esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Results  
#### 4.2.1 Data Preprocessing  
During the in-depth interview about smart TV gesture interaction, a total of 240 meaningful responses were gathered from the ten participants. First, each response was decomposed into primary morphemes. As the smallest unit of meaning in a language, the morphemes were regarded as latent values. The Kind Korean Morpheme Analyzer (KKMA) 2.0 was used to decompose the responses into morphemes. Considering that meaningless morphemes such as postposition particles are frequently observed in responses in the Korean language, morphemes consisting of nouns, adjectives, verbs, adverbs, and determiners were selected from all the morphemes. As a result, 333 major morphemes were collected from meaningful responses.  
For instance, one participant stated that \"because the TV's accuracy is low, additional behavior such as an exaggerated gesture or loud voice is needed.\" In this case, morphemes including accuracy, low, additional, behavior, exaggerated, gesture, loud, and voice were selected. Of course, among them, there are still morphemes like 'low' that do not have any meaning in a standalone state and are hence meaningless.  
Much of this was done by hand in a semiautomated way. In English, Natural Language Toolkit (NLTK) package of Python and various topic modeling algorithms can make it easy to analyze, but because the Korean language is complex, this study used a different approach. This study extracted words based on KKMA mentioned above, which is similar to the NLTK package, and investigated all the words that could be collected, rather than automatically extracting only the important words at this stage. Consequently, a semiautomated method required effort was used because the core of this method is in the development of the procedure.  
Second, significant keywords were derived from the major morphemes. As mentioned above, morphemes that have no meaning independently were screened out. Morphemes that are synonyms were merged. For instance, at this stage, terms like fun and pleasure are merged into fun. Note that keywords intermingled core or associated values (e.g., convenience and fun) and design variables (e.g., type of control and gesture shape). As a result, a total of 132 nonoverlapping significant keywords were selected.  
Finally, co-occurrence matrices were generated using the set of significant keywords. In order to transform information into a dataset that could be analyzed with network analysis, a co-occurrence matrix was used. Each row indicates one response and includes several keywords described in the response. A response may have more than one keyword, while responses that do not have any keywords are filtered out. Here, keywords in the same row are defined as a co-occurrence. In other words, they may have relationships that be described as edges in the network structure. Note that associated values are defined as values that have a relationship with core values. As a result, a total of 251 co-occurrence rows composed the co-occurrence matrix.
## 1 Introduction  
Quantum entanglement (QE) is one of the main features of quantum information compared with classical information [1, 2, 3, 4, 5, 6, 7, 8]. It describes nonlocal correlations [9] in quantum systems, which play major roles in quantum computation and quantum communication [10, 11], teleportation [12, 13, 14, 15], dense coding [16, 17], and cryptography [18]. Thus, the purpose of this study was to explore methods of creating entangled states [19, 20]. Von Neumann entropy, concurrence, and negativity are used to measure the properties of quantum coherence [21, 22, 23, 24, 25].  
Matter-cavity interactions are used to produce entangled states [26, 27, 28] and are important examples of the physical realization of quantum information processing (QIP) [29]. The Jaynes-Cummings model (JCM) was introduced [30] to describe a two-level atomic system (TLAS) coupled to a single-mode radiation field. This model has attracted considerable attention for multi-photon transitions and interactions between TLAS and multi-mode EMFs [21]. Increasing the number of atoms interacting with an electromagnetic field (EMF) is another promising direction. Two TLAS with different extensions interacting with one-mode or two-mode fields have been investigated [22; 31].  
Many generalizations depending on the number of atomic levels, such as 2, 3, and 4-level atoms interacting with different types of cavity fields, have been investigated [22]. The quantum correlation dynamics between their subsystems (atom-atom, atom-field, and field-field) have also been studied [32]. The Kerr medium effects on the dynamics of nonlocal correlations between the two atoms that interact with a single mode field have also been studied [33]. Two TLAS interacting with a single mode vacuum field in the presence of atom-field and atom-atom entanglements were investigated using quantum relative entropy [22].  
