esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Marine Environment Monitoring Network Model  
As shown in Figure 1, the marine monitoring system constructed in this paper is composed of three main parts: the land-based shore station platform, the surface floating platforms, and the underwater data vaults along with the nearby central node area. The specific monitoring process is as follows: a notification of clustering initiation is broadcast by the central node connected to the underwater data vault to all mobile nodes within its communication range. Location information is then broadcast via the nodes to the network upon receiving this information. The clustering of all mobile nodes that receive the clustering notification is performed using the GTAC algorithm proposed in this paper. Within the clustered groups, data collected via ordinary member nodes is transmitted to the CH (cluster head) nodes. The CH nodes first fuse all the received data information and then, using CH nodes from other clusters as relays, transmit this information to the underwater central node through a multi-hop method.  
Figure 1: Marine environment monitoring network model.  
Secondly, the collected analog signal data is converted into digital signals via the underwater central node and connected to the watertight connectors installed on the bulkhead of the data center through the submarine cable. The watertight connectors are then connected to the main control unit inside the data vault via the network-electrical composite cables within the vault. After the collected data are parsed according to the communication protocol, one end of the main control unit is connected to the power distribution system on the floating platform, providing continuous electrical power to the underwater central node. On the other end, it is connected to the switch inside the vault via network cables, converting the digital signals into optical signals and transmitting them via fiber-optic cables to the switch and server on the floating surface platform. The power supply system connected to the main control unit on the floating platform is numbered in a one-to-one correspondence with the underwater data vault where the main control unit is located. Data uploaded to the floating platform server are first stored and then transmitted to the land-based shore station monitoring area via wireless satellite communication.  
Finally, the data information received by the shore station monitoring system is transmitted to the shore station server. The server integrates and processes the received data information and pushes it to the user monitoring terminals. The pushing process involves, on the one hand, the selection of appropriate communication protocols based on the data characteristics. On the other hand, information needed for the monitoring terminals, such as alarms and queries, is filtered out based on user requests.  
Before the channel correlation analysis of underwater nodes, it is necessary to use existing algorithms [19] to locate underwater mobile nodes.
# Integration of Microstrip Slot Array Antenna with Dye-Sensitized Solar Cells
## 4 Conclusions  
In this paper, a 2 \(\times\) 2 circularly polarized microstrip slot array antenna integrated with dye-sensitized solar cells is designed. A novel stack design method makes the solar cells and the array antenna well integrated. The simulation and measurement results show that the gain of the array antenna increases by 0.12 dB and the axial ratio decreases to 1.50 dB after the integration of the dye-sensitized solar cells. Whether the antenna works or not has little influence on the performance of the dye-sensitized solar cells. The array antenna integrated with dye-sensitized solar cells has a similar radiation performance to the traditional microstrip slot array antenna, and can also provide electricity. Compared with the existing circularly polarized microstrip slot array antenna, the proposed antenna adds the output of dye-sensitized solar cells into the voltage regulation circuit to form a stable power supply to the radio frequency system, which ensures the operation of the microstrip slot array antenna. In other words, the antenna has the ability of self-sustaining power generation capabilities, so as to provide reliable and long-term communication for the communication system when the power is not easy to obtain.  
Figure 8: **(a)** The axial ratio varies with frequency; **(b)** axial ratio radiation pattern of the proposed antenna.  
Figure 9: Normalized radiation patterns of the proposed antenna.  
