & & \\ \hline Ny-Alesund & IUP Bremen & \(\bullet\) & O & O & \\ Harestua & BIRA-IASB & \(\bullet\) & & & O \\ Bremen & IUP Bremen & \(\bullet\) & \(\bullet\) & O & O \\ De Bilt & KNMI & \(\bullet\) & \(\bullet\) & O & O \\ Cabauw & KNMI & & \(\bullet\) & O & O \\ Uccle & BIRA-IASB & \(\bullet\) & \(\bullet\) & O & O \\ Vielsalm & BIRA-IASB & & O & O & O \\ Mainz & MPIC & & \(\bullet\) & O & O \\ Heidelberg & IUP Heidelberg & \(\bullet\) & \(\bullet\) & O & O \\ Jungfraujoch & BIRA-IASB & O & & O \\ San Pietro Capofiume & CNR-ISAC & O & O & O & O \\ Toronto Downsview & ECCC & & O & O & O \\ Thessaloniki & AUTH-LAP & \(\bullet\) & \(\bullet\) & O & O \\ Xianghe & BIRA-IASB & \(\bullet\) & \(\bullet\) & O & O \\ Athens & IUP Bremen & \(\bullet\) & \(\bullet\) & O & O \\ Izana & INTA & \(\bullet\) & & & O \\ Kinshasa & BIRA-IASB & & O & O & O \\ La Reunion Maido & BIRA-IASB & \(\bullet\) & & & O \\ Lauder & NIVA & \(\bullet\) & \(\bullet\) & O & O \\ Neumayer & IUP Heidelberg & \(\bullet\) & O & O & O \\ Usteinen & BIRA-IASB & O & O & O & O \\ Arrival Heights & IUP Heidelberg & \(\bullet\) & O & O & O \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Distribution of the data products generated by the FRM4 DOAS Central Data Processing System. Total ozone and tropospheric NO\({}_{2}\) vertical profile data that are submitted daily to the NDACC rapid delivery (RD) repository are denoted by filled circles. Other products generated by the system but not submitted to the NDACC RD are denoted by open circles. Figure 14: Overview of the FRM4 DOAS tropospheric NO\({}_{2}\) vertical profile demonstrational data processing covering the period from January 2018 until late September 2024. Note that for double-channel instruments, NO\({}_{2}\) files are produced both for the UV and visible channels.
## 2 Theory and Simulation  
### Straddling Oil and Gas Pipeline Dynamics Theory  
In the process of pressure flow in oil and gas pipelines, when the fluid contacts solid surfaces, it exerts forces that cause structural deformation. Generally, this deformation is small and gradually changes over time, making it relatively stable. However, during an earthquake event, intense pipeline vibrations can result in significant deformations that impact the distribution of fluid loads within the pipeline. This leads to alterations in fluid velocity and pressure fields, ultimately affecting the motion state of the medium within the pipeline. Consequently, these interactions between the fluid medium and solid structure are referred to as fluid-solid coupling effects.  
According to the fluid-structure coupling mechanism, it can be categorized into two manifestations. The first manifestation involves a blending of the solid and fluid domains, making it challenging to distinguish between them. This is observed in phenomena such as turbulence and seepage. The second manifestation occurs at the interface between the solid and fluid domains. In this paper, we focus on the latter type of coupling exhibited by oil and gas pipelines.  
The fluid-solid coupling problem can be classified into unidirectional and bidirectional coupling based on the need for data transmission between the fluid and solid interfaces. Unidirectional coupling assumes that the influence of the solid on the fluid is insufficient to alter the flow field's shape, disregarding deformation and displacement transfer from solid to fluid. It only calculates one-way data transfer results from fluid to solid. However, this method yields inaccurate results as it is only suitable when the fluid significantly affects the solid structure, while neglecting any substantial impact of the solid on the fluid. In contrast, bidirectional coupling considers two-way data transfer at their interface, accounting for interactions between both phases. This approach aligns with real-world behavior and provides more realistic calculation outcomes. Therefore, this study adopts bidirectional fluid-solid coupling to simulate interactions between the oil-gas medium and the pipeline.  
The numerical simulation of convection-solid coupling should adhere to the fundamental equations of fluid mechanics and solid mechanics, specifically the fluid control equation, solid control equation, and coupling control equation.
## 3 Data Routing Approaches in WSNs
### Fault-Tolerant Approaches  
In fault-tolerant data routing approaches, the main focus is providing sufficient reliability to satisfy the needed accuracy, but since using such mechanisms may reduce the energy of the nodes, most of the approaches in this category are aware about the energy level of nodes, too. In general, fault-tolerant approaches have two behaviors against faults: using some methods or adding different levels of redundancy to prevent faults or detecting and recovering the occurred faults.