In real physical models, effective dissipation degrades the purity and entanglement [34; 35; 36; 37; 38; 39; 40]. One cause of dissipation is intrinsic decoherence in which the quantum effects deteriorate as the quantum system evolves. Intrinsic decoherence was studied in several previous articles [41; 42; 43; 44; 45; 46; 47]. Thus, this work introduces a physical model wherein the su\((1,1)\)-system is initially assumed to have a generalized coherent state, and an analytical description is used to investigate the entire system's quantum coherence and its different partitions.  
In this paper, the physical model is introduced in Section 2. The effects of the decay terms and dipole-dipole interactions on the atomic inversion of two atoms are studied in Section 3. The influences of the intrinsic dissipation and coupling rates between the two atoms on the purity are discussed in Section 4. In Sections 5 and 6, QE in different partitions are investigated by using negativity measurements. Section 7 concludes this paper.
# Shear Behavior of Concrete Encased Steel Truss Composite Girders
## 3 Analysis and Discussion  
### Horizontal Shear Failure  
Figure 10 shows the failure surface and resistance area against horizontal shear in the EST composite girder. It is noteworthy that the failure surface due to horizontal shear observed from the shear test was consistent with the area indicated in red in Figure 10, i.e., horizontal shear cracking occurred along the failure surface with the lowest horizontal shear resistance. The horizontal shear strength (\(V_{h}\)), considering the failure surface, can be calculated as follows:  
\[V_{h}=V_{sh}+V_{ch} \tag{1}\]  
\[V_{sh}=f_{sc}b_{f}l_{a} \tag{2}\]  
\[V_{ch}=v_{c}b_{c}l_{a}, \tag{3}\]  
where \(V_{sh}\) is the horizontal shear strength that can be developed via the bond force of the steel member; \(V_{ch}\) is the horizontal shear strength of concrete; \(f_{sc}\) is the bond force between steel and concrete, which was set as 0.66 in this study [19]; \(b_{f}\) and \(b_{c}\) are the widths of the steel and concrete surfaces, respectively, within the resistance area for the horizontal shear force; \(l_{a}\) is the length of the shear span; and \(v_{c}\) is the horizontal shear strength of concrete calculated using shear friction theory [20]. The calculated horizontal shear strength (\(V_{h}\)) can be converted to the vertical shear strength (\(V_{nh}\)) as follows:  
\[V_{nh}=\frac{V_{h}(jd)}{l_{a}}, \tag{4}\]  
where \(jd\) is the length of the moment lever arm. Table 4 shows a comparison of the shear strength (\(V_{nh}\)) calculated using Equations (1)-(4) with that obtained from the tests (\(V_{test}\)) among the P specimen series. It was discovered that Equations (1)-(4) provided relatively accurate estimations and conservative results for the horizontal shear strength of the specimen. However, because the horizontal shear failure of the member observed in this study was not a desirable failure mode, sufficient horizontal shear strength should be secured by applying Equations (1)-(4) to the design of the EST composite girder.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specimen** & \(V_{test}\)**a (KN)** & **Failure Mode** \\ \hline PV & 691.6 & Horizontal shear failure \\ \hline P & 520.5 & Horizontal shear failure \\ \hline WV & 659.5 & Diagonal shear failure \\ \hline W & 661.9 & Diagonal shear failure \\ \hline \hline \end{tabular}  
* \(V_{test}\) = shear strength obtained from test.  
\end{table}
Table 3: Test results.