**Author Contributions:** Conceptualization, B.B.; methodology, B.B., C.S. and Z.Z.; software, B.B., Z.Z.; validation, B.B., Z.Z. and C.S.; formal analysis, Z.Z. and C.S.; investigation, B.B., Z.Z. and X.L.; resources, B.B., X.L. and Y.L.; writing--original draft preparation, Z.Z.; writing--review and editing, B.B. and C.S.; visualization, Z.Z.; supervision, X.L., Y.L.; project administration, B.B., X.L.; funding acquisition, B.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China under Grant 61801343 Grant 61701381, Grant 61431010, and Grant 61627901, in part by the Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2019 JM-177, and in part by the Chinese Postdoctoral Science Foundation.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Thermodynamic Analysis of In-Cylinder Steam Assist Technology within an Internal Combustion Engine
## 3 Results and Discussion
### Analysis of Thermal Efficiency Boundary
Consequently, the increase in compression ratio benefits the optimum thermal efficiency of the in-cylinder steam-assisted cycle, and the gains compensate for the loss in WHR. Figure 8: Relationships between in-cylinder/exhaust temperatures and steam injection mass at different compression ratios (T\({}_{\text{steam}}\) = 450 K; P\({}_{\text{steam}}\) = 20 MPa; P\({}_{\text{intake}}\) = 0.1 MPa). Figure 9: Thermal efficiency and its boundary at different compression ratios (P\({}_{\text{steam}}\) = 20 MPa; P\({}_{\text{intake}}\) = 0.1 MPa). According to the analysis above, there is an efficiency boundary for the in-cylinder steam-assisted cycle under different compression ratios. For a constant-volume heating cycle, the ideal efficiency can be calculated as follows:  
\[\eta_{i}=1-\varepsilon^{1-k} \tag{22}\]  
where \(k\) is the specific heat ratio or adiabatic exponent and \(\varepsilon\) is the compression ratio. In Figure 11, the black line indicates the ideal thermal efficiency, and the adiabatic exponent is 1.4. Similarly, the blue line shows the counterpart of the Otto cycle and the adiabatic exponent is calculated as 1.27. The proposed cycle is also a constant-volume heating cycle so that the thermal efficiency can be summarized according to Equation (22), and is represented by the red line. Obviously, compared with the Otto cycle, the thermal efficiency boundary rises dramatically, and the adiabatic exponent of the combined cycle increases from 1.27 to 1.333. An in-cylinder steam-assisted cycle based on the Otto cycle combines the advantages of a reheated Rankine cycle and steam injection technology. First, exhaust energy is recovered through a heat exchanger. Higher steam temperatures mean higher specific enthalpy and heat energy recovery from exhaust gases, thereby making the most of the engine's waste heat and enhancing its thermal efficiency. Second, in addition to the effective utilisation of waste heat, the overheated steam injection has many advantages, such as controlling abnormal combustion precisely and quickly, increasing the specific heat ratio and heat absorption capacity, and increasing the working medium in the expansion stroke. Hence, in-cylinder steam-assisted cycles have broad application prospects. Figure 11: Thermal efficiency boundaries at different compression ratios. Figure 10: Comparison of thermal efficiencies at different compression ratios.
## 4 Proposed Method
### Modification Point-Aware Test Prioritization and Sampling (MPTPS)  
Figure 2 clearly explains the overview of our proposed method MPTPS (Modification Point-aware Test Prioritization and Sampling). Initially, _Test Runner_ takes _Program Variant_ and _Ordered Test Cases_ from _Test Suite_ as inputs for validation and produces a result. Ordered Test Cases are nothing but prioritized and then sampled test cases (in the same prioritized order). From the _Test Results_, we extract the failing test cases information and _Update MPTTable_ by adding (increasing) value '1' to every failing test case in the patch killing count column of the table. After _MPPTTable_ is updated with the test execution results, _Test Case Prioritization_ takes place by prioritizing the patch killing count in descending order from the table. Prioritized test cases then undergo the _Test Cases Sampling_ phase to split the test suite into multiple subsets. These _Ordered Test Cases_ subsets are executed one by one until the last subset, if there are no failing tests during execution. If all the subsets (test cases) pass, it is delivered as a _Test Suite Adequate Patch_ to the developer. Otherwise, the _Test Runner_ will be stopped after completely executing the subset with a failing test case(s). In this case, the outcome is considered a _Failed Patch_. Subsequently, the fitness score is calculated for the patch and based on the score, and the _Failed Patch_ might undergo modification once again.
## 1 Introduction
Some of these tests can provide results in a matter of hours, whereas other tests must be sent to a laboratory for analysis, providing results in a few days. These tests tend to be costly and can consume a significant amount of resources for certain populations or countries [21]. Moreover, due to the worldwide situation, these tests are scarce in many countries, making it difficult to conduct them on a wider scale. Hence, they cannot always be performed upon the recognition of the first symptom suggesting potential infection with COVID-19. Since COVID-19 generates a respiratory disease that mainly affects the lungs, medical staff usually resort to the use of visual images of the lungs using computed tomography (CT) and X-rays (RX) to assess the medical conditions of their patients. People with respiratory diseases usually exhibit distinct marks in their lungs that can be spotted by a trained eye. These marks exhibit different patterns according to the type of disease affecting the patient [22; 23; 24; 25]. In cases where a person exhibits fever or any other symptom and viral tests are difficult to perform, the analysis of CT scans or RX images can provide strong support to confirm or refute the hypothesis that someone is infected with COVID-19. Nonetheless, as previously stated, highly trained personnel are required to provide accurate interpretations of the aforementioned images and diagnose whether or not the patient is infected with COVID-19. This rationale leads to the question of whether or not we can train machines that can interpret images to identify patients with COVID-19. The same question holds for other pulmonary diseases, but due to the pandemic being ongoing (as of June 2020), we mainly aim to detect the aforementioned disease. It is highly important to emphasize that it is by no means implied that a machine-obtained interpretation of a pulmonary image can be a substitute for a more comprehensive diagnosis made by a highly trained physician. However, this technique could eventually be used to help medical personnel to quickly obtain an indication of whether the symptoms of a given patient imply infection with COVID-19.