# Low-Order Spherical Harmonic HRTF Restoration Using a Neural Network Approach
## 3 Machine Learning HRTF Restoration
### Model Enhancement  
#### 3.3.1 Smaller Model  
To address the over-fitting problem, an effective method is to decrease the complexity of the model, by decreasing the number of parameters. A smaller model was trained with a convolution and transposed convolution layer pair removed in the CAE and a NN layer removed from the fully connected NN (Figure 6 with the yellow highlighted layers removed). The result does not seem to have significant improvement on the SADIE II Subject 20 dataset. However, it substantially increased the MSE with the Bernshutuz data from 67.17 to 89.35 (Table 7 Model B).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{6}{c}{**Compare the Results From Different Models (Lower the Better)**} \\ \hline
**Model** & **Overall Mean** & **Training** & **Validation** & **SADIE 20** & **Bernschutz** & **Test Mean** \\ \hline A. Baseline & 44.46 & 28.17 & 30.17 & 52.34 & 67.17 & 59.76 \\ \hline B. Smaller model & 45.51 & 19.54 & 21.52 & 51.62 & 89.35 & 70.48 \\ \hline C. With weight decay & 46.04 & 28.59 & 30.96 & 52.75 & 71.89 & 62.32 \\ \hline D. With dropout & 46.47 & 29.14 & 30.08 & 54.52 & 72.15 & 63.33 \\ \hline E. With weight decay and dropout (proposed model) & 45.48 & 29.85 & 30.61 & **47.21** & 74.23 & 60.72 \\ \hline F. With weight decay and dropout (early stopped at 111 epoch) & 49.78 & 40.92 & 41.00 & **47.18** & 70.04 & 58.61 \\ \hline G. Baseline trained with extra data & 39.36 & 19.74 & 20.09 & 59.87 & 57.72 & 58.80 \\ \hline H. With weight decay and dropout and trained with extra data & 41.44 & 22.49 & 22.07 & 59.69 & 61.50 & 60.60 \\ \hline I. Bigger model without weight decay and trained with extra data & **31.38** & **7.83** & **10.61** & 56.88 & **50.22** & **53.55** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of MSE among different models. This table shows that the bigger model without weight decay and trained with extra data (Model I) performed the best with the Bernschutz test data, and also generalised better with different measurement methods (in bold). However, for the SADIE II Subject 20 test data, the proposed model (Model E) and the early stopped proposed model (Model F) performed the best among all models (in bold).
# Performance Study and Efficiency Improvement of Ice Slurry Production by Scraped-Surface Method
## 3 Results and Discussion
### Influence of Scraping Speed on the System Performance  
Ice slurry formation characteristics of 4 wt% sodium chloride solution were experimentally measured at different scraping speeds while maintaining the fixed solution flow rate (q = 1.0 m\({}^{3}\)/h).  
Figure 11: Effects of flow rates on COP.  
Figure 10: Relationship between IPF and ice-making time at different flow rates.  
Figure 9: Ice slurry formation characteristics at different flow rates.  
Figure 12 shows that as the scraping speed increased, the temperature of ice slurry formation remained unchanged and the ice slurry generation time was delayed. When the scraping speed was 13 rpm, the ice crystals began to appear after cooling for 1030 s. When the scraping speed increased to 29 rpm, the time required increased to 1180 s. The relationship between IPF and ice-making time is shown in Figure 13. It was found that, at the same ice-making time, the lower the scraping speed, the higher the IPF of ice slurry. Comparing the COPs at different scraping speeds, as shown in Figure 14, it is clear that the COP at the scraping speed of 13 rpm was the highest, which increased by 8.39% with respect to the scraping speed of 29 rpm. This was because when the scraping speed increased, the solution was easily taken away by the scraper, resulting in shorter residence time of the solution on the inner wall. Consequently, the solution could not exchange heat with the refrigerant on the other side sufficiently, which lowered both the cooling volume actually obtained in the system unit time and the system COP. Therefore, the decrease in scraping speed was beneficial to lowering the power consumption of the system and the optimal scraping speed was 13 rpm.  
Figure 12: Ice slurry formation characteristics at different scraping speeds.  
Figure 13: Relationship between IPF and ice-making time at different scraping speeds.
# Task Assignment of the Improved Contract Net Protocol under a Multi-Agent System
## 5 Results
### All Variables Fixed  
On the basis of Experiment 1 and Experiment 2, Experiment 3 sets the number of agents as 30 and the number of tasks as 1000, \(L_{\text{max}}\) is equal to 3, and three contract net protocol algorithms are used for task allocation. The statistics of the number of errors and the overall task completion rate are shown in Table 2 and the assignment of 30 agents is shown in Figure 7.  
Figure 6: Comparative analysis of the three algorithms with different agent numbers. (**a**) The traffic of the improved contract net protocol with a different number of tasks; (**b**) The run-time of the improved contract net protocol with a different number of tasks.  
From Table 2, we can see that the classical contract net protocol has the largest number of errors, followed by the dynamic contract net protocol. The improved contract net not only has the least number of errors, but can also re-forward these error tasks, which can be successfully completed by other contractor agents. This is because the traditional contract net protocol does not respond to the threshold and trust degree, and the number of task errors is relatively large. Although the dynamic contract net protocol has a trust degree, it is only linearly increased and reduced, and the contractor agent with a strong ability cannot be selected.  
As can be seen from Figure 7, most of these error tasks are concentrated on a few agents. When the contractor agents fail under the first two contract net protocols, they do not timely notify the manager agent, resulting in the accumulation of tasks and a failure to complete. In the improved contract net protocol, the manager agent can identify whether the contractor agent is at fault after \(CosT\) time, change its response threshold, and then ensure the re-completion of the task according to the comprehensive evaluation sequence table.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **Number of Error Tasks** & **Number of Successful Re-Forward Tasks** & **Task Completion Rate** \\ \hline CNP & 133 & 0 & 86.7\% \\ DCNP & 68 & 0 & 93.2\% \\ ICNP & 11 & 11 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of errant tasks and task completion rate.  
Figure 7: Task allocation of 30 agents under three algorithms.
# Underwater Sensor Network Redeployment Algorithm Based on Wolf Search
## 2 Related Works  
The research of the UWSN (Underwater wireless sensor network) node deployment algorithm mainly focused on the static deployment and dynamic deployment. Static deployment includes determining deployment and random deployment. They mainly used no mobile node of the network [23; 24]. The random deployment will lead to a lot of redundant nodes. And it uses aircraft to put nodes in the place that human is not convenient to arrive. Static deployment is that the sensor nodes' locations are known. To cover the monitoring area, the disadvantage is that the deployment efficiency is low. Dynamic deployment can solve the two types of deployment listed above [25; 26]. The dynamic deployment can be divided into two types. One of them is the mobile limited dynamic deployment. It means that part of the nodes move limitedly, and they are only allowed to move in one direction. For example, it includes depth regulation deployment. The other one of them is the freedom dynamic deployment. It means that nodes have the ability to move freely, so they can plan a trajectory that is not constrained. Dynamic deployment according to the specific algorithm can automatically adjust the position in UWSNs, until it reaches the network coverage requirements [27]. Therefore, in order to achieve a better effect, we adopt a mobile free node deployment algorithm to solve the problem of underwater node deployment.  
Compared with the existing algorithms, the proposed RAWS (redeployment algorithm based on wolf search) algorithm contributions are as follows:  
1. the underwater deployment scenarios consider real obstacle factors;
2. in order to achieve a better effect, when faced with obstacles, the nodes can avoid obstacles;
3. the obstacle avoidance mechanism avoids node failure due to external force. From a certain extent, it reduces the network energy consumption and guarantees the stability of network topology.
# Multiservice-Based Traffic Scheduling for 5G Access Traffic Steering, Switching and Splitting
## 5 Conclusions  
We implemented and optimized the traffic scheduling in an NWA network to improve user satisfaction and comply with service DQoS. Unlike the throughput in an actual wireless channel, based on multiservice provision, network throughput was evaluated using the effective network capacity obtained under DQoS constraints. First, the smart terminal selected the data transmission path according to the service type. Then, the traffic scheduling optimization problem was transformed into a constrained convex optimization problem, which was, in turn. subdivided into power- and bandwidth-allocation optimizations. The user satisfaction and network capacity achieved using the proposed MSTT  
Figure 10: Relationship between effective network capacity and QoS index for simultaneous multiple services.  