# MDFULog: Multi-Feature Deep Fusion of Unstable Log Anomaly Detection Model
## 4 Experiment and Analysis
### Result Analysis
#### 4.3.1 Original Dataset  
The results of comparing MDFULog versus the baseline models are listed in Table 1, with the highest score expressed in bold. It can be found from the Recall that, compared with the traditional PCA and DeepLog, the recall for the HDFS dataset increased by 30% and 5%, respectively, and for OpenStack increased by 28% and 15%, respectively. This indicates that the MDFULog model has a high Recall rate on the HDFS and OpenStack datasets and can find anomalies more accurately. Precision shows that the model in this paper has a good effect in detecting abnormal log data. MDFULog achieved about 98% and 96% on HDFS and OpenStack, respectively. It can be seen from the F1-score that the proposed model obtained the best F1 value among the six methods, and the average scores on HDFS and OpenStack were about 98% and 96%, respectively. Both PCA and DeepLog use the index of the log template to represent logs, ignoring the template's semantic information. DeepLog models and detects exceptions of the execution path type and parameter type, respectively, in the detection process, ignoring the correlation between multiple exceptions. LogRobust fully considers semantic information, but does not consider the impact of time parameters. The detection method cannot effectively cover the type of time anomaly. DeepSyslog also does not consider the potential dangers of time interval anomalies. ROEAD uses natural language processing technology to decrease the number of log templates. The Recall rates of ROEAD and MDFULog on the HDFS dataset were very close, but the Precision of MDFULog was somewhat higher than that of ROEAD. On OpenStack, the Recall was slightly lower than for DeepSyslog. DeepSyslog achieved a higher Recall rate of 0.97, but its Precision was only 0.87. The lower Precision implies that additional anomalies cannot be detected. MDFULog combines Bert and contrastive learning to withdraw the semantic information of the log better and introduces time information as a feature to capture more types of faults, hence increasing the Precision of anomaly detection. The Informer classifier is used to perform log anomaly detection on multiple features. Finally, a higher F1 value was obtained, showing the model's effectiveness for sequence anomaly detection. Our model showed good stability on the different datasets, and the inspection performance of the MDFULog model identifiedvirtually all the anomalies in the HDFS and OpenStack datasets.
## 3 Some Key Issues on the Use of SLNs and NLCs as Oral Nanovehicles for Nutraceuticals or Bioactives
### Poorly Soluble Bioactive Compounds
#### 4.2.2 Polyphenols
Resveratrol is another important polyphenol found in various common foods like grapes, berries, and red wines. It is recognized as a bioactive nutraceutical with a number of potential beneficial effects on health [159]. [PERSON] and [PERSON] [160] evaluated the in vitro cellular uptake, transport, and internalization of resveratrol-loaded SLNs in keratinocytes, and found that the SLNs could readily cross the cell membrane in a short period (\(<\)15 min), and exhibited greater cytostatic effects, intracellular delivery, solubility, and stability, compared to the free resveratrol suspension. [PERSON] and colleagues [161] showed that, in addition to the improved in vitro cell uptake, the resveratrol encapsulated in SLNs had better anti-lipoperio oxidative activity. In another work, it was confirmed that the incorporation of resveratrol in the SLNs conferred physical and chemical stability and also provided a sustained release behavior to the encapsulated molecule [57]. Additionally, [PERSON] et al. [154] further indicated that the encapsulation of resveratrol in SLNs resulted in a significant 8.035-fold improvement in the oral bioavailability of resveratrol (in rats) as compared to the free resveratrol suspension. [PERSON] et al. [155] demonstrated that, through intraperitoneal administration, the encapsulated resveratrol in the SLNs could be preferentially transferred to the brain, with the brain concentration of resveratrol around 5 times higher than that for free resveratrol.
# A Survey on MLLMs in Education: Application and Future Directions
## 3 Applications of MLLMs in Education
### AI-Powered Learning Management Systems (LMSs)
#### Examples of Applications  
Several AI-powered LMS platforms and initiatives demonstrate the application of MLLMs in education. _Coursera_ is an online learning platform that offers massive open online courses (MOOCs), specializations, and degrees [67]. Coursera utilizes AI to personalize course recommendations and enhance the learning experience [68]. Similarly, _edX_ incorporates AI to provide adaptive learning experiences, including personalized content and assessments [69]. _Udemy_ uses AI algorithms to analyze learner data and provide course recommendations tailored to individual interests and needs [70]. _Knewton's Alta_ is an AI-powered adaptive learning platform that uses data analytics and MLLMs to personalize learning paths and provide real-time feedback [71].