Figure 9: Relationship between normalized effective network capacity and UE-transmit power for unevenly distributed network users.  
algorithm were evaluated through simulation experiments which showed an improvement in user satisfaction. Additionally, the network capacity obtained using the proposed MSTT algorithm was always superior to those achieved using the ABC and fixed-ratio power-allocation algorithms. This is because the proposed algorithm distributes power as freely and reasonably as possible to achieve the optimal network capacity. Furthermore, although the use of a fixed-ratio (i.e., proportional) power-allocation algorithm can enhance the effective network capacity under certain conditions, it is difficult to define percentage of the total user power allocated to the NR link suitable for all situations; thus, network robustness cannot always be guaranteed. This is a topic for further research. The proposed traffic scheduling algorithm will be beneficial for optimizing 5G network resources, such as network bandwidth and power.  
Conceptualization, X.B. and L.J.; methodology, X.B.; software, X.B. and S.L.; validation, L.J. and Z.L.; formal analysis, J.D.; writing--original draft preparation, X.B.; writing--review and editing, X.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Program of China under Grant 2021 YFP0900702-02, in part by the Fundamental Research Funds for the Central Universities under Grant CUC210F005, in part by the National Natural Science Foundation of China (NFSC) under Grant 62071436.  
The authors declare no conflict of interest.
# Hygro-Thermo-Mechanical Analysis of Brick Masonry Walls Subjected to Environmental Actions
## 2 Hygro-Thermal Model
### Hygro-Thermal Coupling
#### 2.3.2 Temperature Dependency of Hygric Parameters
The reported results support the validity of the Lucas-Washburn law, which describes the water penetration depth \(x\) [m] after a period of time \(t\) [s] as [57]:  
\[x=\sqrt{\frac{\sigma\text{r}\text{cos}\theta}{2\eta}}.\sqrt{t}, \tag{12}\]  
where \(\sigma\) [N/m] is the surface tension of water, \(r\) [m] is the equivalent hydraulic capillary radius, \(\theta\) is the contact angle, and \(\eta\) [Pa-s] is the viscosity of water. This expression states that liquid water flow is influenced by a series of factors. In order to understand how the overall liquid flow is affected by variations of temperature, it is convenient to study the impact of temperature on each of those factors separately. First, assuming that the porous structure undergoes negligible changes due to thermal (or hygric) expansion, it is reasonable to assume a constant \(r\). Moreover, temperature has only a slight impact on the contact angle \(\theta\), so its influence can be disregarded. Therefore, the remaining temperature-dependent parameters are the surface tension \(\sigma\) and the viscosity \(\eta\). Comparing the definition of the capillary absorption coefficient with the Lucas-Washburn equation, it is possible to establish a relation of proportionality as suggested by [PERSON] et al. [53]:  
\[A_{w}\propto\sqrt{\sigma/\eta}. \tag{13}\]  
Considering the previous relation, [PERSON] and [PERSON] [44] fitted tabulated values for \(\sigma\) and \(\eta\) (temperatures between 0 \({}^{\circ}\)C and 50 \({}^{\circ}\)C) and established a linear model to describe the relationship between \(A_{w}\) and \(T\):  
\[A_{w}=k\sqrt{\sigma/\eta}=k[0.095(T-273.15)+6.566], \tag{14}\]  
where \(k\) [kg/m\({}^{2.5}\)] is a micro-structure-dependent but temperature-independent factor. As an example, these authors studied the absorption behavior of different building materials under several temperature conditions and determined \(k=0.0059\) for brick (\(A_{w}=0.052\) at 22.5 \({}^{\circ}\)C). For cement mortars, [PERSON] and [PERSON] [56] found \(k=0.0019\)-0.0023 (\(A_{w}=0.016\)-0.020 at 20 \({}^{\circ}\)C), whereas for NHL mortar, the data presented by [PERSON] et al. [58] were reinterpreted by [PERSON] and [PERSON] [55], who established \(k=0.0125\) (\(A_{w}=0.106\) at 20 \({}^{\circ}\)C).