# Building Penetration Losses at 3.5 GHz: Dependence on Polarization and Incidence Angle
## 4 Results and Discussion
Results vary with the incidence angle in an 8 dB range for the facade without windows and just around 1 dB for the facade with windows. The presence of windows in the facade increases the heterogeneity of the surface with its greater constructive complexity, having more corners, borders, metal frames, surfaces at different levels and so on. The presence of the metal frames of the windows may also explain why the attenuation is larger for the facade with windows than for the facade without windows at most incidence angles. The 15 cm facade again presents a similar trend to that of the 30 cm facade with no windows; however, with lower attenuation values and less variability with the incidence angle. The glass facade is the only showing a decreasing attenuation with the incidence angle. This agrees with the theoretical calculations presented in Figure 2 and also with the results given in [19].
## 3 Numerical Validations of Stiffened Ship Panel Strain Reconstruction
### Influence of Different Mesh Partitioning Schemes on the Accuracy of Strain Field Inversion  
Using the fundamental partitioning rules proposed in Section 3.2 for stiffened ship panels, three mesh partitioning schemes were devised:  
Scheme 1: The panel was partitioned into a grid of 4 \(\times\) 4 quadrilateral shell elements, and the top surfaces of the two transverse T-sections were subdivided into 1 \(\times\) 4 quadrilateral shell elements, resulting in a total of 24 elements, as depicted in Figure 6a.  
Figure 5: (**a**) Four paths in the transverse T\(-\)section; (**b**) X\(-\)direction simulated strain curves for the four paths.  
Scheme 2: The panel was divided into a grid of \(4\times 7\) quadrilateral shell elements, and the top surfaces of the two transverse T-sections were subdivided into \(1\times 7\) quadrilateral shell elements, yielding a total of 42 elements, as illustrated in Figure 6b.  
Scheme 3: The panel was segmented into a grid of \(4\times 21\) quadrilateral shell elements, and the top surfaces of the two transverse T-sections were subdivided into \(1\times 21\) quadrilateral shell elements, resulting in a total of 126 elements, as shown in Figure 6c.  
Taking Case 1 as an example, strain data from the inner side of the elements' central points, divided according to the three mentioned mesh partitioning schemes, were extracted from the results of finite element simulations. Utilizing the inverse finite element method based on strain functions, the relative errors in strain reconstruction for each validation point were computed separately for the three different mesh partitioning schemes, as depicted in Figure 7.  
Based on the reconstruction results, it was evident that, with denser mesh partitioning, the accuracy of strain field reconstruction increased, as indicated in Table 2. Considering both reconstruction accuracy and sensor configuration efficiency, we opted for the second mesh partitioning scheme to conduct validation of the reconstruction results based on numerical simulation.
# Patch-Font: Enhancing Few-Shot Font Generation with Patch-Based Attention and Multitask Encoding
## 5 Experimental Results
### Comparison with SOTA Methods
#### 5.2.3 Failure Cases  
In Figures 3 and 4, red boxes mark complex overlaps or cursive strokes that some baselines fail to reproduce. Patch-Font, while not perfect, more accurately maintains stroke continuity due to its patch-level attention. Remaining errors often stem from extremely distorted or ornamental shapes that can confuse local style embeddings.
# A Soft-Voting Ensemble Classifier for Detecting Patients Affected by COVID-19
## 2 Materials and Methods  
As CT scans can consist of hundreds of slices, training DL models able to analyze this huge amount of data is a challenging task. Independently from the specific problem, two main approaches based on analyzing 2D or 3D images have been proposed in the literature.  
The first approach is twofold, as one or more representative slices of a scan can be analyzed. In the former case, no further work is required, whereas in the latter, each slice is analyzed independently, and then individual results are combined to produce the desired outcome. In the second approach, a subset of the available slices is selected and analyzed together. Basically, the first approach requires less memory and is computationally less expensive than the second, but information about chest slices contiguity is lost. The proposed work is based on the latter approach.  
A soft-voting ensemble approach built on Inception networks able to analyze 3D images to detect COVID-19 infections is proposed. Inception networks have been used for their ability to limit overfitting when working with not large datasets. Moreover, the size of these networks allowed fitting the trainable 3D model on the device memory according to different settings. Details of the materials and the implemented methods are reported in the following.  
This section is organized as follows: first, the Inception-based architectures used in this work are recalled; then, a brief summary of the main ensemble classifier methods is given; afterwards, the dataset used to train and test the models is described; and finally, the strategy adopted for dealing with the detection of COVID-19 is presented.